ECG-based biometric under different psychological stress states - Zhou, Ruishi; Wang, Chenshuo; Zhang, Pengfei; Chen, Xianxiang; Du, Lidong; Wang, Peng; Zhao, Zhan; Du, Mingyan; Fang, Zhen (Computer methods and programs in biomedicine, 2021)
•We propose a biometric method that combines manual and automatic features, which provides the possibility of biometric identification under different stress conditions.•We propose a new indicator stress classification Coefficient(SCC) to evaluate the impact of different psychological stress on HRV features. The smaller stress classification Coefficient(SCC), the smaller the influence of different psychological pressures on HRV features.•We propose a method to reduce the influence of different psychological pressures on HRV features. We cluster the HRV features with the GMM model, and use the GMM clustering center parameters to process the HRV features to reduce the stress classification Coefficient(SCC), which means reducing the impact of different psychological pressures on HRV features. 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.