A System for the Analysis of Snore Signals

Abstract

Sleep apnoea syndrome (SAS) is a disease consisting in the nocturnal cessation of oronasal airflow at least 10 seconds in duration. The standard method for SAS diagnosis is the polysomnographic exam (PSG). However it does not permit a mass screening because it has high cost and requires long term monitoring.This paper presents a preliminary software system prototype for snoring signal analysis, whose main goal is to support the doctor in SAS diagnosis and patient follow-up. The design of the system is modular to allow a future hardware implementation in a portable device for personal snore collection and monitoring.

High-precision snore detection method based on deep learning

“…They encode the speech low-level descriptor into the input eigenvector of the model [6] . Advanced speech analysis technologies such as linear predictive coding (LPC), fast Fourier transform and wavelet analysis have been applied to analyze snoring [7] .…”

Snoring detection method in sleep based on MBAM-ResNet

“…A method in comparison with acoustic sensors, piezoelectric sensors, and nasal pressure sensors (cannula) was proposed to measure snoring [5] . Meanwhile, Calabrese proposed a preliminary snoring signal analysis software system to support doctors' diagnosis of sleep apnea syndrome [6] . Advanced speech analysis techniques such as linear predictive coding (LPC), fast Fourier transform, and wavelet analysis have been applied to analyze snoring [7] .…”

Contribution to Time and Frequency Analysis of Irregular Sleep Snoring

“…Statistical parameters such as the number of the snoring episodes , duration , etc. were calculated in [9]- [10].…”