SleepAp: an automated obstructive sleep apnoea screening application for smartphones.

Journal article

Obstructive sleep apnoea (OSA) is a sleep disorder with long-term consequences. Long-term effects include sleep-related issues and cardiovascular diseases. OSA is often diagnosed with an overnight sleep test called a polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper, a novel OSA screening framework and prototype phone application are introduced. A database of 856 patients that underwent at-home polygraphy was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG), and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients and tested on 121 patients. Classification on the test set had an accuracy of up to 92.2% when classifying subjects as having moderate or severe OSA versus being healthy or a snorer based on the clinicians' diagnoses. The signal processing and machine learning algorithms were ported to Java and integrated into the phone application-SleepAp. SleepAp records the body position, audio, actigraphy and PPG signals, and implements the clinically validated STOP-BANG questionnaire. It derives features from the signals and classifies the user as having OSA or not using the SVM trained on the clinical database. The resulting software could provide a new, easy-to-use, low-cost, and widely available modality for OSA screening.

Authors: Behar, J; Roebuck, A; Shahid, M; Daly, J; Hallack, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE journal of biomedical and health informatics
• Volume: 19
• Issue: 1
• Pages: 325-331
• Publication date: 2015-01-01
• DOI: 10.1109/jbhi.2014.2307913
• EISSN: 2168-2208
• ISSN: 2168-2194
• Language: English
• Keywords: Equipment Failure Analysis; Sensitivity and Specificity; Reproducibility of Results; Equipment Design; Algorithms; Mobile Applications; Sleep Apnea, Obstructive; Humans; Mass Screening; Polysomnography; Cell Phones; Diagnosis, Computer-Assisted