Video-based heart rate estimation with spectrogram signal quality ranking and fusion

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2024-10-30 DOI:10.1016/j.bspc.2024.107094

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引用次数: 0

Abstract

Remote photoplethysmography (rPPG) enables non-contact measurement of heart rate (HR). However, the stability of rPPG extraction is a bottleneck limiting its application. To address this issue, a signal quality ranking and fusion (SQRF) approach based on HR continuity in the time–frequency domain is introduced. Firstly, the facial region is divided into multiple regions of interest (ROIs), and the raw blood volume pulse (BVP) signal is extracted from each ROI separately using a conventional rPPG method such as the plane orthogonal to skin (POS) method. Then, wavelet synchrosqueezed transform (WSST) is employed to convert the raw pulse signals into spectrograms, which are further ranked according to the HR instantaneous continuity. The selected spectrograms with high-quality HR continuity are then fused using a weighted average to predict the final HR. The proposed SQRF algorithm is verified on three public datasets DDPM, UBFC-Phys and PURE with real scenarios. The obtained mean absolute error (MAE) was reduced by 58.7%, 47.5%, and 16.0% respectively, compared to the original single-ROI method. The results prove that SORF with spectrogram-based HR continuity can consistently boost the stability of POS.

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基于视频的心率估计与频谱图信号质量排序和融合

远程心电图（rPPG）可以非接触式测量心率（HR）。然而，rPPG 提取的稳定性是限制其应用的瓶颈。为了解决这个问题，我们引入了一种基于时频域心率连续性的信号质量排序和融合（SQRF）方法。首先，将面部区域划分为多个感兴趣区域（ROI），然后使用传统的 rPPG 方法（如与皮肤正交的平面（POS）方法）分别从每个感兴趣区域提取原始血容量脉搏（BVP）信号。然后，采用小波同步萃取变换（WSST）将原始脉搏信号转换为频谱图，并根据心率瞬时连续性对频谱图进一步排序。然后使用加权平均法融合所选的具有高质量心率连续性的频谱图，以预测最终心率。提议的 SQRF 算法在三个公共数据集 DDPM、UBFC-Phys 和 PURE 上进行了真实场景验证。与原始的单 ROI 方法相比，获得的平均绝对误差（MAE）分别减少了 58.7%、47.5% 和 16.0%。结果证明，基于频谱图 HR 连续性的 SORF 可以持续提高 POS 的稳定性。

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来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.