Classification of snoring sound based on a recurrent neural network

Abstract Snoring is a sleep disorder that may have adverse effects on an individual's health and social activities. Polysomnography is the most common way to diagnose snoring but involves considerable time and cost. Many recent studies have attempted to classify snoring and non-snoring. However, since the length, frequency, and period of snoring episodes (SE) differ according to the individual being measured, it is very difficult to develop a general reference point to classify snoring. Therefore, in order to classify different snoring patterns and noise for different individuals, a learning-based snoring classification algorithm is essential. To this end, this study proposes a classification method based on a recurrent neural network (RNN) that can classify SEs and non-snoring episodes (NSEs) by learning the features of an individual's SEs and NSEs, measured in daily life based on the subjects’ sleep recordings using smartphone. The method proposed in this study can be largely divided into segmentation, feature extraction, and classification. The performance of this study was evaluated through statistical parameters. Despite the fact that the proposed RNN-based classifiers were trained using a relative small dataset, they exhibited an extremely high accuracy of 98.9%.

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