Algorithm and Architecture Design of Epilepsy Seizure Prediction System

• Main Authors: Cheng-Yi Chiang, 蔣承毅
• Other Authors: Liang-Gee Chen
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/60513665372301133044

碩士 === 國立臺灣大學 === 電子工程學研究所 === 99 === Epilepsy is one of the most common brain disorders in the world. The spontaneous seizure onset influences the daily life of epilepsy patients. The studies on feature extraction and feature classification from Electroencephalography(EEG) signal in seizure prediction methods have shown great improvement these years. However, the variation issue of EEG signal (being awake, being asleep, severity of epilepsy, etc.) poses a fundamental difficulty in seizure prediction problem. The traditional off-line training method trains the model using a fixed training set, and expects the performance of the model to remain stable even after a long period of time, and thus suffers from variation issue. In this thesis, we propose an on-line retraining method to leverage the recent input data by gradually enlarging the training set and retraining the model. Also, a simple post-processing scheme is incorporated to reduce false alarms. We develop our method based on the state of the art machine learning based classification of bivariate patterns method. The performance of the method is evaluated on Electrocorticogram(ECoG) recording from Freiburg database as well as long-term scalp EEG recording from CHB-MIT EEG Database and National Taiwan University Hospital. The proposed method achieves 74.2% sensitivity on ECoG database and 52.2% sensitivity on scalp EEG database, while improving the sensitivity of off-line training method by 29.0% and 17.4% in ECoG database and EEG database respectively. The experimental result suggests that on-line retraining can greatly improve the reliability and is promising for future seizure prediction method development. To implement the proposed seizure prediction system, the system faces three design challenges including throughput requirement, power consumption, and system flexibility. To deal with the design challenges, we first propose a hybrid dimension reduction technique to reduce the pattern size from over thousands to only 256 dimensions, while the prediction performance was only dropped by 3%. The reduction of the pattern size improves the system regarding to time, area complexity, and power consumption, and enables the capability of online-retraining method. Through a careful analysis on hardware resources and computation estimation, we then map the function into hardware units. The analysis leads to the conclusion that an efficient feature extraction engine and a general purpose processor are two indispensable units to solve the design challenges. An efficient architecture design of multi-channel wavelet coherence feature extraction engine is analyzed and optimized. A reasonable system architecture design which meets the design challenges is then presented. In sum, a novel online retraining seizure prediction method along with the system architecture design are presented. The hardware implementation results of the core DSP function of the system verifies the proposed solution, and the mentioned design challenges in algorithm and architecture are solved through careful analysis.