Direct Estimation of Pediatric Heart Rate Variability from BOLD-fMRI: A Machine Learning Approach Using Dynamic Connectivity
This provides a tool to simplify and enhance pediatric fMRI studies by eliminating the need for peripheral recording devices, though it is incremental as it builds on existing methods for a specific domain.
The researchers tackled the problem of missing or poor-quality cardiac signals in pediatric fMRI studies by developing a machine learning framework to directly reconstruct Heart Rate Variability (HRV) from BOLD-fMRI data, achieving an 8% improvement in HRV accuracy.
In many pediatric fMRI studies, cardiac signals are often missing or of poor quality. A tool to extract Heart Rate Variation (HRV) waveforms directly from fMRI data, without the need for peripheral recording devices, would be highly beneficial. We developed a machine learning framework to accurately reconstruct HRV for pediatric applications. A hybrid model combining one-dimensional Convolutional Neural Networks (1D-CNN) and Gated Recurrent Units (GRU) analyzed BOLD signals from 628 ROIs, integrating past and future data. The model achieved an 8% improvement in HRV accuracy, as evidenced by enhanced performance metrics. This approach eliminates the need for peripheral photoplethysmography devices, reduces costs, and simplifies procedures in pediatric fMRI. Additionally, it improves the robustness of pediatric fMRI studies, which are more sensitive to physiological and developmental variations than those in adults.