Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Comparative Analysis of Machine Learning Techniques for the Classification of Knee Abnormality

Knee abnormality is a major problem in elderly people these days. It can be diagnosed by using Magnetic Resonance Imaging (MRI) or X-Ray imaging techniques. X-Ray is only used for primary evaluation, while MRI is an efficient way to diagnose knee abnormality, but it is very expensive. In this work, Surface EMG (sEMG) signals acquired from healthy and knee abnormal individuals during three different lower limb movements: Gait, Standing and Sitting, were used for classification. Hence, first Discrete Wavelet Transform (DWT) was used for denoising the input signals; then, eleven different time-domain features were extracted by using a 256 msec windowing with 25% of overlapping. After that, the features were normalized between 0 (zero) to 1 (one) and then selected by using the backward elimination method based on the p-value test. Five different machine learning classifiers: K-nearest neighbor, support vector machine, decision tree, random forest and extra tree, were studied for the classification step. Our result shows that the Extra Tree Classifier with ten cross-validations gave the highest accuracy (91%) in detecting knee abnormality from the sEMG signals under analysis. (c) 2020 IEEE

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Real‐time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre‐recorded datasets. While real‐time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real‐time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real‐time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real‐time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able‐bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p &lt; 0.05) outperformed other clas-sifiers, with an average classification accuracy of above 97%. On the other hand, the real‐time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively

Human knee abnormality detection from imbalanced sEMG data

The classification of imbalanced datasets, especially in medicine, is a major problem in data mining. Such a problem is evident in analyzing normal and abnormal subjects about knee from data collected during walking. In this work, surface electromyography (sEMG) data were collected during walking from the lower limb of 22 individuals (11 with and 11 without knee abnormality). Subjects with a knee abnormality take longer to complete the walking task than healthy subjects. Therefore, the SEMG signal length of unhealthy subjects is longer than that of healthy subjects, resulting in a problem of imbalance in the collected sEMG signal data. Thus, the development of a classification model for such datasets is challenging due to the bias towards the majority class in the data. The collected sEMG signals are challenging due to the contribution of multiple motor units at a time and their dependency on neuromuscular activity, physiological and anatomical properties of the involved muscles. Hence, automated analysis of such sEMG signals is an arduous task. A multi-step classification scheme is proposed in this research to overcome this limitation. The wavelet denoising (WD) scheme is used to denoise the collected sEMG signals, followed by the extraction of eleven time-domain features. The oversampling techniques are then used to balance the data under analysis by increasing the training minority class. The competency of the proposed scheme was assessed using various computational classifiers with 10 fold cross-validation. It was found that the oversampling techniques improve the performance of all studied classifiers when applied to the studied imbalanced sEMG data. (c) 2021 Elsevier Lt