A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis

TL;DR

AI-HEART is a comprehensive cloud-based digital health platform that uses deep learning to analyze multi-day ECG recordings, supporting clinicians with automated, accurate, and scalable arrhythmia detection and signal quality assessment.

Contribution

This work introduces a fully integrated end-to-end AI system for ECG analysis that combines advanced neural networks, data augmentation, and clinician feedback for improved accuracy and operational deployment.

Findings

High accuracy in ECG delineation and arrhythmia classification

Reliable noise and quality detection in ambulatory ECGs

Scalable deployment with clinician review and feedback mechanisms

Abstract

This study presents AI-HEART, a cloud-based information system for managing and analysing long-duration ambulatory electrocardiogram (ECG) recordings and supporting clinician decision-making. The platform operationalises an end-to-end pipeline that ingests multi-day three-lead ECGs, normalises inputs, performs signal preprocessing, and applies dedicated deep neural networks for wave delineation, noise/quality detection, and beat- and rhythm-level multi-class arrhythmia classification. To address class imbalance and real-world signal variability, model development combines large clinically annotated datasets with expert-in-the-loop curation and generative augmentation for under-represented rhythms. Empirical evaluation on three-lead ambulatory ECG data shows that delineation accuracy is sufficient for automated interval measurement, noise detection reliably flags poor-quality segments, and arrhythmia classification achieves high specificity with clinically useful macro-averaged performance across common and rarer rhythms. Beyond predictive accuracy, AI-HEART provides a scalable deployment approach for integrating AI into routine ECG services, enabling traceable outputs, audit-friendly storage of recordings and derived annotations, and clinician review/editing that captures feedback for controlled model improvement. The findings demonstrate the technical feasibility and operational value of a noise-aware AI-ECG platform as a digital health information system.