Accepted Manuscript Title: Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation Author: Anne-Mari Vitikainen Elina M¨akel¨a Pantelis Lioumis Veikko Jousm¨aki Jyrki P. M¨akel¨a PII: DOI: Reference:
S0165-0270(15)00194-6 http://dx.doi.org/doi:10.1016/j.jneumeth.2015.05.015 NSM 7234
To appear in:
Journal of Neuroscience Methods
Received date: Revised date: Accepted date:
3-2-2015 19-5-2015 21-5-2015
Please cite this article as: Vitikainen A-M, M¨akel¨a E, Lioumis P, Jousm¨aki V, M¨akel¨a JP, Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation, Journal of Neuroscience Methods (2015), http://dx.doi.org/10.1016/j.jneumeth.2015.05.015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1 Title: Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation
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Authors: Anne-Mari Vitikainen a,b Lic.Phil, Elina Mäkeläa B.Sc., Pantelis Lioumisa,c Ph.D., Veikko Jousmäkid,e Ph.D., Jyrki P. Mäkelä a M.D., Ph.D. a
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Article type: Research article Number of text pages: 20 Number of figures: 2 Number of tables: 1 Number of supplementary figures: 2 Number of supplementary tables: 3
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BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 Helsinki b Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki c Neuroscience Center, University of Helsinki, P.O. Box 56, FI-00014 Helsinki d Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 15100, FI-00076 AALTO, Espoo, Finland e Aalto NeuroImaging, Aalto University School of Science, P.O. Box 15100, FI-00076 AALTO, Espoo, Finland
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Address for correspondence: Anne-Mari Vitikainen BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital P.O. Box 340, FI-00029 Helsinki Tel: +358 5042 72020 Fax: +358 9471 74404 E-mail: [email protected]
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All authors’ e-mails: Anne-Mari Vitikainen Elina Mäkelä Pantelis Lioumis Veikko Jousmäki Jyrki P. Mäkelä
[email protected] [email protected] [email protected] [email protected] [email protected]
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2 Abstract
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Background: The use of navigated repetitive transcranial magnetic stimulation (rTMS) in
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mapping of speech-related brain areas has recently shown to be useful in preoperative
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workflow of epilepsy and tumor patients. However, substantial inter- and intraobserver
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variability and non-optimal replicability of the rTMS results have been reported, and a
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need for additional development of the methodology is recognized. In TMS motor cortex
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mappings the evoked responses can be quantitatively monitored by electromyographic
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recordings; however, no such easily available setup exists for speech mappings.
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New Method: We present an accelerometer-based setup for detection of vocalization-
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related larynx vibrations combined with an automatic routine for voice onset detection for
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rTMS speech mapping applying naming.
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Comparison with Existing Method(s): The results produced by the automatic routine were
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compared with the manually reviewed video-recordings.
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Results: The new method was applied in the routine navigated rTMS speech mapping for
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12 consecutive patients during preoperative workup for epilepsy or tumor surgery. The
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automatic routine correctly detected 96% of the voice onsets, resulting in 96% sensitivity
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and 71% specificity. Majority (63%) of the misdetections were related to visible throat
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movements, extra voices before the response, or delayed naming of the previous stimuli.
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The no-response errors were correctly detected in 88% of events.
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Conclusion: The proposed setup for automatic detection of voice onsets provides
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quantitative additional data for analysis of the rTMS-induced speech response
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modifications. The objectively defined speech response latencies increase the
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repeatability, reliability and stratification of the rTMS results.
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Keywords: Presurgical planning, navigated TMS (nTMS), object naming, speech
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mapping
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4 1. Introduction:
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Localization of speech-related brain areas by navigated repetitive transcranial magnetic
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stimulation (rTMS) during an object naming task has been suggested to be useful in
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planning of brain tumor and epilepsy surgery (1, 2). Use of individual’s magnetic
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resonance imaging (MRI) based navigation with rTMS mapping enables the speech
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related cortical sites to be transferred to the neuronavigation system (3) and to be used in
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surgical planning. Preoperative speech mapping by navigated rTMS may aid in objective
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risk-benefit assessment of the planned surgery, enable more precisely targeted smaller
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craniotomies, faster and safer intraoperative mapping, and safer surgeries for patients that
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cannot undergo awake craniotomy (2, 4). The rTMS speech mapping results have been
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compared to direct cortical stimulation (DCS) during awake craniotomy implying that
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nTMS is remarkably sensitive but relatively non-specific in detecting the sites producing
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speech disturbance in DCS (4, 5). In preoperative navigated TMS (nTMS) mapping of
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motor cortex, shown to have a very good match with DCS (5, 9), the responses to nTMS
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are monitored by motor evoked potentials from the activated muscles. No such
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straightforward, easily recordable marker exists for detection of speech modifications
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induced by rTMS.
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4 The navigated rTMS method has been accepted by US Food and Drug Administration
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(FDA) for presurgical speech mapping in 2012 (10), and, consequently, its use will
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probably expand in the near future. Recently, rTMS speech mapping results in patients
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with brain tumors and healthy subjects have suggested tumor-induced plasticity of speech
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representation areas (11, 12), and demonstrated differences in cortical areas related to
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object and action naming in healthy subjects (13). Thus, nTMS during an object naming
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task may have an impact on surgery planning and provide information about the
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organization of speech-related brain areas in general. Nevertheless, the intraobserver and
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interobserver comparisons of the nTMS speech mapping results show only limited
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replicability, and the currently used protocols need further development (14, 15).
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Additionally, the methodology is not completely standardized between the surgical
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groups applying it.
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The no-response (speech arrest) errors are the most replicable results of nTMS speech
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mapping (11). However, speech disturbances such as semantic and phonological
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paraphasias, and performance errors during the pulse train are more difficult to separate
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quantitatively from the recorded videos. Particularly, the value of hesitations, delayed but
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not completely abolished responses induced by rTMS, is not clear, as their evaluation is
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quite subjective; interpretation of these errors has been considered as a possible reason
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for a high rate of false positive results of rTMS studies as compared with DCS (16).
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Microphone recording of vocalization to detect the voice onsets objectively is hampered
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by ambient noise from TMS pulses and coil cooling (1). Electromyographic (EMG)
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5 signals from cricothyroid muscles have been used in combination with nTMS to monitor
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effects of nTMS to the inferior frontal cortex on larynx muscles during object naming
3
tasks (17). However, the insertion of the EMG wire electrodes to the cricothyroid muscle
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is invasive and requires skill (18).
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Detection of larynx vibrations, coinciding with the fundamental frequency of the voice,
7
with an accelerometer enables non-invasive follow-up of speech vocalizations (19).
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Accelerometers can accurately measure vocal activity (20-22). Compared with
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microphones, accelerometers are not sensitive to ambient environmental sounds and are
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therefore well suitable for voice assessment (21).
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In this study, we tested the feasibility of using an accelerometer to pick up the onsets of
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the vocalizations in navigated rTMS speech mapping.
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2. Materials and Methods
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2.1. Subjects
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We made the accelerometer recordings as part of the rTMS speech mappings for twelve
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consecutive patients (4 females/8 males, age range 12–39 years) going through tumor or
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epilepsy surgery workup. Both hemispheres were stimulated with rTMS in eleven
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patients; patient #4 did not tolerate the right-hemisphere stimulation and only his left
21
hemisphere was stimulated. For one patient, the data during the baseline was not recorded
22
due human error. For one patient the data was lost due technical difficulties, for patient
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#7 the data of the left hemisphere stimulation was inaccessible, and for patient #6 a first
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part of data of left hemisphere stimulation was corrupted. The results of ten patients are
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presented. The study was approved by the local Ethical Committee.
3 2.2. Experimental design
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The experiment started with an initial baseline session without rTMS to select the images
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that were correctly named and pronounced. In order to enable the offline comparison of
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the responses with and without the rTMS, the baseline image set was run two times more.
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During the second baseline session the rTMS coil was held near the patient’s head in the
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navigation field of the rTMS system to ensure the trigger pulse output. The intensity of
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the baseline stimulation was held at 0 or 1 % of the maximum stimulator output, i.e., no
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rTMS stimulation was applied. Images that were not named, not named correctly, not
12
named clearly, not articulated correctly and named with delay or hesitation were removed
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from the image set after the first baseline round. Only the data from the second baseline
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round was used for the analysis and subsequent rTMS sessions. The images were
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displayed in random order. All sessions were video-recorded for offline analysis.
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The rTMS measurements were carried out at the BioMag Laboratory using eXimia NBS
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4.3 (Nexstim Ltd., Helsinki, Finland) and a commercial speech mapping module
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(NexSpeech, Nexstim Ltd., Helsinki, Finland). The navigation system estimates the
20
strength of the maximum electric field at the stimulation location and overlays the
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estimated field strength on-line on the 3-D reconstruction of the individual’s brain (23).
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Each stimulation site is tagged to the structural magnetic resonance (MR) images for
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subsequent analysis.
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7 1 All rTMS stimulations were done with a figure-of-eight coil of a 70-mm outer diameter
3
and biphasic pulse shape. The resting motor threshold (MT) was determined from the
4
abductor pollicis brevis muscle controlled by the hemisphere affected by the epilepsy or
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tumor. The method used by Rossini et al. (24) was used for the MT determination. The
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rTMS intensity for the mapping was adjusted to produce roughly equally strong electric
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field to all perisylvian cortical regions. If the stimulation caused intolerable discomfort to
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the subject, its intensity was lowered in 5-10 % decrements until tolerable. Thus, the
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stimulation intensity varied somewhat across subjects. The estimated induced electric
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field strength at the cortex was registered. The stimulations were done with 5-pulse rTMS
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trains at 5 or 7 Hz (1, 25).
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In nine patients, the images to be named were a subset of color images out of set of 84
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images depicting everyday objects (1). In three patients, a selection of 92 images from a
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standardized image set (26) was used. The selection from the standardized set was chosen
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to represent frequently used items in Finnish every day life, whose names are common in
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Finnish language, and that have only few synonyms. The subjects were asked to name the
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objects in Finnish as quickly and precisely as possible. The images were displayed for
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700 ms on a computer screen with 2.0-3.0 s interstimulus intervals (ISI). The experiment
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started with a 2.5 s ISI. If needed, the ISI was adjusted according to the baseline
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performance of the patient. The rTMS trains started 300 ms after the image onset. The
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coil was hand-held and freely movable between the pulse trains. During the stimulation,
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the coil was moved between the pulse trains following a grid-like pattern so that the
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8 stimulated locations covered systematically a wide fronto-temporo-parietal cortical area.
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The orientation of the coil was adjusted to induce current primarily perpendicular to the
3
fibers of the temporalis muscle to minimize muscle twitching, and secondarily
4
perpendicular to the sulcus at the stimulation location. The cortical sites where rTMS
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produced naming errors were revisited to evaluate the repeatability of the effect.
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2.3. Manual review of the mapping data
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The speech mappings were routinely reviewed offline from the video by a
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neuropsychologist with expertise in effects of DCS on speech. The categories available in
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the speech mapping module (no error, no-response error, performance error, semantic
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paraphasia, muscle stimulation and other) were applied in the analysis. Additionally
12
information about performance errors’ subdivision (e.g. delays, phonological
13
paraphasias) was noted in the free comment field. The speech response latencies were not
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available in the speech mapping module.
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2.4. Vibration recording
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Subject’s vocal activity, i.e. fundamental frequency of the voice, was recorded during
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object naming with a three-axis accelerometer (ADXL330 iMEMS® Accelerometer,
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Analog Devices, Norwood, MA) attached to the skin on left side of the subject’s throat,
20
onto the larynx site producing palpable vibrations during vocalization (Fig. 1). The
21
analog accelerometer signals were connected to the EMG system of the stimulator with a
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custom built interface. The recorded frequency band was 10–500 Hz and sampling rate 3
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kHz. Similar accelerometer has been used previously by Bourguignon and co-workers to
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detect the fundamental frequency of the reader’s voice (19).
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Fig. 1. The accelerometer attached to the throat.
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2.5. Automated routine for voice onset detection For the automated routine, the following files were collected: the tabular data of the
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speech mapping (speech-file containing the speech event related data from the
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commercial speech mapping module, including the name of the picture, speech exam
16
identifications,and rTMS train sequence identifiers, converted to excel file), and
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accelerometer and trigger signals (edf-files). Each accelerometer data file corresponding
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to each of the speech exams (sessions) were identified and verified.
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The accelerometer signals were high-pass filtered with Butterworth filter (4th order, cut-
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off frequency of 80 Hz) to reduce low frequency interference and possible signal level
22
drifts while maintaining the characteristics of the voice (19, 22) ( Figure 2). The data was
23
first filtered in forward and then in reverse direction, preserving the waveform features
24
exactly at the same time point where they occur in the original signal (27). To enable a
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10 robust automatic onset calculation routine, the envelope (see Figure 2) of the filtered
2
signal was calculated using Hilbert transform. The signal envelope captures the varying
3
features of the signal generating the signal outline, and its analytic representation enables
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fast calculations. This approach is commonly used in sound signal processing (28).
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The recordings were split into several sessions (and thus several data files) controlled by
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the commercial program module. Some of the sessions may contain short but extremely
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intensive vibrations due to e.g. coughs while maintaining constant level of the vibrations
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related to silence and speech responses. In order to enable uniform processing of the
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baseline and subsequent rTMS sessions, the mean of the signal envelope during the
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whole session was calculated to represent the overall vibration level of the session. An
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active speech threshold was formed by multiplying this general signal level with an
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individually adjustable constant.
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The first rough estimate of the speech periods was formed by taking into account the
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signal periods where the envelope of the signal amplitude exceeded the active speech
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threshold (29). This was done sample by sample (sampling rate 3000 Hz). Erroneous
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periods containing e.g. coughs, sighs, swallowing, moving of the jaw etc. were included
18
into analysis, in addition to the speech response. To extract only the true speech response
19
onsets, and not the shorter signals originating from non-desirable events, the envelope
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signal was then modified by applying a moving average filter (implemented as
21
convolution, with a rectangular unit pulse of 40 ms in length; Figure 2). After this the first
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rising edge of the resulting signal between successive rTMS train onsets was determined
23
as the speech response onset corresponding the given rTMS train onset.
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11 1 The active speech threshold limit and the length of the moving average filter window and
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shape of the convolution kernel (rectangular unit pulse) were checked manually to be
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appropriate for our measurement setup with four randomly selected sessions (not
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included in the results).
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12 Fig. 2. An example of the analysis steps of the automated routine and the corresponding audio
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waveform of a sample of data from patient #1. A) The original accelerometer signal, B) filtered
3
signal, C) signal envelope and the active threshold (dashed line), D) resulting modified signal
4
after the moving average filter. The rising edge of the signal after each rTMS train onset is
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determined as the speech response onset, E) the rTMS train trigger pulses, F) The corresponding
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audio waveform. Note strong signals from the clicks induced by rTMS.
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The trigger pulses of the rTMS stimuli, collected together with the accelerometer data,
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enabled the calculation of the onset latencies of vocalizations and the onsets of the stimuli
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(Fig. 2). The responses for each image from the rTMS session were matched with the
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corresponding images in baseline, and the voice onset time difference was calculated. To
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improve the usability of the routine, the rTMS train sequence numbers are shown in the
13
overview figures (see Supplementary Figure A) and the image name in the response
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comparison figures. Finally, a list of the responses which onset time difference exceeding
15
a chosen value (default 100 ms) compared to the baseline was printed. The results were
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visually crosschecked from the recorded video and from the signals for erroneous onset
17
detections.
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Fig. 3. An example of the response comparison of one object naming baseline-rTMS session pair
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from patient #1. The voice onset time is prolonged by 227 ms when rTMS is applied, and instead
4
of naming the image correctly as “pullo” (bottle), the patient named it as “kokis” (coke) (semantic
5
error). This is seen as a divergent signal shape. (For interpretation of the references to color in this
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figure legend, the reader is referred to the web version of the article. The MATLAB figure is
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provided as Supplementary Figure B.)
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2.6. Comparison of the data
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The results produced by the automated routine were compared with the manually
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reviewed results from video with the following five components: a) the number of
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detected rTMS pulse trains compared with the number of the actually occurring rTMS
13
trains, b) the number of the correctly detected rTMS pulse trains, c) the number of
14
detected voice onsets, d) the number of the correctly detected voice onsets, and e) the
15
number of no-response errors. The voice onset latency could not be directly compared, as
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14 1
it could not be defined precisely from the video recordings. However, we defined the
2
delays of responses scored as “delays” by our neurophysiologist.
3 The correctness was evaluated against the response performance observed from the
5
videos for every response in which the correctness was in doubt. The reason for
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misdetection of the rTMS train onset resulted from not detecting the trigger signal, or
7
detecting extra trigger signals. The reason for voice onset misdetections were classified to
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four categories according to the underlying cause: I) throat movement related problems
9
(swallows, jaw movements, muscle stimulation, grimaces, etc.); such activity was
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detected before the actual response or movement was detected without any vocalization,
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II) extra voice and associated movements taking place before the actual response (such as
12
'hmm', 'eeeh', coughs, sighs, etc.), III) delayed naming of the previous image, and IV)
13
other reasons. The reason for misdetection of the no-response errors was analyzed as well.
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3. Results:
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The accelerometer signals were easily recorded with the EMG system of the TMS device,
17
enabling on-line visualization of the signal during the stimulation and detailed off-line
18
analysis.
19
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The rTMS train sequence was detected correctly in 98 % of all patients with sensitivity of
21
99 % and specificity of 86 %. Detailed breakdown of the detection performance for each
22
patient is given in Supplementary Table A. A confirmed rTMS train sequence was
23
associated in 98 % of the shown images. The reason for not having an rTMS train
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15 sequence while the image was present was the movement of the stimulation coil out of
2
the navigation field of the rTMS system. The sequence of images delivered by the speech
3
module can only be interrupted manually and is not directly related to successful
4
detection of the stimulator coil by the navigation system. 78 % of the misdetections were
5
due to lack of the trigger pulses during the images: either the pulse sequence was not
6
delivered, or it was delivered only partially. The rest of the misdetections resulted from
7
extra trigger signals of unknown origin in between the confirmed train sequences.
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8 3.1. Voice onset detection
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The latencies of the vocalizations during rTMS were increased by more than 100 ms in,
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on average, 26±13 trials (range 7-55 trials), and by more than 500 ms in, on average 9±5
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trials (range 2-19 trials) (For values with intermediate delays, see Table 2). Longer
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latency delays were less common than short ones in all patients.
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The voice onset detection performance was evaluated as portion of the correctly detected
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voice onsets of all voice response onsets; details are given in Table 1. The sensitivity of
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the automated routine to correctly detect the voice onsets was 96 %, and the specificity
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71 %. Majority of the misdetections were related to visible throat movements before the
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actual response (26 %), to extra voice before the response (24 %) or other, e.g. trigger
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related problems (36 %). Delayed naming of the previous image was present in 13 % of
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the misdetections. Detailed categorization of the misdetections for each patient separately
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is provided as Supplementary Table B.
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2
39
3
36
4
39
5
12
6
17
7
37
8
15
9
17
F F M M M F
MTa (% of MSOPb)
50 25 25 39
M M M
Condition
Baseline rTMS LHc Baseline rTMS LH Baseline
63
38
25 63 62
Stimulation intensity range (% MT)
rTMS LH Baseline
rTMS LH Baseline
rTMS LH Baseline
rTMS LH Baseline
rTMS RH Baseline rTMS LH Baseline rTMS LH
rTMS train (Hz)
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Sex (F/M)
100 to 90 100
ed
Age (y)
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Patient no.
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Table 1 The patient characteristics, stimulation parameters and response onset detection performance.
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100
100 to 77 95 to 87 100
100 to 120 100 to 71 97 to 89
5, 7
5, 7
5, 7 5, 7 5 5 5 5 5, 7
Occurring (count) 42
Response onsets Correctly detected (count) 42
406 115
404 112
340 200
333 198
334 133
332 133
265 114
251 101
111 157
96 155
172 172
165 164
119 97
111 97
206 78
182 78
292
285
Correctly detected (%) 99.5 97.4 98.8 96.5 85.9 96.5 94.5 90.3 98.3
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a
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Baseline
36
100
5
103
100
176
173
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rTMS LH
97.2 95,7
Grand average
motor threshold, b maximum stimulator output, c left hemisphere, d right hemisphere
Table 2 The response vocalizations with delays and increased latencies. Responses marked as delays
2 3 4
rTMS LH
2
rTMS RHd rTMS LH
0 0
rTMS RH rTMS LH
1 11
6 7 8
Average delay (ms)
>150ms (count)
>200ms (count)
>500ms (count)
18
16
4
-
16 15
13 12
10 11
4
281 407
15 55
12 42
9 33
4 4
6 0
434 -
41 24
32 9
26 5
8 -
7 (9)e
1175
34
33
32
15
3 (4)e 2
758 884
32 45
32 37
31 35
19 17
rTMS RH rTMS RH
5 0
649 -
37 8
34 5
32 5
10 4
rTMS LH
0
-
24
21
20
11
rTMS RH
0
-
7
4
4
2
rTMS RH rTMS LH
rTMS RH rTMS LH
665
>100ms (count)
Responses with increased latency
24
rTMS LH 5
Delayed (count)
c
ed
1
Condition
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Patient no.
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17
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-
25
rTMS RH rTMS LH
0 0
-
11 27
rTMS RH Average ± SD
1 2±3
1221 719 ± 329
23 26 ± 13
22
21
9
8 25
6 21
7
20 21 ± 12
19 19 ± 11
12 9±5
Range 0 - 11 281 - 1221 7 - 55 4 - 42 4 - 35 2 - 19 motor threshold, b maximum stimulator output, c left hemisphere, d right hemisphere, e the average is calculated from those response onsets where the onset was available
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ed
a
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rTMS LH
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3.2. Detection of no-response errors The no-response errors were detected correctly in 88 % of all the no-response errors,
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including the “no-response” events in the baseline sessions. Details for the detection of
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no-response errors for each patient is given in Supplementary Table C. As the
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accelerometer data also contains the first round of the images in the baseline session, and
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thus also the responses to the images named incorrectly, not named, not named clearly,
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not articulated correctly and named with delay or hesitation, the following results are
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calculated separately for baseline and rTMS sessions. In baseline sessions the sensitivity
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and specificity were 100 %. In rTMS sessions the overall sensitivity was 82 % and
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specificity 100 %. The reasons for misdetection followed the same categories as in voice
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onset misdetections.
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The accelerometer-based method presented here measures the voice onset latency to
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specific image stimuli. The accelerometer recording produced high-quality signals and
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enabled automatic voice onset time detection. The recordings were collected from 12
17
consecutive patients who required rTMS speech mapping; thus they reflect overall
18
feasibility of the presented setup in a real clinical situation. The automated routine was
19
compared to a manual review of the rTMS speech mapping videos, which is the present
20
method to analyze the errors in the object naming task. We found that the presented
21
method with the automated routine correctly identified 98 % of all presented rTMS trains
22
onsets and 96 % of the voice onsets. This suggests that the methodology could produce
23
an additional reliable means to stratify the effects of rTMS in an object naming task for
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20 1
presurgical planning. Moreover, it could provide a preliminary indicator to detect the no-
2
response errors and thus speed up the analysis of the videoed responses.
3 Our setup offers fast additional information to the behavioral data from video analysis of
5
the naming performance (1). Short delays may pass unnoticed in video analysis of several
6
hundred images and responses in several sessions, but they can be detected and promptly
7
quantified by the accelerometer recording. Most importantly our presented setup provides
8
numeral values of naming latencies, therefore reducing subjectivity and increasing
9
repeatability and reliability of the analysis. We are not aware of reports studying just
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notifiable differences in delays of naming. Healthy subjects distinguish a reliable speech
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asynchrony if acoustical signal leads lip opening by 80 ms or lags it by 140 ms (30);
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probably minimum perceived differences in naming delay are in the same time range.
13
The delays identified by visual scoring ranged from 300 to 1200 ms with an average of
14
700 ms. This variability may relate, in part, to the regularity of the patient performance
15
generating a background baseline for response variability in visual analysis. Our setup,
16
however, enables selection of any delay for a more precise scrutiny. The final clinical
17
value of different delays can only be identified by comparison with the data obtained by
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DCS during awake craniotomy.
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The automated routine may not recognize all stop consonants in the beginning of a word
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as their signal amplitude is very small during the voice onset. The smaller thresholds
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required for their detection is not feasible, as spikes caused by TMS and other random
23
disturbances would be classified as speech. However, this drawback is not important as
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21 the latencies in rTMS condition are compared to the baseline latencies of the same word,
2
and the beginning of the word is usually lost in both conditions. Only the loudness
3
variation between the baseline and rTMS condition may cause problems despite the use
4
of relative detection threshold: the automated routine may detect the onset only in the
5
louder vocalization and cause an error in comparison of the baseline and rTMS data.
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Therefore, visual evaluation of the automatically detected voice onsets is still important.
7
Most observed erroneous latency detections were induced by coughing or sighing before
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the actual response. These artifacts resulted in too short, not abnormally long latencies.
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Instead, the true effects of rTMS caused a delayed vocalization. Therefore, the design of
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our algorithm minimizes false positive findings.
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The automated analysis detected successfully latencies for the naming of presented
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images. Although the patients rehearsed naming, some of them had particular difficulties
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to name specific images fluently during rTMS. This may indicate that image-related
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factors, instead of rTMS-related ones, are the underlying reason for such variability. Our
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algorithm reliably identifies such images and enables straightforward comparison with
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the DCS data to study this question.
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The analysis of the measured signals can be developed further. For example, a shape
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recognition algorithm (see Figure 3) could recognize rTMS-induced differences of
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pronunciation or word change on the vowel-associated vibration pattern in comparison
22
with the pattern recorded during the baseline.
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22 We lost data in 2 out of 12 patients due to errors in procedures related to data saving.
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Closer integration of the accelerometer analysis into the speech mapping module
3
probably would avoid such errors. Similar recordings could, in principle be done also
4
with ordinary microphones. However, recordings with microphones during the rTMS
5
speech mapping paradigm can be problematic due to the loud rTMS clicks, and also due
6
to other environmental sounds, such as arising from the coil cooling system, present in
7
the stimulation environment (See Figure 2).
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8 Conclusion
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In this study, we developed an accelerometer signal-based automated routine for voice
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onset detection from larynx vibrations to be used with navigated rTMS speech mapping.
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The automated routine was found feasible and it detects excellently the rTMS stimulation
13
train onsets, the corresponding vocalization onsets and the no-response errors. This
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method produces numerical result tables indicating the latency of each response, thus
15
adding reliability, repeatability, and objectivity to the rTMS speech mapping/object
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naming analysis.
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Acknowledgements
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This study was financially supported by a development grant from the HUS Medical
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Imaging Center. We thank Helge Kainulainen and Ronny Schreiber at the department of
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Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland,
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for the technical support with the accelerometer.
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Page 22 of 27
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12. Hernandez-Pavon JC, Mäkelä N, Lehtinen H, Lioumis P, Mäkelä JP. Effects of
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13. Sollmann N, Hauck T, Hapfelmeier A, Meyer B, Ringel F, Krieg SM. Intra- and
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14. Krieg SM, Sollmann N, Hauck T, Ille S, Meyer B, Ringel F. Repeated mapping of
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15. Ille S, Sollmann N, Hauck T, Maurer S, Tanigawa N, Obermueller T, et al.
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Impairment of preoperative language mapping by lesion location: a functional magnetic
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17. Deletis V, Fernández-Conejero I, Ulkatan S, Rogić M, Carbó EL, Hiltzik D.
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18. Bourguignon M, De Tiège X, de Beeck MO, Ligot N, Paquier P, Van Bogaert P, et al.
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19. Hillman RE, Heaton JT, Masaki A, Zeitels SM, Cheyne HA. Ambulatory monitoring
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of disordered voices. Ann Otol Rhinol Laryngol. 2006;115(11):795-801.
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20. Lindstrom F, Ren K, Li H, Waye KP. Comparison of two methods of voice activity
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detection in field studies. J Speech Lang Hear Res. 2009;52(6):1658-1663.
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21. Orlikoff RF. Vocal stability and vocal tract configuration: An acoustic and
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electroglottographic investigation. J Voice. 1995;9(2):173-181.
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22. Ruohonen J, Karhu J. Navigated transcranial magnetic stimulation. Neurophysiol Clin.
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2010;40(1):7-17.
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23. Rossini PM, Barker AT, Berardelli A, Caramia MD, Caruso G, Cracco RQ, et al.
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Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots:
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26 24. Epstein CM, Lah JJ, Meador K, Weissman JD, Gaitan LE, Dihenia B. Optimum
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25. Brodeur MB, Dionne-Dostie E, Montreuil T, Lepage M. The Bank of Standardized
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26. Oppenheim A, V., Schafer R, W., Buck j, R. Discrete-Time Signal Processing. 2nd ed.
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Upper Saddle River, New Jersey 07458: Prentice Hall; 1999.
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27. Samjin C, Zhongwei J. Comparison of envelope extraction algorithms for cardiac
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28. Myers S, Hansen BB. The origin of vowel lenght neutralization in final position:
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Evidence from finnish speakers. Natural Language and Linguistic Theory.
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2007;25(1):157-193.
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29. Summerfield Q. Lipreading and audio-visual speech perception. Philos Trans R Soc
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Lond B Biol Sci. 1992;335(1273):71-78.
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Highlights rTMS-induced modifications of naming are commonly analyzed from video reordings Detection of vocalization-related larynx vibrations via accelerometer is introduced Our setup offers additional information to the behavioral data Automated routine correctly detected 96% of the voice onsets The new method improves the repeatability and objectivity of rTMS language mappings
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S0165-0270(15)00194-6 http://dx.doi.org/doi:10.1016/j.jneumeth.2015.05.015 NSM 7234
To appear in:
Journal of Neuroscience Methods
Received date: Revised date: Accepted date:
3-2-2015 19-5-2015 21-5-2015
Please cite this article as: Vitikainen A-M, M¨akel¨a E, Lioumis P, Jousm¨aki V, M¨akel¨a JP, Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation, Journal of Neuroscience Methods (2015), http://dx.doi.org/10.1016/j.jneumeth.2015.05.015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1 Title: Accelerometer-based automatic voice onset detection in speech mapping with navigated repetitive transcranial magnetic stimulation
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Authors: Anne-Mari Vitikainen a,b Lic.Phil, Elina Mäkeläa B.Sc., Pantelis Lioumisa,c Ph.D., Veikko Jousmäkid,e Ph.D., Jyrki P. Mäkelä a M.D., Ph.D. a
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Article type: Research article Number of text pages: 20 Number of figures: 2 Number of tables: 1 Number of supplementary figures: 2 Number of supplementary tables: 3
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BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 Helsinki b Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki c Neuroscience Center, University of Helsinki, P.O. Box 56, FI-00014 Helsinki d Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 15100, FI-00076 AALTO, Espoo, Finland e Aalto NeuroImaging, Aalto University School of Science, P.O. Box 15100, FI-00076 AALTO, Espoo, Finland
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Address for correspondence: Anne-Mari Vitikainen BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital P.O. Box 340, FI-00029 Helsinki Tel: +358 5042 72020 Fax: +358 9471 74404 E-mail: [email protected]
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All authors’ e-mails: Anne-Mari Vitikainen Elina Mäkelä Pantelis Lioumis Veikko Jousmäki Jyrki P. Mäkelä
[email protected] [email protected] [email protected] [email protected] [email protected]
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2 Abstract
2
Background: The use of navigated repetitive transcranial magnetic stimulation (rTMS) in
3
mapping of speech-related brain areas has recently shown to be useful in preoperative
4
workflow of epilepsy and tumor patients. However, substantial inter- and intraobserver
5
variability and non-optimal replicability of the rTMS results have been reported, and a
6
need for additional development of the methodology is recognized. In TMS motor cortex
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mappings the evoked responses can be quantitatively monitored by electromyographic
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recordings; however, no such easily available setup exists for speech mappings.
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New Method: We present an accelerometer-based setup for detection of vocalization-
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related larynx vibrations combined with an automatic routine for voice onset detection for
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rTMS speech mapping applying naming.
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Comparison with Existing Method(s): The results produced by the automatic routine were
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compared with the manually reviewed video-recordings.
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Results: The new method was applied in the routine navigated rTMS speech mapping for
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12 consecutive patients during preoperative workup for epilepsy or tumor surgery. The
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automatic routine correctly detected 96% of the voice onsets, resulting in 96% sensitivity
17
and 71% specificity. Majority (63%) of the misdetections were related to visible throat
18
movements, extra voices before the response, or delayed naming of the previous stimuli.
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The no-response errors were correctly detected in 88% of events.
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Conclusion: The proposed setup for automatic detection of voice onsets provides
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quantitative additional data for analysis of the rTMS-induced speech response
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modifications. The objectively defined speech response latencies increase the
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repeatability, reliability and stratification of the rTMS results.
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3 1 2
Keywords: Presurgical planning, navigated TMS (nTMS), object naming, speech
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mapping
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4 1. Introduction:
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Localization of speech-related brain areas by navigated repetitive transcranial magnetic
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stimulation (rTMS) during an object naming task has been suggested to be useful in
8
planning of brain tumor and epilepsy surgery (1, 2). Use of individual’s magnetic
9
resonance imaging (MRI) based navigation with rTMS mapping enables the speech
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related cortical sites to be transferred to the neuronavigation system (3) and to be used in
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surgical planning. Preoperative speech mapping by navigated rTMS may aid in objective
12
risk-benefit assessment of the planned surgery, enable more precisely targeted smaller
13
craniotomies, faster and safer intraoperative mapping, and safer surgeries for patients that
14
cannot undergo awake craniotomy (2, 4). The rTMS speech mapping results have been
15
compared to direct cortical stimulation (DCS) during awake craniotomy implying that
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nTMS is remarkably sensitive but relatively non-specific in detecting the sites producing
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speech disturbance in DCS (4, 5). In preoperative navigated TMS (nTMS) mapping of
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motor cortex, shown to have a very good match with DCS (5, 9), the responses to nTMS
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are monitored by motor evoked potentials from the activated muscles. No such
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straightforward, easily recordable marker exists for detection of speech modifications
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induced by rTMS.
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4 The navigated rTMS method has been accepted by US Food and Drug Administration
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(FDA) for presurgical speech mapping in 2012 (10), and, consequently, its use will
3
probably expand in the near future. Recently, rTMS speech mapping results in patients
4
with brain tumors and healthy subjects have suggested tumor-induced plasticity of speech
5
representation areas (11, 12), and demonstrated differences in cortical areas related to
6
object and action naming in healthy subjects (13). Thus, nTMS during an object naming
7
task may have an impact on surgery planning and provide information about the
8
organization of speech-related brain areas in general. Nevertheless, the intraobserver and
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interobserver comparisons of the nTMS speech mapping results show only limited
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replicability, and the currently used protocols need further development (14, 15).
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Additionally, the methodology is not completely standardized between the surgical
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groups applying it.
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The no-response (speech arrest) errors are the most replicable results of nTMS speech
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mapping (11). However, speech disturbances such as semantic and phonological
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paraphasias, and performance errors during the pulse train are more difficult to separate
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quantitatively from the recorded videos. Particularly, the value of hesitations, delayed but
18
not completely abolished responses induced by rTMS, is not clear, as their evaluation is
19
quite subjective; interpretation of these errors has been considered as a possible reason
20
for a high rate of false positive results of rTMS studies as compared with DCS (16).
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Microphone recording of vocalization to detect the voice onsets objectively is hampered
23
by ambient noise from TMS pulses and coil cooling (1). Electromyographic (EMG)
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5 signals from cricothyroid muscles have been used in combination with nTMS to monitor
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effects of nTMS to the inferior frontal cortex on larynx muscles during object naming
3
tasks (17). However, the insertion of the EMG wire electrodes to the cricothyroid muscle
4
is invasive and requires skill (18).
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Detection of larynx vibrations, coinciding with the fundamental frequency of the voice,
7
with an accelerometer enables non-invasive follow-up of speech vocalizations (19).
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Accelerometers can accurately measure vocal activity (20-22). Compared with
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microphones, accelerometers are not sensitive to ambient environmental sounds and are
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therefore well suitable for voice assessment (21).
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In this study, we tested the feasibility of using an accelerometer to pick up the onsets of
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the vocalizations in navigated rTMS speech mapping.
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2. Materials and Methods
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2.1. Subjects
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We made the accelerometer recordings as part of the rTMS speech mappings for twelve
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consecutive patients (4 females/8 males, age range 12–39 years) going through tumor or
19
epilepsy surgery workup. Both hemispheres were stimulated with rTMS in eleven
20
patients; patient #4 did not tolerate the right-hemisphere stimulation and only his left
21
hemisphere was stimulated. For one patient, the data during the baseline was not recorded
22
due human error. For one patient the data was lost due technical difficulties, for patient
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#7 the data of the left hemisphere stimulation was inaccessible, and for patient #6 a first
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6 1
part of data of left hemisphere stimulation was corrupted. The results of ten patients are
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presented. The study was approved by the local Ethical Committee.
3 2.2. Experimental design
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The experiment started with an initial baseline session without rTMS to select the images
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that were correctly named and pronounced. In order to enable the offline comparison of
7
the responses with and without the rTMS, the baseline image set was run two times more.
8
During the second baseline session the rTMS coil was held near the patient’s head in the
9
navigation field of the rTMS system to ensure the trigger pulse output. The intensity of
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the baseline stimulation was held at 0 or 1 % of the maximum stimulator output, i.e., no
11
rTMS stimulation was applied. Images that were not named, not named correctly, not
12
named clearly, not articulated correctly and named with delay or hesitation were removed
13
from the image set after the first baseline round. Only the data from the second baseline
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round was used for the analysis and subsequent rTMS sessions. The images were
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displayed in random order. All sessions were video-recorded for offline analysis.
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The rTMS measurements were carried out at the BioMag Laboratory using eXimia NBS
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4.3 (Nexstim Ltd., Helsinki, Finland) and a commercial speech mapping module
19
(NexSpeech, Nexstim Ltd., Helsinki, Finland). The navigation system estimates the
20
strength of the maximum electric field at the stimulation location and overlays the
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estimated field strength on-line on the 3-D reconstruction of the individual’s brain (23).
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Each stimulation site is tagged to the structural magnetic resonance (MR) images for
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subsequent analysis.
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7 1 All rTMS stimulations were done with a figure-of-eight coil of a 70-mm outer diameter
3
and biphasic pulse shape. The resting motor threshold (MT) was determined from the
4
abductor pollicis brevis muscle controlled by the hemisphere affected by the epilepsy or
5
tumor. The method used by Rossini et al. (24) was used for the MT determination. The
6
rTMS intensity for the mapping was adjusted to produce roughly equally strong electric
7
field to all perisylvian cortical regions. If the stimulation caused intolerable discomfort to
8
the subject, its intensity was lowered in 5-10 % decrements until tolerable. Thus, the
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stimulation intensity varied somewhat across subjects. The estimated induced electric
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field strength at the cortex was registered. The stimulations were done with 5-pulse rTMS
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trains at 5 or 7 Hz (1, 25).
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In nine patients, the images to be named were a subset of color images out of set of 84
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images depicting everyday objects (1). In three patients, a selection of 92 images from a
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standardized image set (26) was used. The selection from the standardized set was chosen
16
to represent frequently used items in Finnish every day life, whose names are common in
17
Finnish language, and that have only few synonyms. The subjects were asked to name the
18
objects in Finnish as quickly and precisely as possible. The images were displayed for
19
700 ms on a computer screen with 2.0-3.0 s interstimulus intervals (ISI). The experiment
20
started with a 2.5 s ISI. If needed, the ISI was adjusted according to the baseline
21
performance of the patient. The rTMS trains started 300 ms after the image onset. The
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coil was hand-held and freely movable between the pulse trains. During the stimulation,
23
the coil was moved between the pulse trains following a grid-like pattern so that the
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8 stimulated locations covered systematically a wide fronto-temporo-parietal cortical area.
2
The orientation of the coil was adjusted to induce current primarily perpendicular to the
3
fibers of the temporalis muscle to minimize muscle twitching, and secondarily
4
perpendicular to the sulcus at the stimulation location. The cortical sites where rTMS
5
produced naming errors were revisited to evaluate the repeatability of the effect.
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2.3. Manual review of the mapping data
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The speech mappings were routinely reviewed offline from the video by a
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neuropsychologist with expertise in effects of DCS on speech. The categories available in
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the speech mapping module (no error, no-response error, performance error, semantic
11
paraphasia, muscle stimulation and other) were applied in the analysis. Additionally
12
information about performance errors’ subdivision (e.g. delays, phonological
13
paraphasias) was noted in the free comment field. The speech response latencies were not
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available in the speech mapping module.
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2.4. Vibration recording
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Subject’s vocal activity, i.e. fundamental frequency of the voice, was recorded during
18
object naming with a three-axis accelerometer (ADXL330 iMEMS® Accelerometer,
19
Analog Devices, Norwood, MA) attached to the skin on left side of the subject’s throat,
20
onto the larynx site producing palpable vibrations during vocalization (Fig. 1). The
21
analog accelerometer signals were connected to the EMG system of the stimulator with a
22
custom built interface. The recorded frequency band was 10–500 Hz and sampling rate 3
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kHz. Similar accelerometer has been used previously by Bourguignon and co-workers to
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detect the fundamental frequency of the reader’s voice (19).
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Fig. 1. The accelerometer attached to the throat.
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2.5. Automated routine for voice onset detection For the automated routine, the following files were collected: the tabular data of the
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speech mapping (speech-file containing the speech event related data from the
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commercial speech mapping module, including the name of the picture, speech exam
16
identifications,and rTMS train sequence identifiers, converted to excel file), and
17
accelerometer and trigger signals (edf-files). Each accelerometer data file corresponding
18
to each of the speech exams (sessions) were identified and verified.
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13
20
The accelerometer signals were high-pass filtered with Butterworth filter (4th order, cut-
21
off frequency of 80 Hz) to reduce low frequency interference and possible signal level
22
drifts while maintaining the characteristics of the voice (19, 22) ( Figure 2). The data was
23
first filtered in forward and then in reverse direction, preserving the waveform features
24
exactly at the same time point where they occur in the original signal (27). To enable a
Page 9 of 27
10 robust automatic onset calculation routine, the envelope (see Figure 2) of the filtered
2
signal was calculated using Hilbert transform. The signal envelope captures the varying
3
features of the signal generating the signal outline, and its analytic representation enables
4
fast calculations. This approach is commonly used in sound signal processing (28).
5
The recordings were split into several sessions (and thus several data files) controlled by
6
the commercial program module. Some of the sessions may contain short but extremely
7
intensive vibrations due to e.g. coughs while maintaining constant level of the vibrations
8
related to silence and speech responses. In order to enable uniform processing of the
9
baseline and subsequent rTMS sessions, the mean of the signal envelope during the
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whole session was calculated to represent the overall vibration level of the session. An
11
active speech threshold was formed by multiplying this general signal level with an
12
individually adjustable constant.
d te
13
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10
The first rough estimate of the speech periods was formed by taking into account the
15
signal periods where the envelope of the signal amplitude exceeded the active speech
16
threshold (29). This was done sample by sample (sampling rate 3000 Hz). Erroneous
17
periods containing e.g. coughs, sighs, swallowing, moving of the jaw etc. were included
18
into analysis, in addition to the speech response. To extract only the true speech response
19
onsets, and not the shorter signals originating from non-desirable events, the envelope
20
signal was then modified by applying a moving average filter (implemented as
21
convolution, with a rectangular unit pulse of 40 ms in length; Figure 2). After this the first
22
rising edge of the resulting signal between successive rTMS train onsets was determined
23
as the speech response onset corresponding the given rTMS train onset.
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11 1 The active speech threshold limit and the length of the moving average filter window and
3
shape of the convolution kernel (rectangular unit pulse) were checked manually to be
4
appropriate for our measurement setup with four randomly selected sessions (not
5
included in the results).
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6
Page 11 of 27
12 Fig. 2. An example of the analysis steps of the automated routine and the corresponding audio
2
waveform of a sample of data from patient #1. A) The original accelerometer signal, B) filtered
3
signal, C) signal envelope and the active threshold (dashed line), D) resulting modified signal
4
after the moving average filter. The rising edge of the signal after each rTMS train onset is
5
determined as the speech response onset, E) the rTMS train trigger pulses, F) The corresponding
6
audio waveform. Note strong signals from the clicks induced by rTMS.
cr
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7
The trigger pulses of the rTMS stimuli, collected together with the accelerometer data,
9
enabled the calculation of the onset latencies of vocalizations and the onsets of the stimuli
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(Fig. 2). The responses for each image from the rTMS session were matched with the
11
corresponding images in baseline, and the voice onset time difference was calculated. To
12
improve the usability of the routine, the rTMS train sequence numbers are shown in the
13
overview figures (see Supplementary Figure A) and the image name in the response
14
comparison figures. Finally, a list of the responses which onset time difference exceeding
15
a chosen value (default 100 ms) compared to the baseline was printed. The results were
16
visually crosschecked from the recorded video and from the signals for erroneous onset
17
detections.
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Page 12 of 27
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1
Fig. 3. An example of the response comparison of one object naming baseline-rTMS session pair
3
from patient #1. The voice onset time is prolonged by 227 ms when rTMS is applied, and instead
4
of naming the image correctly as “pullo” (bottle), the patient named it as “kokis” (coke) (semantic
5
error). This is seen as a divergent signal shape. (For interpretation of the references to color in this
6
figure legend, the reader is referred to the web version of the article. The MATLAB figure is
7
provided as Supplementary Figure B.)
9
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2
2.6. Comparison of the data
10
The results produced by the automated routine were compared with the manually
11
reviewed results from video with the following five components: a) the number of
12
detected rTMS pulse trains compared with the number of the actually occurring rTMS
13
trains, b) the number of the correctly detected rTMS pulse trains, c) the number of
14
detected voice onsets, d) the number of the correctly detected voice onsets, and e) the
15
number of no-response errors. The voice onset latency could not be directly compared, as
Page 13 of 27
14 1
it could not be defined precisely from the video recordings. However, we defined the
2
delays of responses scored as “delays” by our neurophysiologist.
3 The correctness was evaluated against the response performance observed from the
5
videos for every response in which the correctness was in doubt. The reason for
6
misdetection of the rTMS train onset resulted from not detecting the trigger signal, or
7
detecting extra trigger signals. The reason for voice onset misdetections were classified to
8
four categories according to the underlying cause: I) throat movement related problems
9
(swallows, jaw movements, muscle stimulation, grimaces, etc.); such activity was
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detected before the actual response or movement was detected without any vocalization,
11
II) extra voice and associated movements taking place before the actual response (such as
12
'hmm', 'eeeh', coughs, sighs, etc.), III) delayed naming of the previous image, and IV)
13
other reasons. The reason for misdetection of the no-response errors was analyzed as well.
14
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3. Results:
16
The accelerometer signals were easily recorded with the EMG system of the TMS device,
17
enabling on-line visualization of the signal during the stimulation and detailed off-line
18
analysis.
19
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20
The rTMS train sequence was detected correctly in 98 % of all patients with sensitivity of
21
99 % and specificity of 86 %. Detailed breakdown of the detection performance for each
22
patient is given in Supplementary Table A. A confirmed rTMS train sequence was
23
associated in 98 % of the shown images. The reason for not having an rTMS train
Page 14 of 27
15 sequence while the image was present was the movement of the stimulation coil out of
2
the navigation field of the rTMS system. The sequence of images delivered by the speech
3
module can only be interrupted manually and is not directly related to successful
4
detection of the stimulator coil by the navigation system. 78 % of the misdetections were
5
due to lack of the trigger pulses during the images: either the pulse sequence was not
6
delivered, or it was delivered only partially. The rest of the misdetections resulted from
7
extra trigger signals of unknown origin in between the confirmed train sequences.
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8 3.1. Voice onset detection
an
9
The latencies of the vocalizations during rTMS were increased by more than 100 ms in,
11
on average, 26±13 trials (range 7-55 trials), and by more than 500 ms in, on average 9±5
12
trials (range 2-19 trials) (For values with intermediate delays, see Table 2). Longer
13
latency delays were less common than short ones in all patients.
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The voice onset detection performance was evaluated as portion of the correctly detected
16
voice onsets of all voice response onsets; details are given in Table 1. The sensitivity of
17
the automated routine to correctly detect the voice onsets was 96 %, and the specificity
18
71 %. Majority of the misdetections were related to visible throat movements before the
19
actual response (26 %), to extra voice before the response (24 %) or other, e.g. trigger
20
related problems (36 %). Delayed naming of the previous image was present in 13 % of
21
the misdetections. Detailed categorization of the misdetections for each patient separately
22
is provided as Supplementary Table B.
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Page 15 of 27
ip t 31
2
39
3
36
4
39
5
12
6
17
7
37
8
15
9
17
F F M M M F
MTa (% of MSOPb)
50 25 25 39
M M M
Condition
Baseline rTMS LHc Baseline rTMS LH Baseline
63
38
25 63 62
Stimulation intensity range (% MT)
rTMS LH Baseline
rTMS LH Baseline
rTMS LH Baseline
rTMS LH Baseline
rTMS RH Baseline rTMS LH Baseline rTMS LH
rTMS train (Hz)
M an
1
Sex (F/M)
100 to 90 100
ed
Age (y)
ce pt
Patient no.
us
Table 1 The patient characteristics, stimulation parameters and response onset detection performance.
Ac
1 2 3
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16
100
100 to 77 95 to 87 100
100 to 120 100 to 71 97 to 89
5, 7
5, 7
5, 7 5, 7 5 5 5 5 5, 7
Occurring (count) 42
Response onsets Correctly detected (count) 42
406 115
404 112
340 200
333 198
334 133
332 133
265 114
251 101
111 157
96 155
172 172
165 164
119 97
111 97
206 78
182 78
292
285
Correctly detected (%) 99.5 97.4 98.8 96.5 85.9 96.5 94.5 90.3 98.3
Page 16 of 27
a
ip t M
Baseline
36
100
5
103
100
176
173
us
rTMS LH
97.2 95,7
Grand average
motor threshold, b maximum stimulator output, c left hemisphere, d right hemisphere
Table 2 The response vocalizations with delays and increased latencies. Responses marked as delays
2 3 4
rTMS LH
2
rTMS RHd rTMS LH
0 0
rTMS RH rTMS LH
1 11
6 7 8
Average delay (ms)
>150ms (count)
>200ms (count)
>500ms (count)
18
16
4
-
16 15
13 12
10 11
4
281 407
15 55
12 42
9 33
4 4
6 0
434 -
41 24
32 9
26 5
8 -
7 (9)e
1175
34
33
32
15
3 (4)e 2
758 884
32 45
32 37
31 35
19 17
rTMS RH rTMS RH
5 0
649 -
37 8
34 5
32 5
10 4
rTMS LH
0
-
24
21
20
11
rTMS RH
0
-
7
4
4
2
rTMS RH rTMS LH
rTMS RH rTMS LH
665
>100ms (count)
Responses with increased latency
24
rTMS LH 5
Delayed (count)
c
ed
1
Condition
ce pt
Patient no.
Ac
1 2 3 4
17
M an
10
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17
Page 17 of 27
ip t All
-
25
rTMS RH rTMS LH
0 0
-
11 27
rTMS RH Average ± SD
1 2±3
1221 719 ± 329
23 26 ± 13
22
21
9
8 25
6 21
7
20 21 ± 12
19 19 ± 11
12 9±5
Range 0 - 11 281 - 1221 7 - 55 4 - 42 4 - 35 2 - 19 motor threshold, b maximum stimulator output, c left hemisphere, d right hemisphere, e the average is calculated from those response onsets where the onset was available
ce pt
ed
a
Ac
1 2
0
us
10
rTMS LH
M an
9
cr
18
Page 18 of 27
19 1
3.2. Detection of no-response errors The no-response errors were detected correctly in 88 % of all the no-response errors,
3
including the “no-response” events in the baseline sessions. Details for the detection of
4
no-response errors for each patient is given in Supplementary Table C. As the
5
accelerometer data also contains the first round of the images in the baseline session, and
6
thus also the responses to the images named incorrectly, not named, not named clearly,
7
not articulated correctly and named with delay or hesitation, the following results are
8
calculated separately for baseline and rTMS sessions. In baseline sessions the sensitivity
9
and specificity were 100 %. In rTMS sessions the overall sensitivity was 82 % and
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specificity 100 %. The reasons for misdetection followed the same categories as in voice
11
onset misdetections.
M
10
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12 4. Discussion:
14
The accelerometer-based method presented here measures the voice onset latency to
15
specific image stimuli. The accelerometer recording produced high-quality signals and
16
enabled automatic voice onset time detection. The recordings were collected from 12
17
consecutive patients who required rTMS speech mapping; thus they reflect overall
18
feasibility of the presented setup in a real clinical situation. The automated routine was
19
compared to a manual review of the rTMS speech mapping videos, which is the present
20
method to analyze the errors in the object naming task. We found that the presented
21
method with the automated routine correctly identified 98 % of all presented rTMS trains
22
onsets and 96 % of the voice onsets. This suggests that the methodology could produce
23
an additional reliable means to stratify the effects of rTMS in an object naming task for
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Page 19 of 27
20 1
presurgical planning. Moreover, it could provide a preliminary indicator to detect the no-
2
response errors and thus speed up the analysis of the videoed responses.
3 Our setup offers fast additional information to the behavioral data from video analysis of
5
the naming performance (1). Short delays may pass unnoticed in video analysis of several
6
hundred images and responses in several sessions, but they can be detected and promptly
7
quantified by the accelerometer recording. Most importantly our presented setup provides
8
numeral values of naming latencies, therefore reducing subjectivity and increasing
9
repeatability and reliability of the analysis. We are not aware of reports studying just
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4
notifiable differences in delays of naming. Healthy subjects distinguish a reliable speech
11
asynchrony if acoustical signal leads lip opening by 80 ms or lags it by 140 ms (30);
12
probably minimum perceived differences in naming delay are in the same time range.
13
The delays identified by visual scoring ranged from 300 to 1200 ms with an average of
14
700 ms. This variability may relate, in part, to the regularity of the patient performance
15
generating a background baseline for response variability in visual analysis. Our setup,
16
however, enables selection of any delay for a more precise scrutiny. The final clinical
17
value of different delays can only be identified by comparison with the data obtained by
18
DCS during awake craniotomy.
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19
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10
20
The automated routine may not recognize all stop consonants in the beginning of a word
21
as their signal amplitude is very small during the voice onset. The smaller thresholds
22
required for their detection is not feasible, as spikes caused by TMS and other random
23
disturbances would be classified as speech. However, this drawback is not important as
Page 20 of 27
21 the latencies in rTMS condition are compared to the baseline latencies of the same word,
2
and the beginning of the word is usually lost in both conditions. Only the loudness
3
variation between the baseline and rTMS condition may cause problems despite the use
4
of relative detection threshold: the automated routine may detect the onset only in the
5
louder vocalization and cause an error in comparison of the baseline and rTMS data.
6
Therefore, visual evaluation of the automatically detected voice onsets is still important.
7
Most observed erroneous latency detections were induced by coughing or sighing before
8
the actual response. These artifacts resulted in too short, not abnormally long latencies.
9
Instead, the true effects of rTMS caused a delayed vocalization. Therefore, the design of
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10
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1
our algorithm minimizes false positive findings.
M
11
The automated analysis detected successfully latencies for the naming of presented
13
images. Although the patients rehearsed naming, some of them had particular difficulties
14
to name specific images fluently during rTMS. This may indicate that image-related
15
factors, instead of rTMS-related ones, are the underlying reason for such variability. Our
16
algorithm reliably identifies such images and enables straightforward comparison with
17
the DCS data to study this question.
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19
The analysis of the measured signals can be developed further. For example, a shape
20
recognition algorithm (see Figure 3) could recognize rTMS-induced differences of
21
pronunciation or word change on the vowel-associated vibration pattern in comparison
22
with the pattern recorded during the baseline.
23
Page 21 of 27
22 We lost data in 2 out of 12 patients due to errors in procedures related to data saving.
2
Closer integration of the accelerometer analysis into the speech mapping module
3
probably would avoid such errors. Similar recordings could, in principle be done also
4
with ordinary microphones. However, recordings with microphones during the rTMS
5
speech mapping paradigm can be problematic due to the loud rTMS clicks, and also due
6
to other environmental sounds, such as arising from the coil cooling system, present in
7
the stimulation environment (See Figure 2).
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8 Conclusion
an
9
In this study, we developed an accelerometer signal-based automated routine for voice
11
onset detection from larynx vibrations to be used with navigated rTMS speech mapping.
12
The automated routine was found feasible and it detects excellently the rTMS stimulation
13
train onsets, the corresponding vocalization onsets and the no-response errors. This
14
method produces numerical result tables indicating the latency of each response, thus
15
adding reliability, repeatability, and objectivity to the rTMS speech mapping/object
16
naming analysis.
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Acknowledgements
19
This study was financially supported by a development grant from the HUS Medical
20
Imaging Center. We thank Helge Kainulainen and Ronny Schreiber at the department of
21
Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland,
22
for the technical support with the accelerometer.
23
Page 22 of 27
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Highlights rTMS-induced modifications of naming are commonly analyzed from video reordings Detection of vocalization-related larynx vibrations via accelerometer is introduced Our setup offers additional information to the behavioral data Automated routine correctly detected 96% of the voice onsets The new method improves the repeatability and objectivity of rTMS language mappings
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Page 27 of 27
