Bivariate Empirical Mode Decomposition for ECG-based Biometric Identification with Emotional Data

Ferdinando, Hany and Seppänen, Tapio and Alasaarela, Esko (2017) Bivariate Empirical Mode Decomposition for ECG-based Biometric Identification with Emotional Data. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 15-07-2017 - 15-07-2017, Seogwipo - Korea Selatan.

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Abstract

Emotions modulate ECG signals such that they might affect ECG 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 when the emotional states of the subjects were ignored. Tested under 3-level sub-classes of valence and arousal, the proposed method also delivered high accuracy, around 99.4%, for each level sub-class with high consistency as well. These findings also occurred for eight discrete emotions, i.e. anger, disgust, fear, joy, sadness, surprise, amusement, and anxiety, plus neutral. We concluded that the proposed method offers emotionindependent features for ECG-based biometric identification. The proposed method needs more evaluation related to variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

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