Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107227
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Xu, SS | en_US |
dc.creator | Mak, MW | en_US |
dc.creator | Cheung, CC | en_US |
dc.date.accessioned | 2024-06-13T01:04:44Z | - |
dc.date.available | 2024-06-13T01:04:44Z | - |
dc.identifier.isbn | 978-1-5386-0560-8 (Electronic) | en_US |
dc.identifier.isbn | 978-1-5386-0561-5 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107227 | - |
dc.description | 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 10-14 July 2017, Hong Kong, China | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Sean shensheng Xu, Man-Wai Mak and Chi-Chung Cheung, "Deep neural networks versus support vector machines for ECG arrhythmia classification," 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Hong Kong, China, 2017, pp. 127-132 is available at https://doi.org/10.1109/ICMEW.2017.8026250. | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | ECG | en_US |
dc.subject | Fisher discriminant ratio | en_US |
dc.subject | Heart arrhythmia classification | en_US |
dc.subject | SVM | en_US |
dc.title | Deep neural networks versus support vector machines for ECG arrhythmia classification | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 127 | en_US |
dc.identifier.epage | 132 | en_US |
dc.identifier.doi | 10.1109/ICMEW.2017.8026250 | en_US |
dcterms.abstract | Heart arrhythmia is a condition in which the heartbeat is too fast, too slow, or irregular. As Electrocardiography (ECG) is an efficient measurement of heart arrhythmia, lots of research efforts have been spent on the identification of heart arrhythmia by classifying ECG signals for health care. Among them, support vector machines (SVMs) and artificial neural networks (ANNs) are the most popular. However, most of the previous studies reported the performance of either the SVMs or the ANNs without in-depth comparisons between these two methods. Also, a large number of features can be extracted from ECG signals, and some may be more relevant to heart arrhythmia than the others. This paper is to enhance the performance of heart arrhythmia classification by selecting relevant features from ECG signals, applying dimension reduction on the feature vectors, and applying deep neural networks (DNNs) for classification. A holistic comparison among DNNs, SVMs, and ANNs will be provided. Experimental results suggest that DNNs outperform both SVMs and ANNs, provided that relevant features have been selected. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 10-14 July 2017, Hong Kong, China | en_US |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85031692757 | - |
dc.relation.conference | IEEE International Conference on Multimedia and Expo Workshops (ICMEW) | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0658 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University; The Hong Kong Hong Kong Innovation and Technology Commission | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9605728 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Mak_Deep_Neural_Networks.pdf | Pre-Published version | 1.22 MB | Adobe PDF | View/Open |
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