UBC Theses and Dissertations
Automatic sleep arousal detection based on C-ELM and MRMR feature selection Liang, Yuemeng
Sleep arousals are sudden awakenings from sleep which can be identified as an abrupt shift in EEG frequency and can be manually scored from various physiological signals by sleep experts. Frequent sleep arousals can degrade sleep quality, result in sleep fragmentation and lead to daytime sleepiness. Visual inspection of arousal events from PSG recordings is cumbersome, and manual scoring results can vary widely among different expert scorers. The main goal of this project is to design and evaluate the performance of an effective and efficient algorithm to automatically detect sleep arousals using a single channel EEG. In the first part of the thesis, a detection model based on a Curious Extreme Learning Machine (C-ELM) using a set of 22 features is proposed. The performance was evaluated using the term Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and the Accuracy (ACC). The proposed C-ELM based model achieved an average AUC and ACC of 0.85 and 0.79 respectively. In comparison, the average AUC and ACC of a Support Vector Machine (SVM) based model were 0.69 and 0.67 respectively. This indicates that the proposed C-ELM based model works well for the sleep arousal detection problem. In the second part of the thesis, an improved detection model is proposed by adding a Minimum Redundancy Maximum Relevance (MRMR) feature selection into the C-ELM based model proposed in the first part. The efficiency of the model is improved by reducing dimensionality (reducing the number of features) of the dataset while the performance is largely unaffected. The achieved average AUC and ACC were 0.85 and 0.80 when a reduced set of 6 features were used, while the AUC and ACC were 0.86 and 0.79 for a full set of 22 features. The result indicates MRMR feature selection step is important for sleep arousal detection. By using the improved sleep arousal detection model, the system runs faster and achieves a good performance for the dataset utilized in our study.
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