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

• Published in: International journal of applied mathematics and computer science Vol. 33; no. 3; pp. 493 - 506
• Main Authors: Kandukuri, Usha Rani, Prakash, Allam Jaya, Patro, Kiran Kumar, Neelapu, Bala Chakravarthy, Tadeusiewicz, Ryszard, Pławiak, Paweł
• Format: Journal Article
• Language: English
• Published: Zielona Góra Sciendo 01-09-2023; De Gruyter Poland
• Subjects: apnea; Artificial neural networks; constant Q-transform; convolutional neural network; Deep learning; Electrocardiography; Electroencephalography; Electromyography; Engineering; Image segmentation; Machine learning; Neural networks; non-apnea; obstructive sleep apnea; Oxygen content; Oxygen saturation; Respiration; Signal monitoring; single-lead ECG signals; Sleep apnea; Two dimensional models; Wavelet transforms
• ISSN: 1641-876X; 2083-8492
• DOI: 10.34768/amcs-2023-0036

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.