Deep neural network detects heart murmur with high accuracy.
problem Detecting heart murmur from heart sound recordings.
method Parallel combination of RNN-BiLSTM and CNN.
result 96-100% sensitivity and specificity, 98% F1 score.
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48 results for “heart murmur”
Novel method segments heart sound signals using LSTMs with attention.
problem Heart sound segmentation for diagnostic applications.
method Bidirectional LSTMs with attention mechanisms.
result State-of-the-art performance on multiple benchmarks.
New arithmetic phenomenon 'murmurations' detected using AI.
problem Detecting new arithmetic patterns in large datasets.
method Machine learning interpretability tools (PCA, saliency, convolutional filters).
result Murmurations encode Frobenius traces and connect to number theory.
Convolutional neural networks predict the analytic rank of elliptic curves accurately.
problem Predicting the analytic rank of elliptic curves over Q.
method Applied one-dimensional convolutional neural networks to Frobenius traces.
result High accuracy predictions for analytic rank across various conductors.
Study ranks of elliptic curves via prime averages.
problem Classifying elliptic curves by rank.
method Average Frobenius trace over primes, data science experiments.
result Oscillating pattern in average trace values, correlates with rank.
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classificatio…
New method locates heart arrhythmia points quickly and reliably.
problem Locating a point-source heart arrhythmia using catheter data.
method Developed a nonconvex feasibility problem and optimization algorithm.
result Robust and fast localization of arrhythmia sources without prior anatomy knowledge.
PPGnet model estimates heart rate from PPG signals without motion artifacts.
problem Wearable PPG devices struggle with motion artifacts.
method End-to-end deep learning model using 8-second PPG signals.
result Achieved mean absolute error of 3.36+-4.1 BPM on IEEE SPC 2015 dataset.
Framework for imputing missing heart data to simulate brain-heart interactions.
problem Lack of multi-modal patient data representing heart and brain processes.
method Probabilistic framework for joint cardiac data imputation and mechanistic model personalization.
result Accurate imputation of missing cardiac features in incomplete datasets.
Deep neural networks improve heart disease diagnosis accuracy.
problem Improving accuracy of heart disease diagnosis.
method Design and use of deep neural networks (DNNs) for detecting heart disease based on clinical data.
result HEARO-5 architecture yields 99% accuracy and 0.98 MCC.
Smartphones can estimate heart rate from other sensor data.
problem Gaps in heart rate data from wearable sensors.
method Regression, SVM, and random forest algorithms to estimate heart rate from smartphone data.
result Smartphone data can improve heart rate estimation from wearable sensors.
A new method classifies heart sounds using i-vectors and machine learning.
problem Heart sound classification for disease diagnostics.
method Extract i-vectors from MFCC features, apply PCA and VAE for dimensionality reduction, then use GMMs and SVM for classification.
result The method improves heart sound classification by 16% on the Physionet dataset.
Improved heart rate and activity recognition with low-power wrist sensors.
problem Challenges in battery life, cost, and sensor performance in wrist-worn sensing applications.
method Used photoplethysmography (PPG) for heart rate and activity recognition, applying transfer learning and CNNs.
result Low sampling frequencies (5 Hz and 10 Hz) achieved good performance in heart rate and activity recognition.
Short-segment heart sound classification using an ensemble of deep convolutional neural networkscs.SD
Paper uses CNNs to classify heart sounds from short segments.
problem Classifying heart sounds from short segments of individual beats.
method Developed a 1D-CNN and 2D-CNN ensemble for feature learning and score-level fusion.
result ECNN ensemble achieved 89.22% accuracy and 89.94% sensitivity on the PhysioNet CinC 2016 database.
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.
Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercisescs.LG
Machine learning predicts exercise load from heart rate data post-exercise.
problem Monitoring energy expenditure in real life.
method Machine learning methods (linear regression, etc.) applied to heart rate data.
result Random forest and k-nearest neighbors classifiers predict load levels accurately.
A Novel Approach to the Diagnosis of Heart Disease using Machine Learning and Deep Neural Networkscs.LG
AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.
problem Misdiagnosis of heart disease leads to unnecessary deaths.
method Developed an AI application using ML and DNN algorithms on a dataset from the Cleveland Clinic Foundation.
result DNN model achieved a 92% accuracy rate, reducing misdiagnosis.
Paper classifies heart sound recordings as normal or abnormal.
problem Classifying normal/abnormal heart sound recordings.
method Four steps: preprocessing, feature extraction, training, validation. Back propagation neural network used.
result Optimal threshold determined for distinguishing normal and abnormal.
Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithmcs.LG
Study predicts heart failure patient survival using stacked ensemble ML.
problem Predicting survival of heart failure patients.
method Collect and analyze patient data, apply SMOTE, use K-Means, Fuzzy C-Means clustering, Random Forest, XGBoost, Decision Tree, and propose a stacked ensemble model.
result Supervised ML algorithms outperform unsupervised models, achieving high accuracy and F1 score.
Interpretable survival analysis improves heart failure risk prediction.
problem Improving heart failure risk prediction using survival analysis.
method Survival stacking, ControlBurn, Explainable Boosting Machines.
result Achieves state-of-the-art performance and provides novel insights.
Sleep-wake classification via quantifying heart rate variability by convolutional neural networkstat.AP
CNN classifies sleep-wake states from heart rate variability.
problem Classifying sleep-wake states from heart rate data.
method Convolutional Neural Network (CNN) trained on ECG data.
result CNN achieves high accuracy in classifying wake/sleep stages.
Study uses machine learning to predict heart failure in cancer patients.
problem Early detection of cancer patients at risk for cardiotoxicity.
method Examined four machine learning algorithms on 143,199 cancer patients.
result Gradient boosting model achieved best AUC score of 0.9077.
Embeddings from EHR diagnoses and procedures predict heart failure risk.
problem Challenges in feature engineering from EHR data due to high dimensionality and heterogeneity.
method Used GloVe to learn word embeddings for diagnoses and procedures in UK EHR data.
result Embeddings enable robust disease risk prediction models for congestive heart failure.
This paper uses fuzzy C-Means clustering and sonification to analyze heart rate variability.
problem Identifying suitable features from HRV analysis for sonification.
method Unsupervised machine learning (fuzzy C-Means clustering) and sonification techniques.
result Improves sonification interpretability by selecting appropriate HRV features.
A CNN-based method improves DTI of the human heart, compensating for motion.
problem Signal loss due to heart motion in DTI.
method Invertible Wavelet Scattering using CNN.
result Effective motion compensation and improved fiber structures.
Liu-type Shrinkage Estimators for Mixture of Poisson Regressions with Experts: A Heart Disease Studystat.ME
Developed shrinkage methods for Poisson regression models with experts to handle multicollinearity.
problem Multicollinearity in Poisson regression models with experts.
method Ridge and Liu-type shrinkage methods.
result Shrinkage methods offer more reliable estimates for coefficients in multicollinearity.
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.
Paper explores using machine learning to predict cyclists' heart rate.
problem Predicting cyclists' heart rate during training sessions.
method Used LSTM machine learning algorithm to model heart rate response.
result Demonstrated successful prediction of cyclists' heart rate.
Study examines heart and football-shaped metrics, verifying geometric structure.
problem Analyzing reducible spherical conical metrics and their geometric properties.
method Examined 1-parameter heart shape and 3-parameter football shape families, verified structure theorem, used explicit metric and geodesic calculations.
result Naturally arise from Abelian differentials of the third kind, offer new evidence for spherical geometry and complex analytic structure interaction.
Smartwatch HRV measurements improved with machine learning.
problem Systematic error in HRV measurements from consumer smartwatches.
method Explanatory and predictive modeling using accelerometer data.
result Error in HRV measurements can be minimized by machine learning.
The study examines machine learning classification algorithms and their generalizability using Framingham Heart Study data.
problem Addressing biases and generalizability issues in machine learning classification algorithms.
method Comparison of eight machine learning classification algorithms on Framingham Heart Study data.
result Double discriminant scoring of type I is the most generalizable algorithm.
Deep learning speeds up whole heart MRI to 30 seconds.
problem Long acquisition times in whole heart MRI.
method Deep learning, specifically a 3D residual U-Net, to reconstruct high-resolution images from low-resolution data.
result Super-resolution images show better edge sharpness and fewer artefacts than low-resolution images.
Machine learning improves CHD screening accuracy from 70% to 87.7%.
problem Predicting coronary heart disease using echocardiography and clinical features.
method Ensemble machine learning approach with model stacking and two-step stacking.
result Improved CHD classification accuracy from 70% to 87.7%.
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.
Improved robustness of 1D CNNs for heart arrhythmia classification.
problem Improving the robustness of 1D CNNs for classification tasks.
method Parameterization using Cayley transform and controllability Gramian for Lipschitz-bounded CNNs.
result Improved robustness of trained Lipschitz-bounded 1D CNNs for heart arrhythmia classification.
Proposes a hybrid deep learning network for better heart failure survival prediction.
problem Improving survival prediction in heart failure patients.
method Joint analysis of cardiac motion features and clinical risk factors using a hybrid deep learning network.
result Optimal integration of clinical risk factors into deep prediction networks.
Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failurestat.ML
Causal analysis reveals regional discrepancies in TOPCAT trial results.
problem Inconclusive results in TOPCAT trial for heart failure treatment.
method Causal discovery methods with domain knowledge integration.
result Significant causal effects shown for some subgroups globally.
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.
Study identifies mental stress in firefighters using heart rate variability data.
problem Unsupervised identification of mental stress in firefighters from heart rate variability data.
method Exploration and comparison of three unsupervised methods: K-Means, convolutional autoencoders, and LSTM autoencoders.
result Convolutional and LSTM autoencoders successfully stratify stressed versus normal samples using HRV markers.
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.
Deep learning predicts heart failure readmission from clinical notes.
problem Predicting and preventing heart failure readmission.
method Convolutional Neural Networks (CNN) trained on clinical notes.
result Deep learning models outperform traditional machine learning methods in readmission prediction.
The paper discovers patterns in Maass forms' coefficients related to Fricke signs.
problem Identifying Fricke signs in Maass forms with unknown signs.
method Averaging Fourier coefficients, Linear Discriminant Analysis (LDA), neural networks.
result 96% accuracy in predicting Fricke signs for forms with even parity, 94% for odd parity.
Method synthesizes 4D CMR images from XCAT model using GAN and SPADE.
problem Synthesizing realistic 4D CMR images with annotations and adaptable styles.
method Hybrid GAN approach with XCAT anatomical ground truth and SPADE for semantic preservation.
result Synthesized images with modality-specific features learned from real CMR data.
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Recordsstat.ML
The paper investigates causal relationships in heart failure prediction using machine learning.
problem Understanding the causal relationships between clinical variables and heart failure.
method Proposes a new computational framework for causal structure discovery (CSD) of mixed-type clinical variables for binary disease outcomes.
result Feature importance from nonlinear classifiers strongly correlates with causal strength of variables, but not differentiating cause and effect.
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.
Modeling individual cardiovascular responses from wearable sensor data.
problem Capturing and understanding cardiovascular responses to physical activity and sleep changes.
method Attentional convolutional neural network to learn signatures from minute-level sensor data.
result Generated signatures generalize and outperform baseline models in predicting cardiovascular variables.
ECGDetect uses deep learning to detect ischemia in heart ECGs.
problem Detecting early signs of acute coronary syndrome in patients.
method Developed a deep learning model using the LTST database.
result Deep neural network achieved 90.31% ROC-AUC, 89.34% sensitivity, 87.81% specificity.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
problem Difficulty in comparing surgical outcomes due to racial/ethnic and geographic differences.
method Causal inference framework and transfer learning to incorporate data from multiple populations.
result Racial and ethnic differences in surgical outcomes are found, with non-Hispanic Black patients experiencing wide variability.