Abstract
The definitive diagnosis of obstructive sleep apnea syndrome (OSAS) is made using an overnight polysomnography (PSG) test. This test requires that a patient wears multiple measurement sensors during an overnight hospitalization. However, this setup imposes physical constraints and a heavy burden on the patient. Recent studies have reported on another technique for conducting OSAS screening based on snoring/breathing episodes (SBEs) extracted from recorded data acquired by a noncontact microphone. However, SBEs have a high dynamic range and are barely audible at intensities >90 dB. A method is needed to detect SBEs even in low-signal-to-noise-ratio (SNR) environments. Therefore, we developed a method for the automatic detection of low-intensity SBEs using an artificial neural network (ANN). However, when considering its practical use, this method required further improvement in terms of detection accuracy and speed. To accomplish this, we propose in this study a new method to detect low SBEs based on neural activity pattern (NAP)-based cepstral coefficients (NAPCC) and ANN classifiers. Comparison results of the leave-one-out cross-validation demonstrated that our proposed method is superior to previous methods for the classification of SBEs and non-SBEs, even in low-SNR conditions (accuracy: 85.99 ± 5.69% vs. 75.64 ± 18.8%).
Highlights
Obstructive sleep apnea syndrome (OSAS) is characterized by complete or incomplete obstruction of the upper airway during sleep
We describe a new method based on the use of auditory model-based features wherein artificial neural network (ANN) classifiers were used to detect quickly low-intensity snoring/breathing episodes (SBEs) in the sleep sound records
Times; a performance evaluation of the proposed method was conducted the initial value of the multilayer perceptron (MLP)-ANN was changed, and validation was repeated 10 based times; on a the trial results that maximized the performance evaluation of the proposed method was conducted based on the trial
Summary
Obstructive sleep apnea syndrome (OSAS) is characterized by complete or incomplete obstruction of the upper airway during sleep. The main symptoms of OSAS are light sleep, excessive daytime sleepiness, and snoring; these are said to increase the risk of developing serious illnesses, such as ischemic heart disease, hypertension, stroke, and cognitive dysfunction [1]. It is said that 6–19% of females and 13–33% of males have OSAS, with the prevalence rate increasing with age [2,3]. A definitive diagnosis of OSAS is currently made using polysomnography (PSG) tests. This test requires multiple measurement sensors (e.g., oral thermistor, nasal pressure cannula, chest belt) to be worn directly on the body all night, which imposes a heavy burden on the patient. Previous studies suggested that the discomfort of wearing multiple sensors during PSG and restricted movements affect sleep efficiency, electrocardiographic (EEG) spectral power, and rapid-eye movements [4–7]
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