What does this research mean for the field?

A pre-trained deep-learning model utilizing transfer learning can predict sudden cardiac arrest risk using single-lead ECG data, enabling practical application in wearable devices. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

March 3, 2026Open Access

Transfer Learning for Multi-Lead ECG Analysis in Cardiac Arrest Risk Classification

Key Points

Single-lead ECG data effectively predicts cardiac arrest risk, offering a practical solution.
Sudden cardiac arrest accounts for 20% of fatalities in developed nations, highlighting urgent need for effective detection.

Structured PICO

Can a pre-trained deep-learning model using single-lead ECG data predict cardiac arrest risk?

Intervention

Pre-trained deep-learning model using single-lead ECG data

Outcome

Prediction of cardiac arrest risk

A pre-trained deep-learning model utilizing single-lead ECG data is proposed to predict sudden cardiac arrest risk, potentially enabling continuous monitoring via wearable devices.

Abstract

Sudden cardiac arrest (SCA) is the sudden loss of all heart activity due to an irregular heart rhythm, accounting for about 20% of fatalities in developed countries. Detecting SCA early is challenging because it occurs suddenly and unpredictably. Electrocardiograms (ECGs) provide valuable heart activity data utilized by Machine Learning (ML) models to predict known conditions like arrhythmias rather than predicting cardiac arrest risk. Additionally, these ML models typically rely on the full 12-lead ECGs, which is impractical for wearable devices. The proposed method in this study utilizes a pre-trained deep-learning model to predict cardiac arrest risk using single-lead ECG data.

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