A Novel Quantitative Spasticity Evaluation Method Based on Surface Electromyogram Signals and Adaptive Neuro Fuzzy Inference System

Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often practical demands for objective and quantitative assessment of muscle spasticity. However, the common quantitative spasticity assessment method, the tonic stretch reflex threshold (TSRT), is time-consuming and complicated to implement due to the requirement of multiple passive stretches. To evaluate spasticity conveniently, a novel spasticity evaluation method based on surface electromyogram (sEMG) signals and adaptive neuro fuzzy inference system (i.e., the sEMG-ANFIS method) was presented in this paper. Eleven stroke patients with spasticity and four healthy subjects were recruited to participate in the experiment. During the experiment, the Modified Ashworth scale (MAS) scores of each subject was obtained and sEMG signals from four elbow flexors or extensors were collected from several times (4–5) repetitions of passive stretching. Four time-domain features (root mean square, the zero-cross rate, the wavelength and a 4th-order autoregressive model coefficient) and one frequency-domain feature (the mean power frequency) were extracted from the collected sEMG signals to reflect the spasticity information. Using the ANFIS classifier, excellent regression performance was achieved [mean accuracy = 0.96, mean root-mean-square error (RMSE) = 0.13], outperforming the classical TSRT method (accuracy = 0.88, RMSE = 0.28). The results showed that the sEMG-ANFIS method not only has higher accuracy but also is convenient to implement by requiring fewer repetitions (4–5) of passive stretches. The sEMG-ANFIS method can help stroke patients develop proper rehabilitation training programs and can potentially be used to provide therapeutic feedback for some new spasticity interventions, such as shockwave therapy and repetitive transcranial magnetic stimulation.

[1]  Atul T Patel,et al.  Successful Treatment of Long-Term, Poststroke, Upper-Limb Spasticity With OnabotulinumtoxinA , 2011, Physical Therapy.

[2]  W. Rymer,et al.  Effects of changing wrist positions on finger flexor hypertonia in stroke survivors , 2006, Muscle & nerve.

[3]  Alireza B. Dariane,et al.  Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models , 2016 .

[4]  Hamid Reza Kobravi,et al.  Generating the Visual Biofeedback Signals Applicable to Reduction of Wrist Spasticity: A Pilot Study on Stroke Patients , 2018, Basic and clinical neuroscience.

[5]  Angkoon Phinyomark,et al.  Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors , 2018, Sensors.

[6]  Eduardo Palermo,et al.  Spasticity Measurement Based on Tonic Stretch Reflex Threshold in Children with Cerebral Palsy Using the PediAnklebot , 2017, Front. Hum. Neurosci..

[7]  David J. Magee.  Physical Rehabilitation of the Injured Athlete , 1993 .

[8]  A. Hulme,et al.  Baclofen in the elderly stroke patient its side-effects and pharmacokinetics , 2004, European Journal of Clinical Pharmacology.

[9]  V Reggie Edgerton,et al.  Spasticity: a switch from inhibition to excitation , 2010, Nature Medicine.

[10]  J. Gracies,et al.  Physical modalities other than stretch in spastic hypertonia. , 2001, Physical medicine and rehabilitation clinics of North America.

[11]  Anatol G. Feldman,et al.  The role of stretch reflex threshold regulation in normal and impaired motor control , 1994, Brain Research.

[12]  D Chaffin,et al.  High-pass filtering to remove electrocardiographic interference from torso EMG recordings. , 1993, Clinical biomechanics.

[13]  Hui Wang,et al.  Normalization factor for the assessment of elbow spasticity with passive stretch measurement: maximum torque VS. body weight , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Hermie J Hermens,et al.  Motor unit properties of biceps brachii during dynamic contractions in chronic stroke patients , 2011, Muscle & nerve.

[15]  Xiufeng Zhang,et al.  Spasticity Measurement Based on the HHT Marginal Spectrum Entropy of sEMG Using a Portable System: A Preliminary Study , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Longhan Xie,et al.  sEMG-Based Trunk Compensation Detection in Rehabilitation Training , 2019, Front. Neurosci..

[17]  Thomas Watanabe,et al.  The Role of Therapy in Spasticity Management , 2004, American journal of physical medicine & rehabilitation.

[18]  Richard W. Bohannon,et al.  Interrater reliability of a modified Ashworth scale of muscle spasticity. , 1987, Physical therapy.

[19]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[20]  A. G. Feldman.  Once More on the Equilibrium-Point Hypothesis (λ Model) for Motor Control , 1986 .

[21]  Xun Chen,et al.  A Regression-Based Framework for Quantitative Assessment of Muscle Spasticity Using Combined EMG and Inertial Data From Wearable Sensors , 2019, Front. Neurosci..

[22]  M. Levin,et al.  Deficits in the coordination of agonist and antagonist muscles in stroke patients: implications for normal motor control , 2000, Brain Research.

[23]  Oluwarotimi Williams Samuel,et al.  Spasticity Assessment Based on the Maximum Isometrics Voluntary Contraction of Upper Limb Muscles in Post-stroke Hemiplegia , 2019, Front. Neurol..

[24]  G. Cruccu,et al.  A mechanism-based classification of pain in multiple sclerosis , 2012, Journal of Neurology.

[25]  James W. Lance,et al.  The control of muscle tone, reflexes, and movement , 1980, Neurology.

[26]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[27]  Blair A. Lock,et al.  Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Chul-Gyu Song,et al.  Portable measurement system for the objective evaluation of the spasticity of hemiplegic patients based on the tonic stretch reflex threshold. , 2011, Medical engineering & physics.

[29]  Andrei Nakagawa Silva,et al.  An improved approach for measuring the tonic stretch reflex response of spastic muscles , 2017, Comput. Biol. Medicine.

[30]  Hyung-Soon Park,et al.  Development of a Haptic Elbow Spasticity Simulator (HESS) for Improving Accuracy and Reliability of Clinical Assessment of Spasticity , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Ching‐Jen Wang,et al.  Extracorporeal shockwave therapy in musculoskeletal disorders , 2012, Journal of Orthopaedic Surgery and Research.

[32]  M. Levin.  Principles of Motor Recovery After Neurological Injury Based on a Motor Control Theory. , 2016, Advances in experimental medicine and biology.

[33]  Huosheng Hu,et al.  Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb , 2008, IEEE Transactions on Biomedical Engineering.

[34]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[35]  Mindy F Levin,et al.  Tonic Stretch Reflex Threshold as a Measure of Spasticity: Implications for Clinical Practice , 2009, Topics in stroke rehabilitation.

[36]  A. G. Feldman.  Once more on the equilibrium-point hypothesis (lambda model) for motor control. , 1986, Journal of motor behavior.

[37]  H Rodgers,et al.  A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity , 1999, Clinical rehabilitation.

[38]  Sheng Li,et al.  New insights into the pathophysiology of post-stroke spasticity , 2015, Front. Hum. Neurosci..

[39]  Preeti Raghavan,et al.  Peripheral Mechanisms Contributing to Spasticity and Implications for Treatment , 2014, Current Physical Medicine and Rehabilitation Reports.

[40]  Hong Kai Yap,et al.  Design of a Soft Robotic Elbow Sleeve with Passive and Intent-Controlled Actuation , 2017, Front. Neurosci..

[41]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[42]  R. Merletti,et al.  Electrically evoked myoelectric signals. , 1992, Critical reviews in biomedical engineering.

[43]  Andrei Nakagawa Silva,et al.  Measurement of post-stroke spasticity based on tonic stretch reflex threshold: implications of stretch velocity for clinical practice , 2019, Disability and rehabilitation.

[44]  E. Wassermann.  Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the International Workshop on the Safety of Repetitive Transcranial Magnetic Stimulation, June 5-7, 1996. , 1998, Electroencephalography and clinical neurophysiology.

[45]  Laura Mori,et al.  Pathophysiology of Spasticity: Implications for Neurorehabilitation , 2014, BioMed research international.

[46]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[47]  U. Emre,et al.  Hand Splinting for Poststroke Spasticity: A Randomized Controlled Trial , 2012, Topics in stroke rehabilitation.

[48]  Hui Wang,et al.  Assessment of elbow spasticity with surface electromyography and mechanomyography based on support vector machine , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[49]  Simona Crea,et al.  Phase-II Clinical Validation of a Powered Exoskeleton for the Treatment of Elbow Spasticity , 2017, Front. Neurosci..

[50]  Retheep Raj,et al.  Comparative study on estimation of elbow kinematics based on EMG time domain parameters using neural network and ANFIS NARX model , 2017, J. Intell. Fuzzy Syst..

[51]  Blaz Zupan,et al.  Relating clinical and neurophysiological assessment of spasticity by machine learning , 1997, Proceedings of Computer Based Medical Systems.

[52]  Tegoeh Tjahjowidodo,et al.  EMG based classification of hand gestures using PCA and ANFIS , 2017, 2017 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics).