Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model

AbstractBackgroundElectrocardiogram (ECG) is widely used to detect cardiac arrhythmia (CA) and heart diseases. The development of deep learning modeling tools and publicly available large ECG data in recent years has made accurate machine diagnosis of CA an attractive task to showcase the power of artificial intelligence (AI) in clinical applications.Methods and FindingsWe have developed a convolution neural network (CNN)-based model to detect and classify nine types of heart rhythms using a large 12-lead ECG dataset (6877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model achieved a median overall F1-score of 0.84 for the 9-type classification on CPSC2018’s hidden test set (2954 ECG recordings), which ranked first in this latest AI competition of ECG-based CA diagnosis challenge. Further analysis showed that concurrent CAs observed in the same patient were adequately predicted for the 476 patients diagnosed with multiple CA types in the dataset. Analysis also showed that the performances of using only single lead data were only slightly worse than using the full 12 lead data, with leads aVR and V1 being the most prominent. These results are extensively discussed in the context of their agreement with and relevance to clinical observations.ConclusionsAn AI model for automatic CA diagnosis achieving state-of-the-art accuracy was developed as the result of a community-based AI challenge advocating open-source research. In- depth analysis further reveals the model’s ability for concurrent CA diagnosis and potential use of certain single leads such as aVR in clinical applications.AbbreviationsCA, cardiac arrhythmia; AF, Atrial fibrillation; I-AVB, first-degree atrioventricular block; LBBB, left bundle branch block; RBBB, right bundle branch block; PAC, premature atrial contraction; PVC, premature ventricular contraction; STD, ST-segment depression; STE, ST-segment elevation.

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Intraventricular conduction defects in patients with st-segment elevation myocardial infarction – the paradox of right bundle branch block

European Heart Journal ◽

10.1093/ehjci/ehaa946.1615 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

S Fernandes ◽

F Montenegro ◽

M Cabral ◽

R Carvalho ◽

L Santos ◽

...

Keyword(s):

Hospital Mortality ◽

Right Bundle Branch Block ◽

Independent Predictor ◽

Left Ventricular Ejection ◽

Bundle Branch Block ◽

Hospital Outcomes ◽

St Segment Elevation ◽

Intraventricular Conduction ◽

St Segment ◽

Worse Prognosis

Abstract Intraventricular conduction defects (IVCD) in patients with acute myocardial infarct (AMI) are predictors of a worse prognosis. When acquired they can be the result of an extensive myocardial damage. Purpose To assess the impact of IVCD, regardless of being previously known or presumed new, on in-hospital outcomes of patients with AMI with ST segment elevation (STEMI) or undetermined location. Methods From a series of patients included in the National Registry of Acute Coronary Syndrome between 10/1/2010 and 9/1/2019, were selected patients with STEMI or undetermined AMI, undergoing coronary angiography. Results 7805 patients were included: 461 (5.9%) presenting left bundle branch block (LBBB), 374 (4.8%) with right bundle branch block (RBBB) and 6970 (89.3%) with no IVCD. Clinical characteristics as well as in-hospital outcomes are described in the table 1. An unexpected worse prognosis in patients with RBBB has motivated a multivariate analysis. RBBB remained an independent predictor of in-hospital mortality (OR 1.91, 95% CI 1.04–3.50, p=0.038), along with female gender (OR 1.73, 95% CI 1.11–2.68, p=0.015), Killip Class>1 (OR 2.26, 95% CI 1.45–3.53, p<0.001), left ventricular ejection fraction <50% (OR 3.93, 95% CI 2.19–7.05, p<0.001) and left anterior descending artery as the culprit lesion (OR 1.85, 95% CI 1.16–2.91, p=0.009). Conclusion In spite of an apparent better clinical profile, in the current large series of unselected STEMI patients, the presence of RBBB is associated with the worst in-hospital outcome. RBBB doubles the risk of death, being an independent predictor of in-hospital mortality. Funding Acknowledgement Type of funding source: None

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Right Bundle Branch Block, Right Precordial ST-Segment Elevation, and Sudden Death in Young People

Circulation ◽

10.1161/01.cir.103.5.710 ◽

2001 ◽

Vol 103 (5) ◽

pp. 710-717 ◽

Cited By ~ 140

Author(s):

Domenico Corrado ◽

Cristina Basso ◽

Gianfranco Buja ◽

Andrea Nava ◽

Lino Rossi ◽

...

Keyword(s):

Young People ◽

Sudden Death ◽

Right Bundle Branch Block ◽

Bundle Branch Block ◽

St Segment Elevation ◽

St Segment

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Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence

Journal of Electrocardiology ◽

10.1016/j.jelectrocard.2021.10.009 ◽

2021 ◽

Author(s):

Manuel Martínez-Sellés ◽

Miriam Juárez ◽

Manuel Marina-Breysse ◽

José María Lillo-Castellano ◽

Albert Ariza

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Learning System ◽

St Segment Elevation ◽

St Segment ◽

Cardiac Necrosis

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P982 Correlation between electrocardiographic and imaging data in patients with acute myocarditis: a single centre retrospective analysis

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jez319.611 ◽

2020 ◽

Vol 21 (Supplement_1) ◽

Author(s):

A Caretta ◽

L A Leo ◽

V L Paiocchi ◽

G M Viani ◽

S A Schlossbauer ◽

...

Keyword(s):

Retrospective Analysis ◽

Wall Motion ◽

Acute Myocarditis ◽

Left Ventricular Ejection ◽

Imaging Data ◽

Bundle Branch Block ◽

St Segment Elevation ◽

St Segment ◽

Wall Motion Abnormalities ◽

Ecg Abnormalities

Abstract Funding Acknowledgements None Background Acute myocarditis is a clinical and pathological condition defined as an inflammation of the myocardium. Its diagnosis is often challenging and requires multiple information derived from different diagnostic modalities. Purpose The aim of the study is to evaluate the correlation between electrocardiographic and imaging data in patients with acute myocarditis. Methods We made a retrospective analysis of 102 patients admitted to our Centre between January 2012 and April 2019 for suspected acute myocarditis. Diagnosis was confirmed with cardiac magnetic resonance (CMR) by identification of myocardial edema in T2-weighted images and/or typical subepicardial or midwall pattern of late gadolinium enhancement (LGE). Significant coronary artery disease was ruled out with coronary angiography. Electrocardiogram (ECG) was analysed on admission - in order to evaluate the presence of ST segment abnormalities, atrio-ventricular or bundle-branch block and heart rhythm disorders - and at the time of discharge. Every patient underwent echocardiography and CMR: from both these exams we reported the presence of regional wall motion abnormalities and left ventricular ejection fraction (LVEF). Results Mean age of our population was 39 ± 18 years; 92 people (90%) were males. At admission, 85 patients (83%) presented ECG abnormalities; the most frequent was ST-segment elevation (65 cases). Conduction or rhythm disorders were observed in 26 cases (25%). At the time of discharge, 41 out of 85 patients had complete regression of ECG changes. Mean value of LVEF measured with echocardiography was 56.4 ± 7.6%. In patients with normal ECG on admission it was 59.9 ± 3.1%, whereas in patients with abnormal ECG 55.7 ± 7.9% (p = 0.045). Considering CMR, mean LVEF in the population was 58.5 ± 8.6%, varying between 64.0 ± 8.9% in the group with normal ECG and 57.4 ± 10.1% in the group with ECG abnormalities (p = 0.02). Moreover, subjects with altered ECG on admission had a higher prevalence of wall motion abnormalities both in echocardiography (47/85 – 55% vs 3/17 – 18%, p < 0.01) and in CMR (45/85 – 53% vs 3/17 – 18%, p < 0.01). Patients with ECG normalization at discharge had a higher prevalence of ST-segment elevation (88 vs 66%, p = 0.02), while the group with persistent ECG alterations had a higher incidence of AV or bundle-branch block (23 vs 7%, p = 0.048). No statistical difference was noted between these two groups regarding echocardiographic or CMR values. Conclusion In our experience evaluation of ECG at admission in patients with suspected acute myocarditis identifies a subgroup of individuals with lower values of LVEF and a higher prevalence of wall motion abnormalities both in echocardiography and in CMR, while data derived by imaging techniques had no significant predictive value on ECG evolution at the time of discharge.

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