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<p>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.</p>
</abstract>

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.

Constant Q–Transform–Based Deep Learning Architecture for Detection of Obstructive Sleep Apnea

Department of Biotechnology and Medical Engineering

Department of Electronics and Communication Engineering

Department of Biocybernetics and Biomedical Engineering

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the...

Constant Q–Transform–Based Deep Learning Architecture for Detection of Obstructive Sleep Apnea

Publié en ligne: 21 sept. 2023

Pages: 493 - 506

Reçu: 24 nov. 2022

Accepté: 18 mai 2023

DOI: https://doi.org/10.34768/amcs-2023-0036

Mots clés
apnea, convolutional neural network, constant Q-transform, deep learning, single-lead ECG signals, non-apnea, obstructive sleep apnea

© 2023 Usha Rani Kandukuri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Constant Q–Transform–Based Deep Learning Architecture for Detection of Obstructive Sleep Apnea

Publié en ligne: 21 sept. 2023

Pages: 493 - 506

Reçu: 24 nov. 2022

Accepté: 18 mai 2023

DOI: https://doi.org/10.34768/amcs-2023-0036

Mots clésapnea, convolutional neural network, constant Q-transform, deep learning, single-lead ECG signals, non-apnea, obstructive sleep apnea

© 2023 Usha Rani Kandukuri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
apnea, convolutional neural network, constant Q-transform, deep learning, single-lead ECG signals, non-apnea, obstructive sleep apnea