HeartCycle: A comprehensive dataset of synchronized impedance cardiography and echocardiography for accurate hemodynamic predictions

The "HeartCycle" dataset offers a comprehensive collection of synchronized
impedance cardiography (ICG) and echocardiography (ECHO) signals, supplemented
with finger photoplethysmography (PPG), heart sounds, and electrocardiography
(ECG) data from 17 healthy volunteers. Collected during the HeartCycle project
(FP7-216695), this dataset aims to address biases in the ICG waveform,
particularly the ABEXYOZ complex, where the B and X points do not precisely
align with the aortic valve opening and closing notches. The biases in B and X
point detection are critical for hemodynamic prediction because these
characteristic points are used to calculate essential diagnostic parameters,
including systolic time intervals (PEP and LVET), contractility, stroke
volume, and cardiac output. By providing synchronized ICG and ECHO signals,
researchers can better understand these biases and develop more accurate
models for hemodynamic parameter computation. The dataset is stored in HDF5
format, facilitating the storage of complex data structures and easy access to
various physiological parameters. It is ideal for developing machine learning
models to enhance the detection of characteristic points in ICG signals. For
instance, machine learning models can be used to detect characteristic points
for improved left ventricular ejection time (LVET) estimation or mapping the
ICG signal to the different mechanical events in the cardiac cycle using the
ECHO as a reference. Detailed metadata and usage notes are included to support
data utilization across different software environments. Ethical approval was
obtained from the University of Coimbra Hospital's ethics committee, and
informed consent was provided by all participants.