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| 題 名 | 類神經網路應用於腦病變源定位分析=Artifical Neural Network for Dipole Localization |
|---|---|
| 作 者 | 羅佩禎; 蘇祝鼎; | 書刊名 | 中華醫學工程學刊 |
| 卷 期 | 19:2 1999.06[民88.06] |
| 頁 次 | 頁105-112 |
| 分類號 | 415.81 |
| 關鍵詞 | 腦病變源定位; 類神經網路; 多頻道腦電波; 三層均質球型; 單一電偶極模型; Localization of focal source; Artificial neural network; Multi-channel EEG; Electroencephalograph; A single current dipole in a three-shell; Homogeneous sphere; |
| 語 文 | 中文(Chinese) |
| 中文摘要 | 本論文旨在嘗試以類神經網路來探討腦病變源的定位問題,主要目的在估測出引 發癲癇症(epilepsy)的單一病變源位置及強度向量。由多頻道腦電波 (electroencephalograph, EEG)反推病變源是一個高度非線性的問題,藉由數值方法中「三 層均質球型、單一電偶極模型」來模擬病變源並產生所對應之腦電波分佈,以此作為訓練樣 本來訓練類神經網路學習該對應關係;亦即,以類神經網路取代數學函數所處理之問題。 本文中首先探討三種類神經網路對於不同分佈區域及特性之電偶極(病變源)的定位效 果,包括多層認知(Multi-Layer Perceptrons, MLPs)網路、幅射式(Radial Basis Function, RBF)網路、以及串接上述二種網路之混合式網路(Hybrid)。與單純之MLPs和RBF網路比較, 混合式網路具有較短的訓練時間與較佳的定位能力的優點。其次,我們提出一組合網路架構 用於定位不分型態之電偶極;其定位精度與傳統數值方法相當,而所需運算時間遠少於數值 方法。 |
| 英文摘要 | This study aims to investigate the problem of focal source localization based on artificial neural network (ANN) techniques. Our major goal is to estimate the source location and strength in a single-source model. Estimating the focal source from the multi-channel EEG (electroencephalograph) is a highly nonlinear approximation process. We applied a 3-layer, homogeneous spherical current dipole model to simulate the brain potential distribution on the scalp. The dipole source and its corresponding brain potentials were used as the training patterns for the neural networks. Then the neural network was trained to learn the correlation between the dipole and brain potential distribution, that is, the neural network model was used to substitute for the mathematical model. This paper first presents the capability of three neural networks, including the multi-layer perceptrons (MLPs), radial basis function (RBF), and the hybrid (cascade of MLPs and RBF) networks, in localizing the dipole sources in five different regions. In comparison with the simple MLPs and RBF networks, the hybrid network requires less training time and has better localization accuracy. Next, a composite network model is proposed to localize the dipole without any a priori knowledge of its depths and orientation. Our experiment shows that the neural network approach, compared with the conventional numerical approach, is much more efficient in computation. And the localization accuracy is comparable to the numerical approach. |
本系統中英文摘要資訊取自各篇刊載內容。