Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework

• URL: http://arxiv.org/abs/2603.28532v2
• Date: Sun, 05 Apr 2026 15:40:07 GMT
• Title: Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
• Abstract summary: Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure.<n>Existing approaches rely solely on end-to-end black-box models with limited interpretability.<n>We introduced ECG-based Predictor-Driven LEF (ECGPD-LEF), a structured framework that integrates foundation model-derived diagnostic probabilities with interpretable modeling.
• Score: 9.789732284839884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for scalable screening strategies. Although artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise, existing approaches rely solely on end-to-end black-box models with limited interpretability or on tabular systems dependent on commercial ECG measurement algorithms with suboptimal performance. We introduced ECG-based Predictor-Driven LEF (ECGPD-LEF), a structured framework that integrates foundation model-derived diagnostic probabilities with interpretable modeling for detecting LEF from ECG. Trained on the benchmark EchoNext dataset comprising 72,475 ECG-echocardiogram pairs and evaluated in predefined independent internal (n=5,442) and external (n=16,017) cohorts, our framework achieved robust discrimination for moderate LEF (internal AUROC 88.4%, F1 64.5%; external AUROC 86.8%, F1 53.6%), consistently outperforming the official end-to-end baseline provided with the benchmark across demographic and clinical subgroups. Interpretability analyses identified high-impact predictors, including normal ECG, incomplete left bundle branch block, and subendocardial injury in anterolateral leads, driving LEF risk estimation. Notably, these predictors independently enabled zero-shot-like inference without task-specific retraining (internal AUROC 75.3-81.0%; external AUROC 71.6-78.6%), indicating that ventricular dysfunction is intrinsically encoded within structured diagnostic probability representations. This framework reconciles predictive performance with mechanistic transparency, supporting scalable enhancement through additional predictors and seamless integration with existing AI-ECG systems.

Related papers

• FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection [5.65079592827682]
  Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.<n>Recent ECG foundation models offer transferable representations for automated AF detection.<n>This study presents FOUND-AF, a unified, leakage-controlled, and deployment-oriented benchmarking framework that evaluates the quality of pretrained ECG representations.
  arXiv  Detail & Related papers  (2026-08-04T12:48:05Z)
• ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning [50.82313761980655]
  ECG-LLM is an ECG-conditioned large language model trained across four cohorts 679,112 ECG studies from 186,409 patients.<n>It is trained on clinically structured question-answer pairs derived from ECG signals, clinical context, CMR, and ECHO.<n>It successfully recovers conventional ECG measurements, such as heart rate, and strongly predicts complex CMR-derived phenotypes.
  arXiv  Detail & Related papers  (2026-07-15T11:06:35Z)
• Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening [9.031528782284935]
  We evaluated whether open pretrained electrocardiogram (ECG) foundation models can support echo-confirmed multi-label structural heart disease detection.<n>We compared engineered ECG features with gradient boosting, end-to-end waveform learning from scratch, and transfer from open ECG foundation models.<n>Adapted ECG-FM models achieved the best overall performance.
  arXiv  Detail & Related papers  (2026-04-25T17:27:42Z)
• 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)
• Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors [8.817617912039616]
  Structural heart disease (SHD) is a prevalent condition with many undiagnosed cases.<n>Recent studies show that artificial intelligence (AI)-based analysis of electrocardiograms (ECGs) can detect SHD.<n>Existing methods are fully black-box models, limiting interpretability and clinical adoption.
  arXiv  Detail & Related papers  (2026-03-03T05:39:32Z)
• Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes [18.45168797720226]
  We developed an interpretable AI-ECG model to predict severe or complete stenosis of the four major coronary arteries.<n>Performance remained stable in a clinically normal-ECG subset, indicating beyond overt ECG abnormalities.
  arXiv  Detail & Related papers  (2025-11-29T05:21:24Z)
• Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters [39.9470953186283]
  Continuous electrocardiogram (ECG) monitoring via wearables offers significant potential for early cardiovascular disease (CVD) detection.<n> deploying deep learning models for automated analysis in resource-constrained environments faces reliability challenges due to Out-of-Distribution data.<n>This paper explores Unsupervised Anomaly Detection (UAD) as an independent, upstream filtering mechanism to improve robustness.
  arXiv  Detail & Related papers  (2025-10-30T13:54:37Z)
• Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients [14.778478113602675]
  Malignant ventricular arrhythmias (VT/VF) following acute myocardial infarction (AMI) are a major cause of in-hospital death.<n>This study aimed to develop a hybrid predictive framework that integrates a large-scale electrocardiogram foundation model (ECGFounder) with an interpretable XGBoost classifier.<n>We analyzed 6,634 ECG recordings from AMI patients, among whom 175 experienced in-hospital VT/VF.
  arXiv  Detail & Related papers  (2025-10-20T05:26:55Z)
• LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR [51.11296719862485]
  We propose a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences.<n>By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings.
  arXiv  Detail & Related papers  (2025-08-23T07:21:23Z)
• Latent Representations of Intracardiac Electrograms for Atrial Fibrillation Driver Detection [37.72464514643607]
  This study proposes a deep learning framework using convolutional autoencoders for unsupervised feature extraction.<n> latent representations of atrial electrical activity enable the characterization and automation of EGM analysis.<n>The proposed method can operate in real-time and enables integration into clinical electroanatomical mapping systems to assist in identifying arrhythmogenic regions during ablation procedures.
  arXiv  Detail & Related papers  (2025-07-24T09:40:24Z)
• Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG [40.407824759778784]
  PTACL (Patient and Temporal Alignment Contrastive Learning) is a multimodal contrastive learning framework that enhances ECG representations by integrating-temporal information from CMR.<n>We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank.<n>Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG.
  arXiv  Detail & Related papers  (2025-06-24T17:19:39Z)
• EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification [1.5162243843944596]
  We propose EXGnet, a novel ECG arrhythmia classification network tailored for single-lead signals.<n>XAI supervision during training directs the model's attention to clinically relevant ECG regions.<n>We introduce an innovative multiresolution block to efficiently capture both short and long-term signal features.
  arXiv  Detail & Related papers  (2025-06-14T08:48:44Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients [3.676588766498097]
  Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia and is associated with increased morbidity and mortality. This work presents an ensemble approach that integrates multiple machine learning models, including Unet, ResNet, EfficientNet and VGG, to perform automatic bi-atrial segmentation from LGE-MRI data.
  arXiv  Detail & Related papers  (2024-09-24T13:33:46Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.