Xu Yuanyuan, Wang Zhi, Liu Xiaochang
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
School of Information Engineering, Hubei University of Economics, Wuhan 430205, China.
Sensors (Basel). 2025 Aug 7;25(15):4849. doi: 10.3390/s25154849.
Using Photoplethysmogram (PPG) signals for identity recognition has been proven effective in biometric authentication. However, in real-world applications, PPG signals are prone to interference from noise, physical activity, diseases, and other factors, making it challenging to ensure accurate user recognition and verification in complex environments. To address these issues, this paper proposes an improved MSF-SE ResNet50 (Multi-Scale Feature Squeeze-and-Excitation ResNet50) model based on 2D PPG signals. Unlike most existing methods that directly process one-dimensional PPG signals, this paper adopts a novel approach based on two-dimensional PPG signal processing. By applying Continuous Wavelet Transform (CWT), the preprocessed one-dimensional PPG signal is transformed into a two-dimensional time-frequency map, which not only preserves the time-frequency characteristics of the signal but also provides richer spatial information. During the feature extraction process, the SENet module is first introduced to enhance the ability to extract distinctive features. Next, a novel Lightweight Multi-Scale Feature Fusion (LMSFF) module is proposed, which addresses the limitation of single-scale feature extraction in existing methods by employing parallel multi-scale convolutional operations. Finally, cross-stage feature fusion is implemented, overcoming the limitations of traditional feature fusion methods. These techniques work synergistically to improve the model's performance. On the BIDMC dataset, the MSF-SE ResNet50 model achieved accuracy, precision, recall, and F1 scores of 98.41%, 98.19%, 98.27%, and 98.23%, respectively. Compared to existing state-of-the-art methods, the proposed model demonstrates significant improvements across all evaluation metrics, highlighting its significance in terms of network architecture and performance.
在生物特征认证中，利用光电容积脉搏波描记图（PPG）信号进行身份识别已被证明是有效的。然而，在实际应用中，PPG信号容易受到噪声、身体活动、疾病和其他因素的干扰，这使得在复杂环境中确保准确的用户识别和验证具有挑战性。为了解决这些问题，本文提出了一种基于二维PPG信号的改进型MSF-SE ResNet50（多尺度特征挤压与激励ResNet50）模型。与大多数直接处理一维PPG信号的现有方法不同，本文采用了一种基于二维PPG信号处理的新颖方法。通过应用连续小波变换（CWT），将预处理后的一维PPG信号转换为二维时频图，该时频图不仅保留了信号的时频特征，还提供了更丰富的空间信息。在特征提取过程中，首先引入SENet模块以增强提取独特特征的能力。接下来，提出了一种新颖的轻量级多尺度特征融合（LMSFF）模块，该模块通过采用并行多尺度卷积运算解决了现有方法中单一尺度特征提取的局限性。最后，实现了跨阶段特征融合，克服了传统特征融合方法的局限性。这些技术协同工作以提高模型的性能。在BIDMC数据集上，MSF-SE ResNet50模型的准确率、精确率、召回率和F1分数分别达到了98.41%、98.19%、98.27%和98.23%。与现有的最先进方法相比，所提出的模型在所有评估指标上都有显著改进，突出了其在网络架构和性能方面的重要性。
