Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx
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Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy Marco K. Butugan a, Cristina D. Sartor a, Ricky Watari a, Maria Cecília S. Martins a, Neli R.S. Ortega b, Vincent A.M. Vigneron c, Isabel C.N. Sacco a,⇑ a
University of Sao Paulo, School of Medicine, Physical Therapy, Speech and Occupational Therapy Dept., Sao Paulo, SP, Brazil University of Sao Paulo, School of Medicine, Center of Fuzzy Systems in Health, Sao Paulo, SP, Brazil c University of Evry, Electrical Engineering Department, Evry, France b
a r t i c l e
i n f o
Article history: Received 10 September 2013 Received in revised form 10 April 2014 Accepted 14 April 2014 Available online xxxx Keywords: Diabetic polyneuropathies Neural conduction Muscle fiber Isometric contraction Electromyography
a b s t r a c t This study compares muscle fiber conduction velocities estimated using surface electromyography during isometric maximal voluntary contraction in different stages of diabetic neuropathy. Eighty-five adults were studied: 16 non-diabetic individuals and 69 diabetic patients classified into four neuropathy stages, defined by a fuzzy expert system: absent (n = 26), mild (n = 21), moderate (n = 11) and severe (n = 11). Average muscle fiber conduction velocities of gastrocnemius medialis, tibialis anterior, vastus lateralis and biceps femoris were assessed using linear array electrodes, and were compared by ANOVA. Conduction velocities were significantly decreased in the moderate neuropathy group for the vastus lateralis compared to other groups (from 18% to 21% decrease), and were also decreased in all diabetic groups for the tibialis anterior (from 15% to 20% from control group). Not only the distal anatomical localization of the muscle affects the conduction velocity, but also the proportion of muscle fiber type, where the tibialis anterior with greater type I fiber proportion is affected earlier while the vastus lateralis with greater type II fiber proportion is affected in later stages of the disease. Generally, the muscles of the lower limb have different responsiveness to the effects of diabetes mellitus and show a reduction in the conduction velocity as neuropathy progresses. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Progression of diabetes mellitus is chronically accompanied by both sensory and motor neuropathies, with marked decrease in quality of life (IJzerman et al., 2012). Sensory nerve degeneration has more conspicuous early signs, with slowing of nerve conduction velocity, clear signs of demyelination, and expressive symptoms related to somatosensory impairment. Motor dysfunction is more subtle and considered as a late symptom in the progression of diabetes mellitus (Andreassen et al., 2006). Even though loss of muscle strength is frequently reported for both type 1 (Andersen, 1996) and type 2 (Andersen et al., 2004b) diabetes, motor nerve conduction velocity is largely preserved due to axonal sprouting and reinnervation (Meijer et al., 2008), and animal ⇑ Corresponding author. Address: Departamento Fisioterapia, Fonoaudiologia e Terapia Ocupacional, R. Cipotânea, 51, Cidade Universitária, São Paulo, SP 05360160, Brazil. Tel.: +55 (11) 3091 8426; fax: +55 (11) 3091 7461. E-mail address: [email protected] (I.C.N. Sacco).
models indicate that the motor neuron seems to be more preserved in the course of the disease (Zochodne et al., 2008). Invasive EMG studies in diabetic patients have shown increased jitter, fiber density (Bril et al., 1996), and motor unit area, which indicate the presence of chronic muscle fiber re-innervation, even before symptoms of neuropathy occur (Andersen et al., 1998). This early neuromuscular impairment can also be identified by needle muscle fiber conduction velocity (MFCV) measurement (Van der Hoeven et al., 1993). Different from what was believed (Boulton et al., 2004), a reduction in MFCV was observed using needle electrodes even before loss of muscle strength or onset of neuropathy symptoms, suggesting that the progression of diabetes is likely coincident for both neuromuscular and sensory systems, and not a late complication of neuropathy (Meijer et al., 2008). The spatial distribution of impairments in the somatosensory system is characteristic, starting from distal segments and moving on to proximal ones, and it is believed to be due to diffuse axonal demyelination, which would affect more intensely axons of greater length and smaller diameter (Forbes and Cooper, 2013). The
http://dx.doi.org/10.1016/j.jelekin.2014.04.007 1050-6411/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx
earliest signs of motor involvement are also reported for distal leg muscles, with decrease in ankle joint torque (Andersen et al., 2004b) and atrophy of the intrinsic foot muscles (Andersen et al., 2004a), following the same order of the progression of somatosensory impairment. Moreover, it has been shown that severity of peripheral nerve dysfunction is related to reduced isokinetic strength, suggesting that both conditions are correlated (Andreassen et al., 2009). In spite of the similar spatial distribution, different mechanisms are responsible for neuromuscular impairment caused by diabetes, since muscle contraction is related to other factors besides nerve integrity such as the state of the motor end plate and skeletal muscle fibers. The motor end plate appears to be affected before nerve impairment in murine models, which is reflected by distal axonal retraction (Ramji et al., 2007). There is also a shift in fiber type proportion favoring the presence of more type II fibers in patients with diabetes mellitus (Fritzsche et al., 2008; Oberbach et al., 2006), which is likely due to factors such as increased intracellular calcium concentration (Nakagawa et al., 1989), leading type I fibers, which are more sensitive to this ion, to early apoptosis (Ruff and Whittlesey, 1991; Widrick et al., 1996). Newly diagnosed patients displayed muscle fiber atrophy, disruption of Z-lines, and morphological abnormalities in the mitochondria (Krause et al., 2011) in the absence of structural indications of neuropathy such as axonal degeneration, motor nerve conduction loss or increased vibratory threshold (Reske-Nielsen et al., 1977). These are important findings as they indicate that skeletal muscle is acutely sensitive to diabetes mellitus, prior to neuropathic complications (Andreassen et al., 2009, Krause et al., 2011). Recent studies assessed the entropy in contractions of the vastus lateralis muscle in diabetic patients and discussed the metabolic patterns using near-infrared spectroscopy (Molinari et al., 2013) and the neuromuscular system using high-density surface EMG (Watanabe et al., 2013). These studies found a decreased entropy suggesting an alteration in the muscle fiber recruitment (Watanabe et al., 2012) and in the metabolic state of the muscle fibers in this population (Molinari et al., 2013). Upon further investigation, it was observed that individual motor units had a lower firing rate in patients with diabetes, suggesting that in addition to changes in the metabolic and vascular states of the muscle fibers, changes in the neuromuscular system like excitation–contraction decoupling or delayed repolarization of the membrane could cause these recruitment changes (Watanabe et al., 2013). It is important to highlight that a delay in the repolarization of the membrane could induce a decrease in conduction velocity of the motor unit action potential in these patients. Non-invasive MFCV estimation can assess the average muscle fiber conduction velocity of innervated fibers (Merletti et al., 2003). In addition, these data could be used to understand other mechanisms underlying the progression of motor dysfunction caused by diabetes mellitus in lower limb muscles. Although there is evidence of early motor involvement, the way the muscle function deteriorates throughout the progression of this disease stays unknown. MFCV estimation could be a sensitive tool to detect muscle function status and signs of motor axonal dysfunction in individuals where diabetes and neuropathy are progressing (Meijer et al., 2008). Therefore, for this study, we tested if different diabetic neuropathy stages have distinct muscle fiber conduction velocities in four lower limb muscles during isometric contractions. We hypothesized that: (i) as diabetic polyneuropathy progresses, the MFCV values would be progressively slower, (ii) distal muscles would be affected earlier and in a greater magnitude than proximal muscles, and (iii) muscles with larger proportion of type I fiber would be affected earlier and in greater magnitude than muscles with smaller proportion of type I fiber.
2. Methods 2.1. Participants For this prospective study, 16 healthy adults participated as a control group (C) and 69 type 2 diabetic patients were divided into four groups with different neuropathy severity stages, classified by a fuzzy expert system: absent neuropathy (AbN; n = 26), mild (MiN; n = 21), moderate (MoN; n = 11), and severe groups (SeN; n = 11). Inclusion criteria were: age under 65 years old, not having partial or total lower limb amputation or other neurological or orthopedic impairments due to stroke, cerebral palsy, poliomyelitis, rheumatoid arthritis, prosthesis, or moderate or severe osteoarthritis; without venous or arterial ulcers; not having severe retinopathy, severe nephropathy causing edema or requiring hemodialysis; and absence of plantar ulcer at the time of evaluation. This study was approved by the local ethics committee (522/11) and all participants provided written informed consent prior to participation. Clinical assessment instruments for the evaluation of the diabetic neuropathy provide simple and inexpensive options (Armstrong et al., 1998), but their results have shown to be variable when reproducibility and accuracy were analyzed (Dyck et al., 2010; England et al., 2005; Taksande et al., 2011), even when used as a composite score (England et al., 2005). These assessments involve a great number of uncertainties in both measurement and formulation of diagnosis. Since neuropathy evolves continuously from the onset of diabetes mellitus, the Theory of Fuzzy Sets is a useful method for diagnosing and classifying patients, as it takes into account the uncertainties of the clinical assessment of the disease, and it is capable of objectively measuring a subjective judgment. All participants were assessed by a trained physical therapist, who evaluated (i) vibratory perception (128 Hz tuning fork), (ii) tactile sensitivity (using a 10 g Semmes–Weinstein monofilament), and (iii) presence of typical neuropathy symptoms (based on the Michigan Neuropathy Screening Instrument). These three groups of variables were used as linguistic inputs in a fuzzy expert system, based on (Picon et al., 2012) and Watari et al. (2014) to classify the patients into different disease severity stages. This model presented a very strong correlation (Zou et al., 2003) with the experts’ opinion (Pearson’s coefficient r = 0.943) and a high accuracy level when classifying real patients that underwent the model’s analysis (ROC curve area = 0.91). The fuzzy expert system classifies each input variable into fuzzy sets (fuzzification process) and performs a combinatory analysis of those variables by the Mamdani inference process (Mamdani and Assilian, 1975), linking those combinations with fuzzy output sets. Then, by (center of area) defuzzification method, the resulting output sets are transformed into numerical values arranged into neuropathy classes corresponding to the following division, with x being the score value: (i) x 6 2.5: absent; (ii) 2.5 < x < 5.0: mild; (iii) 5.0 6 x < 8.0: moderate; and (iv) x P 8.0: severe (Watari et al., 2014). Glycated hemoglobin levels were measured with a HbA1c monitor (A1CNow SELFCHECK, Bayer LLC Diabetes Care, New York, USA). The groups did not differ in gender distribution and body mass index. Diabetes duration did not differ between groups, HbA1c levels were lower in C compared to all diabetic groups, and the neuropathy output score from the fuzzy model increased significantly among diabetic groups (Table 1).
2.2. Data acquisition Surface EMG was acquired during maximum isometric voluntary contractions of four lower limb muscles using a multi-channel
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx Table 1 Demographic and grouping variables. Groups
a b c
Age (years)
Male/female (%)
Body mass index (kg/m2)
HbA1c (%) c
Diabetes duration (years)
Fuzzy score
Control (n = 16) Absent Neuropathy (n = 26) Mild Neuropathy (n = 21) Moderate Neuropathy (n = 11) Severe Neuropathy (n = 11)
56.6 ± 7.5 58.0 ± 5.5 58.3 ± 6.0 60.3 ± 4.5 59.4 ± 4.3
56 62 28 56 70
29.3 ± 4.1 27.0 ± 2.7 27.3 ± 7.7 28.7 ± 4.9 31.1 ± 4.1
5.9 ± 0.5 8.5 ± 1.7 9.0 ± 1.8 9.5 ± 1.5 9.5 ± 2.5
– 13.1 ± 8.7 10.2 ± 7.8 17.4 ± 6.8 14.6 ± 9.5
0.9 ± 0.4 1.3 ± 0.6 2.7 ± 0.9 6.1 ± 1.0 8.9 ± 1.0
p
0.56a
0.09b
0.22a
1012 ohm, amplified by a factor of 2000, band-pass filtered with a 5th order Bessel band pass filter at 10–900 Hz and converted to digital form by its 12bit analog-to-digital converter at 2048 Hz. 2.3. Procedures We used a dry linear array of eight 5 mm width by 1 mm diameter silver bars with 10 mm inter-electrode distance (SA8/10, OT Bioelettronica, Torino, Italy) to determine the best electrode position to acquire maximum voluntary isometric contractions. For that, we used adhesive linear arrays with 4 electrodes (Ag/AgCl, 5 mm 1 mm electrodes with 10 mm inter-electrode distance, ELSCH004, OT Bioelettronica, Torino, Italy) filled with conductive paste (CC1, OT Bioelettronica, Torino, Italy) and attached with disposable foam spacers (KITAD004, OT Bioelettronica, Torino, Italy), after skin cleaning and abrasion with alcohol and gauze. A reference electrode was placed on the tibial tuberosity. The dry electrode was initially placed in each muscle following the electrode placement orientation recommended by the surface EMG for the Non-Invasive Assessment of Muscles (SENIAM, 2012), avoiding the muscle innervation zones (Barbero et al., 2012). Then, the position of the innervation zone, tendon and the proper fiber alignment for each muscle were determined in submaximal contractions by visual inspection of the signals (Fig. 1) (Merletti et al., 2001). The disposable linear arrays were then placed between the distal tendon and the most distal innervation zone (Fig. 2). We acquired three trials of five seconds each of EMG data for the vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius medialis muscles while the individuals performed a maximum voluntary isometric contraction against manual resistance provided by a trained physiotherapist (Fig. 3).
To ensure that maximal voluntary contraction was achieved during the trials, subjects were first instructed on how to perform each task, and then were verbally encouraged to produce maximum effort during data acquisition. The assessor was instructed to keep the participant’s joint positions constant for the whole test. For each muscle assessment, a different body posture was required. For the vastus lateralis, the participant was in supine, with the hip and the knee in a neutral position. The patient was instructed to apply pressure with the back of the knee joint and the anterior aspect of the ankle joint against the assessor’s resistance, extending the knee joint. Resistance was applied on the posterior aspect of the knee joint and distally on the anterior aspect of the ankle joint. For the biceps femoris, the participant was in prone, with the hip in a neutral position and the knee at 45° of flexion. The patient was instructed to flex the knee joint and resistance was applied on the posterior aspect of the ankle joint. For the tibialis anterior, the participant was in supine, with the ankle in neutral position. The patient was instructed to flex the ankle joint and resistance was exerted over the foot. For the gastrocnemius medialis, the participant was in prone, with the ankle at 45° of flexion and the knee in a neutral position. The patient was instructed to extend the ankle and resistance was applied on the foot sole to flex the ankle joint. All joint positions were measured by a manual goniometer. 2.4. Signal processing The acquired signals were band-pass filtered at 10–500 Hz, with a zero-lag 4th order Butterworth digital filter. They were also conditioned by spectral subtraction, which requires known spectral properties of the noise, using a segment of the signal without muscle activity that was assumed to contain white noise with a power r2. The short term amplitude spectrum of the denoised signal was estimated by the expression:
b ðkÞ ¼ GðkÞXðkÞ X
Fig. 1. Sample single-differential signals from dry electrode acquired from the tibialis anterior. Dashed lines indicate the approximate location of innervation zones. To the left, the signal was acquired at the position and orientation recommended by SENIAM: the black arrow indicates the recommended position, smaller amplitude and reversal propagation direction of the action potential can be observed in the signal closest to the innervation zone. To the right, the signal was acquired at the final/used position: the dashed line indicates the last innervation zone detected; the gray signals have the smallest amplitude indicating the tendon region; in black adequate action potentials can be observed for three single differential channels.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx
Fig. 2. Electrode positioning for each muscle: the lighter lines indicate the position (short dashed line) and orientation recommended by SENIAM (long dashed line). The darker black lines indicate the final/used position for the electrodes (short continuous darker black lines) and the dashed darker lines indicate the location of the innervation zone.
Fig. 3. Single differential signals obtained during maximal voluntary contraction for each muscle: the dashed line indicates the action potential propagation direction in each signal.
where X(k) is the discrete Fourier transform of a noisy signal x(n) of length
XðkÞ ¼
N 1 X xðnÞe2jpnk=N n¼0
and where G(k) can be estimated by:
GðkÞ ¼
( pffiffiffi pffiffiffi r N r N Abs p = 0.02; C > MiN p = 0.02; C > MoN p = 0.03; C > SeN p = 0.01. For Vastus Lateralis p values were: C > MoN p = 0.03; AbN > MoN p = 0.02;MiN > MoN p = 0.02 SeN > MoN p = 0.01.
Fig. 5. Mean MFCV values and standard error of gastrocnemius medialis and biceps femoris muscles during isometric contraction.
alterations observed did not follow a homogeneous progressive pattern of impairment throughout the disease progression, with tibialis anterior affected earlier, and vastus lateralis being affected only at moderate stage of neuropathy. In general, the MFCV did not follow a distal to proximal order of alteration from absent to severe stages. We observe a significant decrease in MFCV values of the tibialis anterior at an early stage of the disease (AbN) that remained almost unchanged during the following stages. It is interesting to note that this reduction occurred even without the presence of signs and symptoms of neuropathy. Meijer et al. (2008) reported a similar result using invasive EMG, where diabetic patients without polyneuropathy presented a slower MFCV in tibialis anterior. These results corroborate to the understanding that motor impairment develops early in diabetes progression along with sensorial losses and following distal to proximal impairment. Since tibialis anterior has 73% type I fibers (Johnson et al., 1973), which is more than the other muscles tested in this study, it is reasonable to assume that this muscle would be more likely affected in earlier stages of diabetes because this disease affects particularly the distal muscles (Andersen et al., 2004a,b). Since type I fiber proportion of the diabetic population is diminished (Oberbach et al., 2006), it is also reasonable to assume that those fibers can be more susceptible to the effects of diabetic neuropathy, thus making muscles composed primarily of these fiber types more easily affected. The tibialis anterior results are also consistent with those from neuropathic patients during gait and stair negotiation who exhibited a delayed activity (Abboud et al., 2000; Onodera et al., 2011; Sacco and Amadio, 2003), suggesting a failure in its function. Our findings together with previous results in locomotion suggest that this muscle contributes the most for distinguishing diabetic patients in early stages of neuromuscular dysfunction. The vastus lateralis muscle shows a significant decrease at the moderate neuropathy stage when compared to the other groups. Late involvement of the vastus lateralis muscle was expected because of its proximal position in relation to calf muscles, being most likely less affected until this stage of progression. This late decrease in conduction velocity is also consistent with previous results during gait that showed impaired activity of the vastus lateralis in diabetic individuals with neuropathy mostly in patients in later stages (Akashi et al., 2008; Sacco et al., 2010). The smaller proportion of type I fiber in vastus lateralis (38%) when compared with tibialis anterior (Johnson et al., 1973) may also account for the
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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reduced MFCV observed in this later stage of the disease, unlike tibialis anterior which showed an earlier decrease in average MFCV values. The SeN group presents a reverse trend of recuperation in vastus lateralis conduction velocity. This possible recovery, occurring late in diabetes progression, can also be attributed to its lower proportion of type I muscle fibers, since this neural impairment seems to affect primarily type I muscle fibers (Oberbach et al., 2006). With vastus lateralis having less type I muscle fibers, a greater degree of resistance is expected. In addition to that, for later stages of disease, axonal sprouting could enlarge the motor end plate areas (Hansen and Ballantyne, 1977), increasing the amount of non-propagating potentials under the electrodes, which could lead to an increase in the MFCV, as it was observed for vastus lateralis. Even though, it is expected that axonal sprouting should happen for all affected muscles in response to denervation (Andersen et al., 1998), this process would be more evident for this muscle and it would be reflected in the MFCV estimation. However, an isolated increase in MFCV observed in the SeN group for vastus lateralis muscles does not mean that muscle function was restored. There was a trend to a linear reduction in the MFCV of the gastrocnemius and biceps throughout the neuropathy progression, opposed to the tibialis anterior results which demonstrated a marked reduction between control and absent groups. Both tibialis anterior and gastrocnemius are distal muscles, therefore we expected that a decrease in MFCV of these muscles would happen at the same stage worsening as neuropathy develops. However, it is important to highlight that gastrocnemius medialis has less type I fibers compared to tibialis anterior (51%) (Johnson et al., 1973), and could be less susceptible to losses due to diabetes than tibialis anterior. In addition, this muscle has several methodological issues that compromise the reliability of the MFCV. The pennation angle of the gastrocnemius fibers (Manal et al., 2006) affects the measurement of MFCV, since this angle increases during maximal voluntary contraction under the electrode array (Mesin et al., 2007). Furthermore, gastrocnemius muscle has also a very distal innervation zone (Saitou et al., 2000) and above the endplate, it is not possible to get a clear motor unit action potential shape (Gallina et al., 2013), which limits the possible positions of the electrodes. All these characteristics make it difficult to find an optimal array position to acquire reliable signals. The lack of significant differences between groups in earlier stages of the disease could be attributed to these issues. Although we have used the criteria established in (McIntosh and Gabriel, 2011), where values between 2 and 13 m/s are considered physiological, according to the probability density function described by (Hunter et al., 1987), we believe that both end-offiber effect and complex fibers muscle architecture (Mesin et al., 2007) may have influenced our MFCV estimation. Even so, we tried to minimize the impact of these factors by placing the electrodes in sites where the muscle fibers were relatively parallel the electrode by visually inspecting the signal, checking for asymmetry in the waveforms (Merletti et al., 2001) and choosing very distal sites, especially for the gastrocnemius. We have also used double-differentiation to improve the removal of end-of-fiber effect. In summary, our first hypothesis was confirmed since as neuropathy progresses, an overall decrease in MFCV of all muscles was observed. Aside from a possible recovery, it does not return to initial and healthier condition, except for the vastus lateralis. Our second hypothesis was not confirmed, since not all distal muscles were affected earlier than the proximal ones. Because of the nature of distal to proximal evolution in diabetic neuropathy, it would be expected that changes in muscle dynamics would appear first in distal, instead of proximal muscles. It appears that the fiber type constitution and capability for reinnervation are also important factors to determine loss of MFCV than only the anatom-
ical location. Future studies should address if these factors can indeed affect MFCV. And finally, our third hypothesis was partially confirmed since the MFCV values of the studied muscles decreased first for the tibialis anterior, which has the greater proportion of type I fiber among the studied muscles, and only showing an isolated MFCV decrease for vastus lateralis much later in disease progression. Further studies should explore and access dynamic muscle activities, either during maintained contraction or different voluntary contraction intensities to better comprehend muscle fiber recruitment patterns in the diabetic population. 5. Conclusion Muscles of the lower limb have different responsiveness to diabetes mellitus progression and the neuropathy severity, but in general alterations in MFCV do not follow a homogeneous/linear progressive pattern of impairment throughout disease progression. There is no clear distal to proximal pattern of muscle involvement, however muscles with greater proportion of type I fiber, like the tibialis anterior, have a pronounced and earlier decrease in MFCV, while muscles with greater type II fiber proportion, like the vastus lateralis, are affected in more severe neuropathy levels. Conflict of interest None. Acknowledgements São Paulo State Research Support Foundation (FAPESP) for Butugan (2011/15770-0) and Sartor (2011/19304-4) scholarships and The National Council for Scientific and Technological Development (CNPq) for Watari scholarship (Master 556374/2010-0). References Abboud RJ, Rowley DI, Newton RW. Lower limb muscle dysfunction may contribute to foot ulceration in diabetic patients. Clin Biomech (Bristol, Avon) 2000;15:37–45. Akashi PM, Sacco IC, Watari R, Hennig E. The effect of diabetic neuropathy and previous foot ulceration in EMG and ground reaction forces during gait. Clin Biomech (Bristol, Avon) 2008;23:584–92. Andersen H. Reliability of isokinetic measurements of ankle dorsal and plantar flexors in normal subjects and in patients with peripheral neuropathy. Arch Phys Med Rehabil 1996;77:265–8. Andersen H, Stalberg E, Gjerstad MD, Jakobsen J. Association of muscle strength and electrophysiological measures of reinnervation in diabetic neuropathy. Muscle Nerve 1998;21:1647–54. Andersen H, Gjerstad MD, Jakobsen J. Atrophy of foot muscles: a measure of diabetic neuropathy. Diabetes Care 2004a;27:2382–5. Andersen H, Nielsen S, Mogensen CE, Jakobsen J. Muscle strength in type 2 diabetes. Diabetes 2004b;53:1543–8. Andreassen CS, Jakobsen J, Andersen H. Muscle weakness: a progressive late complication in diabetic distal symmetric polyneuropathy. Diabetes 2006;55:806–12. Andreassen CS, Jakobsen J, Ringgaard S, Ejskjaer N, Andersen H. Accelerated atrophy of lower leg and foot muscles – a follow-up study of long-term diabetic polyneuropathy using magnetic resonance imaging (MRI). Diabetologia 2009;52:1182–91. Armstrong DG, Lavery LA, Vela SA, Quebedeaux TL, Fleischli JG. Choosing a practical screening instrument to identify patients at risk for diabetic foot ulceration. Arch Intern Med 1998;158:289–92. Barbero M, Merletti R, Rainoldi A. Atlas of muscle innervation zones. 1st ed. Italy: Springer-Verlag; 2012. Boulton AJ, Malik RA, Arezzo JC, Sosenko JM. Diabetic somatic neuropathies. Diabetes Care 2004;27:1458–86. Bril V, Werb MR, Greene DA, Sima AA. Single-fiber electromyography in diabetic peripheral polyneuropathy. Muscle Nerve 1996;19:2–9. Dyck PJ, Overland CJ, Low PA, Litchy WJ, Davies JL, O’Brien PC, et al. Signs and symptoms versus nerve conduction studies to diagnose diabetic sensorimotor polyneuropathy: Cl vs. NPhys trial. Muscle Nerve 2010;42:157–64. England JD, Gronseth GS, Franklin G, Miller RG, Asbury AK, Carter GT, et al. Distal symmetric polyneuropathy: a definition for clinical research: report of the American Academy of Neurology, the American association of electrodiagnostic
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Marco K. Butugan is a graduate student at the Laboratory of Human Movement and Posture Biomechanics, Department of Physical Therapy, Speech and Occupational Therapy, Faculty of Medicine, University of São Paulo, Brazil and is currently working for his M. Sc. degree under Professor Isabel de Camargo Neves Sacco. His research interests include biological signal processing and biomechanical analysis of the human movement.
Cristina D. Sartor obtained her Ph.D. from the University of São Paulo (São Paulo, Brazil) in 2013 and is currently a post-doctoral researcher in the Laboratory of Human Movement and Posture Biomechanics. Her research interests include the biomechanical analysis of human movement in pathological conditions, especially Diabetes Mellitus and musculoskeletal disorders.
Ricky Watari obtained his M. Sc. in Rehabilitation Science (2013) from University of São Paulo (São Paulo, Brazil) and he is currently an associate researcher at the Laboratory of Biomechanics of Human Movement and Posture, Department of Physical Therapy, Speech and Occupational Therapy, Faculty of Medicine, University of São Paulo, Brazil. His research interests include biomechanical analysis of the human movement in pathological conditions and on the Pilates method.
Maria Cecília S. Martins is a certified Physical Therapist and worked in the Laboratory of Biomechanics of Human Movement and Posture, University of São Paulo under Professor Isabel de Camargo Neves Sacco. Currently working validating and translating specific questionnaires for ankle sprain assessment in athletes at the Center of Sport Traumatology of the University of Sâo Paulo.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx Neli R.S. Ortega obtained her M.Sc. (1997) and Ph.D. (2001) from the Physics Institute of the University of Sâo Paulo (São Paulo, Brazil). Currently she is an associate professor and researcher in the Univesity of Sâo Paulo, researcher of the Nacional Council of Scientific and Technological Development in Brazil, ad hoc consultant for the same organization and reviewer in journals such as the Bulletin of Mathematical Biology, Mathematical Biosciencas and Medical Care among others. Her research focuses on the mathematical modelling of problems in public health with focus on fuzzy set theory, epidemiology and decision support systems.
Isabel C.N. Sacco has a BSc in Physical Education and MSc and PhD degrees in Sciences by University of Sao Paulo, Brazil. From 1997 till 1999, he was with the Biomechanics Laboratory at The Pennsylvania State University as a post-doctoral fellow. She is an Associate Professor in the School of Medicine at the University of São Paulo, Brazil since 1999. She runs the Laboratory of Biomechanics of Movement and Human Posture – LABIMPH – whose main areas of research includeintelligent systems for decision making in health and biomechanics of human locomotion in diabetic neuropathy and osteoarthritis. She is currently on the editorial specialists’ board of the Brazilian Journal of Physical Therapy and serves as a reviewer of several Journals in the Biomechanics and Rehabilitation fields.
Vincent A.M. Vigneron, before becoming a faculty member of Evry University, Vincent Vigneron was working part-time for companies as a computer science engineer. He received the Dipl.-Ing. degree from the ENSIMEV engineering school, Valenciennes, France in 1990, the PhD degree in applied mathematics from the University d’Evry, France and the Habilitation to Direct Research (HDR) degree from Universite d’Evry, Evry, France, in 2007. Currently, Vincent Vigneron is an associate Professor in the Electrical Engineering department. He is at the head of the SIMOB group of the IBISC lab. His main research topics are statistical signal processing and pattern recognition, more precisely blind source separation, parsimonious models and tensor factorization. He is reviewer of several journals and conferences, including IEEE Transactions on Signal Processing, IEEE Signal Processing letters, Signal Processing (Elsevier), IEEE Transactions on Neural Networks.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy Marco K. Butugan a, Cristina D. Sartor a, Ricky Watari a, Maria Cecília S. Martins a, Neli R.S. Ortega b, Vincent A.M. Vigneron c, Isabel C.N. Sacco a,⇑ a
University of Sao Paulo, School of Medicine, Physical Therapy, Speech and Occupational Therapy Dept., Sao Paulo, SP, Brazil University of Sao Paulo, School of Medicine, Center of Fuzzy Systems in Health, Sao Paulo, SP, Brazil c University of Evry, Electrical Engineering Department, Evry, France b
a r t i c l e
i n f o
Article history: Received 10 September 2013 Received in revised form 10 April 2014 Accepted 14 April 2014 Available online xxxx Keywords: Diabetic polyneuropathies Neural conduction Muscle fiber Isometric contraction Electromyography
a b s t r a c t This study compares muscle fiber conduction velocities estimated using surface electromyography during isometric maximal voluntary contraction in different stages of diabetic neuropathy. Eighty-five adults were studied: 16 non-diabetic individuals and 69 diabetic patients classified into four neuropathy stages, defined by a fuzzy expert system: absent (n = 26), mild (n = 21), moderate (n = 11) and severe (n = 11). Average muscle fiber conduction velocities of gastrocnemius medialis, tibialis anterior, vastus lateralis and biceps femoris were assessed using linear array electrodes, and were compared by ANOVA. Conduction velocities were significantly decreased in the moderate neuropathy group for the vastus lateralis compared to other groups (from 18% to 21% decrease), and were also decreased in all diabetic groups for the tibialis anterior (from 15% to 20% from control group). Not only the distal anatomical localization of the muscle affects the conduction velocity, but also the proportion of muscle fiber type, where the tibialis anterior with greater type I fiber proportion is affected earlier while the vastus lateralis with greater type II fiber proportion is affected in later stages of the disease. Generally, the muscles of the lower limb have different responsiveness to the effects of diabetes mellitus and show a reduction in the conduction velocity as neuropathy progresses. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction Progression of diabetes mellitus is chronically accompanied by both sensory and motor neuropathies, with marked decrease in quality of life (IJzerman et al., 2012). Sensory nerve degeneration has more conspicuous early signs, with slowing of nerve conduction velocity, clear signs of demyelination, and expressive symptoms related to somatosensory impairment. Motor dysfunction is more subtle and considered as a late symptom in the progression of diabetes mellitus (Andreassen et al., 2006). Even though loss of muscle strength is frequently reported for both type 1 (Andersen, 1996) and type 2 (Andersen et al., 2004b) diabetes, motor nerve conduction velocity is largely preserved due to axonal sprouting and reinnervation (Meijer et al., 2008), and animal ⇑ Corresponding author. Address: Departamento Fisioterapia, Fonoaudiologia e Terapia Ocupacional, R. Cipotânea, 51, Cidade Universitária, São Paulo, SP 05360160, Brazil. Tel.: +55 (11) 3091 8426; fax: +55 (11) 3091 7461. E-mail address: [email protected] (I.C.N. Sacco).
models indicate that the motor neuron seems to be more preserved in the course of the disease (Zochodne et al., 2008). Invasive EMG studies in diabetic patients have shown increased jitter, fiber density (Bril et al., 1996), and motor unit area, which indicate the presence of chronic muscle fiber re-innervation, even before symptoms of neuropathy occur (Andersen et al., 1998). This early neuromuscular impairment can also be identified by needle muscle fiber conduction velocity (MFCV) measurement (Van der Hoeven et al., 1993). Different from what was believed (Boulton et al., 2004), a reduction in MFCV was observed using needle electrodes even before loss of muscle strength or onset of neuropathy symptoms, suggesting that the progression of diabetes is likely coincident for both neuromuscular and sensory systems, and not a late complication of neuropathy (Meijer et al., 2008). The spatial distribution of impairments in the somatosensory system is characteristic, starting from distal segments and moving on to proximal ones, and it is believed to be due to diffuse axonal demyelination, which would affect more intensely axons of greater length and smaller diameter (Forbes and Cooper, 2013). The
http://dx.doi.org/10.1016/j.jelekin.2014.04.007 1050-6411/Ó 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx
earliest signs of motor involvement are also reported for distal leg muscles, with decrease in ankle joint torque (Andersen et al., 2004b) and atrophy of the intrinsic foot muscles (Andersen et al., 2004a), following the same order of the progression of somatosensory impairment. Moreover, it has been shown that severity of peripheral nerve dysfunction is related to reduced isokinetic strength, suggesting that both conditions are correlated (Andreassen et al., 2009). In spite of the similar spatial distribution, different mechanisms are responsible for neuromuscular impairment caused by diabetes, since muscle contraction is related to other factors besides nerve integrity such as the state of the motor end plate and skeletal muscle fibers. The motor end plate appears to be affected before nerve impairment in murine models, which is reflected by distal axonal retraction (Ramji et al., 2007). There is also a shift in fiber type proportion favoring the presence of more type II fibers in patients with diabetes mellitus (Fritzsche et al., 2008; Oberbach et al., 2006), which is likely due to factors such as increased intracellular calcium concentration (Nakagawa et al., 1989), leading type I fibers, which are more sensitive to this ion, to early apoptosis (Ruff and Whittlesey, 1991; Widrick et al., 1996). Newly diagnosed patients displayed muscle fiber atrophy, disruption of Z-lines, and morphological abnormalities in the mitochondria (Krause et al., 2011) in the absence of structural indications of neuropathy such as axonal degeneration, motor nerve conduction loss or increased vibratory threshold (Reske-Nielsen et al., 1977). These are important findings as they indicate that skeletal muscle is acutely sensitive to diabetes mellitus, prior to neuropathic complications (Andreassen et al., 2009, Krause et al., 2011). Recent studies assessed the entropy in contractions of the vastus lateralis muscle in diabetic patients and discussed the metabolic patterns using near-infrared spectroscopy (Molinari et al., 2013) and the neuromuscular system using high-density surface EMG (Watanabe et al., 2013). These studies found a decreased entropy suggesting an alteration in the muscle fiber recruitment (Watanabe et al., 2012) and in the metabolic state of the muscle fibers in this population (Molinari et al., 2013). Upon further investigation, it was observed that individual motor units had a lower firing rate in patients with diabetes, suggesting that in addition to changes in the metabolic and vascular states of the muscle fibers, changes in the neuromuscular system like excitation–contraction decoupling or delayed repolarization of the membrane could cause these recruitment changes (Watanabe et al., 2013). It is important to highlight that a delay in the repolarization of the membrane could induce a decrease in conduction velocity of the motor unit action potential in these patients. Non-invasive MFCV estimation can assess the average muscle fiber conduction velocity of innervated fibers (Merletti et al., 2003). In addition, these data could be used to understand other mechanisms underlying the progression of motor dysfunction caused by diabetes mellitus in lower limb muscles. Although there is evidence of early motor involvement, the way the muscle function deteriorates throughout the progression of this disease stays unknown. MFCV estimation could be a sensitive tool to detect muscle function status and signs of motor axonal dysfunction in individuals where diabetes and neuropathy are progressing (Meijer et al., 2008). Therefore, for this study, we tested if different diabetic neuropathy stages have distinct muscle fiber conduction velocities in four lower limb muscles during isometric contractions. We hypothesized that: (i) as diabetic polyneuropathy progresses, the MFCV values would be progressively slower, (ii) distal muscles would be affected earlier and in a greater magnitude than proximal muscles, and (iii) muscles with larger proportion of type I fiber would be affected earlier and in greater magnitude than muscles with smaller proportion of type I fiber.
2. Methods 2.1. Participants For this prospective study, 16 healthy adults participated as a control group (C) and 69 type 2 diabetic patients were divided into four groups with different neuropathy severity stages, classified by a fuzzy expert system: absent neuropathy (AbN; n = 26), mild (MiN; n = 21), moderate (MoN; n = 11), and severe groups (SeN; n = 11). Inclusion criteria were: age under 65 years old, not having partial or total lower limb amputation or other neurological or orthopedic impairments due to stroke, cerebral palsy, poliomyelitis, rheumatoid arthritis, prosthesis, or moderate or severe osteoarthritis; without venous or arterial ulcers; not having severe retinopathy, severe nephropathy causing edema or requiring hemodialysis; and absence of plantar ulcer at the time of evaluation. This study was approved by the local ethics committee (522/11) and all participants provided written informed consent prior to participation. Clinical assessment instruments for the evaluation of the diabetic neuropathy provide simple and inexpensive options (Armstrong et al., 1998), but their results have shown to be variable when reproducibility and accuracy were analyzed (Dyck et al., 2010; England et al., 2005; Taksande et al., 2011), even when used as a composite score (England et al., 2005). These assessments involve a great number of uncertainties in both measurement and formulation of diagnosis. Since neuropathy evolves continuously from the onset of diabetes mellitus, the Theory of Fuzzy Sets is a useful method for diagnosing and classifying patients, as it takes into account the uncertainties of the clinical assessment of the disease, and it is capable of objectively measuring a subjective judgment. All participants were assessed by a trained physical therapist, who evaluated (i) vibratory perception (128 Hz tuning fork), (ii) tactile sensitivity (using a 10 g Semmes–Weinstein monofilament), and (iii) presence of typical neuropathy symptoms (based on the Michigan Neuropathy Screening Instrument). These three groups of variables were used as linguistic inputs in a fuzzy expert system, based on (Picon et al., 2012) and Watari et al. (2014) to classify the patients into different disease severity stages. This model presented a very strong correlation (Zou et al., 2003) with the experts’ opinion (Pearson’s coefficient r = 0.943) and a high accuracy level when classifying real patients that underwent the model’s analysis (ROC curve area = 0.91). The fuzzy expert system classifies each input variable into fuzzy sets (fuzzification process) and performs a combinatory analysis of those variables by the Mamdani inference process (Mamdani and Assilian, 1975), linking those combinations with fuzzy output sets. Then, by (center of area) defuzzification method, the resulting output sets are transformed into numerical values arranged into neuropathy classes corresponding to the following division, with x being the score value: (i) x 6 2.5: absent; (ii) 2.5 < x < 5.0: mild; (iii) 5.0 6 x < 8.0: moderate; and (iv) x P 8.0: severe (Watari et al., 2014). Glycated hemoglobin levels were measured with a HbA1c monitor (A1CNow SELFCHECK, Bayer LLC Diabetes Care, New York, USA). The groups did not differ in gender distribution and body mass index. Diabetes duration did not differ between groups, HbA1c levels were lower in C compared to all diabetic groups, and the neuropathy output score from the fuzzy model increased significantly among diabetic groups (Table 1).
2.2. Data acquisition Surface EMG was acquired during maximum isometric voluntary contractions of four lower limb muscles using a multi-channel
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx Table 1 Demographic and grouping variables. Groups
a b c
Age (years)
Male/female (%)
Body mass index (kg/m2)
HbA1c (%) c
Diabetes duration (years)
Fuzzy score
Control (n = 16) Absent Neuropathy (n = 26) Mild Neuropathy (n = 21) Moderate Neuropathy (n = 11) Severe Neuropathy (n = 11)
56.6 ± 7.5 58.0 ± 5.5 58.3 ± 6.0 60.3 ± 4.5 59.4 ± 4.3
56 62 28 56 70
29.3 ± 4.1 27.0 ± 2.7 27.3 ± 7.7 28.7 ± 4.9 31.1 ± 4.1
5.9 ± 0.5 8.5 ± 1.7 9.0 ± 1.8 9.5 ± 1.5 9.5 ± 2.5
– 13.1 ± 8.7 10.2 ± 7.8 17.4 ± 6.8 14.6 ± 9.5
0.9 ± 0.4 1.3 ± 0.6 2.7 ± 0.9 6.1 ± 1.0 8.9 ± 1.0
p
0.56a
0.09b
0.22a
1012 ohm, amplified by a factor of 2000, band-pass filtered with a 5th order Bessel band pass filter at 10–900 Hz and converted to digital form by its 12bit analog-to-digital converter at 2048 Hz. 2.3. Procedures We used a dry linear array of eight 5 mm width by 1 mm diameter silver bars with 10 mm inter-electrode distance (SA8/10, OT Bioelettronica, Torino, Italy) to determine the best electrode position to acquire maximum voluntary isometric contractions. For that, we used adhesive linear arrays with 4 electrodes (Ag/AgCl, 5 mm 1 mm electrodes with 10 mm inter-electrode distance, ELSCH004, OT Bioelettronica, Torino, Italy) filled with conductive paste (CC1, OT Bioelettronica, Torino, Italy) and attached with disposable foam spacers (KITAD004, OT Bioelettronica, Torino, Italy), after skin cleaning and abrasion with alcohol and gauze. A reference electrode was placed on the tibial tuberosity. The dry electrode was initially placed in each muscle following the electrode placement orientation recommended by the surface EMG for the Non-Invasive Assessment of Muscles (SENIAM, 2012), avoiding the muscle innervation zones (Barbero et al., 2012). Then, the position of the innervation zone, tendon and the proper fiber alignment for each muscle were determined in submaximal contractions by visual inspection of the signals (Fig. 1) (Merletti et al., 2001). The disposable linear arrays were then placed between the distal tendon and the most distal innervation zone (Fig. 2). We acquired three trials of five seconds each of EMG data for the vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius medialis muscles while the individuals performed a maximum voluntary isometric contraction against manual resistance provided by a trained physiotherapist (Fig. 3).
To ensure that maximal voluntary contraction was achieved during the trials, subjects were first instructed on how to perform each task, and then were verbally encouraged to produce maximum effort during data acquisition. The assessor was instructed to keep the participant’s joint positions constant for the whole test. For each muscle assessment, a different body posture was required. For the vastus lateralis, the participant was in supine, with the hip and the knee in a neutral position. The patient was instructed to apply pressure with the back of the knee joint and the anterior aspect of the ankle joint against the assessor’s resistance, extending the knee joint. Resistance was applied on the posterior aspect of the knee joint and distally on the anterior aspect of the ankle joint. For the biceps femoris, the participant was in prone, with the hip in a neutral position and the knee at 45° of flexion. The patient was instructed to flex the knee joint and resistance was applied on the posterior aspect of the ankle joint. For the tibialis anterior, the participant was in supine, with the ankle in neutral position. The patient was instructed to flex the ankle joint and resistance was exerted over the foot. For the gastrocnemius medialis, the participant was in prone, with the ankle at 45° of flexion and the knee in a neutral position. The patient was instructed to extend the ankle and resistance was applied on the foot sole to flex the ankle joint. All joint positions were measured by a manual goniometer. 2.4. Signal processing The acquired signals were band-pass filtered at 10–500 Hz, with a zero-lag 4th order Butterworth digital filter. They were also conditioned by spectral subtraction, which requires known spectral properties of the noise, using a segment of the signal without muscle activity that was assumed to contain white noise with a power r2. The short term amplitude spectrum of the denoised signal was estimated by the expression:
b ðkÞ ¼ GðkÞXðkÞ X
Fig. 1. Sample single-differential signals from dry electrode acquired from the tibialis anterior. Dashed lines indicate the approximate location of innervation zones. To the left, the signal was acquired at the position and orientation recommended by SENIAM: the black arrow indicates the recommended position, smaller amplitude and reversal propagation direction of the action potential can be observed in the signal closest to the innervation zone. To the right, the signal was acquired at the final/used position: the dashed line indicates the last innervation zone detected; the gray signals have the smallest amplitude indicating the tendon region; in black adequate action potentials can be observed for three single differential channels.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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Fig. 2. Electrode positioning for each muscle: the lighter lines indicate the position (short dashed line) and orientation recommended by SENIAM (long dashed line). The darker black lines indicate the final/used position for the electrodes (short continuous darker black lines) and the dashed darker lines indicate the location of the innervation zone.
Fig. 3. Single differential signals obtained during maximal voluntary contraction for each muscle: the dashed line indicates the action potential propagation direction in each signal.
where X(k) is the discrete Fourier transform of a noisy signal x(n) of length
XðkÞ ¼
N 1 X xðnÞe2jpnk=N n¼0
and where G(k) can be estimated by:
GðkÞ ¼
( pffiffiffi pffiffiffi r N r N Abs p = 0.02; C > MiN p = 0.02; C > MoN p = 0.03; C > SeN p = 0.01. For Vastus Lateralis p values were: C > MoN p = 0.03; AbN > MoN p = 0.02;MiN > MoN p = 0.02 SeN > MoN p = 0.01.
Fig. 5. Mean MFCV values and standard error of gastrocnemius medialis and biceps femoris muscles during isometric contraction.
alterations observed did not follow a homogeneous progressive pattern of impairment throughout the disease progression, with tibialis anterior affected earlier, and vastus lateralis being affected only at moderate stage of neuropathy. In general, the MFCV did not follow a distal to proximal order of alteration from absent to severe stages. We observe a significant decrease in MFCV values of the tibialis anterior at an early stage of the disease (AbN) that remained almost unchanged during the following stages. It is interesting to note that this reduction occurred even without the presence of signs and symptoms of neuropathy. Meijer et al. (2008) reported a similar result using invasive EMG, where diabetic patients without polyneuropathy presented a slower MFCV in tibialis anterior. These results corroborate to the understanding that motor impairment develops early in diabetes progression along with sensorial losses and following distal to proximal impairment. Since tibialis anterior has 73% type I fibers (Johnson et al., 1973), which is more than the other muscles tested in this study, it is reasonable to assume that this muscle would be more likely affected in earlier stages of diabetes because this disease affects particularly the distal muscles (Andersen et al., 2004a,b). Since type I fiber proportion of the diabetic population is diminished (Oberbach et al., 2006), it is also reasonable to assume that those fibers can be more susceptible to the effects of diabetic neuropathy, thus making muscles composed primarily of these fiber types more easily affected. The tibialis anterior results are also consistent with those from neuropathic patients during gait and stair negotiation who exhibited a delayed activity (Abboud et al., 2000; Onodera et al., 2011; Sacco and Amadio, 2003), suggesting a failure in its function. Our findings together with previous results in locomotion suggest that this muscle contributes the most for distinguishing diabetic patients in early stages of neuromuscular dysfunction. The vastus lateralis muscle shows a significant decrease at the moderate neuropathy stage when compared to the other groups. Late involvement of the vastus lateralis muscle was expected because of its proximal position in relation to calf muscles, being most likely less affected until this stage of progression. This late decrease in conduction velocity is also consistent with previous results during gait that showed impaired activity of the vastus lateralis in diabetic individuals with neuropathy mostly in patients in later stages (Akashi et al., 2008; Sacco et al., 2010). The smaller proportion of type I fiber in vastus lateralis (38%) when compared with tibialis anterior (Johnson et al., 1973) may also account for the
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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reduced MFCV observed in this later stage of the disease, unlike tibialis anterior which showed an earlier decrease in average MFCV values. The SeN group presents a reverse trend of recuperation in vastus lateralis conduction velocity. This possible recovery, occurring late in diabetes progression, can also be attributed to its lower proportion of type I muscle fibers, since this neural impairment seems to affect primarily type I muscle fibers (Oberbach et al., 2006). With vastus lateralis having less type I muscle fibers, a greater degree of resistance is expected. In addition to that, for later stages of disease, axonal sprouting could enlarge the motor end plate areas (Hansen and Ballantyne, 1977), increasing the amount of non-propagating potentials under the electrodes, which could lead to an increase in the MFCV, as it was observed for vastus lateralis. Even though, it is expected that axonal sprouting should happen for all affected muscles in response to denervation (Andersen et al., 1998), this process would be more evident for this muscle and it would be reflected in the MFCV estimation. However, an isolated increase in MFCV observed in the SeN group for vastus lateralis muscles does not mean that muscle function was restored. There was a trend to a linear reduction in the MFCV of the gastrocnemius and biceps throughout the neuropathy progression, opposed to the tibialis anterior results which demonstrated a marked reduction between control and absent groups. Both tibialis anterior and gastrocnemius are distal muscles, therefore we expected that a decrease in MFCV of these muscles would happen at the same stage worsening as neuropathy develops. However, it is important to highlight that gastrocnemius medialis has less type I fibers compared to tibialis anterior (51%) (Johnson et al., 1973), and could be less susceptible to losses due to diabetes than tibialis anterior. In addition, this muscle has several methodological issues that compromise the reliability of the MFCV. The pennation angle of the gastrocnemius fibers (Manal et al., 2006) affects the measurement of MFCV, since this angle increases during maximal voluntary contraction under the electrode array (Mesin et al., 2007). Furthermore, gastrocnemius muscle has also a very distal innervation zone (Saitou et al., 2000) and above the endplate, it is not possible to get a clear motor unit action potential shape (Gallina et al., 2013), which limits the possible positions of the electrodes. All these characteristics make it difficult to find an optimal array position to acquire reliable signals. The lack of significant differences between groups in earlier stages of the disease could be attributed to these issues. Although we have used the criteria established in (McIntosh and Gabriel, 2011), where values between 2 and 13 m/s are considered physiological, according to the probability density function described by (Hunter et al., 1987), we believe that both end-offiber effect and complex fibers muscle architecture (Mesin et al., 2007) may have influenced our MFCV estimation. Even so, we tried to minimize the impact of these factors by placing the electrodes in sites where the muscle fibers were relatively parallel the electrode by visually inspecting the signal, checking for asymmetry in the waveforms (Merletti et al., 2001) and choosing very distal sites, especially for the gastrocnemius. We have also used double-differentiation to improve the removal of end-of-fiber effect. In summary, our first hypothesis was confirmed since as neuropathy progresses, an overall decrease in MFCV of all muscles was observed. Aside from a possible recovery, it does not return to initial and healthier condition, except for the vastus lateralis. Our second hypothesis was not confirmed, since not all distal muscles were affected earlier than the proximal ones. Because of the nature of distal to proximal evolution in diabetic neuropathy, it would be expected that changes in muscle dynamics would appear first in distal, instead of proximal muscles. It appears that the fiber type constitution and capability for reinnervation are also important factors to determine loss of MFCV than only the anatom-
ical location. Future studies should address if these factors can indeed affect MFCV. And finally, our third hypothesis was partially confirmed since the MFCV values of the studied muscles decreased first for the tibialis anterior, which has the greater proportion of type I fiber among the studied muscles, and only showing an isolated MFCV decrease for vastus lateralis much later in disease progression. Further studies should explore and access dynamic muscle activities, either during maintained contraction or different voluntary contraction intensities to better comprehend muscle fiber recruitment patterns in the diabetic population. 5. Conclusion Muscles of the lower limb have different responsiveness to diabetes mellitus progression and the neuropathy severity, but in general alterations in MFCV do not follow a homogeneous/linear progressive pattern of impairment throughout disease progression. There is no clear distal to proximal pattern of muscle involvement, however muscles with greater proportion of type I fiber, like the tibialis anterior, have a pronounced and earlier decrease in MFCV, while muscles with greater type II fiber proportion, like the vastus lateralis, are affected in more severe neuropathy levels. Conflict of interest None. Acknowledgements São Paulo State Research Support Foundation (FAPESP) for Butugan (2011/15770-0) and Sartor (2011/19304-4) scholarships and The National Council for Scientific and Technological Development (CNPq) for Watari scholarship (Master 556374/2010-0). References Abboud RJ, Rowley DI, Newton RW. Lower limb muscle dysfunction may contribute to foot ulceration in diabetic patients. Clin Biomech (Bristol, Avon) 2000;15:37–45. Akashi PM, Sacco IC, Watari R, Hennig E. The effect of diabetic neuropathy and previous foot ulceration in EMG and ground reaction forces during gait. Clin Biomech (Bristol, Avon) 2008;23:584–92. Andersen H. Reliability of isokinetic measurements of ankle dorsal and plantar flexors in normal subjects and in patients with peripheral neuropathy. Arch Phys Med Rehabil 1996;77:265–8. Andersen H, Stalberg E, Gjerstad MD, Jakobsen J. Association of muscle strength and electrophysiological measures of reinnervation in diabetic neuropathy. Muscle Nerve 1998;21:1647–54. Andersen H, Gjerstad MD, Jakobsen J. Atrophy of foot muscles: a measure of diabetic neuropathy. Diabetes Care 2004a;27:2382–5. Andersen H, Nielsen S, Mogensen CE, Jakobsen J. Muscle strength in type 2 diabetes. Diabetes 2004b;53:1543–8. Andreassen CS, Jakobsen J, Andersen H. Muscle weakness: a progressive late complication in diabetic distal symmetric polyneuropathy. Diabetes 2006;55:806–12. Andreassen CS, Jakobsen J, Ringgaard S, Ejskjaer N, Andersen H. Accelerated atrophy of lower leg and foot muscles – a follow-up study of long-term diabetic polyneuropathy using magnetic resonance imaging (MRI). Diabetologia 2009;52:1182–91. Armstrong DG, Lavery LA, Vela SA, Quebedeaux TL, Fleischli JG. Choosing a practical screening instrument to identify patients at risk for diabetic foot ulceration. Arch Intern Med 1998;158:289–92. Barbero M, Merletti R, Rainoldi A. Atlas of muscle innervation zones. 1st ed. Italy: Springer-Verlag; 2012. Boulton AJ, Malik RA, Arezzo JC, Sosenko JM. Diabetic somatic neuropathies. Diabetes Care 2004;27:1458–86. Bril V, Werb MR, Greene DA, Sima AA. Single-fiber electromyography in diabetic peripheral polyneuropathy. Muscle Nerve 1996;19:2–9. Dyck PJ, Overland CJ, Low PA, Litchy WJ, Davies JL, O’Brien PC, et al. Signs and symptoms versus nerve conduction studies to diagnose diabetic sensorimotor polyneuropathy: Cl vs. NPhys trial. Muscle Nerve 2010;42:157–64. England JD, Gronseth GS, Franklin G, Miller RG, Asbury AK, Carter GT, et al. Distal symmetric polyneuropathy: a definition for clinical research: report of the American Academy of Neurology, the American association of electrodiagnostic
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Marco K. Butugan is a graduate student at the Laboratory of Human Movement and Posture Biomechanics, Department of Physical Therapy, Speech and Occupational Therapy, Faculty of Medicine, University of São Paulo, Brazil and is currently working for his M. Sc. degree under Professor Isabel de Camargo Neves Sacco. His research interests include biological signal processing and biomechanical analysis of the human movement.
Cristina D. Sartor obtained her Ph.D. from the University of São Paulo (São Paulo, Brazil) in 2013 and is currently a post-doctoral researcher in the Laboratory of Human Movement and Posture Biomechanics. Her research interests include the biomechanical analysis of human movement in pathological conditions, especially Diabetes Mellitus and musculoskeletal disorders.
Ricky Watari obtained his M. Sc. in Rehabilitation Science (2013) from University of São Paulo (São Paulo, Brazil) and he is currently an associate researcher at the Laboratory of Biomechanics of Human Movement and Posture, Department of Physical Therapy, Speech and Occupational Therapy, Faculty of Medicine, University of São Paulo, Brazil. His research interests include biomechanical analysis of the human movement in pathological conditions and on the Pilates method.
Maria Cecília S. Martins is a certified Physical Therapist and worked in the Laboratory of Biomechanics of Human Movement and Posture, University of São Paulo under Professor Isabel de Camargo Neves Sacco. Currently working validating and translating specific questionnaires for ankle sprain assessment in athletes at the Center of Sport Traumatology of the University of Sâo Paulo.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
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M.K. Butugan et al. / Journal of Electromyography and Kinesiology xxx (2014) xxx–xxx Neli R.S. Ortega obtained her M.Sc. (1997) and Ph.D. (2001) from the Physics Institute of the University of Sâo Paulo (São Paulo, Brazil). Currently she is an associate professor and researcher in the Univesity of Sâo Paulo, researcher of the Nacional Council of Scientific and Technological Development in Brazil, ad hoc consultant for the same organization and reviewer in journals such as the Bulletin of Mathematical Biology, Mathematical Biosciencas and Medical Care among others. Her research focuses on the mathematical modelling of problems in public health with focus on fuzzy set theory, epidemiology and decision support systems.
Isabel C.N. Sacco has a BSc in Physical Education and MSc and PhD degrees in Sciences by University of Sao Paulo, Brazil. From 1997 till 1999, he was with the Biomechanics Laboratory at The Pennsylvania State University as a post-doctoral fellow. She is an Associate Professor in the School of Medicine at the University of São Paulo, Brazil since 1999. She runs the Laboratory of Biomechanics of Movement and Human Posture – LABIMPH – whose main areas of research includeintelligent systems for decision making in health and biomechanics of human locomotion in diabetic neuropathy and osteoarthritis. She is currently on the editorial specialists’ board of the Brazilian Journal of Physical Therapy and serves as a reviewer of several Journals in the Biomechanics and Rehabilitation fields.
Vincent A.M. Vigneron, before becoming a faculty member of Evry University, Vincent Vigneron was working part-time for companies as a computer science engineer. He received the Dipl.-Ing. degree from the ENSIMEV engineering school, Valenciennes, France in 1990, the PhD degree in applied mathematics from the University d’Evry, France and the Habilitation to Direct Research (HDR) degree from Universite d’Evry, Evry, France, in 2007. Currently, Vincent Vigneron is an associate Professor in the Electrical Engineering department. He is at the head of the SIMOB group of the IBISC lab. His main research topics are statistical signal processing and pattern recognition, more precisely blind source separation, parsimonious models and tensor factorization. He is reviewer of several journals and conferences, including IEEE Transactions on Signal Processing, IEEE Signal Processing letters, Signal Processing (Elsevier), IEEE Transactions on Neural Networks.
Please cite this article in press as: Butugan MK et al. Multichannel EMG-based estimation of fiber conduction velocity during isometric contraction of patients with different stages of diabetic neuropathy. J Electromyogr Kinesiol (2014), http://dx.doi.org/10.1016/j.jelekin.2014.04.007
