Convolutional networks struggle with repeating patterns in ECGs.
problem Modeling repeating patterns in electrocardiogram signals.
method Demonstrated through ECG examples, highlighting systemic issues in deep learning.
result Counterintuitive effects on generalization in deep networks.
Paper explores LSTM visualization techniques for ECGs.
problem Visualizing LSTM models for ECG classification.
method Four visualization techniques applied to ECGs, focusing on input deletion masks.
result Best technique is learning an input deletion mask to reduce class score.
Improves cardiac simulator fit to real patient ECG data.
problem Intractable inference over non-differentiable cardiac simulators.
method Variational inference combined with Bayesian optimization.
result Significant improvement in simulator fit to real patient ECG data.
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.
Model reconstructs missing EKG data more accurately than baseline.
problem Reconstruct missing EKG data with high accuracy.
method Developed a probabilistic model of cardiac electrophysiology and EKG measurement process.
result Model outperforms baseline in reconstructing missing EKG data.
Generative adversarial network system improves ECG arrhythmia classification.
problem Improving automatic ECG arrhythmia classification accuracy.
method Generative adversarial network with patient-specific normal beats and generated abnormal beats.
result Superior overall classification performance for SVEB and VEB on MIT-BIH arrhythmia database.
Method generates natural ECGs with 25 interpretable features.
problem Lack of labeled ECGs for supervised learning and automatic diagnostics.
method Variational autoencoder for ECG generation and feature extraction.
result Low Maximum Mean Discrepancy (0.00383) indicates good ECG generation quality.
System detects and predicts cardiac anomalies from ECG data.
problem Early detection of cardiac anomalies to reduce healthcare costs and mortality.
method Discrete Wavelet Transform (DWT) and Undecimated Wavelet Transform (UWT) for feature extraction; Bayesian Network Classifier for anomaly prediction.
result Average accuracy of 96.6% for PAC, 92.8% for MI, and 87% for PVC on real ECG datasets.
Active stacking improves heart rate estimation accuracy with minimal labeled data.
problem Inconsistent heart rate estimation across subjects due to signal quality and individual differences.
method Active learning and stacking ensemble regression to aggregate base estimators.
result Active stacking significantly outperforms other methods with minimal labeled data.
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.
A new method for time series analysis that highlights important signals.
problem Finding signals that matter most in time series data.
method Contrastive Multivariate Singular Spectrum Analysis (CMSA) using a background dataset.
result CMSA identifies signals that are more relevant to the analyst than those with the highest variance.
Algorithm finds critical subsequences in ECG beats for classification.
problem Classifying ECG beats accurately and understanding why.
method Optimized implementation of nearest neighbor algorithm with DTW distance.
result Algorithm discovers important subsequences for ECG classification.
AI detects heart disease from ECGs with improved interpretability and performance.
problem Undiagnosed structural heart disease due to high cost and accessibility of echocardiography.
method Generalized additive model integrating clinically meaningful ECG predictors.
result Improved AUROC, AUPRC, and F1 score compared to deep-learning baselines.
Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.
problem Diagnosing cardiac abnormalities from 12-lead ECGs.
method Training an enhanced deep convolutional neural network with hand-crafted features, data preprocessing, and augmentation.
result Promising generalization performance in ECG diagnosis.
The paper uses AI to analyze ECG data, revealing age-related changes and identifying key features.
problem Investigating age-related changes in ECG data to distinguish healthy from disease-related changes.
method Employed deep-learning and tree-based models on raw ECG signals and features from a diverse age group.
result Identified age-related declines in breathing rates and high SDANN values in elderly individuals.
Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.
problem Improving temporal data understanding with capsule networks.
method Generated capsules along temporal and channel dimensions, learning contrasting relationships.
result Achieved 96.21% accuracy on ECG signal beat categories, surpassing state-of-the-art.
EKG-based models show better stability across patient populations than EHR-based models.
problem Model generalization issues in EHR and EKG-based predictive models.
method Two tests to measure model generalization, comparing EHR and EKG data.
result EKG-based models are more stable across different patient populations.
Improved ECG classification using multi-task learning.
problem Low frequency of rare diagnoses in ECGs.
method Developed a multi-task CNN to classify multiple diagnoses from 12-lead ECGs.
result Adding common classes improves performance on rarer classes.
Bayesian method clusters time series with varying dynamics.
problem Modeling and clustering time series with unknown number of clusters and dynamics.
method Hierarchical Dirichlet process and Gaussian process for modeling time series patterns and variations.
result Efficiently clusters time series with varying dynamics without unnecessary proliferation of clusters.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
problem Limited understanding of deep learning models in ECG analysis.
method Extracts time-localized clusters from model's internal representations.
result Enhances trust in AI-driven diagnostics and reveals clinically relevant patterns.
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.
Paper establishes a comprehensive benchmark for ECG time-series analysis.
problem Incomplete understanding of ECG signal properties and limitations in evaluation metrics.
method Categorization of downstream applications, identification of limitations, introduction of a novel metric, benchmarking of time-series models.
result Validation of the effectiveness of the proposed metric and model architecture.
Enhanced ECG biometric system improves authentication accuracy.
problem Traditional authentication methods have security issues.
method Developed an ECG-based authentication system using machine learning.
result Achieved up to 92% identification accuracy.
SCRIB assigns multiple labels to each example to control class-specific prediction risks.
problem Lack of a sound mechanism to decide when to refrain from predicting in DL classifiers.
method Set-classifier with Class-specific Risk Bounds (SCRIB) that assigns multiple labels to each example and controls class-specific prediction risks.
result SCRIB obtained class-specific risks 35%-88% closer to the target risks than baseline methods.
Deep learning detects myocardial infarction with high accuracy from ECG data.
problem Detecting myocardial infarction from electrocardiogram data.
method Adapted ConvNetQuake model to analyze 15 ECG leads, focusing on v6, vz, and ii leads.
result Achieved cardiologist-level performance in detecting myocardial infarction.
Deep learning detects arrhythmia from RR-interval ECG data.
problem Diagnosing arrhythmia using ECG data.
method Convolutional neural network (CNN) on time-sliced RR-interval data.
result Compact system achieves accurate arrhythmia detection.
This work uses encoder-decoder networks to denoise one-dimensional signals by aligning clean and noisy signal latent representations.
problem Noise removal in one-dimensional signals, especially in medical and motion signals.
method Encoder-decoder architecture with adversarial learning to align clean and noisy signal latent representations.
result Better performance on electrocardiogram and motion signal denoising compared to learning-based and non-learning approaches.
Study shows DL models trained on healthy subjects perform worse on patients' ECG data.
problem Inefficiency of DL models on heterogeneous datasets for heart beat detection.
method Investigated and evaluated the use of Transfer Learning to adapt DL models to different datasets.
result Transfer Learning improves classification performance on small sample size datasets.
Semi-supervised learning method augments minority class examples for robust anomaly detection in clinical signals.
problem Class imbalance in minority class instances impairs robustness of clinical analytics solutions.
method Intelligent augmentation of minority class examples to balance class distribution and construct a smooth decision boundary.
result The proposed method outperforms state-of-the-art algorithms in anomaly detection for clinical signals.
Generative model improves TMP reconstruction from ECG data.
problem Reconstructing cardiac transmembrane potential from surface ECG data is an ill-posed inverse problem.
method A novel model-constrained inference framework using a variational auto-encoder (VAE) with LSTM networks to learn the conditional likelihood of TMP and the prior distribution of generative factors.
result The method significantly improves TMP reconstruction accuracy compared to conventional methods.
Compact neural network for ECG classification reduces resource needs.
problem Current reliance on deep learning for ECG analysis requires extensive resources and large datasets.
method Simple ANN architecture with advanced feature engineering.
result Achieved 97.36% accuracy in classifying 4 types of arrhythmias.
Algorithm selects optimal segment for physiological signal analysis.
problem Physiological signals are often corrupted by noise, requiring selective analysis.
method Combines deep neural networks for signal analysis and combinatorial optimization for segment selection.
result Significant improvement in system performance, e.g. 2.4% increase in sensitivity for heart sound segmentation.
GeoECG augments ECG data to improve heart disease detection.
problem Insufficient labeled ECG data and vulnerability to adversarial attacks.
method Wasserstein geodesic perturbation for data augmentation.
result Improved accuracy and robustness in ECG-based heart disease detection.
Study examines error correction in ECG segmentation neural networks.
problem Improving accuracy of ECG segmentation using neural networks.
method Training deep convolutional neural networks for ECG segmentation and analyzing ensemble errors.
result Outliers in ensemble can be used to evaluate data representation quality.
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.
ECG-DelNet uses neural networks to accurately delineate ECGs, even with low-quality data.
problem Lack of model explainability and small databases limit deep learning applicability in ECG detection.
method Adapted U-Net architecture for 1D data, used PhysioNet's QT database, applied data augmentation and regularization techniques.
result Best configuration achieved high precision and recall for P, QRS, and T waves.
DDGM generates realistic ECG signals for clinical use.
problem Generating accurate ECG signals from noisy data.
method Bayesian ECG reconstruction using DDGM trained on healthy ECG data.
result DDGM successfully generates realistic ECG signals for clinical applications.
LLT-ECG classifies ECG signals without backpropagation using linear laws.
problem ECG signal classification efficiency and verifiability.
method Forward linear law identification for ECG signal features.
result State-of-the-art performance on real-world ECG datasets.
Researchers use VAEs to create understandable heart beat representations.
problem Lack of explainable models for ECG beat classification.
method Variational Auto-Encoders (VAEs) with linear dense networks.
result Interpretable ECG beat space generated.
VPNet uses variable projection for efficient neural network training.
problem Efficient and interpretable neural network training for signal processing.
method Variable projection (VP) applied to neural networks.
result VPNet achieves fast learning and good accuracy with low computational cost.
Deep learning detects arrhythmias from ECGs using multidimensional representations.
problem Detecting arrhythmias from ECGs using traditional methods.
method Convert 1-D ECG data into 2-D images, then use deep learning for classification.
result Deep learning outperforms existing methods in arrhythmia detection.
A deep learning algorithm for ECG segmentation.
problem ECG signal segmentation for various sampling rates and monitors.
method UNet-like neural network for adaptive and generalized ECG segmentation.
result F1-measures for ECG segmentation are at least 97.8%, 99.5%, and 99.9%.
This work identifies eigenvalues of unknown linear dynamics without full system identification.
problem Identifying parameters of a linear dynamical system is challenging.
method Developed a computationally efficient algorithm to estimate eigenvalues of the state-transition matrix.
result The algorithm can efficiently cluster multi-dimensional time series with temporal offsets and varying lengths.
Bayesian model predicts emotion from fitness tracker heartbeat data.
problem Predicting emotional valence from consumer fitness tracker heartbeat data.
method End-to-end Bayesian deep learning model using PPG data.
result Peak F1 score of 0.7 for emotional valence classification.
Wavesplit separates speech from mixtures using clustering.
problem Permutation problem in speech separation.
method End-to-end system infers speaker representations and estimates signals.
result Robust separation of long recordings, new benchmarks set.
Novel algorithm compresses ECG signals with preserved R peaks.
problem Efficiently compressing ECG signals while preserving R peak information.
method Blaschke unwinding AFD for faster convergence and higher fidelity.
result The proposed algorithm outperforms state-of-the-art approaches in ECG signal compression.
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.
We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…