Detection of sleep breathing sound based on artificial neural network analysis

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is known to cause daytime drowsiness and an association with diseases such as Type II diabetes, cardiovascular disease, and stroke. A polysomnography (PSG) test is the traditional method for diagnosing OSAHS. However, this test is expensive, inconvenient, and requires the placement of body contact sensors during sleep. Recently, in several studies, the snoring/breathing episodes (SBEs) acquired by non-contact microphones have been used for OSAHS diagnosis. SBEs may range from barely audible to loud. SBE detection, especially low-intensity SBEs, can be challenging in noisy environments because of the low signal-to-noise ratio (SNR). In this paper, we propose a novel method for the rapid detection of low-intensity SBEs from data recorded during sleep. Our method is based on an artificial neural network (ANN) technique. When an ANN is trained as subject-specific classifier, we show that the proposed method is capable of detecting low-intensity SBEs more rapidly compared to our previous method. When an ANN is used as subject-independent classifier, we show that the proposed method can classify low-intensity SBEs and low-intensity non-SBEs that may occur during actual sleep with an average accuracy of 75.10%.