Self-supervised ECG embedding model inspired by BERT/RoBERTa from natural language processing, designed for efficient medical signal analysis. HeartBERT translates ECG signals into an intermediate synthetic 'language' via signal quantization and discretization (Lloyd-Max quantization), then trains a RoBERTa-style encoder from scratch on this text-like representation using the MIT-BIH Arrhythmia Database, PTB-XL, and European ST-T Database. The resulting embeddings are evaluated on two downstream tasks -- sleep-stage classification and heartbeat classification -- using bidirectional LSTM heads, showing particular strength when only small labeled training datasets are available.

Deep learning strategy for cost-effective, comprehensive cardiac screening from ECG alone, by transferring domain-specific structural information from cardiac magnetic resonance (CMR) imaging into ECG representations. Combines multimodal contrastive learning with masked data modelling during pretraining on paired ECG-CMR data, then uses only ECG at inference. On 40,044 UK Biobank subjects, the multimodal pretraining improved subject-specific CVD risk prediction by up to 12.19% and cardiac phenotype prediction by up to 27.59% versus ECG-only baselines, with learned ECG representations shown to incorporate information from CMR regions of interest.

Self-supervised deep learning model that extracts cardiovascular-risk-relevant patterns from multimodal polysomnography (PSG) signals -- EEG, ECG, and respiratory signals -- without relying on manual sleep-stage annotations. Trained on 4,398 participants, the model derives 'projection scores' by contrasting embeddings from individuals with and without cardiovascular disease (CVD) outcomes. Externally validated in an independent cohort of 1,093 participants, ECG-derived projection scores were predictive of prevalent and incident cardiac conditions (particularly CVD mortality), and combining projection scores with the Framingham Risk Score consistently improved prediction (AUC 0.607-0.965 internally, 0.710-0.807 externally across most outcomes).

Optimized Hierarchical Fused Fuzzy Deep Reinforcement Learning model for predicting non-sinus (higher-risk) cardiac rhythms from PQRST-analyzed 12-lead ECG data. The three-stage approach combines data preprocessing, reinforcement learning, and fuzzy deep learning to classify sinus vs. non-sinus rhythms. Evaluated on a 12-lead ECG dataset of 10,646 patients, OHFFDRL achieved 94% accuracy, an AUC of 0.91, and was interpreted using SHAP, LIME, calibration curves, adversarial vulnerability analysis, and integrated gradients; TAxis (ventricular repolarization movement range) was found to be the most important distinguishing feature.

Self-supervised learning (SSL) model that identifies chronically stressed mother-fetus dyads from raw maternal abdominal ECG (aECG), which contains both maternal and fetal cardiac signals. Built on a self-supervised representation-learning approach originally developed for ECG-based emotion recognition, the model is pretrained on public ECG datasets and evaluated on a cohort of pregnant women with chronic stress exposure validated by psychological inventory, maternal hair cortisol, and the fetal stress index (FSI). Using maternal ECG alone with the publicly pretrained model, it detected the chronic-stress-exposure group with AUROC 0.982 and predicted psychological stress score (R2 0.943), FSI (R2 0.946), and maternal hair cortisol (R2 0.931).

Real-time deep-learning model that fuses serial 12-lead ECG waveforms with sequential vital signs and routinely available clinical data to predict hospital admission early during emergency department (ED) encounters with cardiac presentations (chest pain, dyspnea, syncope, presyncope). Developed and validated on the public MIMIC-IV, MIMIC-IV-ED, and MIMIC-IV-ECG databases (n=30,421 ED stays with >=1 ECG; n=11,273 with >=2 ECGs), the model improves on baseline tabular (random forest) and ECG-only models by leveraging how a patient's risk evolves with successive ECGs during the visit, addressing a key limitation of single-time-point risk scores.

Self-supervised representation-learning method for 12-lead ECG signals, designed to reduce reliance on large labeled datasets for downstream ECG classification. TSSL exploits two structural properties of ECG data: temporally, it encourages stable representations for the same individual across time while keeping different leads distinguishable; spatially, it enforces consistency in the relationships between signals and their representations across the different leads of a single recording. Evaluated on three public ECG datasets (CPSC2018, Chapman, PTB-XL), TSSL-pretrained models approached the performance of fully supervised training while using only about 10% of the labeled data.

Two-stage deep learning pipeline that localizes premature ventricular contraction (PVC) beats directly from raw, unsegmented ECG signal, without relying on hand-crafted features or pre-existing R-peak annotations. An encoder-decoder network first localizes the R-peak of every heartbeat (normal or anomalous); the resulting R-peak positions are then passed to CardioIncNet, a 1D InceptionTime-based classifier, which delineates each beat as healthy or PVC. Evaluated with both single-dataset and cross-dataset protocols across three public ECG databases, the pipeline reached F1 scores above 0.99 (single-dataset) and 0.979 (cross-dataset) for R-peak localization, and above 0.96 and 0.85 respectively for PVC beat classification.

Deep learning model that non-invasively estimates cardiac output (CO) from wearable seismocardiography (SCG), a single-lead ECG, and body mass index (BMI), as a potential alternative to invasive right heart catheterization (RHC). Parallel 1D-CNN branches extract features from the SCG and ECG waveforms, which are fused with BMI and passed through a lightweight regression head to predict CO directly. Trained and evaluated via leave-pair-out cross-validation on 73 heart-failure patients (83 RHC encounters) from an open PhysioNet dataset, the model achieved an RMSE of 1.00 L/min (22%) and Pearson correlation of 0.75 versus catheterization-derived CO, with particularly strong performance in low-output states.

Explainable machine learning model that detects and localizes left ventricular (LV) scar in hypertrophic cardiomyopathy (HCM) patients directly from 12-lead ECG, as a faster and cheaper alternative to late-gadolinium-enhancement (LGE) cardiac MRI, the clinical gold standard. XplainScar first uses an HCM-specific ECG segmentation algorithm to extract morphological features (duration, amplitude, slope, energy) from the QRS complex, ST segment and T wave of each lead, then combines unsupervised and self-supervised representation learning to predict scar presence and reveal which ECG features are associated with scar location (basal, mid, or apical LV). Trained on 500 HCM patients from the Johns Hopkins HCM Registry and validated on a held-out cohort of 248 HCM patients from UCSF, it reached 88% precision, 90% sensitivity, 78% specificity and an F1-score of 89% for scar detection on the external test set, analyzing a 10-patient batch of ECGs in under one minute.

Clinically-informed modification of the ResNet-18 architecture for identifying occlusion myocardial infarction (OMI) -- a severe, often ST-elevation-negative heart attack caused by complete blockage of a coronary artery -- from a single 12-lead ECG. The network first learns lead-specific temporal features via 1xk temporal convolutions, then learns cross-lead spatial concordance/discordance (e.g. reciprocal ST changes) via a 12x1 spatial convolution placed after the residual blocks, with saliency maps highlighting the most relevant leads and waveform regions for explainability. Benchmarked against ResNet-18 and other CNN/random-forest baselines on a multisite real-world clinical dataset of 10,893 ECGs (OMI rate 6.5%), reaching a test AUROC of 0.889 and an average precision of 0.587, outperforming the compared models.

Ensemble classifier combining hand-engineered expert features with a deep convolutional neural network for classifying cardiac rhythm from a single-lead ECG recording into normal sinus rhythm, atrial fibrillation, another rhythm, or too noisy to classify. A large set of expert features (from time-, frequency-, and template-based analysis) is fed into a gradient-boosted tree classifier (AdaBoost), and its output is combined with a separate deep CNN operating directly on the raw waveform; combining both feature families measurably outperformed either alone. ENCASE won 1st place in the PhysioNet/Computing in Cardiology Challenge 2017 (single-lead AF classification) with an overall F1 score of 0.83 on the official hidden test set, and remains a widely cited example of combining classical signal-processing features with deep representations for ECG classification.

Multimodal machine learning model that flags patients at risk of transthyretin amyloid cardiomyopathy (ATTR-CM) -- a progressive, underdiagnosed disease with expanding disease-modifying treatment options -- from routinely available ECG waveforms, echocardiographic measurements, demographics, and diagnosis codes for orthopedic manifestations of amyloidosis (e.g. carpal tunnel syndrome, spinal stenosis). Developed on 799 patients with 5-fold cross-validation (AUROC 0.85) and externally validated on 422 patients at a separate site (AUROC 0.82), with consistent accuracy across Hispanic, non-Hispanic Black, and non-Hispanic White patients. In a subsequent nonrandomized, single-system, multisite clinical trial (the Cardiac Amyloidosis Discovery Trial), patients flagged by ATTRACTnet and referred for confirmatory amyloid scintigraphy were positive for ATTR-CM 48% of the time, more than 2.8x the positivity rate of historical (15.3%) and contemporary (17.0%) controls referred by usual clinical judgment (P < .001 for both). This is a proprietary clinical AI program; no public code or model weights have been released.

Multimodal large language model for ECG medical-report generation and cardiology conversational question-answering. An ECG-CoCa encoder (contrastive ECG-report pretraining in the style of OpenCLIP) is paired with a LLaVA-style vision-language architecture and an LLM backbone, fine-tuned on a purpose-built 45k-example ECG-instruction dataset (19k diagnosis examples + 25k multi-turn dialogue examples) built from five public 12-lead ECG datasets. Produces free-text diagnostic reports and supports zero-shot ECG-report retrieval classification.

First multimodal LLM to unify ECG time series, 12-lead ECG images, and text for grounded, clinician-aligned ECG interpretation. A dual-encoder framework (ECG-CoCa time-series encoder plus a LLaVA-style vision-language backbone) extracts complementary time-series and image features with cross-modal alignment, trained on knowledge-guided instruction data (ECG-Grounding, linking diagnoses to measurable waveform parameters such as QRS/PR intervals) plus the 1.15-million-conversation ECG-Instruct corpus. Introduces the "Grounded ECG Understanding" benchmark and improves predictive performance, explainability, and grounding over prior ECG-language models such as ECG-Chat and PULSE.

Multi-scale ECG-language pretraining model that aligns 12-lead ECG signals with clinical text reports at three granularities -- token, beat, and rhythm level -- rather than a single global embedding. First fine-tunes a cardiology-specialized text encoder to improve understanding of ECG report language, then trains an ECG-FM-initialized ECG encoder against it with hierarchical contrastive supervision. Outperforms prior ECG-language and self-supervised baselines including MERL, ST-MEM, and HeartLang on zero-shot classification, linear probing, and ECG report generation, with especially large gains at low label fractions. Developed at the University of Hong Kong (HKU-MedAI).

Foundation model that encodes single-lead (lead I) ECGs with information from paired transthoracic echocardiography reports, aimed at label-efficient screening for structural heart disease (SHD) on wearable and portable single-lead ECG devices. A 7-layer 1D-CNN ECG encoder and a RoBERTa-based text encoder are contrastively pretrained (CLIP-style) on 194,551 ECG-echo report pairs from 77,378 adults in the Yale New Haven Health System, then the ECG encoder is fine-tuned on a temporally-distinct cohort to detect reduced LVEF, diastolic dysfunction, and a composite SHD label. Matches a randomly-initialized CNN at full training-data volume but substantially outperforms it in label-scarce regimes (e.g. with only 0.5% of labeled data).

Multi-agent LLM framework, deployed as a Software-as-a-Medical-Device on AWS, that assists cardiologists reading 24-hour Holter/patch ECG monitoring studies. Three fine-tuned LLM agents divide the diagnostic workflow the way a cardiologist would: a table-to-text agent (Llama-3.1-8B) extracts findings from tabular arrhythmia metrics, an image-to-text agent (LLaVA-v1.5-13B) extracts findings from ECG tracing images, and a findings-to-interpretation agent (Llama-3.1-8B) synthesizes both against clinical guidelines with a fact-checking step. Each agent is instruction-tuned on cardiologist-adjudicated reports from 2,000+ real-world patients and further steered at inference with in-context demonstrations matched to the patient's age, sex and arrhythmia class. In blinded cardiologist ratings across eight clinical/security metrics (1-5 scale), ZODIAC outperformed GPT-4o, Gemini-Pro, Llama-3.1-405B, Mixtral-8x22B, and medical-specialist LLMs (BioGPT, Meditron, Med42) on every metric while using under 30B total parameters, and has been integrated into commercial ECG monitoring devices. This is a proprietary product; no public code or model weights have been released.

Dual-encoder single-lead ECG classifier for atrial fibrillation detection that fuses a raw-signal branch with a spectrogram branch via axial attention and a Transformer. Originally developed as a graduate-course project at TU Darmstadt for the 2017 PhysioNet/CinC Challenge, and later extended in a 2023 follow-up study. Released in four sizes up to 130M parameters (S/M/L/XL), alongside a simpler CNN+LSTM variant.

Multimodal ECG model that pairs a 1D ConvNeXtV2 signal encoder with a BioLinkBERT text encoder, trained with a joint contrastive-and-captioning objective using LLM-generated descriptions of ECG demographics and waveform patterns in place of raw clinical reports. Validated on arrhythmia diagnosis and ECG-based subject identification, reaching an AUROC of 0.938 fine-tuned and 0.812 zero-shot on PTB-XL diagnostic classification. Developed at Rice University.

Detects 12 categories of echocardiogram-confirmed structural heart disease from 12-lead ECG waveforms combined with demographic and clinical covariates. Uses the same architecture as the original, larger EchoNext model but is trained entirely on the public EchoNext-Mini dataset - 100,000 de-identified ECGs from Columbia University Irving Medical Center released on PhysioNet - making it one of the more fully reproducible models of its kind, with public weights, a Docker image, and inference code.

Distills knowledge from EchoCLIP, a vision-language echocardiography model, into ECG embeddings, aiming to improve how well ECG signals alone can predict echo-derived measures of cardiac function. Combines a 1D ECG encoder with a BioBERT text encoder under a probabilistic cross-modal embedding objective that captures uncertainty. Published at MICCAI 2025 by the University of Toronto's McIntosh Lab.

Multimodal model that learns a shared representation space for ECG signals and their clinical text reports, pretrained on paired MIMIC-IV-ECG recordings and reports. Supports zero-shot ECG classification via text prompts, tested across six public benchmark datasets including PTB-XL and CPSC2018 without any downstream training data. Developed at Imperial College London and published at ICML 2024.

CLIP-style model that aligns 12-lead ECG signals with free-text echocardiography reports for zero-shot detection of structural heart disease directly from an ECG. Extends the MERL framework, and was trained on 45,016 paired ECG-echo reports from two Hong Kong hospitals, with external validation on the public EchoNext dataset from Columbia University. Developed by researchers at the University of Hong Kong and Imperial College London; described in a 2025 medRxiv preprint.