Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography...

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Machine Learning in Healthcare

Background:

• Beat-by-beat hemodynamic monitoring is crucial for diagnosing cardiovascular diseases like heart failure.
• Current invasive methods are impractical for daily monitoring, necessitating non-invasive alternatives.

Purpose of the Study:

• To propose a novel method for estimating intraventricular hemodynamic parameters using non-invasive phonocardiogram (PCG) and photoplethysmography (PPG) signals.
• To develop a deep neural network capable of real-time, beat-to-beat hemodynamic parameter estimation.

Main Methods:

• Synchronous collection of PCG, PPG, ECG, and invasive left ventricular pressure signals in beagle dogs.
• Development of a deep neural network combining residual convolutional and bidirectional recurrent modules.
• Training and testing the model using a regression approach with mean squared error loss.

Main Results:

• The model accurately estimated left ventricular systolic blood pressure (SBP), diastolic blood pressure (DBP), maximum rate of pressure rise (MRR), and maximum rate of pressure decline (MRD) with correlation coefficients >0.90 within subjects.
• Cross-subject validation showed slightly reduced performance (CCs ~0.7-0.85), indicating a need to address individual differences for improved generalizability.

Conclusions:

• Non-invasive PCG and PPG signals can effectively estimate beat-by-beat hemodynamic parameters.
• The developed deep learning model demonstrates potential for wearable devices in home healthcare and self-monitoring applications.
• Further research is needed to enhance model generalizability across diverse individuals.