arXiv research

PPGnet model estimates heart rate from PPG signals without motion artifacts.

Smartphones can estimate heart rate from other sensor data.

Improved heart rate and activity recognition with low-power wrist sensors.

Active stacking improves heart rate estimation accuracy with minimal labeled data.

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…

Developed shrinkage methods for Poisson regression models with experts to handle multicollinearity.

Machine learning predicts exercise load from heart rate data post-exercise.

This paper uses fuzzy C-Means clustering and sonification to analyze heart rate variability.

New scalable method balances hospital profit status and heart attack outcomes.

Smartwatch HRV measurements improved with machine learning.

Fluctuations in heart rate are intimately tied to changes in the physiological state of the organism. We examine and exploit this relationship by classifying a human subject's wake/sleep status using his instantaneous heart rate (IHR) series. We use a convolutional neural network (CNN) to build features from the IHR se…

Study identifies mental stress in firefighters using heart rate variability data.

The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.

Wavesplit separates speech from mixtures using clustering.

Hybrid Amortized Inference improves PPG model interpretability.

AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.

Framework for imputing missing heart data to simulate brain-heart interactions.

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…

Machine learning improves CHD screening accuracy from 70% to 87.7%.

CardiacGen generates realistic ECG signals for training deep learning models.

Framework uses human annotations to make models robust to spurious correlations.

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition t…

We consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions. We use an attentional convolutional neural network to learn parsimonious signatures of individual cardiovascular response from data recorded at the minute level res…

PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.

A new estimator for state values in reinforcement learning reduces complexity and improves convergence.

Study improves CAD diagnosis accuracy by selecting significant features.

Deep learning predicts heart failure readmission from clinical notes.

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…

Study reviews machine learning techniques for stress monitoring.

Deep learning speeds up whole heart MRI to 30 seconds.

New system uses wearable bio-signals for easy authentication.

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are…

Develops a parameter-free SGD algorithm with optimal convergence rate.

A new method classifies heart sounds using i-vectors and machine learning.

We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard convergence rate in the stochastic optimization setting. To the best of…

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 …

AI detects heart disease from ECGs with improved interpretability and performance.

Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In real…

In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnos…

Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various methodologies have been proposed to analyze the disease factors aiming to decrease the phys…

Method discovers nonlinear relations from time series data.

Generative ODE model learns unknown variables in medical systems.

Proposes a novel anomaly detection method for echocardiogram videos.

Novel method segments heart sound signals using LSTMs with attention.

Echo noise improves compression in autoencoders with exact rate-distortion.

New method estimates sparse mean from noisy data without knowing sparsity level.

Study predicts heart failure patient survival using stacked ensemble ML.

Interpretable survival analysis improves heart failure risk prediction.