A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908894668783616 |
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| author | Kontou, Artemis Miroshnikova, Natalia Matheou, Costakis Sophocleous, Sophocles Tsekouras, Nicholas Malialis, Kleanthis Kolios, Panayiotis |
| author_facet | Kontou, Artemis Miroshnikova, Natalia Matheou, Costakis Sophocleous, Sophocles Tsekouras, Nicholas Malialis, Kleanthis Kolios, Panayiotis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16891 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis Kontou, Artemis Miroshnikova, Natalia Matheou, Costakis Sophocleous, Sophocles Tsekouras, Nicholas Malialis, Kleanthis Kolios, Panayiotis Signal Processing Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis |
| topic | Signal Processing Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2603.16891 |