多导睡眠图
睡眠呼吸暂停
阻塞性睡眠呼吸暂停
呼吸暂停
计算机科学
人工神经网络
人工智能
睡眠(系统调用)
呼吸暂停-低通气指数
医学
微控制器
机器学习
模式识别(心理学)
语音识别
嵌入式系统
内科学
操作系统
作者
Heng Li,Xu Lin,Yun Lu,Mingjiang Wang,Hongyan Cheng
出处
期刊:Physiological Measurement
[IOP Publishing]
日期:2023-08-01
卷期号:44 (8): 085003-085003
标识
DOI:10.1088/1361-6579/acebb5
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI