Ferdinando Hany, Seppanen Tapio, Alasaarela Esko
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:450-453. doi: 10.1109/EMBC.2017.8036859.
Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.
情绪会调节心电图信号，以至于在现实生活应用中可能会影响基于心电图的生物特征识别。这促使人们寻找好的特征提取方法，使受试者的情绪状态影响最小。本文评估了在考虑情绪因素时基于双变量经验模式分解（BEMD）的生物特征识别特征提取方法。使用来自Mahnob-HCI数据库的心电图信号进行情感识别，特征是对心电图信号应用BEMD分析后主导频率的统计分布。在10折交叉验证中使用kNN分类器识别26名受试者时，当忽略受试者的情绪状态时，准确率达到99.5%，一致性很高。当考虑受试者的情绪状态时，所提出的方法也具有较高的准确率，约为99.4%。我们得出结论，所提出的方法为基于心电图的生物特征识别提供了与情绪无关的特征。所提出的方法需要更多与使用其他分类器进行测试以及心电图信号变化相关的评估，例如正常心电图与心律失常心电图、不同年龄的心电图以及来自其他情感数据库的心电图。
