期刊
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)
卷 -, 期 -, 页码 469-474出版社
IEEE
DOI: 10.1109/BigComp.2018.00075
关键词
Electroencephalograph; slope; wavelet; seizure; diagnosis of epilepsy; classification; hidden markov model
资金
- Industrial Technology Innovation Program - Ministry of Trade, industry & Energy (MI, Korea) [10073154]
- Basic Science Research Program through the National Research Foundation of Korea(NRF) - Ministry of Education [2015R1D1A1A01061112]
- Next-generation Medical Device Development Program for Newly-Created Market of the National Research Foundation (NRF) - Korean government, MSIP [2015M3D5A1066100]
- Bio & Medical Technology Development Program of the NRF - Korean government, MSIP [NRF-2014M3A9D7070128]
- National Research Foundation of Korea [2015M3D5A1066100, 2015R1D1A1A01061112] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this paper, we propose a novel feature extraction method, a slope of counting wavelet coefficients over various thresholds (SCOT) method based hidden markov model (HMM) for seizure detection. The purpose of the proposed method is to aid in the diagnosis of epilepsy, which requires long-term electroencephalography (EEG) monitoring. The interpretation of long-term EEG monitoring takes a lot of time and requires the assistance of experienced experts. In order to overcome these limitations, it is important to apply the optimized feature extraction algorithm to the seizure detection system. The proposed SCOT method based HMM has a robust detection accuracy, and a short feature extraction time; whereas the existing methods require a large amount of training data and a long feature extraction time for learning the seizure detection model. Experimental result shows that with the proposed method, the average detection accuracies are 96.5% and 98.4% using the HMM in seizure and non-seizure, respectively. In addition, the proposed method has robust detection performance regardless of the given window sizes (0.15, 0.25, 0.5, 1, and 2 seconds) are used.
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