Wu Mengshan, Chen Xiang
School of Microelectronics, University of Science and Technology of China, Hefei, China.
Med Biol Eng Comput. 2025 Jun 4. doi: 10.1007/s11517-025-03379-x.
This paper proposes a novel approach for exercise heart rate (HR) estimation by integrating PPG quality assessment with deep neural network (DNN) modeling. A frequency-domain kurtosis (kurtF) metric is introduced to identify high-quality PPG samples, optimizing DNN training data and mitigating motion artifacts. An E-K scatter plot is used to visualize sample quality distribution, aiding dataset variability analysis. The proposed DNN model, designed for single-channel PPG input, demonstrates strong HR estimation performance on public datasets, achieving a mean absolute error (MAE) values of 3.76 bpm (PPG_DaLiA) and 3.18 bpm (IEEE-Training). Theoretical analysis and experimental validation confirm that prioritizing high-quality samples enhances model stability, accuracy, and generalizability. Additionally, a dataset quality analysis method is introduced to facilitate comparative assessments. The kurtF metric and quality-driven sample selection strategy provide a robust framework for improving HR estimation, even in data-limited scenarios. This study underscores the importance of integrating sample quality assessment into HR estimation workflows, paving the way for more accurate and reliable PPG-based HR monitoring during exercise.
本文提出了一种通过将PPG质量评估与深度神经网络（DNN）建模相结合来估计运动心率（HR）的新方法。引入了频域峰度（kurtF）指标来识别高质量的PPG样本，优化DNN训练数据并减轻运动伪影。使用E-K散点图来可视化样本质量分布，辅助数据集变异性分析。所提出的DNN模型专为单通道PPG输入设计，在公共数据集上展示了强大的HR估计性能，在PPG_DaLiA数据集上平均绝对误差（MAE）值为3.76 bpm，在IEEE-Training数据集上为3.18 bpm。理论分析和实验验证证实，优先选择高质量样本可提高模型的稳定性、准确性和通用性。此外，还引入了一种数据集质量分析方法以促进比较评估。kurtF指标和质量驱动的样本选择策略为改进HR估计提供了一个强大的框架，即使在数据有限的情况下也是如此。本研究强调了将样本质量评估纳入HR估计工作流程的重要性，为运动期间基于PPG的更准确可靠的HR监测铺平了道路。
