Spectral analysis of snoring events from an Emfit mattress

Detection of Snores Using Source Separation on an Emfit Signal

Snoring (SN) is an early sign of upper airway dysfunction, and it is strongly associated with obstructive sleep apnea. SN detection is important to monitor SN objectively and to improve the diagnostic sensitivity of sleep-disordered breathing. In this study, an automatic snore detection method using an electromechanical film transducer (Emfit) signal is presented. Representative polysomnographs of normal breathing and SN periods from 30 subjects were selected. Individual SN events were identified using source separation applying nonnegative matrix factorization deconvolution. The algorithm was evaluated using manual annotation of the polysomnographic recordings. According to our results, the sensitivity, and the positive predictive value of the developed method to reveal snoring from the Emfit signal were 82.81% and 86.29%, respectively. Compared to other approaches, our method adapts to the individual spectral snoring profile of the subject rather than matching a particular spectral profile, estimates the snoring intensity, and obtains the specific spectral profile of the snores in the epoch. Additionally, no training is necessary. This study suggests that it is possible to detect individual SN events with Emfit mattress, which can be used as a contactless alternative to more conventional methods such as piezo-snore sensors or microphones.

Assessment of support vector machines and convolutional neural networks to detect snoring using Emfit mattress

Snoring (SN) is an essential feature of sleep breathing disorders, such as obstructive sleep apnea (OSA). In this study, we evaluate epoch-based snoring detection methods using an unobtrusive electromechanical film transducer (Emfit) mattress sensor using polysomnography recordings as a reference. Two different approaches were investigated: a support vector machine (SVM) classifier fed with a subset of spectral features and convolutional neural network (CNN) fed with spectrograms. Representative 10-min normal breathing (NB) and SN periods were selected for analysis in 30 subjects and divided into thirty-second epochs. In the evaluation, average results over 10 fold Monte Carlo cross-validation with 80% training and 20% test split were reported. Highest performance was achieved using CNN, with 92% sensitivity, 96% specificity, 94% accuracy, and 0.983 area under the receiver operating characteristics curve (AROC). Results showed a 6% average increase of performance of the CNN over SVM and greater robustness, and similar performance to ambient microphones.