# Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

arXiv: https://arxiv.org/abs/1807.02569 · HTML: https://mlsanity.com/paper/1807.02569/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 14 of 14)

- [3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations](https://mlsanity.com/paper/2106.04452/) (2021)
- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs](https://mlsanity.com/paper/2102.08540/) (2021)
- [AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings](https://mlsanity.com/paper/2411.17711/) (2024)
- [ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis](https://mlsanity.com/paper/2409.16612/) (2024)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)
- [Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation](https://mlsanity.com/paper/2111.12996/) (2021)
- [Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care](https://mlsanity.com/paper/2312.11050/) (2023)
- [Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac Condition](https://mlsanity.com/paper/2411.17702/) (2024)
- [Translation from Wearable PPG to 12-Lead ECG](https://mlsanity.com/paper/2509.25480/) (2025)
- [AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography](https://mlsanity.com/paper/2505.07347/) (2025)
- [MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management](https://mlsanity.com/paper/2603.22179/) (2026)
- [Learning ECG Image Representations via Dual Physiological-Aware Alignments](https://mlsanity.com/paper/2604.01526/) (2026)
- [Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure](https://mlsanity.com/paper/2606.30665/) (2026)

## Abstract (arXiv)

The electrocardiogram or ECG has been in use for over 100 years and remains the most widely performed diagnostic test to characterize cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, innovations in machine learning algorithms, and availability of large-scale digitized ECG data would enable extending the utility of the ECG beyond its current limitations, while at the same time preserving interpretability, which is fundamental to medical decision-making. We identified 36,186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease. We derived a novel model for ECG segmentation using convolutional neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output by comparing electrical interval estimates to 141,864 measurements from the clinical workflow. We built a 725-element patient-level ECG profile using downsampled segmentation data and trained machine learning models to estimate left ventricular mass, left atrial volume, mitral annulus e' and to detect and track four diseases: pulmonary arterial hypertension (PAH), hypertrophic cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP). CNN-HMM derived ECG segmentation agreed with clinical estimates, with median absolute deviations (MAD) as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative estimates of left ventricular and mitral annulus e' velocity with good discrimination in binary classification models of left ventricular hypertrophy and diastolic function. Models for disease detection ranged from AUROC of 0.94 to 0.77 for MVP. Top-ranked variables for all models included known ECG characteristics along with novel predictors of these traits/diseases.