Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107174
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Xu, SS | en_US |
dc.creator | Mak, MW | en_US |
dc.creator | Cheung, CC | en_US |
dc.date.accessioned | 2024-06-13T01:04:23Z | - |
dc.date.available | 2024-06-13T01:04:23Z | - |
dc.identifier.issn | 2168-2194 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107174 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2019 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 S. S. Xu, M. -W. Mak and C. -C. Cheung, "Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks," in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1574-1584, July 2019 is available at https://doi.org/10.1109/JBHI.2018.2871510. | en_US |
dc.subject | Arrhythmia classification | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | ECG classification | en_US |
dc.subject | End-to-end | en_US |
dc.subject | Heartbeat alignment | en_US |
dc.title | Towards end-to-end ECG classification with raw signal extraction and deep neural networks | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1574 | en_US |
dc.identifier.epage | 1584 | en_US |
dc.identifier.volume | 23 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.doi | 10.1109/JBHI.2018.2871510 | en_US |
dcterms.abstract | This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points are extracted from raw ECG signals, and consecutive vectors are extracted from a sliding time-window covering these sample points. Each of these vectors comprises the consecutive sample points of a complete heartbeat cycle, which includes not only the QRS complex but also the P and T waves. Unlike existing heartbeat classification methods in which medical doctors extract handcrafted features from raw ECG signals, the proposed end-to-end method leverages a deep neural network for both feature extraction and classification based on aligned heartbeats. This strategy not only obviates the need to handcraft the features but also produces optimized ECG representation for heartbeat classification. Evaluations on the MIT-BIH arrhythmia database show that at the same specificity, the proposed patient-independent classifier can detect supraventricular- and ventricular-ectopic beats at a sensitivity that is at least 10% higher than current state-of-the-art methods. More importantly, there is a wide range of operating points in which both the sensitivity and specificity of the proposed classifier are higher than those achieved by state-of-the-art classifiers. The proposed classifier can also perform comparable to patient-specific classifiers, but at the same time enjoys the advantage of patient independence. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE journal of biomedical and health informatics, July 2019, v. 23, no. 4, p. 1574-1584 | en_US |
dcterms.isPartOf | IEEE journal of biomedical and health informatics | en_US |
dcterms.issued | 2019-07 | - |
dc.identifier.scopus | 2-s2.0-85053605758 | - |
dc.identifier.pmid | 30235153 | - |
dc.identifier.eissn | 2168-2208 | en_US |
dc.description.validate | 202403 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0361 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20150601 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
---|---|---|---|---|
Xu_Towards_End-To-End_Ecg.pdf | Pre-Published version | 2.23 MB | Adobe PDF | View/Open |
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