NDLI: Automated epilepsy diagnosis using interictal scalp EEG

Content Provider	IEEE Xplore Digital Library
Author	Forrest Sheng Bao Jue-Ming Gao Jing Hu Donald Lie Yuanlin Zhang Oommen, K.J.
Copyright Year	2009
Description	Author affiliation: Department of Neurosurgery, Jiang-Su Provincial Hospital of Chinese Medicine, Nanjing, Jiangsu, China (Jue-Ming Gao; Jing Hu) \|\| Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas 79409 (Forrest Sheng Bao; Donald Lie) \|\| Department of Computer Science, Texas Tech University, Lubbock, Texas 79409 (Yuanlin Zhang) \|\| Department of Neurology, Texas Tech University Health Sciences Center, Lubbock Texas 79430 (Oommen, K.J.)
Abstract	Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy.
Starting Page	6603
Ending Page	6607
File Size	437402
Page Count	5
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5332550
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Epilepsy Scalp Electroencephalography Artificial neural networks Video recording Medical diagnostic imaging Biological neural networks Voting USA Councils Computer science seizure Electroencephalogram (EEG) Probabilistic Neural Network (PNN)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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