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

Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset

Epileptic seizure or epilepsy is a chronic neurological disorder that occurs due to brain neurons\u27 abnormal activities and has affected approximately 50 million people worldwide. Epilepsy can affect patients’ health and lead to life-threatening emergencies. Early detection of epilepsy is highly effective in avoiding seizures by intervening in treatment. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result varies with different neurophysiologists for an identical reading. Thus, automatically classifying epilepsy into different epileptic states with a high accuracy rate is an urgent requirement and has long been investigated. This PhD thesis contributes to the epileptic seizure detection problem using Machine Learning (ML) techniques. Machine learning algorithms have been implemented to automatically classifying epilepsy from EEG data. Imbalance class distribution problems and effective feature extraction from the EEG signals are the two major concerns towards effectively and efficiently applying machine learning algorithms for epilepsy classification. The algorithms exhibit biased results towards the majority class when classes are imbalanced, while effective feature extraction can improve classification performance. In this thesis, we presented three different novel frameworks to effectively classify epileptic states while addressing the above issues. Firstly, a deep neural network-based framework exploring different sampling techniques was proposed where both traditional and state-of-the-art sampling techniques were experimented with and evaluated for their capability of improving the imbalance ratio and classification performance. Secondly, a novel integrated machine learning-based framework was proposed to effectively learn from EEG imbalanced data leveraging the Principal Component Analysis method to extract high- and low-variant principal components, which are empirically customized for the imbalanced data classification. This study showed that principal components associated with low variances can capture implicit patterns of the minority class of a dataset. Next, we proposed a novel framework to effectively classify epilepsy leveraging summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis and replaced outliers with k-NN imputer. Next, window level features were extracted from statistical, temporal, and spectral domains. Basic summary statistics are then computed from the extracted features to feed into different machine learning classifiers. An optimal set of features are selected leveraging variance thresholding and dropping correlated features before feeding the features for classification. Finally, we applied traditional machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, and k-Nearest Neighbors along with Deep Neural Networks to classify epilepsy. We experimented the frameworks with a benchmark dataset through rigorous experimental settings and displayed the effectiveness of the proposed frameworks in terms of accuracy, precision, recall, and F-beta score

Stationary wavelet processing and data imputing in myoelectric pattern recognition on a low-cost embedded system

Pattern recognition-based decoding of surface electromyography allows for intuitive and flexible control of prostheses but comes at the cost of sensitivity to in-band noise and sensor faults. System robustness can be improved with wavelet-based signal processing and data imputing, but no attempt has been made to implement such algorithms on real-time, portable systems. The aim of this work was to investigate the feasibility of low-latency, wavelet-based processing and data imputing on an embedded device capable of controlling upper-arm prostheses. Nine able-bodied subjects performed Motion Tests while inducing transient disturbances. Additional investigation was performed on pre-recorded Motion Tests from 15 able-bodied subjects with simulated disturbances. Results from real-time tests were inconclusive, likely due to the low number of disturbance episodes, but simulated tests showed significant improvements in most metrics for both algorithms. However, both algorithms also showed reduced responsiveness during disturbance episodes. These results suggest wavelet-based processing and data imputing can be implemented in portable, real-time systems to potentially improve robustness to signal distortion in prosthetic devices with the caveat of reduced responsiveness for the typically short duration of signal disturbances. The trade-off between large-scale signal corruption robustness and system responsiveness warrants further studies in daily life activities

Stationary Wavelet Processing and Data Imputing in Myoelectric Pattern Recognition on a Low-Cost Embedded System

Pattern recognition-based decoding of surface electromyography allows for intuitive and flexible control of prostheses but comes at the cost of sensitivity to in-band noise and sensor faults. System robustness can be improved with wavelet-based signal processing and data imputing, but no attempt has been made to implement such algorithms on real-time, portable systems. The aim of this work was to investigate the feasibility of low-latency, wavelet-based processing and data imputing on an embedded device capable of controlling upper-arm prostheses. Nine able-bodied subjects performed Motion Tests while inducing transient disturbances. Additional investigation was performed on pre-recorded Motion Tests from 15 able-bodied subjects with simulated disturbances. Results from real-time tests were inconclusive, likely due to the low number of disturbance episodes, but simulated tests showed significant improvements in most metrics for both algorithms. However, both algorithms also showed reduced responsiveness during disturbance episodes. These results suggest wavelet-based processing and data imputing can be implemented in portable, real-time systems to potentially improve robustness to signal distortion in prosthetic devices with the caveat of reduced responsiveness for the typically short duration of signal disturbances. The trade-off between large-scale signal corruption robustness and system responsiveness warrants further studies in daily life activities

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

A Survey on the Project in title

In this paper we present a survey of work that has been done in the project ldquo;Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theoryrdquo;we summarised the following papers, (Mohammed J Alhaddad amp; 2011), (Mohammed J. Alhaddad amp; Kamel M, 2012), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2013), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2013), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2014), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2014), (Mohammed J Alhaddad, Kamel, amp; Kadah, 2014), (Mohammed J Alhaddad, Kamel, Makary, Hargas, amp; Kadah, 2014), (Mohammed J Alhaddad, Mohammed, Kamel, amp; Hagras, 2015).We developed a new pre-processing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing pre-processing and allowing low channel counts to be used. We also developed a novel approach for brain-computer interface data that requires no prior training. The proposed approach is based on interval type-2 fuzzy logic based classifier which is able to handle the usersrsquo; uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximize the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. The basic principle of this new class of techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new methods were verified using various experiments which were performed on standard data sets and using real-data sets obtained from real subjects experiments performed in the BCI lab in King Abdulaziz University. The results were compared to the classification results of the same data using previous methods. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. It will be shown that the produced type-2 fuzzy logic based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl