Seizure detection using least eeg channels by deep convolutional neural network

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buir.contributor.authorAvcu, Mustafa Talha
dc.citation.epage1124en_US
dc.citation.spage1120en_US
dc.contributor.authorAvcu, Mustafa Talhaen_US
dc.contributor.authorZhang, Z.en_US
dc.contributor.authorChan, D. W. S.en_US
dc.coverage.spatialBrighton, United Kingdomen_US
dc.date.accessioned2020-01-28T11:57:12Z
dc.date.available2020-01-28T11:57:12Z
dc.date.issued2019
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 12-17 May 2019en_US
dc.descriptionConference Name: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019en_US
dc.description.abstractThis work aims to develop an end-to-end solution for seizure onset detection. We design the SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with traditional machine learning approach, a baseline classifier is implemented using spectrum band power features with Support Vector Machines (BPsvm). We explore the possibility to use the least number of channels for accurate seizure detection by evaluating SeizNet and BPsvm approaches using all channels and two channels settings respectively. EEG Data is acquired from 29 pediatric patients admitted to KK Woman's and Children's Hospital who were diagnosed as typical absence seizures. We conduct leave-one-out cross validation for all subjects. Using full channel data, BPsvm yields a sensitivity of 86.6% and 0.84 false alarm (per hour) while SeizNet yields overall sensitivity of 95.8 % with 0.17 false alarm. More interestingly, two channels seizNet outperforms full channel BPsvm with a sensitivity of 93.3% and 0.58 false alarm. We further investigate interpretability of SeizNet by decoding the filters learned along convolutional layers. Seizure-like characteristics can be clearly observed in the filters from third and forth convolutional layers.en_US
dc.description.sponsorshipThe Institute of Electrical and Electronics Engineers Signal Processing Societyen_US
dc.identifier.doi10.1109/ICASSP.2019.8683229en_US
dc.identifier.eisbn9781479981311
dc.identifier.eissn2379-190X
dc.identifier.isbn9781479981328
dc.identifier.issn1520-6149
dc.identifier.urihttp://hdl.handle.net/11693/52877
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/ICASSP.2019.8683229en_US
dc.source.titleProceedings of the 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019en_US
dc.subjectConvolutional neural netsen_US
dc.subjectData acquisitionen_US
dc.subjectElectroencephalographyen_US
dc.subjectFeature extractionen_US
dc.subjectFiltering theoryen_US
dc.subjectMedical disordersen_US
dc.subjectMedical signal detectionen_US
dc.subjectMedical signal processingen_US
dc.subjectNeurophysiologyen_US
dc.subjectSignal classificationen_US
dc.subjectSupport vector machinesen_US
dc.titleSeizure detection using least eeg channels by deep convolutional neural networken_US
dc.typeConference Paperen_US
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