Detecting breathing and snoring episodes using a wireless tracheal sensor - a feasibility study
PBN-AR
Instytucja
Wydział Mechatroniki (Politechnika Warszawska)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
IEEE Journal of Biomedical and Health Informatics
ISSN
1089-7771
EISSN
Wydawca
Institute of Electrical and Electronics Engineers
DOI
URL
Rok publikacji
2016
Numer zeszytu
99
Strony od-do
1-7
Numer tomu
PP
Identyfikator DOI
Liczba arkuszy
0.5
Autorzy
(liczba autorów: 4)
Pozostali autorzy
+ 3
Słowa kluczowe
en
smartphone application, sleep breathing disorders, snoring, tracheal sound analysis, machine learning
Streszczenia
Język
en
Treść
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 1,520 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. Conclusions: The system is a compromise between non-medical 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 wellrecorded data which are reliable and comparable. Significance: The system opens unexplored possibilities in sleep monitoring and study enabling a multi-night recording strategy involving the collection and analysis of abundant data from thousands of people.
Inne
System-identifier
WUT6bb0d4f4d83f4fd7a843a87d660cdbbf
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