Sleep Apnea Detection Algorithms and Methods using Electrophysiological Signals: A Review of the Literature

Abstract

Aim: Sleep apnea is extremely prevalent in patients with snoring, heart disease, stroke and obesity. The real-time detection of sleep apnea is essential for the society to enhance the health service sector. This review aims to demonstrate the algorithms and methods for detection of sleep apnea using electrophysiological signals. Materials and Methods: We categorize the detection and diagnosing techniques into two: Category 1 establishes the algorithms based features extraction using electrocardiogram (ECG) and electroencephalogram (EEG) signals and Category 2 explores the different methods for detecting sleep apnea using sensors, smart devices, hydrophobic electrodes, bluetooth system, principle component analysis (PCA) and radar monitoring system. Results: Observational studies of sleep apnea detection from recent years are summarized shortly in this review of the literature. This integrative review compared the accuracy significance and the limitations of different studies. Conclusion: The review of the literature discusses some important algorithms and methods which can optimistically be useful to design a substantial device for sleep apnea detection.