Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea - Kandukuri, Usha Rani; Prakash, Allam Jaya; Patro, Kiran Kumar; Neelapu, Bala Chakravarthy; Tadeusiewicz, Ryszard (1947- ); Pławiak, Paweł (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: apnea; convolutional neural network (CNN); constant Q-transform; deep learning; single-lead ECG signals; non-apnea; obstructive sleep apnea