PhysKANNet: A KAN-based model for multiscale feature extraction and contextual fusion in remote physiological measurement

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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Abstract

Physiological indicator reflects the health status of the human body, and remote photoplethysmography (rPPG) is a highly promising technology for contactless measurement of these indicators through facial video. However, current deep learning methods mainly rely on traditional neural networks with limited spatiotemporal receptive fields, overlooking the importance of multi-scale features and noise resistance in rPPG signal modeling. This results in challenges when addressing subtle color changes and noise interference. To overcome these limitations, we leverage the advantages of the Kolmogorov-Arnold Network (KAN) in handling sparse data and propose PhysKANNet, a novel KAN-based encoder–decoder architecture that integrates multi-scale feature extraction and contextual information fusion to enhance rPPG signal extraction. We introduce three new plug-and-play modules for PhysKANNet: the rPPG-Aware Convolutional Attention Block, which extracts features at different scales through a multi-branch structure and enhances multi-scale representation using KAN’s nonlinear modeling capabilities; the Multi-Dimensional Feature Fusion Block, which combines high-dimensional features from the encoder with low-dimensional features from the decoder; and the rPPG Edge Sampling Block, which fuses edge and semantic information to further optimize signal extraction accuracy. We employ unsupervised learning for training PhysKANNet and conducted comprehensive experiments on multiple benchmark datasets. The results show that PhysKANNet significantly improves feature learning from unlabeled data, achieving excellent performance across various testing scenarios.
PhysKANNet:基于 KAN 的远程生理测量多尺度特征提取和上下文融合模型
生理指标反映了人体的健康状况,而远程血压计(rPPG)是一种通过面部视频进行非接触式测量的极具前景的技术。然而,目前的深度学习方法主要依赖于时空感受野有限的传统神经网络,忽视了多尺度特征和抗噪性在 rPPG 信号建模中的重要性。这导致在处理微妙的颜色变化和噪声干扰时面临挑战。为了克服这些局限性,我们利用柯尔莫哥洛夫-阿诺德网络(KAN)在处理稀疏数据方面的优势,提出了基于 KAN 的新型编码器-解码器架构 PhysKANNet,该架构集成了多尺度特征提取和上下文信息融合,以增强 rPPG 信号提取。我们为 PhysKANNet 引入了三个新的即插即用模块:rPPG 感知卷积注意力模块(rPPG-Aware Convolutional Attention Block),它通过多分支结构提取不同尺度的特征,并利用 KAN 的非线性建模能力增强多尺度表示;多维特征融合模块(Multi-Dimensional Feature Fusion Block),它将来自编码器的高维特征与来自解码器的低维特征相结合;以及 rPPG 边缘采样模块(rPPG Edge Sampling Block),它融合边缘和语义信息,进一步优化信号提取的准确性。我们采用无监督学习来训练 PhysKANNet,并在多个基准数据集上进行了综合实验。结果表明,PhysKANNet 显著提高了无标记数据的特征学习能力,在各种测试场景中都取得了优异的性能。
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来源期刊
Biomedical Signal Processing and Control
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.
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