Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

doi:10.1016/j.neunet.2018.04.018

科研成果详情

题名	Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram
作者	Nhan Duy Truong 1; Anh Duy Nguyen 2; Levin Kuhlmann 3; Mohammad Reza Bonyadi 4; Jiawei Yang 5; Samuel Ippolito 6; Omid Kavehei 7
发表期刊	Neural networks : the official journal of the International Neural Network Society 影响因子和分区
语种	英语
原始文献类型	Journal article (JA)
关键词	Convolutional neural network Intracranial EEG Machine learning Scalp EEG Seizure prediction.
摘要	Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets.
资助项目	National Health and Medical Research Council[APP1065638];
出版者	Elsevier Ltd
ISSN	0893-6080
EISSN	1879-2782
卷号	105 页码:104-111.
DOI	10.1016/j.neunet.2018.04.018
页数	35
收录类别	PUBMED ; EI ; SCOPUS
发表日期	2018-09-01
EI入藏号	20182105226377
EI主题词	Electroencephalography
URL	查看原文
Pubmed记录号	29793128
Scopus记录号	2-s2.0-85047158073
引用统计	被引频次：201[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/33454
专题	温州医科大学
作者单位	1.School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia; Nano-Neuro-inspired Research Laboratory, School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia; School of Engineering, Royal Melbourne Institute of Technology, Melbourne, VIC 3000, Australia. Electronic address: duy.truong@sydney.edu.au.; 2.Nano-Neuro-inspired Research Laboratory, School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia. Electronic address: ngduyanhece@gmail.com.; 3.Centre for Human Psychopharmacology, Swinburne University, Hawthorn, VIC 3122, Australia; Neuroengineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia; Department of Medicine, St. Vincent&s Hospital Melbourne, University of Melbourne, Parkville, VIC 3010, Australia. Electronic address: lkuhlmann@swin.edu.au.; 4.Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD 4072, Australia; Optimization and Logistics Group, University of Adelaide, Adelaide, SA 5005, Australia. Electronic address: reza@cai.uq.edu.au.; 5.Nanochap Electronics and Wenzhou Medical University, 268 Xueyuan West Rd., Wenzhou, China; 6.School of Engineering, Royal Melbourne Institute of Technology, Melbourne, VIC 3000, Australia. Electronic address: samuel.ippolito@rmit.edu.au.; 7.School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia; Nano-Neuro-inspired Research Laboratory, School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia. Electronic address: omid.kavehei@sydney.edu.au.
推荐引用方式 GB/T 7714	Nhan Duy Truong,Anh Duy Nguyen,Levin Kuhlmann,et al. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram[J]. Neural networks : the official journal of the International Neural Network Society,2018,105:104-111..
APA	Nhan Duy Truong., Anh Duy Nguyen., Levin Kuhlmann., Mohammad Reza Bonyadi., Jiawei Yang., ... & Omid Kavehei. (2018). Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural networks : the official journal of the International Neural Network Society, 105, 104-111..
MLA	Nhan Duy Truong,et al."Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram".Neural networks : the official journal of the International Neural Network Society 105(2018):104-111..