Direct Estimation of Pediatric Heart Rate Variability from BOLD-fMRI: A Machine Learning Approach Using Dynamic Connectivity

Motivation: In many pediatric fMRI studies, cardiac signals are missing or have poor quality. Therefore, it would be highly beneficial to have a tool to extract Heart Rate Variation (HRV) waveforms directly from fMRI data without the need for peripheral recording devices. Goal(s): Develop a machine learning framework to accurately reconstruct HRV tailored for pediatric applications. Approach: A hybrid model using one-dimensional Convolutional Neural Networks (1D-CNN) and Gated Recurrent Units (GRU) analyzed BOLD signals from 628 ROIs, integrating past and future data. Results: Achieved an 8% improvement in HRV accuracy, evidenced by enhanced performance metrics, demonstrating the model's effectiveness. Impact: This method enhances pediatric fMRI by eliminating the need for peripheral photoplethysmography devices, reducing costs and simplifying procedures. It could also improve the robustness of pediatric fMRI studies, which are more affected by physiological and developmental variations than in adults.