Abstract
A stand-alone, custom-made biomedical system was introduced for long-term monitoring of sleep and detection of snoring events. Commercially available electronic components were assembled for recording audio, pulse, and respiration signals. Its software was implemented for off-line processing of the acquired signals in C++ and MATLAB environments. The linear and nonlinear features of the signals were extracted and characterized using spectral energy distribution, entropy, and largest Lyapunov exponent LLE . The performance of the system was evaluated with real physiological data gathered from 14 chronic snorers. Analysis of the cases indicated that the system identified the snoring events with an accuracy of 88.22%, sensitivity of 94.91%, and positive predictive value of 90.95%. This high level of validation confirmed the reliability and utility of the system in detecting snoring.
Highlights
Snoring is a widespread problem affecting 20%-–40% of the population [1]. It is considered as a sleep-related breathing disorder (SRBD) and represents a risk factor [2]
To address the existing issues above, the purpose of this paper is to present the construction of a new portable system, SnoreBox, for a portable home-based long-term sleep monitoring and snore detection
SnoreBox is a wearable device with belts to hang on to the body and have sensor attachments
Summary
Snoring is a widespread problem affecting 20%-–40% of the population [1] It is considered as a sleep-related breathing disorder (SRBD) and represents a risk factor [2]. PSG is thorough and reliable, the process requires a whole night’s evaluation at a sleep laboratory while the subject is connected to a set of numerous sensors. It is an expensive operation and the waiting list is typically long. Sleep conditions are unnatural and the procedure may extend to long-term monitoring in the home environment
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