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

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.