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.
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbea…
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.
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency fea…
We developed a new approach for the analysis of physiological time series. An iterative convolution filter is used to decompose the time series into various components. Statistics of these components are extracted as features to characterize the mechanisms underlying the time series. Motivated by the studies that show …
We describe a novel neural network architecture for the prediction of ventricular tachyarrhythmias. The model receives input features that capture the change in RR intervals and ectopic beats, along with features based on heart rate variability and frequency analysis. Patient age is also included as a trainable embeddi…
Heart rate estimation from electrocardiogram signals is very important for the early detection of cardiovascular diseases. However, due to large individual differences and varying electrocardiogram signal quality, there does not exist a single reliable estimation algorithm that works well on all subjects. Every algorit…
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.
Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models a…
N-BEATS-MOE improves time series forecasting by adapting to series characteristics.
problem Forecasting heterogeneous time series with varying characteristics.
method Mixture-of-Experts layer with dynamic block weighting.
result Consistent improvements across 12 benchmark datasets, especially for heterogeneous series.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series 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.
Paper optimizes portfolio selection with ICX order constraints.
problem Minimizing portfolio variance with ICX order constraints.
method Optimal and efficient portfolios are derived in closed form.
result Closed-form solutions for optimal and efficient portfolios.
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…
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.
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 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.
Python models predict stock sentiment for market-beating returns.
problem Predicting public sentiment for stock trading.
method Crowd-sourced labeled data, trained and evaluated various models.
result Best models predict market-beating returns from public sentiment.
We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart. We model this situation as a …
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.
The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we face both challe…
A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data ana…
Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in free living conditions. PPG based heart rate measurement is unfortunately highly susceptible…
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.
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.
This study compares two neural models for financial forecasting, showing their superiority.
problem Improving financial market trend predictions using neural networks.
method Systematic comparison of N-HiTS and N-BEATS with conventional models.
result N-HiTS and N-BEATS enhance forecast accuracy and robustness in financial time series data.
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.
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.
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.
Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classificatio…
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.
Adversarial policies beat superhuman Go AI systems.
problem Vulnerability of superhuman AI systems to adversarial attacks.
method Training adversarial policies to trick KataGo into making blunders.
result Adversarial policies achieve >97% win rate against KataGo at superhuman settings.
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…
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.
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.
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.
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.
IBPF algorithm tackles high-dimensional parameter learning for complex systems.
problem Learning high-dimensional parameters in complex, partially observed, and nonlinear systems.
method Iterated Block Particle Filter (IBPF) for graphical state space models.
result IBPF algorithm consistently beats the curse of dimensionality across various experiments.
N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.
problem Efficiently forecasting millions of time series with high accuracy.
method Global parallel variant of N-BEATS model designed for multi-step time series forecasting.
result Significant reduction in training time and memory usage with comparable accuracy.
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%.
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 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.
The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wear…