基于卷积神经网络的癫痫病预测模型

Epilepsy prediction with convolutional neural network

肖彬

癫痫病 ； 卷积神经网络 ； 深度学习 ； epilepsy prediction ； deep learning ； transfer learning

交通大學資訊科學與工程研究所學位論文

2016年

碩士

莊仁輝;劉建良

英文

癲癇病是一種常見的腦疾病，其給患者帶來的最大困擾在於癲癇病會毫無徵兆的在任何地方，任何時間發作。針對於此，一個好的癲癇病發作預測的系統可以極大的改善患者的生活品質，減少患者的精神壓力。當前，如何建立一個可靠的癲癇病發作預測系統已經成為一個熱門的研究主題。EEG信號是一種大腦電位變化的信號。EEG信號被用來診斷癲癇病已經有數十年的歷史。一般癲癇病的預測問題，都會被轉換成二元分類的問題，主要是把preictal狀態的EEG信號和interictal狀態的EEG信號分開。本文中，我們分別使用了快速傅裡葉變換（FFT）和主成分分析法（PCA），分別在EEG信號的頻域和時域提取信號特徵。以這些特徵為基礎，我們提出了multi-view的卷積神經網路架構來解決癲癇病預測的問題。實驗結果表明，我們提出的方法要優於現有的方法。除此之外，我们研究了使用transfer learning 的方法来提升演算法的效能，实验结果表明，transfer learning能对演算法的效能带来一定的提升。

Epilepsy is one of the most common brain diseases, which can break out at anytime, anywhere. The unpredictability of seizure is often considered the most problematic aspect of epilepsy by the patients. A good epilepsy seizure predictor can help patients reduce the burden of unpredictability and improve patients’ life quality greatly. Therefore, a central theme in epilepsy treatments is to predict epilepsy seizure, so that patients can get a warning before epilepsy seizures take place. Electroencephalograms (EEGs) are recordings of the electrical potentials produced by the brain. EEG signals, together with patient behavior, have been used in the diagnosis of epilepsy for decades. Typically, researchers treat epilepsy seizure prediction as a binary classification problem aiming at discriminate cerebral state preictal state or interictal state. In this work, we apply two of the simplest and most popular EEG signal processing methods, Discrete Fourier Transform (FFT) and Principal Component Analysis (PCA), to generate features in frequency domain and time domain separately. With these features as input, we propose a multi-view Convolutional Neural Network model to solve seizure prediction problem. Experimental results show that our approach outperforms other existing solutions. We also explore to use transfer learning to improve the performance of our solution. The experiments show that our solution can benefit from transfer learning.

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