Abstract：
Objective. Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system. Most researchers employed deep learning IPPG algorithms to extract the blood volume pulse (BVP) signal, analyzing it predominantly through the heart rate (HR). However, this approach often overlooks the inherent intricate time-frequency domain characteristics in the BVP signal, which cannot be comprehensively deduced solely from HR. The analysis of HRV metrics through the BVP signal is imperative.
METHODS: In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC).
RESULTS: The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively.
CONCLUSIONS: This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.
METHODS: In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC).
RESULTS: The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively.
CONCLUSIONS: This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.
摘要：
监测人类HRV(心率变异性)的变化对于保护生命和健康具有重要意义。.研究表明，基于普通彩色相机的成像光电容积描记术（IPPG）可以检测由心肺系统引起的皮肤像素的颜色变化。大多数研究人员采用深度学习IPPG算法来提取血容量脉冲(BVP)信号，主要通过心率（HR）进行分析。然而,这种方法通常忽略了BVP信号中固有的复杂时频域特性，这不能仅仅从人力资源中全面推导出来。通过BVP信号分析HRV度量是必要的。
方法：在本文中，首次将具有距离平衡的变换不变损失函数（TIDLE）损失函数应用于IPPG，能较好地恢复BVP信号的细节。详细来说,TIDLE在四种常用的IPPG深度学习模型中进行了测试，是DeepPhys,EfficientPhys,Physnet和TS_CAN，与其他三个损失函数相比，是MAE,MSE,NPCC。
结果：实验表明，MAE和MSE在预测四个模型的LF/HF方面表现出次优的性能，实现了25.94%和34.05%的平均绝对误差(MAES)统计，分别。相比之下,NPCC和TIDLE取得了更有利的结果，分别为13.51％和11.35％，分别。考虑到BVP信号的形态特征，关于预测HRV度量的两个最优模型，即DeepPhys和TS_CAN，与黄金标准BVP信号相比，TIDLE预测的BVP信号的皮尔逊系数分别达到0.627和0.605的值。相比之下,基于NPCC的结果明显较低，分别只有0.545和0.533。
结论：本文对有效恢复BVP信号的形态和频域特征做出了重大贡献。
方法：在本文中，首次将具有距离平衡的变换不变损失函数（TIDLE）损失函数应用于IPPG，能较好地恢复BVP信号的细节。详细来说,TIDLE在四种常用的IPPG深度学习模型中进行了测试，是DeepPhys,EfficientPhys,Physnet和TS_CAN，与其他三个损失函数相比，是MAE,MSE,NPCC。
结果：实验表明，MAE和MSE在预测四个模型的LF/HF方面表现出次优的性能，实现了25.94%和34.05%的平均绝对误差(MAES)统计，分别。相比之下,NPCC和TIDLE取得了更有利的结果，分别为13.51％和11.35％，分别。考虑到BVP信号的形态特征，关于预测HRV度量的两个最优模型，即DeepPhys和TS_CAN，与黄金标准BVP信号相比，TIDLE预测的BVP信号的皮尔逊系数分别达到0.627和0.605的值。相比之下,基于NPCC的结果明显较低，分别只有0.545和0.533。
结论：本文对有效恢复BVP信号的形态和频域特征做出了重大贡献。
