Machine learning with electrocardiograms: A call for guidelines and best practices for 'stress testing' algorithms.
This paper provides a brief description of how computer programs are used to automatically interpret electrocardiograms (ECGs), and also provides a discussion regarding new opportunities. The algorithms that are typically used today in hospitals are knowledge engineered where a computer programmer m...
| Published in: | Journal of Electrocardiology Vol. 69; pp. 1 - 7 |
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| Main Authors: | , , , |
| Format: | research Journal Article |
| Published: |
W B Saunders
2021 Supplement
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153954902&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153954902 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00220736 1276 jtl: Journal of Electrocardiology issn: 00220736 maglogo: N pubinfo: dt: 2021 Supplement vid: 69 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 153954902 153954902 NLM34340817 153954902 10.1016/j.jelectrocard.2021.07.003 NLM34340817 153954902 ppf: 1 ppct: 6 formats: tig: atl: Machine learning with electrocardiograms: A call for guidelines and best practices for 'stress testing' algorithms. aug: au: Bond, Raymond Finlay, Dewar Al-Zaiti, Salah Shafiq Macfarlane, Peter affil: Faculty of Computing, Engineering and the Built Environment, Ulster University, Jordanstown Campus, Northern Ireland, UK sug: subj: Algorithms Electrocardiography Human Exercise Test Comparative Studies Multicenter Studies Evaluation Research Validation Studies Questionnaires ab: This paper provides a brief description of how computer programs are used to automatically interpret electrocardiograms (ECGs), and also provides a discussion regarding new opportunities. The algorithms that are typically used today in hospitals are knowledge engineered where a computer programmer manually writes computer code and logical statements which are then used to deduce a possible diagnosis. The computer programmer's code represents the criteria and knowledge that is used by clinicians when reading ECGs. This is in contrast to supervised machine learning (ML) approaches which use large, labelled ECG datasets to induct their own 'rules' to automatically classify ECGs. Although there are many ML techniques, deep neural networks are being increasingly explored as ECG classification algorithms when trained on large ECG datasets. Whilst this paper presents some of the pros and cons of each of these approaches, perhaps there are opportunities to develop hybridised algorithms that combine both knowledge and data driven techniques. In this paper, it is pointed out that open ECG data can dramatically influence what international ECG ML researchers focus on and that, ideally, open datasets could align with real world clinical challenges. In addition, some of the pitfalls and opportunities for ML with ECGs are outlined. A potential opportunity for the ECG community is to provide guidelines to researchers to help guide ECG ML practices. For example, whilst general ML guidelines exist, there is perhaps a need to recommend approaches for 'stress testing' and evaluating ML algorithms for ECG analysis, e.g. testing the algorithm with noisy ECGs and ECGs acquired using common lead and electrode misplacements. This paper provides a primer on ECG ML and discusses some of the key challenges and opportunities. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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