Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds

This paper proposes a new perspective of analyzing non-linear acoustic characteristics of the snore sounds. According to the ERB (Equivalent Rectangular Bandwidth) scale used in psychoacoustics, the ERB correlation dimension (ECD) of the snore sound was computed to feature different severity levels of sleep apnea hypopnea syndrome (SAHS). For the training group of 93 subjects, snore episodes were manually segmented and the ECD parameters of the snores were extracted, which established the gaussian mixture models (GMM). The nocturnal snore sound of the testing group of another 120 subjects was tested to detect SAHS snores, thus estimating the apnea hypopnea index (AHI), which is called AHIECD. Compared to the AHIPSG value of the gold standard polysomnography (PSG) diagnosis, the estimated AHIECD achieved an accuracy of 87.5% in diagnosis the SAHS severity levels. The results suggest that the ECD vectors can be effective parameters for screening SAHS.

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

Ensemble Heterogeneous Feature Selection (EHFS) and Heterogeneous Ensemble Classifier with VOTE (HECV) for Automatic Detection of Snore Sounds

Obstructive Sleep Apnea (OSA) is generally considered as a sleep co-related breathing difficulty with some important well known disabling indication. This research work tends to differentiate the condition of OSA victims. The significant work is to propose an innovative Heterogeneous Ensemble Classifier with Velum Oropharyngeal Tongue Epiglottis (HECV) method with a high level of effectiveness. In the formulated study, data recording, features extraction, multifeature selection, classification and performance evaluation are the five stages of processing. At the initial stage, the noises represented in the audio signals which were eliminated through Adaptive Fuzzy Median Filter (AFMF) algorithm. After thatCrest Factor, original Frequency, Spectral Frequency Features, Subband Energy Ratio, Mel-Scale Frequency Cepstral Coefficients (MFCC), Empirical Mode Decomposition (EMD) Features, and Wavelet Energy Features are collected from the noise suppressed audio signals and inputs are fed into Ensemble Heterogeneous Feature Selection (EHFS) technique. EHFS algorithm fuses the outputs of filter and wrapper oriented feature selection methods. These identified features are classified using HECV technique which renders a good classification outputs by validation without the regard of subjects. The outputs show that the formulated HECV approach gives better performance in snore detection when co-related with other classifiers.