Robust performances of a nocturnal long-term ECG algorithm for the evaluation of sleep apnea syndrome: A pilot study

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is one of the most common sleep disorders affecting nearly one billion of the global adult population, making it a major public health issue. Even if in-lab polysomnography (PSG) remains the gold standard to diagnose OSAHS, there is a growing interest to develop new solutions with more convenient at home devices enhanced with AI-based algorithms for the detection of sleep apnea. This retrospective study aimed to assess the performances of a new method based on nocturnal long-term electrocardiogram signal to detect apneas and hypopneas, in patients who performed attended in-lab PSG. After assessing the quality of the ECG signal, the new method automatically detected apneas and hypopneas using dedicated machine learning algorithm. The agreement between the new ECG-based detection method and the standard interpretation of PSG by a sleep clinician was determined in a blind manner. Eighty-five exams were included into the study with a mean bias between the proposed method and the scorer of 3.5 apneas-hypopneas/hour (/h) (95% CI -48.1 to 55.1). At a threshold of 15/h, sensibility and specificity were 93.3% and 66.7% respectively, and positive and negative predictive values were 87.5% and 80%, respectively. The proposed method using nocturnal long-term electrocardiogram signals showed very high performances to detect apneas and hypopneas. Its implementation in a simple ECG-based device would offer a promising opportunity for preliminary evaluation of patients suspected or at-risk of OSAHS.