Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 6; pp. 2354 - 2364
• Main Authors: Van Steenkiste, Tom, Groenendaal, Willemijn, Deschrijver, Dirk, Dhaene, Tom
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aged; Algorithms; Apnea; Automation; Beds; Blood pressure; Congestive heart failure; Databases, Factual; Deep learning; Electrocardiography; Feature extraction; Female; Hidden Markov models; Humans; Informatics; Long short-term memory; LSTM; Machine Learning; Male; Middle Aged; Monitoring, Physiologic - methods; Neural networks; Neural Networks, Computer; Physiology; Recording; Respiratory Physiological Phenomena; Sensitivity analysis; SHHS-1; Signal Processing, Computer-Assisted; Sleep; Sleep apnea; Sleep Apnea Syndromes - diagnosis; Sleep disorders
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2018.2886064

Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this paper, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state of the art. Furthermore, when these predictions are mapped to the apnea-hypopnea index, a considerable improvement in per-patient scoring is achieved over conventional methods. This paper presents a powerful aid for trained staff to quickly diagnose sleep apnea.