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Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.
Rocznik
Tom
Strony
493--506
Opis fizyczny
Bibliogr. 47 poz., rys., tab., wykr.
Twórcy
autor
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Sector-1, Rourkela, Odisha, 769008, India
autor
- School of Computing Science and Engineering (SCOPE), VIT Vellore, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India
autor
- Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Main Street, Tekkali, Andhra Pradesh, 532201, India
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Sector-1, Rourkela, Odisha, 769008, India
autor
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Cracow, Poland
autor
- Department of Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44-100 Gliwice, Poland
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-952598a3-4f4e-4d38-a300-04cbb0cbfa26