PMID- 34970885
OWN - NLM
STAT- MEDLINE
DCOM- 20220103
LR  - 20230701
IS  - 1001-5515 (Print)
IS  - 1001-5515 (Linking)
VI  - 38
IP  - 6
DP  - 2021 Dec 25
TI  - [Automatic epileptic seizure detection algorithm based on dual density dual tree 
      complex wavelet transform].
PG  - 1035-1042
LID - 10.7507/1001-5515.202105075 [doi]
AB  - It is very important for epilepsy treatment to distinguish epileptic seizure and 
      non-seizure. In this study, an automatic seizure detection algorithm based on 
      dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial 
      electroencephalogram (iEEG) was proposed. The experimental data were collected 
      from 15 719 competition data set up by the National Institutes of Health (NINDS) 
      in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 
      non-seizure epochs. Each epoch was 1 second long and contained 174 sampling 
      points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG 
      signal processing. Four kinds of features include wavelet entropy, variance, 
      energy and mean value were extracted from the signal. Finally, these features 
      were sent to least squares-support vector machine (LS-SVM) for learning and 
      classification. The appropriate decomposition level was selected by comparing the 
      experimental results under different wavelet decomposition levels. The 
      experimental results showed that the features selected in this paper were 
      different between seizure and non-seizure. Among the eight patients, the average 
      accuracy of three-level decomposition classification was 91.98%, the sensitivity 
      was 90.15%, and the specificity was 93.81%. The work of this paper shows that our 
      algorithm has excellent performance in the two classification of EEG signals of 
      epileptic patients, and can detect the seizure period automatically and 
      efficiently.
FAU - Kang, Tongzhou
AU  - Kang T
AD  - School of Electronic Science and Engineering, University of Electronic Science 
      and Technology, Chengdu 610054, P.R.China.
FAU - Zuo, Rundong
AU  - Zuo R
AD  - School of Electronic Science and Engineering, University of Electronic Science 
      and Technology, Chengdu 610054, P.R.China.
FAU - Zhong, Lanfeng
AU  - Zhong L
AD  - School of Electronic Science and Engineering, University of Electronic Science 
      and Technology, Chengdu 610054, P.R.China.
FAU - Chen, Wenjing
AU  - Chen W
AD  - West China Hospital, Sichuan University, Chengdu 610041, P.R.China.
FAU - Zhang, Heng
AU  - Zhang H
AD  - West China Hospital, Sichuan University, Chengdu 610041, P.R.China.
FAU - Liu, Hongxiu
AU  - Liu H
AD  - 29th Research Institute of CETC, Chengdu 610093, P.R.China.
FAU - Lai, Dakun
AU  - Lai D
AD  - School of Electronic Science and Engineering, University of Electronic Science 
      and Technology, Chengdu 610054, P.R.China.
LA  - chi
PT  - Journal Article
PL  - China
TA  - Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
JT  - Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = 
      Shengwu yixue gongchengxue zazhi
JID - 9426398
SB  - IM
MH  - Algorithms
MH  - Electroencephalography
MH  - *Epilepsy/diagnosis
MH  - Humans
MH  - Seizures/diagnosis
MH  - Signal Processing, Computer-Assisted
MH  - Support Vector Machine
MH  - *Wavelet Analysis
PMC - PMC9927122
OTO - NOTNLM
OT  - dual density dual tree complex wavelet
OT  - epilepsy
OT  - epileptic seizure detection
OT  - intracranial electroencephalogram
OT  - wavelet entropy
COIS- 利益冲突声明：本文全体作者均声明不存在利益冲突。
EDAT- 2022/01/01 06:00
MHDA- 2022/01/04 06:00
PMCR- 2021/12/25
CRDT- 2021/12/31 06:22
PHST- 2021/12/31 06:22 [entrez]
PHST- 2022/01/01 06:00 [pubmed]
PHST- 2022/01/04 06:00 [medline]
PHST- 2021/12/25 00:00 [pmc-release]
AID - swyxgcxzz-38-6-1035 [pii]
AID - 10.7507/1001-5515.202105075 [doi]
PST - ppublish
SO  - Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Dec 25;38(6):1035-1042. doi: 
      10.7507/1001-5515.202105075.