English  |  正體中文  |  简体中文  |  Items with full text/Total items : 26988/38789
Visitors : 2357771      Online Users : 36
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/11277

Title: 整合複數個神經網路弱分類器之癲癇腦波判讀
Robust Epilepsy Classification via Integrating Plural Neural Network Weak Classifiers
Authors: 王榮華
Contributors: NTOU:Department of Electrical Engineering
國立臺灣海洋大學:電機工程學系
Keywords: 希爾伯特-黃轉換;分類器;癲癇認知神經網路;適應性提升演算法( Adaboost);腦電圖;癲癇症
Hilbert-Huang Transformation;Classifier;Epilepsy Recognition Neural Network;Adaptive Boosting;Electroencephalogram (EEG);Epilepsy
Date: 2010
Issue Date: 2011-06-28T08:08:53Z
Publisher: 行政院國家科學委員會
Abstract: 腦波信號對於特定疾病具有獨特的特徵表現性,將特徵表現歸納出對應人體之疾病係眾多研究人員努力的目標。然而特定病徵如癲癇,其需處理的腦波資料量相當龐大且屬於非穩態(non-stationary)、非線性,故研發一套精確且客觀的癲癇腦波輔助診斷系統是兼具學術及應用價值。承續前期計畫已完成針對數個spatio-temporal神經網路模型(TDNN, SRNN, ACNN等)適用性篩選及HHT拆解多通道腦波信號,基於該研究成果與經驗累積,本期計畫擬進一步提出一整合複數個弱分類器之癲癇判讀方法,每一個弱分類器係指一具有不同延遲窗口之癲癇認知神經網路(ERNN,修改自TDNN),其特點有三 (1)不同於SRNN以補零擴張輸入資料,吾人擬藉鏡射擴張以達到對神經網路的充分訓練(2)由於窗口的大小可以控制輸入資料的解析度(resolution),具有等效量化的效果,藉此克服龐大的腦波資料量(3)藉由腦波訊號在希爾伯特頻譜能量上的表現選取數個延遲窗口大小,據以決定ERNN的數量。 最後,使用Adaboost學習法將數個延遲窗口大小不同的ERNN結合成為一強建性癲癇腦波分類器(Robust epilepsy waves classifier),由於係採用整合複數個弱分類器的架構,並非僅有固定之單一窗口大小,可有效克服腦波的非穩態、非線性。期能輔助醫師作客觀的診斷,提昇醫療品質。 特別感謝財團法人長庚紀念醫院基隆院區-神經內科主任彭宗義醫師定期提供專業意見與病患腦波資料,藉此吾人得以將神經網路技術與醫療應用做跨領域的結合。
Abstract-Brain disorders often present specific and unique EEG wave patterns, how to utilize this unique characteristic to assist medical diagnosis is an important research subject in medical field, and developing an objective and accurate epilepsy diagnosis system is both academically and practically potential. However, the key difficulty lies in that EEG wave associated with disorders such as epilepsy contains enormous (not to mention multiple channels), non-stationary and non-linear data. This project proposes a robust epilepsy classifying method constructed by integrating plural weak classifiers called ERNN (Epilepsy Recognition Neural Network), each with different delay window size. ERNN is based on modified TDNN in corporation with HHT (Hilbert-Huang Transformation) features. ERNN characterizes in threefold: (1) Unlike SRNN, ERNN can be sufficiently trained by a mirroring process (2) The problem of enormous brain data is dealt with changing resolution of input data through varying the delay window size (3) The energy distribution of Hilbert spectrum of an input EEG wave is used to determine the number of delay window, so is the number of ERNN. Finally, a robust epilepsy classifier is constructed by training plural ERNN with Adaboost training method, and it is expected that problems of non-stationary and non-linear inherent with epilepsy brain waves can be effectively overcome with various ERNNs, each with different window size and quantized input data.
Relation: NSC99-2221-E019-040
URI: http://ntour.ntou.edu.tw/ir/handle/987654321/11277
Appears in Collections:[電機工程學系] 研究計畫

Files in This Item:

There are no files associated with this item.



All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback