Cardiac Time Intervals from Electrocardiogram and Phonocardiogram for Heart Failure Assessment

Deep Learning-Based Automated Extraction of Cardiac Time Intervals from Electrocardiogram and Phonocardiogram for Heart Failure Assessment

Prediction of Systolic Heart Failure from Real-World ECG Data

It is suggested that ECG models for systolic HF can be developed using ICD-based labels from routine clinical data, with model output associated with echo-derived LVEF, however, the performance decrease in external evaluation underscores the importance of careful interpretation and validation against imaging-derived en...

Within a Heartbeat: A Machine Learning Approach Using VCG for Heart Failure Assessment in Sinus Rhythm

The findings suggest that ECG/VCG-based machine learning approaches may contribute to HF subtype stratification and have the potential to broaden access to precision diagnosis and personalized medicine.

Classification of Valvular Heart Diseases Using Nonlinear Heart Rate Variability Indices in Concurrent Electrocardiograms, Seismocardiograms, and Gyrocardiograms

Abstract Valvular heart disease (VHD) remains a major clinical burden, while simple signal-based screening methods are limited. This study tested whether nonlinear heart rate variability (HRV) indices from concurrent electrocardiograms (ECG), seismocardiograms (SCG), and gyrocardiograms (GCG) can differentiate five VHD...

Beat-to-beat aortic valve opening detection from impedance cardiography using machine learning

Accurate detection of the aortic valve opening, represented by the B-point in the impedance cardiogram (ICG), is a critical step in non-invasive hemodynamic monitoring. The B-point is essential for deriving key parameters like the pre-ejection period (PEP) and left ventricular ejection time (LVET). However, accurat...

Clinical feasibility and reliability of automated cardiac interval assessment from smartwatch ECG in lung cancer patients