Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

• Published in: Physiological measurement Vol. 44; no. 8; pp. 85003 - 85020
• Main Authors: Li, Heng, Lin, Xu, Lu, Yun, Wang, Mingjiang, Cheng, Hanrong
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 14.08.2023
• Subjects: attention mechanism; Bi-LSTM; embedded platform; Humans; Pilot Projects; Polysomnography - methods; SAS detection; Sleep Apnea, Obstructive - diagnosis; snore; Snoring - diagnosis; Bi-LSTM; embedded platform; SAS detection; attention mechanism; snore
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/acebb5

Objective. Sleep apnea has a high incidence and is a potentially dangerous disease, and its early detection and diagnosis are challenging. Polysomnography (PSG) is considered the best approach for sleep apnea detection, but it requires cumbersome and complicated operations. Thus, it cannot satisfy the family healthcare needs. Approach. To facilitate the initial detection of sleep apnea in the home environment, we developed a sleep apnea classification model based on snoring and hybrid neural network, and implemented the well trained model in an embedded hardware platform. We used snore signals from 32 patients at Shenzhen People's Hospital. The Mel-Fbank features were extracted from snore signals to build a sleep apnea classification model based on Bi-LSTM with attention mechanism. Main results. The proposed model classified snore signals into four types: hypopnea, normal condition, obstructive sleep apnea, and central sleep apnea, with 83.52% and 62.31% accuracies, corresponding to the subject-dependence and subject-independence validation, respectively. After pruning and model quantization, at the cost of 0.81% and 0.95% accuracy loss of the subject dependence and subject independence classification, respectively, the number of model parameters and model storage space were reduced by 32.12% and 60.37%, respectively. The model exhibited accuracies of 82.71% and 61.36% based on the subject dependence and subject independence validations, respectively. When the well trained model was successfully porting and running on an STM32 ARM-embedded platform, the model accuracy was 58.85% for the four classifications based on leave-one-subject-out validation. Significance. The proposed sleep apnea detection model can be used in home healthcare for the initial detection of sleep apnea.