Cardiac Arrhythmia Classification From Lead I ECG Recorded in a Free-Living Environment.

Abstract

Cardiac diseases are a leading cause of global mortality. Electrocardiograms (ECGs) are essential for detecting abnormal cardiac rhythms. Smartwatches can record ECGs, similar to lead I ECGs recorded by a patient vitals monitor in a hospital, potentially helping clinicians in early diagnosis and improved management of cardiovascular diseases. While AI models have classified arrhythmias with human-level accuracy, their potential for broad screening remains underutilized. We propose a deep learning based framework for diagnosing various cardiac arrhythmias using 10-second lead I ECG recordings, demonstrating lead I's utility in remote monitoring. Robustness was tested by introducing noise to simulate real-world conditions. Additionally, a novel data similarity assessment metric was developed to enhance transfer learning and external dataset validation. Using over 60,000 ECGs from the PhysioNet Challenge 2021, the trained model classified clean lead I ECGs in one dataset with a test- fold area under receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.915, 0.867 and 0.858 respectively. For signals with 0 decibel signal-to-noise ratio from the same dataset, the respective performance metrics dropped slightly to 0.899, 0.862 and 0.818. External validation across three separate datasets showed a minimum AUC of 0.807. The data similarity metric outperformed an existing method in improving classification, particularly with limited target dataset samples, i.e. 50. The proposed Cardiac Arrhythmia Risk Evaluation from Lead-I ECG (CARE-I) framework enables accurate arrhythmia detection across diverse populations in real-world noisy environments, thus enhancing model generalisation and early diagnosis.