DeepEMGNet: An Application for Efficient Discrimination of ALS and Normal EMG Signals
This paper proposes a deep learning application for efficient classification of amyotrophic lateral sclerosis (ALS) and normal Electromyogram (EMG) signals. EMG signals are helpful in analyzing of the neuromuscular diseases like ALS. ALS is a well-known brain disease, which progressively degenerates the motor neurons. Most of the previous works about EMG signal classification covers a dozen of basic signal processing methodologies such as statistical signal processing, wavelet analysis, and empirical mode decomposition (EMD). In this work, a different application is implemented which is based on time-frequency (TF) representation of EMG signals and convolutional neural networks (CNN). Short Time Fourier Transform (STFT) is considered for TF representation. Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.69% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.
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Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| ALS Detection | ALS EMG (University of Copenhagen) | STFT CNN | Accuracy | 96.69% | # 1 |
