Treffer: Improved Spider Monkey Optimization for EEG Feature Selection in Deep Learning-Based Sleep Stage Classification.

Maintaining human physical and mental health depends on sleep; insufficient sleep results in illness. Many deep learning (DL) and machine learning (ML) based sleep stage classification (SSC) algorithms have been proposed in the last ten years. However, insufficient feature uniqueness, intricate deep learning architectures, a great deal of hyperparameter adjustment, and low accuracy in classifying sleep stages make SSC difficult. This paper presents SSC based on spectral, texture, and temporal features of electroencephalogram (EEG) features and a lightweight Deep Convolution Neural Network (DCNN) and Long Short-Term Memory (LSTM). The DCNN helps to improve the correlation and connectivity features of EEG, and the LSTM helps to boost the temporal depiction and long-term connectivity of EEG features. It uses Wavelet Packet Transform (WPT) based soft thresholding to minimize noise and artifacts in the EEG signal. The improved Spider Monkey Optimization (ISMO) algorithm selects the decisive features from the multiple EEG features. The suggested WPT-ISMO-DCNN-LSTM-based SSC scheme's effectiveness is estimated on the sleep-European Data Format (Sleep-EDF) dataset based on accuracy, recall, precision, F1-score, trainable parameters, and recognition time. The WPT-ISMO-DCNN-LSTM-based SSC scheme provides an accuracy of 99%, precision of 1, Kohen's Kappa rate of 0.9878, 0.9921, recall of 0.99, and F1-score of 0.99, outperforming the existing state of the art. The WPT-ISMO-DCNN-LSTM provides an accuracy of 99% for 2-class SSC, 99.70% for 3-class SSC, 98.50% for 4-class SSC, 99.30% for 5-class SSC, and 99% for 6-class SSC for 350 features. The proposed algorithm offers 5.9 M trainable parameters and a total training time of 498 s for a 6-class SSC. [ABSTRACT FROM AUTHOR]

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