Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques

Abstract

Purpose: Breathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing the breathing sounds collected from a large number of subjects during entire overnight sleep.

Methods: The participants were patients who presented at a sleep center with snoring or cessation of breathing during sleep. They were subjected to full-night polysomnography (PSG) during which the breathing sound was recorded using a microphone. Then, audio features were extracted and a group of features differing significantly between different SDB severity groups was selected as a potential acoustic biomarker. To assess the validity of the acoustic biomarker, classification tasks were performed using several machine learning techniques. Based on the apnea-hypopnea index of the subjects, four-group classification and binary classification were performed.

Results: Using tenfold cross validation, we achieved an accuracy of 88.3% in the four-group classification and an accuracy of 92.5% in the binary classification. Experimental evaluation demonstrated that the models trained on the proposed acoustic biomarkers can be used to estimate the severity of SDB.

Conclusions: Acoustic biomarkers may be useful to accurately predict the severity of SDB based on the patient's breathing sounds during sleep, without conducting attended full-night PSG. This study implies that any device with a microphone, such as a smartphone, could be potentially utilized outside specialized facilities as a screening tool for detecting SDB.

Keywords: Acoustic biomarker; Apnea–hypopnea index; Deep neural network; Polysomnography screening test; Sleep disordered breathing.

Fig. 1

Acquisition of sleep sounds and PSG reports in sleep laboratory. Audio data and PSG reports were recorded from the PSG system. After acquisition, two filtering stages were adopted to eliminate unwanted noises for 120 patients

Fig. 2

Audio feature extraction framework. Audio features were extracted in every 5 s windows. Then statistical values (means and standard deviations) of features were calculated over whole sleep period

Fig. 3

Selection of acoustic biomarker. Among statistical values of audio features, SDB-severity group discriminators were determined with Tukey-HSD tests. The union of all SDB-severity group discriminators is defined as the acoustic biomarker

Fig. 4

Process of qTM extraction. First, absoluted magnitude values are quantized into three levels (silence, low-level signal, high-level signal) for simplification. Among silence periods, apnea candidate periods were determined under standards of AASM and finally signals were quantized into four levels. Temporal transitions of quantized magnitudes were derived and transition probabilities were calculated over whole sleep

Fig. 5

Structure of the deep neural network. The network contains two hidden layers with 50 and 25 nodes respectively, two dropout layers, and an output layer with 4 nodes for 4-class classification

Fig. 6

Distribution of subject groups. Using the t-SNE algorithm, distributions of 120 subjects using a whole audio features and b discriminators (acoustic biomarker) respectively. When using the acoustic biomarker, group separability was increased and it has a direct effect on the classification performance

Fig. 7

Performance of classification using all audio features (baseline). Specificity, sensitivity and area under ROC curve are depicted when all audio features are adopted as input features. A confusion matrix of the 4-group classification is also presented

Fig. 8

Comparison of performance of using audio features extracted under various window sizes

Fig. 9

Performance of classification when components of qTM are added as input features

Fig. 10

Performance of classification when the acoustic biomarkers are adopted instead of all audio features

Fig. 11

Performance of classification when both the acoustic biomarker and the qTM are adopted

Fig. 12

Comparison of performance of using various classifiers

Fig. 13

Comparison of performance of using different feature sets chosen with SVM-based feature selection algorithm

Fig. 14

Performance of binary classifications under various thresholds