A new portable device for the snore/non-snore classification

Ankishan, Haydar; Tuncer, A. Turgut

doi:10.1109/icengtechnol.2017.8308212

Cited by 1 publication

(1 citation statement)

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“…In the study of snoring, the researchers first studied the technique of extracting snoring sounds from breathing sounds. For example, the nonlinear classification algorithm to identify snoring sounds was studied by Ankishan [ 13 ]; Lim proposed a snoring recognition method based on RNN [ 14 , 15 ]. The study of OSAHS recognition based on snoring has also been proposed after the effective extraction of snoring signals: After extracting the time-domain features of snoring after apnea events, Temrat et al judged the severity degree of OSAHS through distinguishing different types of snoring by the leave-one-out cross-validation technique [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Snore from OSAHS Patients Based on Deep Learning

Shen

Cheng

Zhu

et al. 2020

Journal of Healthcare Engineering

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

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