Synthesis of standard 12‑lead electrocardiograms using two-dimensional generative adversarial networks.
This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals-left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACU...
| Publicado en: | Journal of Electrocardiology Vol. 69; pp. 6 - 15 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
W B Saunders
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153730205&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153730205 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00220736 1276 jtl: Journal of Electrocardiology issn: 00220736 maglogo: N pubinfo: dt: Nov2021 vid: 69 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 153730205 153730205 NLM34474312 10.1016/j.jelectrocard.2021.08.019 NLM34474312 153730205 ppf: 6 ppct: 9 formats: tig: atl: Synthesis of standard 12‑lead electrocardiograms using two-dimensional generative adversarial networks. aug: au: Zhang, Yu-He Babaeizadeh, Saeed affil: Advanced Algorithm Research Center, Philips Healthcare, Cambridge, MA, USA sug: subj: Electrocardiography Myocardial Infarction Generative Adversarial Networks Resource Databases Bundle-Branch Block Hypertrophy, Left Ventricular Diagnosis ab: This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals-left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACUTMI), and Normal. It uses a fully automatic end-to-end process to generate and verify the synthetic ECGs that does not require any visual inspection. The proposed model is able to produce synthetic standard 12-lead ECG signals with success rates of 98% for LVH, 93% for LBBB, 79% for ACUTMI, and 59% for Normal. Statistical evaluation of the data confirms that the synthetic ECGs are not biased towards or overfitted to the training ECGs, and span a wide range of morphological features. This study demonstrates that it is feasible to use a 2D GAN to produce standard 12-lead ECGs suitable to augment artificially a diverse database of real ECGs, thus providing a possible solution to the demand for extensive ECG datasets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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