New method learns disentangled representations from ECG data for VT origin classification.
problem Challenges in learning subject-specific models from ECG data due to inter-subject variations.
method Conditional Variational Autoencoder (VAE) with maximum mean discrepancy regularization and contrastive regularization.
result Demonstrated efficacy in classifying VT origin segments compared to standard VAE.
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.
Model predicts ventricular tachyarrhythmias with high accuracy.
problem Predicting ventricular tachyarrhythmias for patient care.
method Multi-task neural network architecture with patient metadata.
result 74.02% prediction accuracy 60 seconds in advance.
VFPred combines signal processing and machine learning for VF detection from short ECG signals.
problem Detecting Ventricular Fibrillation from short ECG signals.
method VFPred uses Empirical Mode Decomposition, Discrete Time Fourier Transform, and Support Vector Machine.
result VFPred achieves high sensitivity and specificity even from short 5-second signals.
Paper introduces rational Gaussian wavelets for efficient signal approximation.
problem Efficiently approximating complex signals with few coefficients.
method Continuous wavelet transform using rational Gaussian wavelets with adjustable parameters.
result Proposed rational Gaussian wavelets provide accurate signal approximations.
Personalized deep learning reduces inappropriate shocks in VA detection.
problem High inappropriate shock rate in traditional VA detection methods.
method Personalized deep learning framework using CNN for real-time VA detection and collaborative inference.
result 6.6% reduction in inappropriate shock rate compared to traditional methods.
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.
Background: Cardiac MRI derived biventricular mass and function parameters, such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically well established. Image segmentation can be challenging and time-consuming, due to the complex a…
In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prio…
GANs improve echo frame generation with labeled patches.
problem Automated echocardiography with limited labels.
method Conditional GAN with patch-based discriminator.
result GAN-enhanced echo frames match given segmentation masks.
MS-FCN improves cardiac left ventricle segmentation accuracy.
problem Accurate segmentation of left ventricle from MRI images.
method Multi-scale fully convolutional network with dense connectivity.
result MS-FCN achieves state-of-the-art Dice scores (0.93 on endocardium, 0.96 on epicardium).
Proposes a novel anomaly detection method for echocardiogram videos.
problem Anomaly detection in echocardiogram videos.
method Dynamic Variational Trajectory Models (TVAE-C, TVAE-R, TVAE-S) trained on healthy infant echocardiogram videos.
result Superior performance in detecting congenital heart defects and pulmonary hypertension.
Causal graph aids observational study insights in aSAH patients.
problem Lack of clear objectives and tools for identifying necessary adjustments in observational studies.
method Uses causal directed acyclic graphs (DAGs) to provide insights mid-study and identify necessary data enhancements.
result Midway insights and necessary data enhancements identified for meaningful causal questions.
Study forecasts aortic pressure with deep learning models.
problem Forecasting noisy, non-stationary aortic pressure.
method Used deep learning models, specifically recurrent neural networks with Legendre Memory Unit, on 25 Hz time series data.
result Recurrent neural networks with Legendre Memory Unit achieved the best performance with an overall forecasting error of 1.8 mmHg.
Generative Adversarial Model improves RV segmentation accuracy in MRI.
problem Accurate segmentation of the right ventricular blood pool from cine MRI sequences.
method Combines FCNN, Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN), and L1 loss function.
result Improves Dice Index and Hausdorff Distance by 0.05 and 3.49 mm respectively.
Semi-supervised learning classifies cardiac pathology using motion features from cine MRI.
problem Classifying cardiac pathology based on motion features from cine MRI.
method Semi-supervised learning of apparent flow to generate motion features from non-segmented images.
result The model achieves 95% classification accuracy on ACDC test set.
LU-Net improves cardiac segmentation accuracy and robustness.
problem Robustness and accuracy of deep learning cardiac segmentation.
method Multi-task end-to-end network designed to improve cardiac segmentation.
result Outperforms current best deep learning solution, reducing outliers and improving clinical indices.
Deep learning improves LV segmentation and volume estimation from cardiac MRI.
problem Accurate LV segmentation and volume estimation for cardiac MRI.
method Image preprocessing, U-Net architecture, postprocessing, end-to-end analytics pipeline.
result Improved accuracy in LV segmentation and volume estimation.
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
problem Accuracy of U-Net-based segmentation models trained on adult cardiac MRI data when applied to rare congenital heart diseases like Tetralogy of Fallot.
method Cross-validation with four-fold, evaluation on unseen data from different pathologies.
result Deep learning models overfit to the training data, leading to significant accuracy drops when applied to other pathologies.
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.
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.