A Study On Acoustic Characteristics Of Snoring In Patients With Obstructive Sleep Apnea Hypopnea Syndrome

Obstructive sleep apnea hypopnea syndrome(OSAHS)is a common sleep-related respiratory disease and snoring is the most direct and typical characteristic of OSAHS patients.In recent years,many researchers at home and abroad have tried to assist the diagnosis of OSAHS patients using the snoring analysis technology,attempting to explore a cheap,portable and effective OSAHS monitoring system.In this paper,acoustic features of snore sounds were analyzed to identify the patients with OSAHS and simple snorers,and then seven types of snore sounds from OSAHS patients were classified to predict the AHI values of OSAHS patients.To automatically identify snore episodes,a method based on sound images and neural network is proposed in this paper.After detecting the potential snore episodes using the subband spectral entropy method,the time-domain waveform,spectrum,spectrogram,Mel-spectrogram and CQT-spectrogram of potential snore episodes were extracted,and then these sound images were fed into model CNNs-DNNs and model CNNs-LSTMs-DNNs,respectively.The results show that significant difference between snores and non-snores is found in frequency-domain characteristics,especially in the low frequency.Among the five sound images extracted in this paper,Mel-spectrogram can better reflect the difference between snores and non-snores.The best performance with 95.07% accuracy,95.42% sensitivity and 95.82% specificity is achieved by the combination of Mel-spectrogram and model CNNs-LSTMs-DNNs.As the snore of OSAHS patients is different from that of simple snorers,this paper discusses the common frequency-domain characteristics of snores and the performance ability of different classifiers in the classification of OSAHS patients and simple snorers.After identifying the snore episodes,Mel-frequency cepstral coefficients,800 Hz power ratio,spectral entropy and other 10 kinds of acoustic features were extracted from snore episodes and a feature selection algorithm based on random forest was applied to these features to screen Top-6 features.The effectiveness of Top-6 features was verified by 5 kinds of machine learning model.The results show that the combination of logistic regression model and Top-6 features performs best which can successfully distinguish the OSAHS patients from simple snorers considering the classification performance and calculation efficiency comprehensively.The proposed method has low computational complexity and high recognition rate for OSAHS snores,which can assess the patients whether suffer OSAHS based on the identified OSAHS snore sounds.The whole night snore sounds of OSAHS patients are different.In this paper,the snore sounds were classified as snore before apnea,snore during apnea,snore after apnea,snore before hypopnea,snore during hypopnea,snore after hypopnea and simple snore.This paper extracted the acoustic features of seven types of snore sounds,such as Mel-frequency cepstral coefficients,spectral entropy,800 Hz power ratio and other features.Then,a Relief F algorithm was used to screen some important features and the support vector machine,logistic regression and random forest were used to classify seven types of snores,respectively.The experimental results show that the classification ability of random forest is stronger than other classification models.Furthermore,the combination of random forest and Top-20 features perform best in the classification of seven types of snores which can achieve overall accuracy of 80.36%.These results provide the basis for predicting AHI values of patients using seven types of snores.