An attention-based multi-resolution deep learning model for automatic A-phase detection of cyclic alternating pattern in sleep using single-channel EEG

Halder, Barproda; Anjum, Tanvir; Bhuiyan, Mohammed Imamul Hassan

doi:10.1016/j.bspc.2023.104730

Cited by 4 publications

(2 citation statements)

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“…The system achieved an accuracy of 83.06% with the children's sleep dataset using the F4-M1 channel and 86.41% with the Sleep-EDFx dataset with manual feature extraction. In [ 26 ], the authors used multi-branch one-dimensional convolutional neural networks and extracted different frequency domain features from single-channel EEG data. The model resulted from 90.31% accuracy, 95.30% specificity, and 65.73% F1score.…”

Section: Related Researchmentioning

confidence: 99%

“…The model resulted from 90.31% accuracy, 95.30% specificity, and 65.73% F1score. In reference [ 26 ], the authors employed multi-branch one-dimensional convolutional neural networks (CNNs) and extracted various frequency domain features and achieved an accuracy of 90.31%, specificity of 95.30%, and an F1 score of 65.73%. Some of the recent studies on sleep staging are presented in Table 1 .…”

Section: Related Researchmentioning

confidence: 99%

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Machine learning-empowered sleep staging classification using multi-modality signals

Satapathy,

Brahma,

Panda

et al. 2024

BMC Med Inform Decis Mak

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The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%