New algorithm separates maternal and fetal ECG from two channels in pregnant women.
problem Affordable ECG monitors for maternal-fetal health monitoring.
method Diffusion-based channel selection for accurate separation.
result Algorithm accurately separates maternal and fetal ECG from two channels.
New method extracts fetal ECG from maternal abdominal ECG.
problem Extract fetal ECG from single-lead maternal abdominal ECG.
method De-shape short time Fourier transform for heart rate estimation, beat tracking, and nonlocal median for waveform reconstruction.
result The method accurately extracts fetal ECG signals from maternal abdominal ECG signals.
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with…
Study uses deep learning with attention to predict fetal brain gestational age.
problem Accurately predicting fetal brain gestational age for early diagnosis.
method Attention-based deep learning model combining multi-view MRI data.
result Age prediction performance with R2 = 0.94 using multi-view MRI and attention.
Deep CNNs identify age-related patterns in fetal brain activity.
problem Understanding age effects in fetal brain development.
method Supervised 3D Convolutional Neural Networks applied to fetal fMRI data.
result Deep CNNs can distinguish age groups in fetal brain activity.
The paper discovers a hidden component in data using an autoencoder with a discriminator.
problem Discovering a single independent latent variable in data.
method An autoencoder with a discriminator is used to recover the hidden component.
result The approach can recover the hidden component up to entropy-preserving transformations.
Method automatically estimates fetal abdominal circumference from ultrasound images.
problem Challenges in accurately estimating fetal abdominal circumference from ultrasound images.
method Proposes a CNN-based approach that classifies ultrasound images and uses Hough transformation for measuring AC.
result CNN provides sufficient classification results for AC estimation with small training samples.
EchoFusion tracks and reconstructs fetal images without external trackers.
problem Limited capture range and view-dependent artefacts in fetal ultrasound imaging.
method Combining deep learning and SLAM for image-based tracking and volume reconstruction.
result Demonstrated robust tracking and accurate volume reconstruction in fetal ultrasound.
Real-time fetal abdominal aorta measurement from ultrasound images.
problem Automating the challenging task of measuring fetal abdominal aorta diameter from ultrasound images.
method Proposes a neural network architecture with three blocks: convolutional layer, Convolution Gated Recurrent Unit (C-GRU), and CyclicLoss.
result Significantly improved accuracy and real-time execution speed compared to previous methods.
Paper proposes a method to extract disentangled features for multi-task learning in medical images.
problem Indiscriminate mixing of image properties leads to poor generalization in deep learning.
method Uses deep neural networks and adversarial regularization to disentangle features.
result Demonstrates improved performance on images with new properties like artifacts.
Improved IVIM imaging accuracy with neural networks and uncertainty estimation.
problem Low accuracy in IVIM imaging due to SNR and motion artifacts.
method Implicit IVIM signal acquisition model using neural networks for full posterior distribution.
result Improved parameter estimation accuracy by 65% on synthetic data.
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.
Paper presents DL models for ECG signal denoising.
problem Efficient denoising of ECG signals for wearable devices.
method CNNs, LSTM, RBM, filtering methods, wavelet-based technique.
result CNN model performs well for offline denoising.
This research synthesizes 12-lead ECG from a single-lead ECG device.
problem Limited cardiac diagnostics from single-lead ECG devices.
method Random forest machine learning model using historical 12-lead recordings.
result Synthesized 12-lead ECG with accuracies exceeding 90%.
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.
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.
Develops a method to denoise and analyze wearable ECGs.
problem Noisy ECGs from wearable devices.
method Statistical model, beat-to-beat representation, factor analysis.
result Upper bound on performance quantified and compared.
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.
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.
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.
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.
Self-supervised ECG learning improves emotion recognition.
problem Improving emotion recognition from ECG signals.
method Multi-task deep learning framework with signal transformations as pretext tasks.
result Significant performance improvement in emotion classification.
Deep neural network with attention detects multiple ECG abnormalities.
problem Detecting multiple ECG abnormalities from 12-lead recordings.
method Preprocessing, deep residual network with attention, ensemble model.
result Overall F1 score of 0.875 on test set.
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.
Novel ECG classification method using deep time-frequency representation and progressive decision fusion.
problem Challenges in classifying abnormal ECG rhythms due to broad taxonomy, noises, and lack of annotated data.
method Transform ECG signal into time-frequency domain, train scale-specific deep CNNs, and fuse decisions progressively.
result Effective and efficient ECG classification method validated on synthetic and real-world datasets.
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%.
Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the potential to be used as point-of-care for cardiovascular disease diagnostic systems. Such wearable and wireless ECG systems require automatic detection of cardiovascular disease. Even in the primary care, automation of ECG diagnostic systems will …
Paper proposes a deep learning model for real-time ECG signal segmentation.
problem Real-time analysis of large ECG datasets for tele-health monitoring.
method Combines CNN and LSTM for detecting heartbeats' waveforms.
result Achieved high sensitivity and precision in QRS detection.
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.
ECG outperforms graph clustering algorithms using ensemble method.
problem Graph clustering challenges.
method ECG combines Louvain algorithm and consensus clustering.
result ECG outperforms leading algorithms on artificial networks.
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.
Generates synthetic ECGs conditioned on clinical statements.
problem Privacy issues with sensitive health data.
method Combines diffusion models and structured state space models.
result SSSD-ECG outperforms GAN-based competitors in synthetic data quality.
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.
Study develops algorithm to classify ECG rhythms from short recordings.
problem Detecting atrial fibrillation from short single-lead ECG recordings.
method Signal quality index (SQI) and dense convolutional neural networks (CNN) for classification.
result Best F1 score of 0.80 on blind test set for NSR, AF, and O.
We release a large ECG dataset for arrhythmia subtype discovery.
problem Discovering unknown subtypes of arrhythmia from continuous raw signals.
method Unsupervised representation learning task using semi-supervised evaluation.
result Qualitative evaluations show potential for representation learning in arrhythmia sub-type discovery.
ECG improves graph clustering and resolves resolution limit issues.
problem Graph clustering resolution limit issue.
method ECG uses consensus clustering to improve graph clustering.
result ECG alleviates the resolution limit issue and improves partition stability.
Deep learning improves ECG arrhythmia classification.
problem Classifying ECG patterns accurately.
method Transfer learning from image classification to ECG spectrograms.
result 97.23% accuracy in classifying 7000 ECG instances.
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.
Paper tackles ECG signal classification with active learning.
problem Challenges in obtaining labeled ECG data and class label noise.
method Robust active learning approach with clustering and noisy label reduction.
result Demonstrates effectiveness of the proposed algorithm on ECG signal classification.
ECGID research focuses on rest but not exercise, this study evaluates both.
problem ECGID performance under exercise is insufficiently studied.
method Various learning methods applied to ECG dataset.
result Current ECGID methods fail under exercise conditions.
Structured state space models improve ECG classification and reveal new insights.
problem Improving ECG analysis through deep learning.
method Applying structured state space models to capture long-term dependencies in ECG data.
result SSMs lead to significant improvements in ECG classification over current state-of-the-art.
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.
CNNs improve ECG noise detection for better heart condition monitoring.
problem Signal noise in mobile ECGs hinders accurate diagnosis.
method Developed and trained a 16-layer CNN on a noise-annotated dataset.
result 16-layer CNN achieves 0.977 AUC on ECG noise detection.
A new model classifies multi-lead ECGs better than single-channel models.
problem Classifying multi-lead ECGs for accurate diagnosis.
method Channel-wise attention mechanism in CNNs for multi-lead ECG classification.
result The model achieves better sensitivity and precision than plain ResNet models.
ECG signal learning predicts cardiovascular death risk.
problem Scarce positive ECG event examples and class imbalance.
method Multiple instance learning framework for raw ECG signals.
result Learned risk score outperforms existing metrics.
PLIs improve classifier performance by fine-tuning latent representations.
problem Difficult interpretation of high-dimensional latent representations in neural networks.
method Back-propagation of manual changes to low-dimensional embeddings using t-distributed stochastic neighbourhood embeddings.
result Manual separation of class clusters in latent space enhances classifier performance.