Paper Info
Title
A Novel Spike Detection Algorithm Based On Multi-Channel Of Bect Eeg Signals
Abstract
Benign childhood epilepsy with centro-temporal spikes (BECT) is one of the most common epilepsy syndromes in childhood which is typically characterized by localized discharges in the central and temporal regions. Traditionally, the recognition of spikes requires visual assessment of long-term EEG recordings which is time consuming and subjective because it depends on the knowledge and experience of the doctor. Therefore, a novel multi-step spike detection algorithm based on average reference (AV) channel and bipolar (BP) channel BECT EEG is proposed, including candidate spike detection algorithm, false positive spike (FPS) elimination, spike feature extraction and random forest (RF) classification. The proposed method is evaluated using 7 routine EEG recordings. This brief shows that the sensitivity (Sen), specificity (Spe), selectivity (Sel) and accuracy (AC) obtained by the proposed method are 97.4%, 96.5%, 96.6% and 96.9%, respectively. Experimental results show that the proposed method is capable of detecting BECT spikes efficiently.
Year
DOI
Venue
2020
10.1109/TCSII.2020.2992285
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
Keywords
DocType
Volume
Electroencephalography, Feature extraction, Radio frequency, Detection algorithms, Epilepsy, Discharges (electric), Training, BECT spikes, AV channel, BP channel, candidate spike detection, FPS elimination
Journal
67
Issue
ISSN
Citations 
12
1549-7747
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Zimeng Wang101.35
Duanpo Wu211.75
Fang Dong3474.65
Jiuwen Cao417818.99
Tiejia Jiang531.49
Junbiao Liu632.17