Snoring Analysis And Recognition In Diagnosis Of Patients With Obstructive Sleep Apnea Hypopnea Syndrome

Obstructive sleep apnea hypopnea syndrome(OSAHS)is a serious respiratory disorder.Snoring appears to be the most intuitive characteristic symptom of OSAHS.In recent years,the study on acoustical analysis of snoring for the OSAHS diagnosis have got rapid development.Many researchers have tried to explore a portable monitoring system and to offer lower costs and greater comfort for the patients.The preliminary and essential step in such diagnosis is to automatically segment snore sounds from original sleep sounds.This study presents an automatic snoring detection algorithm to detect potential snoring episodes.The whole night sleep sound recordings and the PSG data of six habitual snorers were obtained in the experimental environment.An adaptive effective-value threshold method is used to detect the potential snoring segments,the feature extraction using maximum power ratio(MPR),sum of positive/negative amplitudes,500 Hz power ratio,spectral entropy(SE)and sample entropy(SampEn),and automatic snoring/nonsnoring classification using a support vector machine.The results show that SampEn provides higher classification accuracy than SE.Furthermore,the proposed automatic detection method achieved over 94.0% accuracy when identifying snoring and nonsnoring sounds.The sensitivity and accuracy of the results demonstrate that the proposed snoring detection method can effectively classify snoring and nonsnoring sounds,thus enabling the automatic detection of snoring.Owing to the inconsistency of the whole night snoring in OSAHS patients,we analyze the acoustic characteristics of snoring to explore the differences of the whole night snoring for the OSAHS diagnosis.The feature analysis extracting spectral centroid,spectral spread,spectral flatness and other feathers,and four types of snoring(the snoring which closed to respiratory disorders event,snoring during apnea,snoring during hypopnea,simple snoring)classification using decision tree and support vector machine(SVM),respectively.The results show that the classification performance of SVM is better than the decision tree,the accuracy of three parameter optimizing methods(grid search,genetic algorithm and particle swarm optimization)in SVM model are above 91.14%.The best result is searched when used the particle swarm optimization as parameters optimization method of SVM.The results demonstrate that the proposed method can classify the four types of snoring and provide guidance to predict AHI in diagnosis of OSAHS.