植基於雲端運算之多通道腦波癲癇預測系統

Abstract

癲癇是一種常見的慢性神經疾病，並且會有不定時的發作情形。由於腦波圖 (Electroencephalogram)訊號在癲癇的診斷上扮演重要的角色。因此，雖然多頻道的腦波圖比單頻道的腦波圖有著更多的資訊以及空間解析度，但是傳統的腦波訊號分析卻缺乏多頻道的演算法。基於腦波多頻道的大量資料運算，因此我們在本篇論文提出了一個雲端架構的多頻道腦波之癲癇分析系統 (EAS)。在訊號分析上，我們同時考慮單極點訊號 (Unipolar)和雙極點訊號 (Bipolar)以抽取特徵，其中包含近似熵 (Approximate Entropy)以及訊號統計數值。同時，我們也採用基因演算法 (Genetic Algorithm)做特徵排序，最後再利用支持向量機 (Support Vector Machines)以及後棘波 (Spike)比對濾波器來辨識腦波訊號。實驗結果顯示，臨床資料II的棘波 (Spike)辨識率是86.69%，而發作 (Seizure)的辨識率是99.77%。同時利用臨床資料II所訓練的模型來偵測臨床資料III也可以得到91.18%的棘波 (Spike) 辨識率以及99.22%的發作 (Seizure) 辨識率。因此，我們建立了一個可靠地、及時地以及完整地 (包含醫療資訊以及訊號處理技術)棘波和發作的多頻道腦波偵測系統。

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.