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Navaneethakrishna Makaram

Bio: Navaneethakrishna Makaram is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Muscle fatigue & Electromyography. The author has an hindex of 2, co-authored 13 publications receiving 26 citations. Previous affiliations of Navaneethakrishna Makaram include Montreal Neurological Institute and Hospital.

Papers
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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
17 Mar 2020
TL;DR: An attempt is made to develop signal processing methods to understand the dynamics of the muscle’s electrical properties and it is observed that at the motif length of 13 all the extracted features are significant, which indicates that the signal has lower complexity.
Abstract: Exercise-induced muscle damage is a condition which results in the loss of muscle function due to overexertion. Muscle fatigue is a precursor of this phenomenon. The characterization of muscle fati...

9 citations

Proceedings ArticleDOI
01 Jan 2014
TL;DR: It appears that, sEMG features, such as activity, mobility, complexity, sample entropy and spectral entropy, are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.
Abstract: Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.

9 citations

Proceedings ArticleDOI
01 Jul 2019
TL;DR: An attempt is made to characterize the variation in the complexity of the surface electromyography (sEMG) signals in fatigue by recording signals from 58 healthy volunteers from the biceps brachii muscle under well-defined dynamic contraction protocol.
Abstract: In this work, an attempt is made to characterize the variation in the complexity of the surface electromyography (sEMG) signals in fatigue. For this, sEMG signals from 58 healthy volunteers are recorded from the biceps brachii muscle under well-defined dynamic contraction protocol. The contractions are segmented, and the initial and final curls are extracted. These are considered as nonfatigue and fatigue respectively. Further, visibility graphs are constructed at multiple scales, and median degree centrality (MSMC) is calculated in them. To quantify the variations in the MSMC, two features namely, the average and standard deviation are calculated. The results reveal that the recorded signals are non-stationary. The constructed networks form distinct clusters in space. The MSMC feature shows a decreasing trend with scale in both nonfatigue and fatigue conditions. Additionally, the extracted features have higher values in fatigue. This may be due to the motor unit synchronization, which causes an increase in connectivity between nodes. All the extracted features showed statistical significance with p<0.005. This approach of analysis can be extended to characterize muscle in other neuromuscular conditions.

4 citations

Proceedings ArticleDOI
01 Jul 2018
TL;DR: An attempt is made to utilize graph signal processing methods such as Sequential Visibility motif for the analysis of muscle fatigue condition and the results show that the signals are unique for each subject.
Abstract: Muscle fatigue is the inability to exert the required force. Surface Electromyography (sEMG) is a technique used to study the muscle’s electrical property. These generated signals are complex and nonstationary in nature. In this work, an attempt is made to utilize graph signal processing methods such as Sequential Visibility motif for the analysis of muscle fatigue condition. The sEMG signals of 41 healthy adult volunteers are acquired from the biceps brachii muscle during isometric contraction with a 6 Kg load. The subjects are asked to perform the exercise until they are unable to continue. The signals are preprocessed, and the first and last 500 ms of the signal are considered for analysis. The segmented signals are subjected to sequential visibility graph algorithm. Further, the number of motifs for a subgraph of four is calculated. The results show that the signals are unique for each subject. The frequency of higher degree motif is more in the case of fatigue. The frequency of each unique motif is capable of differentiating nonfatigue and fatigue conditions. Nonparametric statistical test result indicates all features are significant with p<0.05. This method of analysis can be extended to other varied neuromuscular conditions.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: This book serves as an introduction to the field of biomedical engineering for students with undergraduate training in engineering, physics, and mathematics and serves as a background for students or practitioners whose prior training has not included this material.
Abstract: Biomedical Engineering Principles Arthur B. Ritter, Stanley Reisman, and Bozena B. Michniak, CRC Press, Taylor and Francis Group, 2005. ISBN: 0824796160, 680 pages, US$99.95. This book serves as an introduction to the field of biomedical engineering for students with undergraduate training in engineering, physics, and mathematics. This book can be used for senioror graduate-level classes at universities, for short courses, or as a general knowledge book for practicing engineers wanting to learn more about biomedical engineering. The classic description of biomedical engineering is that it is the application of engineering analysis to problems in medicine and life sciences. Biomedical engineering is not one discipline but several interacting disciplines that coexist within the same field. Since biomedical engineering cuts across several engineering disciplines, the book is divided into several sections. Each section is intended to be complementary and to serve as a background for students or practitioners whose prior training has not included this material. The first section addresses modeling, transport processes, cell physiology, and the cardiovascular system. Chapter 1 presents an overview and introduction to engineering analysis of physiological systems, the nature of biological data, and the role of models and simulation in experimental design. The chapter introduces the concepts of conservation of mass, compartments, convection, and diffusion. It also develops pharmacokinetic models for drug distribution. Chapter 2 covers cell physiology and transport, introducing the primary mechanisms by which water and solutes get into and out of cells. Chapter 3 covers the fundamentals of hemodynamics and the nature of blood and blood vessels as engineering materials. Chapter 4 is an introduction to the cardiovascular system, covering the cardiac conduction pathway, control of heart rate, EKG measurement and interpretation, cardiac output, cardiac work, and autonomic and local regulation of blood flow. The second section of the book reviews the concepts of biomedical signal processing. Chapter 5 discusses biomedical signals and how to represent them. The frequency content of a signal, periodic functions, and Fourier series are reviewed. Chapter 6 discusses signal acquisition and processing. Topics include sampling theorem, sampling rate, and aliasing. Chapter 7 discusses techniques for physiological signal processing. Topics include AR modeling, time-frequency analysis, short-time Fourier transforms, and quadratic distributions. Chapter 8 contains examples of physiological signal processing. The third section of the book contains an introduction to and practical applications of biomechanics. Chapter 9 is an introduction to the principles of biomechanics and discusses the analysis of human movement, human dynamics, measurements of muscle force, electrical stimulation of skeletal muscle, mechanical characteristics of biological materials, bone remodeling, body cycles, thermal regulation, and hypothermia. Chapter 10 contains a discussion of some practical applications of biomechanics, using the principles developed in Chapter 9. The fourth section of the book presents an introduction to tissue engineering. Chapter 11 covers the history of tissue engineering, materials, biological interactions, and the role of cells in tissue engineering. Applications of tissue engineering in skin equivalents, cardiovascular components, bone regrowth, muscle tissue, and nerve regeneration are also discussed. Chapter 12 looks at future developments in biomedical engineering. For university faculty, the book is an excellent textbook for a class. Each chapter contains numerous examples and contains many figures to enhance learning. References and suggestions for further reading are included at the end of each chapter. Problems are included at the end of chapters, where they will best test the student's knowledge. For practicing engineers without a biomedical engineering background, the book provides an excellent resource to explain the many intricacies of biomedical engineering and provides sufficient background material to make the subject understandable. —Richard C. Fries, PE, CRE Baxter Healthcare, Inc.

145 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

Posted Content
TL;DR: This work investigates correlations in information carriers, e.g. texts and pieces of music, which are represented by strings of letters, and calculates the word distribution, the higher order entropy and the transinformation.
Abstract: We investigate correlations in information carriers, e.g. texts and pieces of music, which are represented by strings of letters. For information carrying strings generated by one source (i.e. a novel or a piece of music) we find correlations on many length scales. The word distribution, the higher order entropies and the transinformation are calculated. The analogy to strings generated through symbolic dynamics by nonlinear systems in critical states is discussed.

34 citations

Journal ArticleDOI
TL;DR: A new method for electroencephalogram (EEG) signal classification based on deep learning model, by which relevant features are automatically learned in a supervised learning framework, which exhibits better stability across different classification cases or patients, indicates the worth in practical applications for diagnostic reference in clinics.

32 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