| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 95 === Polysomnography (PSG) is the must common procedures used for diagnosis of sleep states. One of important task of PSG is sleep stages scoring. Sleeping stage of each 30 second segment is determined by sleep specialist, and scoring stages manually consumes time and human resource. So many automatic sleep stages scoring system was developed.
In this thesis, we proposed a feature set to replace the must common features relative frequency band energy. Our feature set includes harmonic parameters with wavelet transform, Hjorht parameters, wavelet entropy, and wavelet energy. We build an automatic sleep stages scoring system using SVM with RBF kernel using the feature set we found.
The objective of this thesis is providing a better set of features form EEG signals. That can decrease the sensor numbers, and that may measure patients’ sleep state in their houses. The automatic sleep stages scoring model can help the sleep specialists save their time of scoring.
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