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.
|Number of pages||6|
|Journal||Journal of Electrocardiology|
|Early online date||17 Jul 2021|
|Publication status||Published (in print/issue) - 31 Dec 2021|
Bibliographical noteFunding Information:
Raymond Bond's research is supported by the European Union's INTERREG VA programme as managed by the Special European Union Programmes Body (SEUPB). His work is associated with the INTERREG funded project entitled, ‘Centre for Personalised Medicine – Clinical Decision Making and Patient Safety’. However, his views and opinions in this paper do not necessarily reflect the views and opinions of the European Commission or SEUPB.
Raymond Bond's research is supported by the European Union's INTERREG VA programme as managed by the Special European Union Programmes Body (SEUPB). His work is associated with the INTERREG funded project entitled, ?Centre for Personalised Medicine ? Clinical Decision Making and Patient Safety?. However, his views and opinions in this paper do not necessarily reflect the views and opinions of the European Commission or SEUPB.
© 2021 Elsevier Inc.
- machine learning
- ECG analysis
- Deep learning
- Automated ECG interpretation
- Call for guidelines
- Machine learning