Neuromuscular disorders detection through time-frequency analysis and classification of multi-muscular EMG signals using Hilbert-Huang transform

Torres-Castillo, Jonathan R.; López-López, Carlos Omar; Castañeda, M.

doi:10.1016/j.bspc.2021.103037

Cited by 42 publications

(21 citation statements)

References 63 publications

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“…Exploring the results obtained by Torres-Castillo et al (2022) [ 15 ], it is noted that the authors have developed various machine learning models with decomposition techniques for classifying normal and abnormal EMG signals using time-frequency features. From the developed models, the authors revealed that the ensemble empirical mode decomposition (EEMD) with K -nearest neighbor has shown the best accuracy, sensitivity, and specificity of 99.5%, 99.6%, and 99.2%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Torres-Castillo et al (2022) [ 15 ] have used the machine learning algorithms with decomposition techniques for detection of neuromuscular disorders using Hilbert transformed time-frequency features. The authors have concluded that the ensemble empirical mode decomposition (EEMD) has exhibited a best result in identifying the normal and abnormal signals.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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Electromyograms (EMG) are a recorded galvanic action of nerves and muscles which assists in diagnosing the disorders associated with muscles and nerves. The efficient discrimination of abnormal EMG signals, myopathy and amyotrophic lateral sclerosis, engage crucial role in automatic diagnostic assistance tools, since EMG signals are nonstationary signals. Hence, for computer-aided identification of abnormalities, extraction of features, selection of superlative feature subset, and developing an efficient classifier are indispensable. Initially, time domain and Wigner-Ville transformed time-frequency features were extracted from abnormal EMG signals for experiments. The selection of substantial characteristics from time and time-frequency features was performed using bat algorithm. Extensively, deep neural network classifier is modelled for selected feature subset using bat algorithm from extracted time and time-frequency features. The performance of deep neural network exerting selected features from bat algorithm was compared with conventional artificial neural network. Results demonstrate that the deep neural network modelled with layers 2 and 3 ( neurons = 2 and 4) using time domain features is efficient in classifying the abnormalities of EMG signals with an accuracy, sensitivity, and specificity of 100% and also exhibited finer performance. Correspondingly, the developed conventional single layer artificial neural network ( neurons = 7 ) with time domain features has shown an accuracy of 83.3%, sensitivity of 100%, and specificity of 71.42%. The work materializes the significance of conventional and deep neural network using time and time-frequency features in diagnosing the abnormal signals exists in neuromuscular system using efficient classification.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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“…For musical tones with two fundamental frequency components, [9] performs multibase frequency detection. Based on [9], [10,11] continue the research and realize the transition from single-base frequency detection to multibase frequency detection. Musicology, instrument physics, psychoacoustics, computer science, and other areas began to use multifundamental frequency detection technology as a result.…”

Section: Introductionmentioning

confidence: 99%

Mobile Music Recognition based on Deep Neural Network

Zhang

2022

Mobile Information Systems

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The piano, as the king of playing instruments, is the most popular instrument for people to learn to play. Learning to play the piano, on the other hand, necessitates professional instruction and a lot of practice. People do not have enough time for systematic training because of the fast pace of life. At the same time, a lack of professional piano teachers and high tuition fees discourage piano students. If the computer can recognize and evaluate the learner’s piano music in real time, the learner will be able to identify and correct errors in real time. There are currently some music recognition technologies, but the majority of them have the following flaws: first and foremost, the recognition accuracy is poor. Second, the identification process is slow and not real-time. Based on the existing problems, this paper proposes a mobile-based music recognition method. The main work of this paper is as follows: (1) a deep neural network (DNN) is applied to the recognition of piano playing music. The use of deep learning models improves the accuracy of music recognition. (2) In order to make the identification of music easier to use, a mobile application is developed in this paper. The app can be installed on mobile phones and tablets. It can input songs in real-time or offline, outputting misplayed notes and scoring the entire composition. In order to evaluate the effect of this study on music recognition, the experimental part uses multiple models for comparison. The experimental results show that the research in this paper is feasible and effective.

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“…[ 102 ] J. R. Torres‐Castillo et al use a rejects‐band filter (notch filter) to remove power line interference and a third‐order Butterworth filter to attenuate the baseline oscillations caused by the subject's involuntary motion. [ 103 ] H. ElMohandes et al used the Kalman filter to decode kinesthetic signals. [ 104 ] K. Strzecha et al used an infinite impulse response (IIR) filter to eliminate frequency drift, power line, and ECG noise, which highly resemble EMG signals.…”

Section: Emg Signal Processing Algorithmsmentioning

confidence: 99%

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Gao

Gong

Chen

et al. 2022

Advanced Intelligent Systems

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Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

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Neuromuscular disorders detection through time-frequency analysis and classification of multi-muscular EMG signals using Hilbert-Huang transform

Cited by 42 publications

References 63 publications

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Mobile Music Recognition based on Deep Neural Network

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

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