none

• Main Authors: shin-hong Shiu, 徐新閎
• Other Authors: 李柏磊
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/8r42bx

碩士 === 國立中央大學 === 電機工程學系 === 106 === Sleep disorder is a popular modern civil disease. It has become an important issue of how to provide adequate treatment for sustaining well sleep. In recent years, many doctors and scientists try to collect big data and apply artificial intelligence to solve this problem. In this paper, we use the deep learning neural network to analyze subjects’ PSG EEG and EOG. We used wavelet transform (WT) to decompose the measured EEG signals into α, β, σ, and θ bands, so that the temporal-frequency parameters were obtained as input data for deep learning neural network. The proposed network architecture contianed three different layers. The first layer is Multilayer Perceptron (MLP). The second layer is the Long Short-Term Memory (LSTM) and the last layer of the normalized exponential function (Softmax) as classifiers for the sleep cycle. The detection accuracy of the our study results was 65 percent. The continuing work of this paper will use hardware on a cloud-based medical system to process the sleep data. With cloud computation service, users will be able to perform self-diagnosis and homecare service in their home.