A Study on Spike Detection and Classification from Epileptic EEG Data
博士 === 國立成功大學 === 資訊工程學系 === 102 === Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this thesis, a new two–stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) i...
Main Authors: | Yung-ChunLiu, 劉勇均 |
---|---|
Other Authors: | Yung-Nien Sun |
Format: | Others |
Language: | en_US |
Published: |
2014
|
Online Access: | http://ndltd.ncl.edu.tw/handle/37413041782524463703 |
Similar Items
-
Model-Based Spike Detection of Epileptic EEG Data
by: Jing-Jane Tsai, et al.
Published: (2013-09-01) -
A Review of EEG and MEG Epileptic Spike Detection Algorithms
by: Fathi E. Abd El-Samie, et al.
Published: (2018-01-01) -
Epileptic Seizure Detection System Using Multi-Channel EEG as Basis for Classification
by: Shih-Ting Liu, et al.
Published: (2012) -
Neural Network Based Epileptic EEG Detection and Classification
by: Shivam Gupta, et al.
Published: (2020-11-01) -
EEG Signal Analysis System for Finger Movement Detection
by: Yung-Chun Liu, et al.
Published: (2004)