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

• URL: http://arxiv.org/abs/2603.16891v1
• Date: Sat, 28 Feb 2026 17:29:37 GMT
• Title: A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis
• Abstract summary: AI-HEART is a cloud-based information system for managing and analysing long-duration ambulatory electrocardiogram (ECG) recordings.<n>System ingests multi-day three-lead ECGs, normalises inputs, performs signal preprocessing, and applies deep neural networks for wave delineation, noise/quality detection, and beat- and rhythm-level arrhythmia classification.
• Score: 1.8665030569912726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis [45.00216485193071]
  Rare cardiac anomalies are difficult to detect from electrocardiograms due to their long-tailed distribution.<n>We developed an AI-assisted two-stage ECG framework integrating self-supervised anomaly detection and demographic-aware representation learning.<n>Our method achieves an AUROC of 94.7% for rare anomalies and reduces the common-rare performance gap by 73%.
  arXiv  Detail & Related papers  (2026-03-20T07:04:00Z)
• BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Classification [1.3909285316906435]
  BEAT-Net is a Biomimetic ECG Analysis with Tokenization framework.<n>It decomposes cardiac physiology through specialized encoders that extract local beat morphology.<n>It exhibits exceptional data efficiency, recovering fully supervised performance using only 30 to 35 percent of annotated data.
  arXiv  Detail & Related papers  (2026-01-12T08:37:47Z)
• Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation [52.19347532840774]
  We propose SE-Diff, a novel physiological simulator and experience enhanced diffusion model for ECG generation.<n> SE-Diff integrates a lightweight ordinary differential equation (ODE)-based ECG simulator into the diffusion process via a beat decoder.<n>Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment.
  arXiv  Detail & Related papers  (2025-11-13T02:57:10Z)
• Explainable AI (XAI) for Arrhythmia detection from electrocardiograms [0.0]
  Deep learning has enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption.<n>This study investigates the application of Explainable AI (XAI) techniques specifically adapted for time-series ECG analysis.
  arXiv  Detail & Related papers  (2025-08-24T10:44:24Z)
• EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation [45.031633614714]
  EEG-MedRAG is a three-layer hypergraph-based retrieval-augmented generation framework.<n>It unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph.<n>We introduce the first cross-disease, cross-role EEG clinical QA benchmark, spanning seven disorders and five authentic clinical perspectives.
  arXiv  Detail & Related papers  (2025-08-19T11:12:58Z)
• Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms [0.0]
  Machine Learning (ML) and Deep Learning (DL) have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions.<n>This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals.
  arXiv  Detail & Related papers  (2024-12-07T08:29:44Z)
• AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings [34.078819572852446]
  Electrocardiogram (ECG) is highly sensitive in detecting acute heart attacks.<n>This paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data.
  arXiv  Detail & Related papers  (2024-11-17T17:32:58Z)
• SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
  Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
  arXiv  Detail & Related papers  (2024-04-15T01:07:08Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning [20.465733855762835]
  Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification. Classic rule-based algorithms are now completely outperformed by deep learning based methods. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout.
  arXiv  Detail & Related papers  (2023-04-10T15:19:00Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)
• A Graph-constrained Changepoint Detection Approach for ECG Segmentation [5.209323879611983]
  We introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of R-peak positions without employing any preprocessing step. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55.
  arXiv  Detail & Related papers  (2020-04-24T23:41:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.