<ul data-eligibleForWebStory="true"><li>Study examines common and exclusive characteristics of ECG and PCG using EPHNOGRAM dataset.</li><li>Linear and nonlinear machine learning models, including non-causal LSTM networks, utilized for reconstruction.</li><li>Nonlinear models, especially non-causal LSTM, show superior reconstruction performance.</li><li>Reconstructing ECG from PCG found to be more feasible than the reverse.</li><li>Exercise and cross-subject scenarios pose challenges in the analysis.</li><li>Envelope-based modeling utilizing instantaneous amplitude features improves cross-subject generalizability.</li><li>Clinically relevant ECG biomarkers like fiducial points and QT intervals can be estimated from PCG in cross-subject scenarios.</li><li>Findings enhance comprehension of the relationship between ECG and PCG for cardiac monitoring technologies.</li></ul>

Cross-Learning Between ECG and PCG: Exploring Common and Exclusive Characteristics of Bimodal Electromechanical Cardiac Waveforms

Discover more