A Review of EMG Techniques for Detection of Gait Disorders

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices

Feature Analysis for Discrimination of Motor Unit Action Potentials

© 2018 IEEE. In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference

Empirical modelling and classification of surface electromyogram

This thesis develops an effective feature extraction technique for sEMG signals. Surface electromyography (sEMG) is the recording of a muscle’s electrical activity from the surface of the skin. The signal contains information that is related to the anatomy and physiology of the muscle. In clinical applications, the signal is used for the diagnosis of neuro-muscular diseases and disorders. Another application of sEMG is for device control application where the signal is used for controlling devices such as prosthetic devices, robots, and human – machine interfaces. Signal classification is used to extract relevant information that represent a particular state (or class) of the sEMG signal. This stater (or class) of the sEMG depicts the information about the underlying pathology or is used as control input for other devices. Therefore it is important that the sEMG is classified in to the relevant class with high accuracy to ensure reliable application in a given field. Many researchers have attempted to improve the classification accuracy of the sEMG signal. Generally the number of electrodes attached to the surface of the skin also needs to be increased in order to increase the classification accuracy. In some cases this number becomes prohibitively high. On the other hand, with a decrease in the number of electrodes the classification accuracy has been reported to decrease. In order to overcome these challenges, in this thesis a new feature extraction technique has been developed. As opposed to the established global time or frequency domain analysis of the sEMG signal, the technique developed in this thesis relies on the well established volume conduction model of sEMG generation. Developed feature extraction technique is then applied to sEMG recorded from low level digital contraction with low signal to noise ratio. A high classification rate of approximately 93% in four classes of low level contraction was achieved by using single channel of sEMG recording. It was further established that the placement of electrode did not have significant effect on the accuracy and reliability of the classification. Further developments that may improve on the methods established in this thesis are presented in the end

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606

Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors

Identifying finger and wrist flexion based actions using a single channel surface electromyogram (sEMG) can lead to a number of applications such as sEMG based controllers for near elbow amputees, human computer interface (HCI) devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized