Bondarenko Maksym, Menon Carlo, Elgendi Mohamed
Biomedical and Mobile Health Technology Research Lab, ETH Zürich, 8008, Zürich, Switzerland.
School of Computation, Information and Technology (CIT), Department of Computer Science, Technical University of Munich (TUM), 80333, Munich, Germany.
NPJ Digit Med. 2025 Jul 26;8(1):479. doi: 10.1038/s41746-025-01814-9.
Heart rate (HR) estimation is crucial for early cardiovascular diagnosis, continuous monitoring, and various health applications. While electrocardiography (ECG) remains the gold standard, its discomfort and impracticality for continuous use have spurred the development of non-contact methods like remote photoplethysmography (rPPG). This systematic review (PROSPERO: CRD 42024592157) examines 70 studies to assess the impact of Region of Interest (ROI) selection on HR estimation accuracy. Most methods (36.8%) use the holistic face, while forehead and cheek areas (24.5% and 21.7%) show superior accuracy. Machine learning-based approaches outperform traditional methods under motion artifacts and poor lighting, achieving Mean Absolute Error and Root Mean Square Error below 1.0 for some datasets. Combining multiple patches improves performance, though increasing ROIs beyond 60 patches results in diminishing returns and higher computational complexity. These findings highlight the significance of ROI optimization for robust rPPG-based HR estimation.
心率（HR）估计对于早期心血管疾病诊断、持续监测以及各种健康应用至关重要。虽然心电图（ECG）仍然是金标准，但其使用时的不适感和持续使用的不便利性促使了诸如远程光电容积脉搏波描记法（rPPG）等非接触式方法的发展。本系统评价（PROSPERO：CRD 42024592157）审查了70项研究，以评估感兴趣区域（ROI）选择对心率估计准确性的影响。大多数方法（36.8%）使用全脸，而额头和脸颊区域（分别为24.5%和21.7%）显示出更高的准确性。在运动伪影和光线不佳的情况下，基于机器学习的方法优于传统方法，对于某些数据集，平均绝对误差和均方根误差低于1.0。组合多个斑块可提高性能，不过，当感兴趣区域超过60个斑块时，收益递减且计算复杂度更高。这些发现凸显了感兴趣区域优化对于基于稳健的rPPG心率估计的重要性。
