Feature selection from multisession electrocardiogram signals for identity verification

This paper proposes a framework for human recognition based on Electrocardiogram (ECG) signals. We particularly consider a verification scenario in which only one recording session is available for enrolling a subject. Capturing the non-stationarity of ECG and constructing a robust model which can be well generalized to unseen data may not be possible via having only one training session. Under this scenario, we propose to use an auxiliary multisession ECG data set to extract a prior knowledge about the behaviour of ECG signal across sessions. A pool of different types of features is formed and a subset of good features is selected using auxiliary data set. By considering only the selected features for enrollment and test, significant performance improvement is achieved. Existing feature selection approaches are designed to be used in conventional classification problems which are based on a set of training samples and a vector of class labels. Our work is different from the previous works in that we not only consider the class labels but also consider session labels. Features selected from a multisession auxiliary data set are used as a prior knowledge to build robust templates in the enrollment stage where only one training session is available. Experimental results demonstrate effectiveness of the proposed method to cope with non-stationarity of ECG signals across different sessions.