评价具有距离均衡的变换不变损失函数在预测成像光体积描记图特征中的应用。

School of Electronic Engineering, Beijing University of Posts and Telecommunications,People's Republic of China.

Department of Mechanical Engineering, Hong Kong City University, Hong Kong, People's Republic of China.

Physiol Meas. 2024 May 7;45(5). doi: 10.1088/1361-6579/ad3dbf.

UNLABELLED

. 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.

APPROACH

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).

MAIN 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.

SIGNIFICANCE

This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.

未加标签

. 监测人类心率变异性（HRV）的变化对于保护生命和健康具有重要意义。研究表明，基于普通彩色摄像机的成像光电容积脉搏波（IPPG）可以检测心肺系统引起的皮肤像素颜色变化。大多数研究人员采用基于深度学习的 IPPG 算法来提取血液体积脉搏（BVP）信号，主要通过心率（HR）来分析它。然而，这种方法往往忽略了 BVP 信号中固有的复杂时频域特征，这些特征不能仅从 HR 来全面推断。因此，通过 BVP 信号分析 HRV 指标至关重要。

方法

本文首次将具有距离均衡的不变损失函数（TIDLE）应用于 IPPG，从而可以更好地恢复 BVP 信号的细节。具体来说，在四个常用的 IPPG 深度学习模型（DeepPhys、EfficientPhys、Physnet 和 TS_CAN）中测试了 TIDLE，并与其他三种损失函数（平均绝对误差（MAE）、均方误差（MSE）、负皮尔逊相关系数（NPCC）进行了比较。

主要结果

实验表明，在预测 LF/HF 方面，MAE 和 MSE 在四个模型中的表现都不太理想，分别达到了 25.94%和 34.05%的统计平均绝对误差（MAES）。相比之下，NPCC 和 TIDLE 的表现更为理想，分别为 13.51%和 11.35%。考虑到 BVP 信号的形态特征，在用于预测 HRV 指标的两个最优模型 DeepPhys 和 TS_CAN 上，TIDLE 预测的 BVP 信号与黄金标准 BVP 信号的皮尔逊系数分别达到了 0.627 和 0.605，而基于 NPCC 的结果则明显较低，分别为 0.545 和 0.533。