Physiological Measurement
Related content
PAPER
Evaluation of postural steadiness before and after sedation: comparison of four nonlinear and three conventional measures
- The effect of age on balancing behavior: complexity analysis of mediolateral force trajectories A L Hilbun and I Karsai
To cite this article: A Tietäväinen et al 2014 Physiol. Meas. 35 139
- Relationships between HRV measures Mirjana M Platisa and Vera Gal
View the article online for updates and enhancements.
- Validation of balance-quality assessment using a modified bathroom scale D J Hewson, J Duchêne and J-Y Hogrel
Recent citations - Baseline adjustment increases accurate interpretation of posturographic sway scores A. Tietäväinen et al
This content was downloaded from IP address 150.216.68.200 on 13/01/2018 at 14:50
Institute of Physics and Engineering in Medicine Physiol. Meas. 35 (2014) 139–151
Physiological Measurement
doi:10.1088/0967-3334/35/2/139
Evaluation of postural steadiness before and after sedation: comparison of four nonlinear and three conventional measures 1 ¨ ainen ¨ ¨ 1 and J E Mandel 2 A Tietav , E Hæggstrom 1
Department of Physics, University of Helsinki, Helsinki, Finland Department of Anesthesiology and Critical Care, University of Pennsylvania School of Medicine, Philadelphia, USA 2
E-mail: [email protected] Received 14 January 2013 Accepted for publication 20 November 2013 Published 7 January 2014 Abstract
Sedative drugs decrease postural steadiness and increase the risk of injury from falls and accidents. The recovery rate is individual, making it hard to predict the patient’s steadiness and hence safe discharge time. 103 outpatients sedated with midazolam and fentanyl were measured posturographically, before (PRE) and after (POST) endoscopy. The ability of conventional and nonlinear sway measures to separate the PRE and POST conditions were compared, and the area under the receiver operating characteristics curve (AUC) was used to quantify the significance of the separation. A nonlinear measure, fuzzy sample entropy, scored the largest AUC (AUCFSE = 0.83, p < 0.0001). While the AUCFSE was not significantly larger than the AUCs of conventional sway measures which offer easy quantification of postural steadiness, nonlinear measures provide more insight into the structure of postural control, which may help understand the effect of sedation on postural steadiness. This study is a step toward developing a tester that indicates a safe discharge time. Keywords: anesthesia recovery, postural steadiness, fuzzy sample entropy, Lyapunov exponent, detrended fluctuation analysis, correlation dimension, midazolam
1. Introduction Fitness for ambulation is an important criterion for discharge after minor procedures that involve sedation. Sedatives impair postural steadiness and ambulatory fitness (Fujisawa et al 2006, Melzer et al 2004). Hence, early discharge may lead to an increased risk of falls among patients. Since both the baseline postural steadiness and the drug wash-out and recovery rate 0967-3334/14/020139+12$33.00
© 2014 Institute of Physics and Engineering in Medicine Printed in the UK
139
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
vary between patients, the assessment of fitness for ambulation is subjective and arbitrary, and an objective test would be useful in determining fitness for discharge. Maintaining balance requires cooperation amongst visual, vestibular, and proprioceptive senses, the central nervous system (CNS), and the musculoskeletal system. A healthy person’s postural control is a physiologically complex system that creates practised and ‘fine-tuned’ movements (Harbourne and Stergiou 2009). Physiological complexity arises from intrinsic nonlinearity and the output of such system manifests highly variable fluctuations that resemble chaos and that are associated with long-range, fractal correlations (Goldberger et al 2002, Harbourne and Stergiou 2009). When facing a challenging postural state such as balance disorders, ageing or sedatives, counterbalancing body movements are large and crude and the physiological complexity is low. Midazolam impedes postural control by decreasing both CNS activation level and skeletal muscle tone (Olkkola and Ahonen 2008). In young, healthy persons balance impairment was significant during 45 min (Gupta et al 1991), and with elderly patients during 50 min after administering midazolam (Fujisawa et al 2006). Static posturography evaluates postural steadiness by recording a subject’s center-ofpressure (COP) deviations in the mediolateral and anterior–posterior (AP) planes while he/she stands erect on a force plate (Prieto et al 1996). Postural steadiness is then quantified with sway measures that conventionally relate to sway amplitude, velocity or frequency (Prieto et al 1996, Chiari et al 2002). Nonlinear measures characterize the structure of the COP signal (Harbourne and Stergiou 2009), exposing the physiological complexity of the balancing act. Nonlinear measures can be interpreted in terms of a person’s motor control (Harbourne and Stergiou 2009, Roerdink et al 2006), and may help to understand the state of the postural control and postural steadiness. No single nonlinear measure can be used to quantify physiological complexity (Goldberger et al 2002). We concentrated on four measures (in the appendix): fuzzy sample entropy (FSE) quantifies the regularity or predictability of the COP signal (Xiong et al 2010), detrended fluctuation analysis (DFA) quantifies long-range correlation in the COP signal (Delignieres et al 2006, Peng et al 1994), correlation dimension (D2) estimates the dimensionality of the underlying postural control (Grassberger and Procaccia 1983, Roerdink et al 2006), and the largest Lyapunov exponent (λmax) quantifies the chaotic nature of the COP signal (Rosenstein et al 1993). These measures have been used in postural analysis in several ways—to study the effect of age on postural control (Amoud et al 2007, Lin et al 2008), recovery in stroke patients (Roerdink et al 2006), and role of attention in postural control (Donker et al 2007). A decreased DFA (or more specifically the scaling exponent, α, see in the appendix), an increased λmax, and a change in D2 and in (non-fuzzy) entropy have been reported to signal impaired postural steadiness (Lin et al 2008, Amoud et al 2007, Roerdink et al 2006, Donker et al 2007, Borg and Lax˚aback 2010, Duarte and Sternad 2008, Ladislao and Fioretti 2007). Previous reports on midazolam sedation utilized conventional measures and dynamic posturography. We hypothesized that decreased COP signal complexity quantified with nonlinear measures of static posturography could distinguish the post-sedative state from the pre-sedative state in patients undergoing elective ambulatory endoscopy. 2. Methods 2.1. Study design
The study was approved by the Institutional Review Board of the University of Pennsylvania School of Medicine. All consenting patients who could stand unaided and who had no diagnosed balance disorder were included. 103 patients undergoing colonoscopy or upper 140
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Table 1. Patients’ anthropometric data, mean (SD) and the given sedatives.
Males (n = 42) Females (n = 61) Total (n = 103) a16
Age (yrs)
Height (cm)
Weight (kg)
Midazolam (μg kg−1)
Fentanyl (μg kg−1)
Benadryl (mg kg−1)a
58 (12) 56 (12) 57 (12)
176 (8) 164 (9) 169 (10)
86 (17) 81 (20) 83 (19)
57 (18) 67 (24) 63 (22)
1.4 (0.4) 1.6 (0.6) 1.5 (0.5)
0.40 (0.09) 0.55 (0.20) 0.49 (0.18)
patients were given Benadryl.
GI endoscopy were measured before the procedure (PRE condition) during 60 s with their eyes open (EO) and during 60 s with their eyes closed (EC). The measurement was repeated after the procedure (POST condition), once the patient was deemed ready to be discharged. The patients had been instructed neither to eat during 24 h nor to drink during 6 h prior to the procedure. They were given sedative drugs midazolam and fentanyl (table 1). Sixteen patients were, in addition, given benadryl to rescue inadequate sedation. Measurements were conducted in the patients’ waiting pod (PRE) and in the recovery pod (POST) with the pod curtain closed. The average time between the PRE and POST measurements was 2.3 ± 0.6 h. During the measurements the patients were instructed to keep their hands at their sides, to keep their feet in a comfortable stance, and to fix their eyes on one spot (of their choice) in the pattern of the curtain hanging in front of them (EO), or to close their eyes (EC). A person stood next to the patient at all times ready to provide support in case the patient lost his/her balance. 2.2. Equipment
The portable equipment consisted of a Nintendo (Kyoto, Japan) Wii Fit balance board3 and a HP laptop (Compaq 6701b) that were connected with a Bluetooth link. The balance board featuring 60 Hz sampling frequency was operated with a custom made C# code that utilized the opensource library WiiMoteLib4. In Clark et al (2010) the Wii Fit board was deemed suitable for clinical use. 2.3. Signal processing
The AP sway direction was analyzed. The signals were standardized to zero-mean, and with the nonlinear measures, also to unit standard deviation. Nonlinear filtering based on the empirical mode decomposition5 (EMD) (Rato et al 2008) was employed to remove high frequency noise and low frequency trends. EMD was chosen because it is specifically designed for nonlinear and nonstationary signals (Huang et al 1998), such as the COP signal. With EMD, each signal was first decomposed into its intrinsic modes (9–12 modes, depending on the signal, the first mode having the highest frequency), and then recomposed leaving out the first two and the last two modes as in Tiet¨av¨ainen et al 2013. All results were also calculated without filtering. A signal before and after EMD filtering is shown in figure 1. Four nonlinear and three conventional measures (in the appendix) were used in the analysis. FSE (Xiong et al 2010) quantified the regularity of the signal. The
2.3.1. Sway measures.
3 A balance board with four transducers, designed originally for gaming. Nintendo Wii home page: www.nintendo.com/wii, balance board’s manual. www.nintendo.com/consumer/downloads/wiiBalanceBoard.pdf, (accessed 10 June 2013). 4 WiiMoteLib: http://wiimotelib.codeplex.com/ (accessed 10 June 2013). 5 The EMD is calculated with a code available in Matlab Central’s File Exchange by Dr Manuel Ortigueira (Rato et al 2008).
141
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Displacement (mm)
Unfiltered COP (a)
10 0 −10 0
20
40
60
Displacement (mm)
Filtered COP (b)
10 0 −10 0
20
40
60
Time (s) Figure 1. (a) Unfiltered 60 s AP COP signal and (b) corresponding EMD filtered signal.
long-range correlation in the COP signals was quantified with DFA (Delignieres et al 2006, Peng et al 1994). DFA provided a scaling exponent, α, as a sway measure. λmax (Rosenstein et al 1993) estimated the chaotic nature of the signal, whereas D2 (Grassberger and Procaccia 1983, Roerdink et al 2006) estimated the active control variables of the underlying balancing dynamics. The conventional measures were sway range (Range), mean velocity (v mean), and mean frequency ( f mean) (Prieto et al 1996).
The Lilliefors test for normality revealed that 37 out of the 56 distributions (2 filters × 4 conditions × 7 sway measures) were non-Gaussian at 5% significance level. Hence, to produce more Gaussian-like distributions, all 56 distributions were log-transformed before the statistical analysis. A three-way MANOVA with repeated measures and a student’s t-test as post hoc test was used to determine the effect of sedatives on postural steadiness. The independent variables were sedatives (PRE/POST conditions), vision (eyes open/closed), and filtering (no filtering/EMD filtering). The sway measures were taken as dependent variables. After applying the Bonferroni correction p2∗ dM+1, M is large enough and dM is the correlation dimension, D2. Here, for unfiltered data, M = 6 ± 1 and J = 21 ± 4, and for filtered data, M = 8 ± 2 and J = 25 ± 6. The region CM = 0.005–0.75 was chosen for the fit (Roerdink et al 2006). Largest Lyapunov exponent (λmax): In chaotic systems small perturbations may lead to large deviations from the equilibrium (Blaszczyk and Klonowski 2001). λmax estimates the local stability of a dynamic system; deterministic and nonlinear signals are chaotic if λmax is positive (Rosenstein et al 1993). We define ‘local stability’ as the postural control system’s sensitivity to small (local), internal perturbations (Harbourne and Stergiou 2003). The λmax algorithm also employs the state-phase presentation of the signal. If initially close trajectories later diverge exponentially, λmax is positive and the system is chaotic. The divergence is quantified using nearest neighbors, Xjˆ; Xjˆ is defined as the Xj that has a temporal separation greater than twice the lag J and that minimizes the Euclidean distance between Xj and Xjˆ. The divergence at an instance i is d(i)j = d(0)jeλmax(it), where d(0)j is the initial distance between Xj and Xjˆ. Hence, λmax is estimated with a least-squares fit to the line y(i) = 1/tlnd(i)j, where is the average value over j neighbors. The region of 0–0.75 s was chosen for the fit (Roerdink et al 2006). References Amoud H, Abadi M, Hewson D, Michel-Pellegrino V, Doussot M and Duchˆene J 2007 Fractal time series analysis of postural stability in elderly and control subjects J. Neuroengineering Rehabil. 4 12 Blaszczyk J W and Klonowski W 2001 Postural stability and fractal dynamics Acta Neurobiol. Exp. 61 105–12 Borg F G and Lax˚aback G 2010 Entropy of balance—some recent results J. Neuroengineering Rehabil. 7 38 Chiari L, Rocchi L and Cappello A 2002 Stabilometric parameters are affected by anthropometry and foot placement Clin. Biomech. 17 666–77 Choy N L, Brauer S and Nitz J 2003 Changes in postural stability in women aged 20 to 80 years J. Gerontol. A Biol. Sci. Med. Sci. 58 M525–30 149
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Clark R A, Bryant A L, Pua Y, McCrory P, Bennell K and Hunt M 2010 Validity and reliability of the Nintendo Wii balance board for assessment of standing balance Gait Posture 31 307–10 Delignieres D, Ramdani S, Lemoine L, Torre K, Fortes M and Ninot G 2006 Fractal analyses for ‘short’ time series: a re-assessment of classical methods J. Math. Psychol. 50 525–44 Donker S F, Roerdink M, Greven A J and Beek P J 2007 Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control Exp. Brain Res. 181 1–11 Duarte M and Sternad D 2008 Complexity of human postural control in young and older adults during prolonged standing Exp. Brain Res. 191 265–76 Fraser A M and Swinney H L 1986 Independent coordinates for strange attractors from mutual information Phys. Rev. A 33 1134–40 Fujisawa T, Takuma S, Koseki H, Kimura K and Fukushima K 2006 Recovery of intentional dynamic balance function after intravenous sedation with midazolam in young and elderly subjects Eur. J. Anaesthesiol. 23 422–5 Ghomashchi H, Esteki A, Sprott J and Nasrabadi A 2010 Identification of dynamic patterns of body sway during quiet standing: is it a nonlinear process? Int. J. Bifurcation Chaos Appl. Sci. Eng. 20 1269–78 Goldberger A, Peng C-K and Lipsitz L 2002 What is physiological complexity and how does it change with aging and disease? Neurobiol. Aging 23 23–26 Grassberger P and Procaccia I 1983 Characterization of strange attractors Phys. Rev. Lett. 50 346–9 ¨ Gupta A, Ledin T, Larsen L E, Lennmarken C and Odkvist L M 1991 Computerized dynamic posturography: a new method for the evaluation of postural stability following anaesthesia Br. J. Anaesth. 66 667–72 Hanley J A and McNeil B J 1982 The meaning and use of the area under a receiver operating characteristic (ROC) curve Radiology 143 29–36 Hanley J A and McNeil B J 1983 A method of comparing the areas under receiver operating characteristic curves derived from the same cases Radiology 148 839–43 Harbourne R T and Stergiou N 2003 Nonlinear analysis of the development of sitting postural control Dev. Psychobiol. 42 368–77 Harbourne R T and Stergiou N 2009 Movement variability and the use of nonlinear tools: principles to guide physical therapist practice Phys. Ther. 89 267–82 Huang N, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N-C, Tung C and Liu H 1998 The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. R. Soc. Lond. A 454 903–95 Ladislao L and Fioretti S 2007 Nonlinear analysis of posturographic data Med. Biol. Eng. Comput. 45 679–88 Lake D E, Richman J S, Griffin M P and Moorman J R 2002 Sample entropy analysis of neonatal heart rate variability Am. J. Physiol.. Regul. Integr. Comp. Physiol. 283 R789–97 Lasko T A, Bhagwat J G, Zou K H and Ohno-Machado L 2005 The use of receiver operating characteristic curves in biomedical informatics J. Biomed. Inform. 38 404–15 Lin D, Seol H, Nussbaum M A and Madigan M L 2008 Reliability of COP-based postural sway measures and age-related differences Gait Posture 28 337–42 Melzer I, Benjuya N and Kaplanski J 2004 Postural stability in the elderly: a comparison between fallers and non-fallers Age Ageing 33 602–7 Olkkola K T and Ahonen J 2008 Midazolam and other benzodiazepines Modern Anesthetics ed J Sch¨uttler and H Schwilden (Berlin: Springer) pp 335–60 Peng C-K, Buldyrev S V, Havlin S, Simons M, Stanley H E and Goldberger A L 1994 Mosaic organization of DNA nucleotides Phys. Rev. E 49 1685–9 Prieto T E, Myklebust J B, Hoffmann R G, Lovett E G and Myklebust B M 1996 Measures of postural steadiness: differences between healthy young and elderly adults IEEE Trans. Biomed. Eng. 43 956–66 Ramdani S, Seigle B, Lagarde J, Bouchara F and Bernard P L 2009 On the use of sample entropy to analyze human postural sway data Med. Eng. Phys. 31 1023–31 Rato R T, Ortigueira M D and Batista A G 2008 On the HHT, its problems, and some solutions Mech. Syst. Signal Process. 22 1374–94 Richman J S and Moorman J R 2000 Physiological time-series analysis using approximate entropy and sample entropy Am. J. Physiol. Heart Circ. Physiol. 278 H2039–49 Roerdink M, De Haart M, Daffertshofer A, Donker S F, Geurts A C H and Beek P J 2006 Dynamical structure of center-of-pressure trajectories in patients recovering from stroke Exp. Brain Res. 174 256–69 150
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Rosenstein M T, Collins J J and DeLuca C J 1993 A practical method for calculating largest Lyapunov exponents from small data sets Physica D: Nonlinear Phenom. 65 117–34 R Wii Fit based Tiet¨av¨ainen A, Gates F K, Meril¨ainen A, Mandel J E and Hæggstr¨om E 2013 Nintendo sleepiness tester detects impairment of postural steadiness due to 24 h of wakefulness Med. Eng. Phys. 35 1850–3 Wrisley D M, Stephens M J, Mosley S, Wojnowski A, Duffy J and Burkard R 2007 Learning effects of repetitive administrations of the sensory organization test in healthy young adults Arch. Phys. Med. Rehabil. 88 1049–54 Xiong G, Zhang L, Liu H, Zou H and Guo W 2010 A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction J. Zhejiang Univ. Sci. A 11 270–9
151
Related content
PAPER
Evaluation of postural steadiness before and after sedation: comparison of four nonlinear and three conventional measures
- The effect of age on balancing behavior: complexity analysis of mediolateral force trajectories A L Hilbun and I Karsai
To cite this article: A Tietäväinen et al 2014 Physiol. Meas. 35 139
- Relationships between HRV measures Mirjana M Platisa and Vera Gal
View the article online for updates and enhancements.
- Validation of balance-quality assessment using a modified bathroom scale D J Hewson, J Duchêne and J-Y Hogrel
Recent citations - Baseline adjustment increases accurate interpretation of posturographic sway scores A. Tietäväinen et al
This content was downloaded from IP address 150.216.68.200 on 13/01/2018 at 14:50
Institute of Physics and Engineering in Medicine Physiol. Meas. 35 (2014) 139–151
Physiological Measurement
doi:10.1088/0967-3334/35/2/139
Evaluation of postural steadiness before and after sedation: comparison of four nonlinear and three conventional measures 1 ¨ ainen ¨ ¨ 1 and J E Mandel 2 A Tietav , E Hæggstrom 1
Department of Physics, University of Helsinki, Helsinki, Finland Department of Anesthesiology and Critical Care, University of Pennsylvania School of Medicine, Philadelphia, USA 2
E-mail: [email protected] Received 14 January 2013 Accepted for publication 20 November 2013 Published 7 January 2014 Abstract
Sedative drugs decrease postural steadiness and increase the risk of injury from falls and accidents. The recovery rate is individual, making it hard to predict the patient’s steadiness and hence safe discharge time. 103 outpatients sedated with midazolam and fentanyl were measured posturographically, before (PRE) and after (POST) endoscopy. The ability of conventional and nonlinear sway measures to separate the PRE and POST conditions were compared, and the area under the receiver operating characteristics curve (AUC) was used to quantify the significance of the separation. A nonlinear measure, fuzzy sample entropy, scored the largest AUC (AUCFSE = 0.83, p < 0.0001). While the AUCFSE was not significantly larger than the AUCs of conventional sway measures which offer easy quantification of postural steadiness, nonlinear measures provide more insight into the structure of postural control, which may help understand the effect of sedation on postural steadiness. This study is a step toward developing a tester that indicates a safe discharge time. Keywords: anesthesia recovery, postural steadiness, fuzzy sample entropy, Lyapunov exponent, detrended fluctuation analysis, correlation dimension, midazolam
1. Introduction Fitness for ambulation is an important criterion for discharge after minor procedures that involve sedation. Sedatives impair postural steadiness and ambulatory fitness (Fujisawa et al 2006, Melzer et al 2004). Hence, early discharge may lead to an increased risk of falls among patients. Since both the baseline postural steadiness and the drug wash-out and recovery rate 0967-3334/14/020139+12$33.00
© 2014 Institute of Physics and Engineering in Medicine Printed in the UK
139
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
vary between patients, the assessment of fitness for ambulation is subjective and arbitrary, and an objective test would be useful in determining fitness for discharge. Maintaining balance requires cooperation amongst visual, vestibular, and proprioceptive senses, the central nervous system (CNS), and the musculoskeletal system. A healthy person’s postural control is a physiologically complex system that creates practised and ‘fine-tuned’ movements (Harbourne and Stergiou 2009). Physiological complexity arises from intrinsic nonlinearity and the output of such system manifests highly variable fluctuations that resemble chaos and that are associated with long-range, fractal correlations (Goldberger et al 2002, Harbourne and Stergiou 2009). When facing a challenging postural state such as balance disorders, ageing or sedatives, counterbalancing body movements are large and crude and the physiological complexity is low. Midazolam impedes postural control by decreasing both CNS activation level and skeletal muscle tone (Olkkola and Ahonen 2008). In young, healthy persons balance impairment was significant during 45 min (Gupta et al 1991), and with elderly patients during 50 min after administering midazolam (Fujisawa et al 2006). Static posturography evaluates postural steadiness by recording a subject’s center-ofpressure (COP) deviations in the mediolateral and anterior–posterior (AP) planes while he/she stands erect on a force plate (Prieto et al 1996). Postural steadiness is then quantified with sway measures that conventionally relate to sway amplitude, velocity or frequency (Prieto et al 1996, Chiari et al 2002). Nonlinear measures characterize the structure of the COP signal (Harbourne and Stergiou 2009), exposing the physiological complexity of the balancing act. Nonlinear measures can be interpreted in terms of a person’s motor control (Harbourne and Stergiou 2009, Roerdink et al 2006), and may help to understand the state of the postural control and postural steadiness. No single nonlinear measure can be used to quantify physiological complexity (Goldberger et al 2002). We concentrated on four measures (in the appendix): fuzzy sample entropy (FSE) quantifies the regularity or predictability of the COP signal (Xiong et al 2010), detrended fluctuation analysis (DFA) quantifies long-range correlation in the COP signal (Delignieres et al 2006, Peng et al 1994), correlation dimension (D2) estimates the dimensionality of the underlying postural control (Grassberger and Procaccia 1983, Roerdink et al 2006), and the largest Lyapunov exponent (λmax) quantifies the chaotic nature of the COP signal (Rosenstein et al 1993). These measures have been used in postural analysis in several ways—to study the effect of age on postural control (Amoud et al 2007, Lin et al 2008), recovery in stroke patients (Roerdink et al 2006), and role of attention in postural control (Donker et al 2007). A decreased DFA (or more specifically the scaling exponent, α, see in the appendix), an increased λmax, and a change in D2 and in (non-fuzzy) entropy have been reported to signal impaired postural steadiness (Lin et al 2008, Amoud et al 2007, Roerdink et al 2006, Donker et al 2007, Borg and Lax˚aback 2010, Duarte and Sternad 2008, Ladislao and Fioretti 2007). Previous reports on midazolam sedation utilized conventional measures and dynamic posturography. We hypothesized that decreased COP signal complexity quantified with nonlinear measures of static posturography could distinguish the post-sedative state from the pre-sedative state in patients undergoing elective ambulatory endoscopy. 2. Methods 2.1. Study design
The study was approved by the Institutional Review Board of the University of Pennsylvania School of Medicine. All consenting patients who could stand unaided and who had no diagnosed balance disorder were included. 103 patients undergoing colonoscopy or upper 140
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Table 1. Patients’ anthropometric data, mean (SD) and the given sedatives.
Males (n = 42) Females (n = 61) Total (n = 103) a16
Age (yrs)
Height (cm)
Weight (kg)
Midazolam (μg kg−1)
Fentanyl (μg kg−1)
Benadryl (mg kg−1)a
58 (12) 56 (12) 57 (12)
176 (8) 164 (9) 169 (10)
86 (17) 81 (20) 83 (19)
57 (18) 67 (24) 63 (22)
1.4 (0.4) 1.6 (0.6) 1.5 (0.5)
0.40 (0.09) 0.55 (0.20) 0.49 (0.18)
patients were given Benadryl.
GI endoscopy were measured before the procedure (PRE condition) during 60 s with their eyes open (EO) and during 60 s with their eyes closed (EC). The measurement was repeated after the procedure (POST condition), once the patient was deemed ready to be discharged. The patients had been instructed neither to eat during 24 h nor to drink during 6 h prior to the procedure. They were given sedative drugs midazolam and fentanyl (table 1). Sixteen patients were, in addition, given benadryl to rescue inadequate sedation. Measurements were conducted in the patients’ waiting pod (PRE) and in the recovery pod (POST) with the pod curtain closed. The average time between the PRE and POST measurements was 2.3 ± 0.6 h. During the measurements the patients were instructed to keep their hands at their sides, to keep their feet in a comfortable stance, and to fix their eyes on one spot (of their choice) in the pattern of the curtain hanging in front of them (EO), or to close their eyes (EC). A person stood next to the patient at all times ready to provide support in case the patient lost his/her balance. 2.2. Equipment
The portable equipment consisted of a Nintendo (Kyoto, Japan) Wii Fit balance board3 and a HP laptop (Compaq 6701b) that were connected with a Bluetooth link. The balance board featuring 60 Hz sampling frequency was operated with a custom made C# code that utilized the opensource library WiiMoteLib4. In Clark et al (2010) the Wii Fit board was deemed suitable for clinical use. 2.3. Signal processing
The AP sway direction was analyzed. The signals were standardized to zero-mean, and with the nonlinear measures, also to unit standard deviation. Nonlinear filtering based on the empirical mode decomposition5 (EMD) (Rato et al 2008) was employed to remove high frequency noise and low frequency trends. EMD was chosen because it is specifically designed for nonlinear and nonstationary signals (Huang et al 1998), such as the COP signal. With EMD, each signal was first decomposed into its intrinsic modes (9–12 modes, depending on the signal, the first mode having the highest frequency), and then recomposed leaving out the first two and the last two modes as in Tiet¨av¨ainen et al 2013. All results were also calculated without filtering. A signal before and after EMD filtering is shown in figure 1. Four nonlinear and three conventional measures (in the appendix) were used in the analysis. FSE (Xiong et al 2010) quantified the regularity of the signal. The
2.3.1. Sway measures.
3 A balance board with four transducers, designed originally for gaming. Nintendo Wii home page: www.nintendo.com/wii, balance board’s manual. www.nintendo.com/consumer/downloads/wiiBalanceBoard.pdf, (accessed 10 June 2013). 4 WiiMoteLib: http://wiimotelib.codeplex.com/ (accessed 10 June 2013). 5 The EMD is calculated with a code available in Matlab Central’s File Exchange by Dr Manuel Ortigueira (Rato et al 2008).
141
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Displacement (mm)
Unfiltered COP (a)
10 0 −10 0
20
40
60
Displacement (mm)
Filtered COP (b)
10 0 −10 0
20
40
60
Time (s) Figure 1. (a) Unfiltered 60 s AP COP signal and (b) corresponding EMD filtered signal.
long-range correlation in the COP signals was quantified with DFA (Delignieres et al 2006, Peng et al 1994). DFA provided a scaling exponent, α, as a sway measure. λmax (Rosenstein et al 1993) estimated the chaotic nature of the signal, whereas D2 (Grassberger and Procaccia 1983, Roerdink et al 2006) estimated the active control variables of the underlying balancing dynamics. The conventional measures were sway range (Range), mean velocity (v mean), and mean frequency ( f mean) (Prieto et al 1996).
The Lilliefors test for normality revealed that 37 out of the 56 distributions (2 filters × 4 conditions × 7 sway measures) were non-Gaussian at 5% significance level. Hence, to produce more Gaussian-like distributions, all 56 distributions were log-transformed before the statistical analysis. A three-way MANOVA with repeated measures and a student’s t-test as post hoc test was used to determine the effect of sedatives on postural steadiness. The independent variables were sedatives (PRE/POST conditions), vision (eyes open/closed), and filtering (no filtering/EMD filtering). The sway measures were taken as dependent variables. After applying the Bonferroni correction p2∗ dM+1, M is large enough and dM is the correlation dimension, D2. Here, for unfiltered data, M = 6 ± 1 and J = 21 ± 4, and for filtered data, M = 8 ± 2 and J = 25 ± 6. The region CM = 0.005–0.75 was chosen for the fit (Roerdink et al 2006). Largest Lyapunov exponent (λmax): In chaotic systems small perturbations may lead to large deviations from the equilibrium (Blaszczyk and Klonowski 2001). λmax estimates the local stability of a dynamic system; deterministic and nonlinear signals are chaotic if λmax is positive (Rosenstein et al 1993). We define ‘local stability’ as the postural control system’s sensitivity to small (local), internal perturbations (Harbourne and Stergiou 2003). The λmax algorithm also employs the state-phase presentation of the signal. If initially close trajectories later diverge exponentially, λmax is positive and the system is chaotic. The divergence is quantified using nearest neighbors, Xjˆ; Xjˆ is defined as the Xj that has a temporal separation greater than twice the lag J and that minimizes the Euclidean distance between Xj and Xjˆ. The divergence at an instance i is d(i)j = d(0)jeλmax(it), where d(0)j is the initial distance between Xj and Xjˆ. Hence, λmax is estimated with a least-squares fit to the line y(i) = 1/tlnd(i)j, where is the average value over j neighbors. The region of 0–0.75 s was chosen for the fit (Roerdink et al 2006). References Amoud H, Abadi M, Hewson D, Michel-Pellegrino V, Doussot M and Duchˆene J 2007 Fractal time series analysis of postural stability in elderly and control subjects J. Neuroengineering Rehabil. 4 12 Blaszczyk J W and Klonowski W 2001 Postural stability and fractal dynamics Acta Neurobiol. Exp. 61 105–12 Borg F G and Lax˚aback G 2010 Entropy of balance—some recent results J. Neuroengineering Rehabil. 7 38 Chiari L, Rocchi L and Cappello A 2002 Stabilometric parameters are affected by anthropometry and foot placement Clin. Biomech. 17 666–77 Choy N L, Brauer S and Nitz J 2003 Changes in postural stability in women aged 20 to 80 years J. Gerontol. A Biol. Sci. Med. Sci. 58 M525–30 149
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Clark R A, Bryant A L, Pua Y, McCrory P, Bennell K and Hunt M 2010 Validity and reliability of the Nintendo Wii balance board for assessment of standing balance Gait Posture 31 307–10 Delignieres D, Ramdani S, Lemoine L, Torre K, Fortes M and Ninot G 2006 Fractal analyses for ‘short’ time series: a re-assessment of classical methods J. Math. Psychol. 50 525–44 Donker S F, Roerdink M, Greven A J and Beek P J 2007 Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control Exp. Brain Res. 181 1–11 Duarte M and Sternad D 2008 Complexity of human postural control in young and older adults during prolonged standing Exp. Brain Res. 191 265–76 Fraser A M and Swinney H L 1986 Independent coordinates for strange attractors from mutual information Phys. Rev. A 33 1134–40 Fujisawa T, Takuma S, Koseki H, Kimura K and Fukushima K 2006 Recovery of intentional dynamic balance function after intravenous sedation with midazolam in young and elderly subjects Eur. J. Anaesthesiol. 23 422–5 Ghomashchi H, Esteki A, Sprott J and Nasrabadi A 2010 Identification of dynamic patterns of body sway during quiet standing: is it a nonlinear process? Int. J. Bifurcation Chaos Appl. Sci. Eng. 20 1269–78 Goldberger A, Peng C-K and Lipsitz L 2002 What is physiological complexity and how does it change with aging and disease? Neurobiol. Aging 23 23–26 Grassberger P and Procaccia I 1983 Characterization of strange attractors Phys. Rev. Lett. 50 346–9 ¨ Gupta A, Ledin T, Larsen L E, Lennmarken C and Odkvist L M 1991 Computerized dynamic posturography: a new method for the evaluation of postural stability following anaesthesia Br. J. Anaesth. 66 667–72 Hanley J A and McNeil B J 1982 The meaning and use of the area under a receiver operating characteristic (ROC) curve Radiology 143 29–36 Hanley J A and McNeil B J 1983 A method of comparing the areas under receiver operating characteristic curves derived from the same cases Radiology 148 839–43 Harbourne R T and Stergiou N 2003 Nonlinear analysis of the development of sitting postural control Dev. Psychobiol. 42 368–77 Harbourne R T and Stergiou N 2009 Movement variability and the use of nonlinear tools: principles to guide physical therapist practice Phys. Ther. 89 267–82 Huang N, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N-C, Tung C and Liu H 1998 The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. R. Soc. Lond. A 454 903–95 Ladislao L and Fioretti S 2007 Nonlinear analysis of posturographic data Med. Biol. Eng. Comput. 45 679–88 Lake D E, Richman J S, Griffin M P and Moorman J R 2002 Sample entropy analysis of neonatal heart rate variability Am. J. Physiol.. Regul. Integr. Comp. Physiol. 283 R789–97 Lasko T A, Bhagwat J G, Zou K H and Ohno-Machado L 2005 The use of receiver operating characteristic curves in biomedical informatics J. Biomed. Inform. 38 404–15 Lin D, Seol H, Nussbaum M A and Madigan M L 2008 Reliability of COP-based postural sway measures and age-related differences Gait Posture 28 337–42 Melzer I, Benjuya N and Kaplanski J 2004 Postural stability in the elderly: a comparison between fallers and non-fallers Age Ageing 33 602–7 Olkkola K T and Ahonen J 2008 Midazolam and other benzodiazepines Modern Anesthetics ed J Sch¨uttler and H Schwilden (Berlin: Springer) pp 335–60 Peng C-K, Buldyrev S V, Havlin S, Simons M, Stanley H E and Goldberger A L 1994 Mosaic organization of DNA nucleotides Phys. Rev. E 49 1685–9 Prieto T E, Myklebust J B, Hoffmann R G, Lovett E G and Myklebust B M 1996 Measures of postural steadiness: differences between healthy young and elderly adults IEEE Trans. Biomed. Eng. 43 956–66 Ramdani S, Seigle B, Lagarde J, Bouchara F and Bernard P L 2009 On the use of sample entropy to analyze human postural sway data Med. Eng. Phys. 31 1023–31 Rato R T, Ortigueira M D and Batista A G 2008 On the HHT, its problems, and some solutions Mech. Syst. Signal Process. 22 1374–94 Richman J S and Moorman J R 2000 Physiological time-series analysis using approximate entropy and sample entropy Am. J. Physiol. Heart Circ. Physiol. 278 H2039–49 Roerdink M, De Haart M, Daffertshofer A, Donker S F, Geurts A C H and Beek P J 2006 Dynamical structure of center-of-pressure trajectories in patients recovering from stroke Exp. Brain Res. 174 256–69 150
¨ ainen ¨ A Tietav et al
Physiol. Meas. 35 (2014) 139
Rosenstein M T, Collins J J and DeLuca C J 1993 A practical method for calculating largest Lyapunov exponents from small data sets Physica D: Nonlinear Phenom. 65 117–34 R Wii Fit based Tiet¨av¨ainen A, Gates F K, Meril¨ainen A, Mandel J E and Hæggstr¨om E 2013 Nintendo sleepiness tester detects impairment of postural steadiness due to 24 h of wakefulness Med. Eng. Phys. 35 1850–3 Wrisley D M, Stephens M J, Mosley S, Wojnowski A, Duffy J and Burkard R 2007 Learning effects of repetitive administrations of the sensory organization test in healthy young adults Arch. Phys. Med. Rehabil. 88 1049–54 Xiong G, Zhang L, Liu H, Zou H and Guo W 2010 A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction J. Zhejiang Univ. Sci. A 11 270–9
151
