Convolutional neural network for detection and classification of seizures in clinical data.
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suf...
| Published in: | Medical & Biological Engineering & Computing Vol. 58; no. 9; pp. 1919 - 1933 |
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| Main Authors: | , |
| Format: | equations & formulas research tables/charts Journal Article |
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Springer Nature
Sep2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145048078&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145048078 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2020 vid: 58 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145048078 144052550 145048078 NLM32533511 145048078 10.1007/s11517-020-02208-7 NLM32533511 145048078 ppf: 1919 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Convolutional neural network for detection and classification of seizures in clinical data. aug: au: Iešmantas, Tomas Alzbutas, Robertas affil: Department of Mathematics and Natural Sciences, Kaunas University of Technology, 44249, Kaunas, Lithuania sug: subj: Seizures Classification Seizures Diagnosis Electroencephalography Statistics and Numerical Data Diagnosis, Computer Assisted Methods Diagnosis, Computer Assisted Statistics and Numerical Data Algorithms Signal Processing, Computer Assisted Resource Databases Funding Source ab: Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data. Graphical abstract. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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