Comparative Study of Brain Signals for Early Detection of Sleep Disorder Using Machine and Deep Learning Algorithm

Satapathy, Santosh Kumar; Patel, Vanshita; Gandhi, Manan; Mohapatra, Rajesh Kumar

doi:10.1109/iatmsi60426.2024.10503066

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) 2024

DOI: 10.1109/iatmsi60426.2024.10503066

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Comparative Study of Brain Signals for Early Detection of Sleep Disorder Using Machine and Deep Learning Algorithm

Santosh Kumar Satapathy,

Vanshita Patel,

Manan Gandhi

et al.

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2024

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Cited by 1 publication

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Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis

Paul,

Singh,

Sharma

et al. 2024

Sensors

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Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

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Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis

Paul,

Singh,

Sharma

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Comparative Study of Brain Signals for Early Detection of Sleep Disorder Using Machine and Deep Learning Algorithm

Cited by 1 publication

References 35 publications

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis

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