ECG-Based Personal Identification Using Empirical Mode Decomposition and Hilbert Transform

It is evident that biological signals of each subject (e.g., electrocardiogram signal) carry his/her unique signature; consequently, several attempts have been made to extract subject-dependent features from these signals with application to human verification. Despite numerous efforts to characterize electrocardiogram (ECG) signals and provide promising results for low population of subjects, the performance of state-of-the-art methods mostly fail in the presence of noise or arrhythmia. This paper presented an efficient and fast-to-compute ECG feature by applying empirical mode decomposition (EMD) to ECG signals, and then, instantaneous frequency, instantaneous phase, amplitude, and entropy features were extracted from the analytical form of the last EMD component. Finally, the k-nearest neighbor (kNN) classifier was utilized to classify the individuals’ features. The proposed method was compared to the conventional features such as fiducial points, correlation, wavelet coefficients, and principal component analysis (PCA). These methods were all applied to ECG signals of 34 healthy subjects derived from the Physikalisch-Technische Bundesanstalt (PTB) database. The results implied the effectiveness of the proposed method, providing 95% verification accuracy, which was not the best among the competitors but provided much lower dimensional feature space compared to the top-rank counterparts.