On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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9 results for “electrocardiography”

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metricsq-bio.QM

Bayesian neural networks improve SHD classification and uncertainty quantification.

problem Improving screening for structural heart disease using noninvasive ECG and echocardiography.

method Comparing frequentist and Bayesian neural network classifiers on the EchoNext dataset.

result Bayesian classifiers provide more robust uncertainty quantification.

Bayesian inference structural heart disease machine learning electrocardiography echocardiography

2026-05-21abs ↗pdf ↗

Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiographystat.ML

New model for time series classification from single example.

problem Classifying time series patterns from limited data.

method Developed a Hidden semi-Markov Model with variable state duration.

result Different representations of state duration have distinct strengths and weaknesses.

Hidden semi-Markov Model time series classification maximum-likelihood estimation Gamma distribution heartbeat classification

2022-11-17abs ↗pdf ↗

Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Careeess.SP

Study finds AI can predict diverse cardiac and non-cardiac diagnoses from a single ECG.

problem Narrow focus of ECG analysis models for diverse medical conditions.

method Exploratory study using a single AI model to predict multiple ICD codes.

result Model achieved AUROC scores > 0.8 for 253 cardiac and 172 non-cardiac diagnoses.

AI ECG Screening Emergency Cardiac

2023-12-18abs ↗pdf ↗

Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGscs.LG

We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to …

2018-01-21abs ↗pdf ↗

Computationally lightweight classifiers with frequentist bounds on predictionscs.LG

Efficient classifier with uncertainty bounds for safety-critical applications.

problem Lack of uncertainty bounds in high-accuracy classifiers for safety-critical tasks.

method Nadaraya-Watson estimator with frequentist bounds.

result Competitive accuracy and uncertainty bounds at reduced computational cost.

classification uncertainty frequentist bounds Nadaraya-Watson electrocardiography

2026-03-23abs ↗pdf ↗

Neural Differential Recurrent Neural Network with Adaptive Time Stepsstat.ML

A neural RNN model adapts time steps for non-stationary time series data.

problem Modeling and forecasting non-stationary time series with sharp changes.

method RNN-ODE-Adap model using neural ODE and adaptive time steps.

result Consistent estimation of intensity function for Hawkes-type data.

neural ODE RNN adaptive time steps non-stationary time series Hawkes process

2023-06-02abs ↗pdf ↗

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLcs.LG

Deep learning benchmarks ECG analysis with strong performance.

problem Lack of appropriate datasets and evaluation procedures for ECG interpretation.

method Benchmarking on PTB-XL and ICBEB2018 datasets using convolutional neural networks.

result Convolutional neural networks, especially resnet- and inception-based architectures, outperform feature-based algorithms.

ECG analysis deep learning PTB-XL ICBEB2018 benchmarking

2020-04-28abs ↗pdf ↗

Newton-Puiseux Analysis for Interpretability and Calibration of Complex-Valued Neural Networkscs.LG

This work improves interpretability and calibration of complex-valued neural networks using Newton-Puiseux analysis.

problem Insufficient interpretability and probability calibration of complex-valued neural networks.

method Newton-Puiseux framework to examine local decision geometry, fitting a polynomial surrogate and factorizing it using Newton-Puiseux expansions.

result Enhanced Expected Calibration Error in ECG and wireless modulation datasets compared to uncalibrated softmax and standard post-hoc baselines.

complex-valued neural networks interpretability probability calibration Newton-Puiseux logit difference

2025-04-27abs ↗pdf ↗

Self-supervised representation learning from 12-lead ECG dataeess.SP

Self-supervised learning improves ECG classification performance.

problem Label scarcity in clinical 12-lead ECG data.

method Adapted self-supervised methods to ECG domain, focusing on contrastive representations and latent forecasting.

result Contrastive predictive coding adaptation yields linear evaluation performance only 0.5% below supervised performance.

self-supervised learning ECG data representation learning clinical biosignals label scarcity

2021-03-23abs ↗pdf ↗