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Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning

文献类型： 会议论文

第一作者： Maziar Hafezi

作者： Maziar Hafezi 1 ; Nasim Montazeri 1 ; Kaiyin Zhu 2 ; Hisham Alshaer 2 ; Azadeh Yadollahi 1 ; Babak Taati 1 ;

作者机构： 1.the Institute of Biomaterial & Biomedical Engineering, University of Toronto, and KITE, Toronto Rehabilitation Institute, University Health Network, ON Canada

2.KITE, Toronto Rehabilitation Institute, University Health Network, ON Canada

关键词： Sleep Apnea;Severity Estimation;Deep Learning

会议名称： International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 1601-1604

摘要： Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The gold standard assessment of sleep apnea requires full night polysomnography in a sleep laboratory which is expensive, time consuming, and inconvenient. Hence, there is an urgent need for a convenient, robust and wearable monitoring device for screening of sleep apnea. A simple and convenient accelerometer-based portable system is presented to estimate the severity of sleep apnea by analyzing tracheal movements. Respiratory related movements were recorded over the suprasternal notch using a 3D accelerometer. Twenty-one physiological features (7 features, 3 accelerometer channels) were extracted. Performance of three different deep learning models - convolutional neural network, recurrent neural network, and their combination - were evaluated for estimating the apnea hypopnea index (AHI). The estimated AHI is compared to the gold standard polysomnography. In 3-fold cross-validation experiments with 20 participants (9 female, age=47.8±18.0 years, BMI=30.8±4.8, AHI=22.2±21.8 events/hr), we achieved a correlation coefficient between gold standard and estimated values (r-value = 0.84). The proposed system is an accurate, convenient, and portable device suitable for home sleep apnea screening.

分类号： r318-532

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