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.

169,051 papers · 148 categories

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16 results for “Cardiology”

Forecasting Cardiology Admissions from Catheterization Laboratorycs.CY

Study forecasts cardiology admissions from cath lab using ARIMA models.

problem Complexity in managing cardiology admissions from cath lab.

method Retrospective data analysis with ARIMA, Holts method, and other models.

result ARIMA (2,0,2) (1,1,1) model selected as best fit.

cardiology admissions catheterization laboratory time series analysis forecasting hospital management

2018-12-28abs ↗pdf ↗

Automatic diagnosis of the 12-lead ECG using a deep neural networkcs.LG

Deep neural networks improve ECG diagnosis accuracy.

problem Limited accuracy of existing ECG analysis models.

method Trained a deep neural network on a large dataset of 12-lead ECG exams.

result DNN outperforms human doctors in recognizing 6 types of ECG abnormalities.

ECG analysis Deep learning Neural networks Clinical practice Cardiology

2019-04-02abs ↗pdf ↗

Dealing with Uncertainty in Contextual Anomaly Detectioncs.LG

Novel framework for contextual anomaly detection models uncertainty.

problem Identifying anomalies in target variables influenced by contextual variables.

method Normalcy score (NS) framework using heteroscedastic Gaussian process regression.

result NS outperforms state-of-the-art methods in detection accuracy and interpretability.

contextual anomaly detection uncertainty modeling heteroscedastic Gaussian process cardiology anomaly detection

2025-07-06abs ↗pdf ↗

Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Networkeess.SP

Deep neural network predicts ECG abnormalities from short-duration exams.

problem Improving accuracy of ECG diagnosis from short-duration exams.

method Residual neural network with 9 convolutional layers trained on large dataset.

result Model outperformed medical doctors on ECG abnormalities.

ECG diagnosis Deep learning Residual networks Telehealth Cardiology

2018-11-28abs ↗pdf ↗

Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregationcs.LG

Predict sepsis early from EHR data with aggregated clinical events.

problem Predict sepsis from clinical data in EHR with temporal interactions.

method Aggregates heterogeneous clinical events, captures temporal interactions with LSTM.

result Achieved high utility score (0.321) in PhysioNet/Computing in Cardiology Challenge 2019.

sepsis electronic health records LSTM temporal interactions aggregation

2019-10-14abs ↗pdf ↗

Probabilistic learning of the Purkinje network from the electrocardiogramstat.ML

Probabilistic method identifies Purkinje network from ECG data.

problem Challenging task of identifying Purkinje conduction system in heart.

method Bayesian optimization and approximate Bayesian computation for probabilistic identification.

result Generates a population of plausible Purkinje networks fitting ECG within tolerance.

Purkinje network Electrocardiogram Bayesian optimization Cardiac digital twins Precision cardiology

2023-12-15abs ↗pdf ↗

ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification Systemcs.LG

This paper explores how to fool ECG diagnosis systems with adversarial ECGs.

problem Vulnerability of DNN-powered ECG diagnosis systems to adversarial attacks.

method Analyzed ECG properties to design effective adversarial attacks under two models.

result Demonstrates weaknesses in DNN-powered ECG diagnosis systems under adversarial attacks.

ECG Deep Learning Adversarial Attacks Cardiology Classification

2019-01-12abs ↗pdf ↗

CNNs, LSTMs, and Attention Networks for Pathology Detection in Medical Datacs.LG

Study combines CNNs and LSTMs for ECG classification, improving performance with attention mechanisms.

problem Improving ECG classification accuracy for weakly supervised tasks.

method Investigates ConvLSTM networks and various attention mechanisms to capture both morphological and temporal features.

result Attention mechanisms improve classification performance and provide interpretable saliency maps.

ECG classification ConvLSTM attention mechanisms long short-term memory saliency maps

2019-12-02abs ↗pdf ↗

Causal Imitation Learning Under Measurement Error and Distribution Shiftcs.LG

Causal Imitation Learning handles noisy measurements and distribution shifts.

problem Learning from noisy state observations and distributional shifts.

method Causal inference framework and adversarial RKHS learning.

result Improved robustness to distribution shifts compared to standard methods.

causal inference imitation learning measurement error distribution shift RKHS

2026-01-29abs ↗pdf ↗

1D Convolutional Neural Network Models for Sleep Arousal Detectioneess.SP

Study uses CNNs to detect sleep arousals more accurately.

problem Accurate detection of sleep arousals for better sleep quality assessment.

method Investigated five 1D CNN models on EEG signals from PhysioNet/Computing in Cardiology Challenge 2018.

result Best 1D CNN model achieved high precision and recall metrics.

sleep arousals convolutional neural networks EEG signals sleep quality physioNet

2019-03-01abs ↗pdf ↗

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detectioneess.SP

Novel ECG classification for AF using spectro-temporal Kalman filtering and deep CNN.

problem Atrial fibrillation (AF) detection in ECG signals.

method Spectro-temporal representation using Kalman filter and deep convolutional neural networks.

result Proposed method achieves an overall F1 score of 80.2% on the PhysioNet/Computing in Cardiology (CinC) 2017 dataset.

Kalman filter spectro-temporal representation deep CNN atrial fibrillation ECG classification

2018-12-12abs ↗pdf ↗

Deep neural networks can predict mortality from 12-lead electrocardiogram voltage dataq-bio.QM

Deep learning predicts one-year mortality from ECGs, even in 'normal' cases.

problem Predicting mortality from 12-lead ECGs using deep learning.

method Deep neural network model trained on 1,775,926 ECGs, validated on 297,548 'normal' ECGs.

result Deep learning model predicts one-year mortality with AUC of 0.85, and Cox Proportional Hazard model reveals a significant hazard ratio.

deep learning ECG mortality prediction prognosis cardiology

2019-04-15abs ↗pdf ↗

An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUscs.LG

Paper tackles ICU false alarms by learning features from ECG signals.

problem High rate of false alarms in ICU due to patient movements and sensor detachment.

method Unsupervised feature learning to extract and cluster high-level features from ECG signals.

result The method reduces false arrhythmia alarms using a few high-level features from a single ECG lead.

ICU false alarms unsupervised feature learning ECG arrhythmia

2019-04-17abs ↗pdf ↗

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomesstat.ME

New scalable method balances hospital profit status and heart attack outcomes.

problem Balancing covariate distributions and minimizing weight dispersion in large datasets.

method Combines kernel basis expansion and convex optimization for efficient and flexible weighting.

result For-profit hospitals use interventional cardiology similarly to other hospitals but have higher mortality and readmission rates.

hospital profit heart attack outcomes kernel balancing reproducing kernel Hilbert space convex optimization

2023-11-01abs ↗pdf ↗

Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operatorscs.LG

Fourier Neural Operators accurately predict dynamics of high-dimensional ionic models.

problem Approximating stiff, multiscale ionic models using neural networks.

method Fourier Neural Operators for learning dynamics of high-dimensional ionic models.

result Fourier Neural Operators can accurately predict dynamics of high-dimensional ionic models.

Fourier Neural Operators Ionic Models Stiff ODEs Artificial Neural Networks High-dimensional Dynamics

2025-05-20abs ↗pdf ↗

Adversarial Examples for Electrocardiogramseess.SP

New method creates indistinguishable but misclassified ECG signals.

problem Adversarial examples fool deep neural networks in ECG classification.

method Developed a technique to generate smoothed adversarial examples for single-lead ECG.

result Adversarial examples are not unique and can be collated and perturbed.

Electrocardiogram Adversarial Examples Deep Learning Medical Applications Physiologically Plausible

2019-05-13abs ↗pdf ↗