February 27, 2024Open Access

An ECG-based artificial intelligence model for assessment of sudden cardiac death risk

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Abstract

Conventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment.

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Cite This Study

Holmström et al. (Tue,) studied this question.

synapsesocial.com/papers/68e77566b6db6435876e9ef0 https://doi.org/https://doi.org/10.1038/s43856-024-00451-9

Also Consider

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1The 12-lead electrocardiogram and risk of sudden death: current utility and future prospects2015 · 47 citations
2Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study2023 · 60 citations
3Electrical risk score beyond the left ventricular ejection fraction: prediction of sudden cardiac death in the Oregon Sudden Unexpected Death Study and the Atherosclerosis Risk in Communities Study2017 · 155 citations
4Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy2020 · 48 citations
5Population-Based Analysis of Sudden Cardiac Death With and Without Left Ventricular Systolic Dysfunction2006 · 533 citations

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