scispace - formally typeset
Search or ask a question
Author

P. A. Karthick

Bio: P. A. Karthick is an academic researcher from National Institute of Technology, Tiruchirappalli. The author has contributed to research in topics: Muscle fatigue & Support vector machine. The author has an hindex of 7, co-authored 25 publications receiving 196 citations. Previous affiliations of P. A. Karthick include Indian Institutes of Technology & Indian Institute of Technology Madras.

Papers
More filters
Journal ArticleDOI
TL;DR: The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions and the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.

103 citations

Journal ArticleDOI
TL;DR: MBD based time–frequency spectrum is able to provide the instantaneous variations of frequency components associated with fatiguing contractions and it is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variability across different subjects in comparison with other two features.

45 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used symbolic transition networks (SNTN) to extract myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions.
Abstract: The measurement and analysis of the electrical activity of muscle provide information that aids in the control of assistive devices. The investigation of these signals under varied physiological conditions, such as fatigue, enables reliable control. Muscle fatigue is a muscular condition associated with loss of muscle function. The early detection of muscle fatigue using surface Electromyography (sEMG)-based electrical measurements is challenging due to the nonlinear variations of the signal. In this work, an attempt has been made to understand the effect of dynamic nonlinear variations in the characteristics of the signal to develop a reliable fatigue index. The methodology involves the acquisition of myoelectric signals from the biceps brachii muscle of 52 healthy participants during dynamic contractions. The acquired signals are preprocessed and are analyzed with symbolic transition networks. Features such as symbolic entropy, network entropy, uniformity, and, minimum and maximum effective degrees (EDs) are extracted for further analysis. Appropriate decision boundaries are established for each feature using receiver operator characteristics (ROCs) and machine learning algorithms. The results indicate a decrease in signal complexity with fatigue. All the extracted features show a statistically significant difference (p < 0.05) between both conditions. Symbolic entropy achieves an accuracy of 89%, and the maximum ED yields an accuracy of 90% based on thresholds estimated with ROC. Furthermore, only a marginal improvement is observed with the combination of these features and the Naive Bayes classifier. It appears that the proposed maximum ED could be used as a reliable fatigue index in real-time applications for the improvement of rehabilitation efficacy.

25 citations

Journal ArticleDOI
TL;DR: The first five seconds of a focal seizure contains information regarding the eventual evolution of the seizure, which could be predicted in most seizures and alert the health care team when a patient is hospitalized for intracerebral EEG and improve safety and eventually result in an implantable device.

24 citations

Journal ArticleDOI
TL;DR: Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue, and these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.
Abstract: Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p?

21 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Clinical predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly.
Abstract: The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 disease. To evaluate the predictive performance of our models, precision, F1-score, recall, AUC, and accuracy scores calculated. Models were tested with 18 laboratory findings from 600 patients and validated with 10 fold cross-validation and train-test split approaches. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. It is observed that predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly. Our models (available at (https://github.com/burakalakuss/COVID-19-Clinical)) can be employed to assists medical experts in validating their initial laboratory findings, and can also be used for clinical prediction studies.

196 citations

Book
01 Jan 2003
TL;DR: Comprehensive in scope, and gentle in approach, this book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.
Abstract: From the Publisher: This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

162 citations

Journal ArticleDOI
01 Aug 2018
TL;DR: The main factors that expand EMG data resources into the era of big data are introduced and directions for future research in EMG pattern recognition are outlined and discussed.
Abstract: The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. Finally, directions for future research in EMG pattern recognition are outlined and discussed.

155 citations

Journal ArticleDOI
TL;DR: In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed and the best method of thumb motion pattern recognition is obtained.
Abstract: Human computer interaction plays an increasingly important role in our life. People need more intelligent, concise and efficient human-computer interaction. It is of great significance to optimize the process of human-computer interaction by using appropriate calculation methods. In order to eliminate the interference data of thumb recognition based on sEMG signal in the process of human-computer interaction, simplify the data processing, and improve the working efficiency of general equipment of sEMG signal. In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed. The redundancy of five groups of action signals is divided into 16 levels and visualized. By comparing the results of thumb motion recognition when different redundant channels are removed, the optimal channel combination in the process of thumb motion recognition is obtained. Finally, two kinds of classifiers suitable for sEMG signal field are selected, and the classification results are compared, and the best method of thumb motion pattern recognition is obtained.

149 citations

Journal ArticleDOI
TL;DR: The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions and the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.

103 citations