Deep learning and the electrocardiogram: review of the current state-of-the-art

Somani, Sulaiman; Russak, Adam; Richter, Felix; Zhao, Shan; Vaid, Akhil; Chaudhry, Fayzan; Freitas, Jessica K De; Naik, Nidhi; Miotto, Riccardo; Nadkarni, Girish N.; Narula, Jagat; Argulian, Edgar; Glicksberg, Benjamin S.

doi:10.1093/europace/euaa377

Cited by 169 publications

(116 citation statements)

References 74 publications

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“…Therefore this review will not focus upon studies analysing ML application to electrocardiogram (ECG) nor cardiac imaging. The extensive research into electrocardiogram (ECG) signal processing and ECG interpretation has been recently synthesised in the state-of-the-art systematic review of application of deep learning to the ECG [26]. Somani et al (2021) provide an overview of deep learning application to ECGs, its benefits, limitations as well as future areas for improvement [26].…”

Section: Common Hf Problems Addressed By MLmentioning

confidence: 99%

What can machines learn about heart failure? A systematic literature review

Jasinska-Piadlo

Bond

Biglarbeigi

et al. 2021

Int J Data Sci Anal

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This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

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Section: Common Hf Problems Addressed By MLmentioning

confidence: 99%

What can machines learn about heart failure? A systematic literature review

Jasinska-Piadlo

Bond

Biglarbeigi

et al. 2021

Int J Data Sci Anal

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“…Deep learning (DL) techniques can reduce the workload in decision-making tasks, leading to faster and more consistent decisions while releasing human resources for other tasks. In this sense, increased computational power and the availability of ECG databases with clinical annotations have driven the development of DL techniques for unsupervised ECG analysis (Parvaneh et al, 2019 ; Somani et al, 2021 ). For the detection of AF different DL methodologies have been proposed, including hierarchical attention networks (Mousavi et al, 2020 ), long short-term memory (Faust et al, 2018 ; Andersen et al, 2019 ; Dang et al, 2019 ; Jin et al, 2020 ), convolutional neural network (CNN) (He et al, 2018 ; Xia et al, 2018 ; Lai et al, 2019 ; Huang and Wu, 2020 ; Zhang et al, 2020 ), and approaches combining recurrent neural networks with CNN (Fujita and Cimr, 2019 ; Shi et al, 2020 ; Wang, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network

Martinez

Bie²,

Marzocchi³

et al. 2021

Front. Physiol.

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Background: Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods: The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results: For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

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“…Recent breakthroughs in artificial intelligence have demonstrated that much more information may be available from the ECG to diagnose such conditions than currently leveraged 31,32 . Deep learning (DL), a class of machine learning that uses hierarchical networks to extract lower-dimensional features from a higher dimensional data input, has demonstrated significant potential for enabling ECG-based predictions and diagnoses 33 . For example, DL has been used to identify patients with atrial fibrillation while in normal sinus rhythm 34 , predict incident atrial fibrillation 35 , identify patients amenable to cardiac resynchronization therapy 36 , evaluate LV diastolic function 37 , evaluation of patients with echocardiographically concealed long QT syndrome 38 , predict risk of sudden cardiac death 39 , and to predict low LVEF.…”

Section: Introductionmentioning

confidence: 99%

Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram

Vaid

Johnson

Badgeley³

et al. 2021

Preprint

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Background Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECG) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, while ones to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. Objectives This study sought to develop deep learning models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. Methods A multi-center study was conducted with data from five New York City hospitals; four for internal testing and one serving as external validation. We created novel DL models to classify Left Ventricular Ejection Fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. Results We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used Natural Language Processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, Area Under Curve (AUC) at detection of LVEF<=40%, 40%<LVEF<=50%, and LVEF>50% was 0.94 (95% CI:0.94-0.94), 0.82 (0.81-0.83), and 0.89 (0.89-0.89) respectively. For external validation, these results were 0.94 (0.94-0.95), 0.73 (0.72-0.74) and 0.87 (0.87-0.88). For regression, the mean absolute error was 5.84% (5.82-5.85) for internal testing, and 6.14% (6.13-6.16) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (0.84-0.84) in both internal testing and external validation. Conclusions DL on ECG data can be utilized to create inexpensive screening, diagnostic, and predictive tools for both LV/RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography, and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease. Keywords Artificial Intelligence, Deep Learning, Machine Learning, HFrEF, Right Ventricular Dilation, Right Ventricular Systolic Dysfunction, echocardiography, electrocardiogram, ECG, EKG, LVEF, Left Ventricular Ejection Fraction, Left Heart Failure, Right Heart Failure

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Deep learning and the electrocardiogram: review of the current state-of-the-art

Cited by 169 publications

References 74 publications

What can machines learn about heart failure? A systematic literature review

What can machines learn about heart failure? A systematic literature review

Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network

Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram

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