文献类型：期刊文献

英文题名：Automatic recognition of epileptic discharges based on shape similarity in time-domain

作者：Wei, Zuo-Chen[1];Zou, Jun-Zhong[1];Zhang, Jian[1];Chen, Lan-Lan[1]

机构：[1]East China Univ Sci & Technol, Dept Automat, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2017

卷号：33

起止页码：236

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20165203191725);WOS:【SCI-EXPANDED(收录号:WOS:000393726500023)】；

基金：This work is partly supported by National Natural Science Foundation of China No. 61201124 and Fundamental Research Funds for the Central Universities WH1414022.

语种：英文

外文关键词：Automatic recognition; Epileptic EEG; Template matching; Modified Hausdorff distance; Shape similarity

摘要：Background: Epilepsy is a common neurological disease, and electroencephalogram (EEG) contains massive epilepsy information. Automatic recognition of epileptic discharges has great significance in diagnosis of epilepsy. New method: This paper proposes a novel automatic recognition of epileptic waves method in EEG signals based on shape similarity in time-series sequence directly. Merger of the increasing and decreasing sequences (MIDS) was used to improve the recognition accuracy and reduce the computation cost. Then shape templates were designed, and the modified Hausdorff distance was employed to measure the shape similarity of waveforms in template matching part. This approach imitates human visual cognitive process to analyze EEG and employs image recognition method into one-dimensional signals, which is a direct, original and effective method. Results: 373 epileptic discharge fragments marked by clinicians from 20 patients' EEG recordings were selected. By fusing significance rules, 98.39% of them were recognized, with the false recognition rate 1.1%. Comparison with existing methods: Experimental results indicate that the proposed approach yielded better performance for interictal epileptiform discharges (IEDS) recognition compared with the previous methods. Conclusions: The proposed approach has good performance and high stability in automatic recognition of epileptic discharges both in ictal and interictal period, which could support the diagnosis of epilepsy greatly. (C) 2016 Elsevier Ltd. All rights reserved.