Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Comput Methods Programs Biomed. 2021 Apr;202:106005. doi: 10.1016/j.cmpb.2021.106005. Epub 2021 Feb 23.
In recent years, people have been exploring methods for biometric identification through electrocardiogram (ECG) signals. Under the same psychological pressure state, biometric identification through ECG signals is a traditional verification method. However, ECG signals are affected by changes in psychological stress, and ECG-Based biometric under different psychological stress states are still challenging. In this paper, we propose a method combining manual and automatic features for ECG-based biometric under different psychological stress states. And propose a new indicator Stress Classification Coefficient (SCC) that assesses the effect of different psychological stress on heart rate variability (HRV) features.
In our method, we obtain manual features to be a three-step process: first, HRV features obtained from the ECG signals. Second, based on HRV features, the mental state of the experimental subjects is assessed by using the Gaussian mixture model (GMM). Finally, use cluster centers to process the original HRV features to reduce the Stress Classification Coefficient (SCC). Also, the one-dimensional convolutional neural network is constructed to automatically extract the implied features of ECG signals. Finally, the manual feature and the automatic feature are combined, and the final recognition result is obtained through the support vector machine (SVM) model. The major attribute of the proposed method is that it can perform ECG biometric under different psychological stress states. The combination of manual and automatic features expands the application scenarios of ECG-based biometric.
Based on this method, we used the Montreal stress model with calculation experiment in the laboratory to induce stress on 23 healthy students (10 women and 13 men, aged 20-37), and obtain their ECG signals under different stress conditions. Through this method to recognize the above data, an average recognition rate of more than 95% can be achieved, the average F1 score is 0.97.
The proposed method in this article is a promising approach to deal with the effects of different psychological stresses on ECG-Based biometric. It provides the possibility of ECG-Based biometric under different psychological stress.
近年来，人们一直在探索通过心电图（ECG）信号进行生物识别的方法。在相同的心理压力状态下，通过 ECG 信号进行生物识别是一种传统的验证方法。然而，ECG 信号会受到心理压力变化的影响，因此在不同的心理压力状态下进行基于 ECG 的生物识别仍然具有挑战性。在本文中，我们提出了一种结合手动和自动特征的方法，用于在不同心理压力状态下进行基于 ECG 的生物识别。并提出了一个新的指标——应激分类系数（SCC），用于评估不同心理应激对心率变异性（HRV）特征的影响。
在我们的方法中，我们获得手动特征是一个三步过程：首先，从 ECG 信号中获取 HRV 特征。其次，基于 HRV 特征，使用高斯混合模型（GMM）评估实验对象的心理状态。最后，使用聚类中心处理原始 HRV 特征以减少应激分类系数（SCC）。同时，构建一维卷积神经网络自动提取 ECG 信号的隐含特征。最后，将手动特征和自动特征相结合，通过支持向量机（SVM）模型得到最终的识别结果。该方法的主要特点是可以在不同的心理压力状态下进行 ECG 生物识别。手动和自动特征的结合扩展了基于 ECG 的生物识别的应用场景。
基于该方法，我们使用实验室计算实验的蒙特利尔应激模型对 23 名健康学生（10 名女性和 13 名男性，年龄 20-37 岁）施加应激，并获得他们在不同应激条件下的 ECG 信号。通过这种方法对上述数据进行识别，可以达到 95%以上的平均识别率，平均 F1 分数为 0.97。
本文提出的方法是一种很有前途的方法，可以处理不同心理应激对基于 ECG 的生物识别的影响。它为不同心理应激下的基于 ECG 的生物识别提供了可能性。
