Detection and Classification of Obstructive Sleep Apnea Using Audio Spectrogram Analysis

• Published in: Electronics (Basel) Vol. 13; no. 13; p. 2567
• Main Authors: Serrano, Salvatore, Patanè, Luca, Serghini, Omar, Scarpa, Marco
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Algorithms; Analysis; Annotations; Audio signals; Cardiovascular disease; Classification; Cost analysis; Datasets; Fourier transforms; Low cost; Machine learning; Neural networks; Patients; Signal classification; Signal processing; Sleep apnea; Sleep apnea syndromes

Sleep disorders are steadily increasing in the population and can significantly affect daily life. Low-cost and noninvasive systems that can assist the diagnostic process will become increasingly widespread in the coming years. This work aims to investigate and compare the performance of machine lea...