Audio signal analysis in combination with noncontact bio-motion data to successfully monitor snoring

This paper proposes a novel algorithm for automatic detection of snoring in sleep by combining non-contact bio-motion data with audio data. The audio data is captured using low end Android Smartphones in a non-clinical environment to mimic a possible user-friendly commercial product for sleep audio monitoring. However snore detection becomes a more challenging problem as the recorded signal has lower quality compared to those recorded in clinical environment. To have an accurate classification of snore/non-snore, we first compare a range of commonly used features extracted from the audio signal to find the best subject-independent features. Thereafter, bio-motion data is used to further improve the classification accuracy by identifying episodes which contain high amounts of body movements. High body movement indicates that the subject is turning, coughing or leaving the bed; during these instances snoring does not occur. The proposed algorithm is evaluated using the data recorded over 25 sessions from 7 healthy subjects who are suspected to be regular snorers. Our experimental results showed that the best subject-independent features for snore/non-snore classification are the energy of frequency band 3150-3650 Hz, zero crossing rate and 1st predictor coefficient of linear predictive coding. The proposed features yielded an average classification accuracy of 84.35%. The introduction of bio-motion data significantly improved the results by an average of 5.87% (p<;0.01). This work is the first study that successfully used bio-motion data to improve the accuracy of snore/non-snore classification.