ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909378450292736 |
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| author | Rahman, Minhajur Rahman, Md Toufiqur Raihan, Md Tanvir Shahnaz, Celia |
| author_facet | Rahman, Minhajur Rahman, Md Toufiqur Raihan, Md Tanvir Shahnaz, Celia |
| contents | Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10756 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals Rahman, Minhajur Rahman, Md Toufiqur Raihan, Md Tanvir Shahnaz, Celia Signal Processing Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%. |
| title | ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2309.10756 |