O’Reilly, C.; Oruganti, S.D.R.; Tilwani, D.; Bradshaw, J. Model-Driven Analysis of ECG Using Reinforcement Learning. Bioengineering2023, 10, 696.
O’Reilly, C.; Oruganti, S.D.R.; Tilwani, D.; Bradshaw, J. Model-Driven Analysis of ECG Using Reinforcement Learning. Bioengineering 2023, 10, 696.
O’Reilly, C.; Oruganti, S.D.R.; Tilwani, D.; Bradshaw, J. Model-Driven Analysis of ECG Using Reinforcement Learning. Bioengineering2023, 10, 696.
O’Reilly, C.; Oruganti, S.D.R.; Tilwani, D.; Bradshaw, J. Model-Driven Analysis of ECG Using Reinforcement Learning. Bioengineering 2023, 10, 696.
Abstract
Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
