Detecting Sleep Apnea by Heart Rate Variability Analysis: Assessing the Validity of Databases and Algorithms

• Published in: Journal of medical systems Vol. 35; no. 4; pp. 473 - 481
• Main Authors: Lado, María J., Vila, Xosé A., Rodríguez-Liñares, Leandro, Méndez, Arturo J., Olivieri, David N., Félix, Paulo
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.08.2011; Springer Nature B.V
• Subjects: Algorithms; Collection; Communities; Dangerous; Data Collection; Databases, Factual - utilization; Electrocardiography; Health Informatics; Health Sciences; Heart rate; Heart Rate - physiology; Humans; Medical diagnosis; Medicine; Medicine & Public Health; Original Paper; Polysomnography; Sensitivity and Specificity; Signal processing; Sleep; Sleep apnea; Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; Statistics for Life Sciences; ECG; OSA; Heart rate variability; Biomedical signal processing
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-009-9383-5

Obstructive sleep apnea (OSA) is a serious disorder caused by intermittent airway obstruction which may have dangerous impact on daily living activities. Heart rate variability (HRV) analysis could be used for diagnosing OSA, since this disease affects HRV during sleep. In order to validate different algorithms developed for detecting OSA employing HRV analysis, several public or proprietary data collections have been employed for different research groups. However, for validation purposes, it is obvious and evident the lack of a common standard database, worldwide recognized and accepted by the scientific community. In this paper, different algorithms employing HRV analysis were applied over diverse public and proprietary databases for detecting OSA, and the outcomes were validated in terms of a statistical analysis. Results indicate that the use of a specific database may strongly affect the performance of the algorithms, due to differences in methodologies of processing. Our results suggest that researchers must strongly take into consideration the database used when quoting their results, since selected cases are highly database dependent and would bias conclusions.