Abstract
This paper presents a bibliometric review of several techniques applied to the EMG signals. We reviewed research papers, which were specifically applied for the EMG signals. The EMG signal contains a huge amount of data, thus the EMG signal research grabs the significance of advanced techniques and analysis of data, which are capable of handling ‘Big Data’. Several noise reduction techniques were discussed and it was found that the wavelet-based noise reduction is a promising technique for EMG classification. More prominent feature extraction and classification techniques and their performance were also reviewed. The modern EMG signal analysis mainly emphasizes feature learning, which is specifically ‘deep learning’, which combines feature extraction and classification, also to improve classification accuracy. A performance analysis of Convolutional Neural Network (CNN) was done in the later sections.
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Emimal, M., Jino Hans, W., Inbamalar, T.M., Mahiban Lindsay, N. (2022). Recent Trends in Electromyography Signal Processing of Neuromuscular Diseases: An Outlook. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_1
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