Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107187
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 | 2024-06-13T01:04:28Z | - |
dc.date.available | 2024-06-13T01:04:28Z | - |
dc.identifier.isbn | 978-153865488-0 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107187 | - |
dc.description | 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 03-06 December 2018, Madrid, Spain | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2018 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, "Patient-Specific Heartbeat Classification Based on I-Vector Adapted Deep Neural Networks," 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018, pp. 784-787 is available at https://doi.org/10.1109/BIBM.2018.8621475. | en_US |
dc.subject | Arrhythmias | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | DNN adaptation | en_US |
dc.subject | ECG classification | en_US |
dc.subject | I-vectors | en_US |
dc.title | Patient-specific heartbeat classification based on i-vector adapted deep neural networks | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 784 | en_US |
dc.identifier.epage | 787 | en_US |
dc.identifier.doi | 10.1109/BIBM.2018.8621475 | en_US |
dcterms.abstract | Automatic heartbeat classification from electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A main challenge in ECG classification is the variability of ECG signals across patients. This paper proposes a patient-specific heartbeat classifier to address the inter-patient variations in ECG signals. Inspired by the success of identity vectors (i-vectors) in speech and speaker recognition, we extracted one i-vector from five minutes of ECG data for each patient and applied it to adapt a patient-independent deep neural network (DNN) to a patient-specific DNN, namely i-vector adapted patient-specific DNN (iAP-DNN). Evaluations on the MIT-BIH arrhythmia database show that the iAP-DNN is able to classify raw ECG signals of the corresponding patient into normal heartbeats and different types of arrhythmias and that it outperforms existing patient-specific classifiers in terms of sensitivity-vs-specificity and Mathews correlation coefficients. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In the Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 03-06 December 2018, Madrid, Spain, p. 784-787 | en_US |
dcterms.issued | 2018 | - |
dc.identifier.scopus | 2-s2.0-85062503913 | - |
dc.relation.conference | IEEE International Conference on Bioinformatics and Biomedicine [BIBM] | - |
dc.description.validate | 202404 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0436 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20150674 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Xu_Patient-Specific_Heartbeat_Classification.pdf | Pre-Published version | 538.41 kB | Adobe PDF | View/Open |
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