Automatic recognition of epileptic discharges based on shape similarity in time-domain. (March 2017)

• Record Type: Journal Article
• Authors: Wei, Zuo-Chen; Zou, Jun-Zhong; Zhang, Jian; Chen, Lan-Lan
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 236
• Page End: 244
• Publication Date: 2017-03
• Subjects: Automatic recognition -- Epileptic EEG -- Template matching -- Modified Hausdorff distance -- Shape similarity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.12.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: We propose a time-domain method to recognize epileptic discharges in EEG. This method imitates the human visual cognitive process. The modified Hausdorff distance is employed to measure the shape similarity. This method can recognize epileptic discharges both in ictal and interictal period. Abstract: 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.