| Summary: | 博士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 102 === Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The Electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multi-channel EEG signals. Due to large data computation, we propose a cloud based Epilepsy Analysis System (EAS) on multi-channel EEG signals. Both unipolar and bipolar EEG and ECG signals are both considered in our approach. We make use of approximate entropy (ApEn) and statistic values to extract features cascaded Genetic Algorithm (GA). Furthermore, EEG was also tested the performance by Support Vector Machine (SVM) and post-spike matching filters. We obtained accuracies of spikes and seizures are 86.69% and 99.77% for Clinical Data Set II. The detection system was further validated using the model trained by Clinical Data Set II on Clinical Data Set III. The system again showed high performance, with accuracies of spikes and seizures are 91.18% and 99.22%. Therefore, we built up a reliable, real-time, and complete (medical information and signal processing technology) system for detecting a large variety of seizures and spikes from multi-channel EEG data.
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