Detection of Obstructive Sleep Apnea Based on Deep Learning Models from ECG Signals: A Review

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder associated with severe health implications, necessitating accurate and timely detection methods. An Electrocardiogram (ECG) signal is one of the biological signals that can be used to detect OSA. This review provides a comprehensive overview of the use of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for OSA detection from ECG signal. We explore various studies and methodologies that have leveraged CNNs to extract spatial features from ECG signals and LSTM networks to model temporal dependencies. We discuss the advantages of combining these deep learning techniques, emphasizing their ability to capture complex patterns within ECG data. Additionally, we assess the performance of these models in terms of accuracy across different datasets. Ultimately, this review sheds light on the promising role of this networks in enhancing the accuracy and efficiency of OSA detection, offering insights into future research directions and clinical applications in sleep medicine.