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 (OSA). SN detection is important to monitor SN objectively and to improve the diagnostic sensitivity of sleep-disordered breathing (SDB). In this study, an automatic snore detection method using an Emfit (Electromechanical film transducer) signal is presented. Representative polysomnographs of normal breathing (NB) and SN periods from 30 subjects were selected. Individual SN events were identified using source separation applying nonnegative matrix factorization deconvolution (NMFD). The algorithm was evaluated using manual annotation of the polysomnographic recordings. According to our results, the sensitivity (Se), and the positive predictive value (PPV) 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.