| dc.description.abstract | Patients who suffer from sleep disorder can affect their overall health, safety and quality of life. Poor sleep has been linked with high blood pressure, atherosclerosis (cholesterol- clogged arteries), heart failure, heart attack,stroke , diabetes, and obesity. However, monitoring of sleep conditions requires physicians to examine huge amount of EEG data which is time-consuming and might fall into the pitfall of subjective judgement. Accordingly, development of an objective platform for automatic and accurate detection of sleep condition in EEG data is important. In this paper, we developed a deep learning based sleep evaluation system for EEG data. We adopted deep learning techniques to analyze sleep EEG data. The EEG data recorded from Fp1, Fp2,Pz and Oz positions were firstly transformed into temporal-frequency domain using wavelet analysis as training features. Eight characteristic features (maximum, minimum, median, average, standard deviation, range, skewness, kurtosis) on temporal-frequency domain were used as input data to train a LSTM network. The accuracy of the network is 83%. Comparing the eight eigenvalues and the results of the direct time-frequency analysis, we found that the performance of extracting eight features is better. The next goal is to use on hardware and the cloud medical system. The development of this sleep EEG analysis technique can reduce medical resources and facilitate the documentation of subject’s sleep history for better treatment in the future. | en_US |