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Automatic Arousal Detection Using Multi-model Deep Neural Network

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收录情况： ◇ CPCI(ISTP)

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机构： [1]Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen, China [2]Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital Capital Medical University Beijing, China

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关键词： deep learning arousal detection neural network sleep disorder polysomnography EEG

摘要：

Arousal labeling is one of the important methods in the diagnosis and treatment of sleep-related diseases, and are usually analyzed manually by doctors based on polysomnography (PSG) signals. In order to solve the problem of time-consuming and labor-intensive manual arousal analysis in sleep physiological signals, we propose an automatic arousal detection method using multi-model deep neural networks. Combining methods such as one-to-many formulation, LSTM, and network structure improvements, the performance of deep neural network models on clinical data set has been significantly improved, and multiple indicators have been improved (precision 86.7%, recall 86.0% and F1 86.3%). At the same time, the model parameters have been greatly streamlined, making them more efficient, laying a foundation for the application of automatic arousal detection methods on wearable sleep monitoring device signal analysis.

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第一作者机构： [1]Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen, China

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