用fMRI数据直接估算儿童心率变异性，省去外接设备
Direct Estimation of Pediatric Heart Rate Variability from BOLD-fMRI: A Machine Learning Approach Using Dynamic Connectivity
- 结合1D-CNN与GRU的混合模型分析628个脑区的BOLD信号
- 心率变异性重建准确率提升8%，性能指标显著改善
- 适合需简化流程、降低成本的儿科fMRI研究
在许多儿科fMRI研究中，心脏信号常缺失或质量不佳。若能直接从fMRI数据中提取心率变异性（HRV）波形，无需外接传感器，将极具价值。本文提出一种机器学习框架，可准确重构儿童HRV。该方法采用一维卷积神经网络（1D-CNN）与门控循环单元（GRU）的混合模型，分析来自628个兴趣区域（ROIs）的BOLD信号，并融合历史与未来信息。结果显示，该模型在HRV重建上实现8%的准确率提升，性能指标显著优化。该方法无需外接光电容积脉搏波描记仪（PPG），降低设备成本，简化实验流程，增强儿科fMRI研究对生理与发育差异的鲁棒性。
原文摘要 · Abstract (English)
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.
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