Detecting Breathing and Snoring Episodes Using a Wireless Tracheal Sensor—A Feasibility Study

• Published in: IEEE journal of biomedical and health informatics Vol. 21; no. 6; pp. 1504 - 1510
• Main Authors: Mlynczak, Marcel, Migacz, Ewa, Migacz, Maciej, Kukwa, Wojciech
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Algorithm design and analysis; Algorithms; Applications programs; Breathing; Data processing; Feasibility Studies; Female; Humans; Informatics; Machine Learning; Male; Medical diagnosis; Medical diagnostic imaging; Middle Aged; Mobile computing; Monitoring, Physiologic - methods; Problem solving; Recording; Respiration; Screening; Segmentation; Signal Processing, Computer-Assisted; Sleep; Sleep apnea; Sleep Apnea Syndromes - diagnosis; sleep breathing disorders; Smartphone; smartphone application; snoring; Snoring - diagnosis; Sound Spectrography - methods; Trachea - physiopathology; tracheal sound analysis; Wireless communication; Wireless sensor networks; Wireless Technology
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2016.2632976

Objective: Sleep-disordered breathing is both a clinical and a social problem. This implies the need for convenient solutions to simplify screening and diagnosis. The aim of the study was to investigate the sensitivity and specificity of a novel wireless system in detecting breathing and snoring episodes during sleep. Methods: A wireless acoustic sensor was elaborated and implemented. Segmentation (based on spectral thresholding and heuristics) and classification of all breathing episodes during recording were implemented through a mobile application. The system was evaluated on 1520 manually labeled episodes registered from 40 real-world, whole-night recordings of 16 generally healthy subjects. Results: The differentiation between normal breathing and snoring had 88.8% accuracy. As the system is intended for screening, high specificity of 95% is reported. Conclusion: The system is a compromise between nonmedical phone applications and medical sleep studies. The presented approach enables the study to be repetitive, personal, and inexpensive. It has additional value in the form of well-recorded data which are reliable and comparable. Significance: The system opens unexplored possibilities in sleep monitoring and study enabling a multinight recording strategy involving the collection and analysis of abundant data from thousands of people.