Efficient Clustering-Based electrocardiographic biometric identification.

The correct identification of individuals through different biometric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative measures have been proposed during the last two decades: electrocardiogram (ECG) and electroencephalogram (EEG) signals, iris or facial recognition, conductual traits, etc. Several works have shown that ECG-based recognition is a feasible alternative, either for stand-alone or multi-biometric recognition systems. In this paper, we propose a novel framework for ECG-based biometric identification, consisting of a simple and robust feature extraction approach and a clustering-based feature reduction method, that enables for an efficient and scalable biometric identification. The proposed feature reduction approach is a two phase method: it uses a clustering algorithm to group features according to their similarities first, and then clusters are represented in terms of a prototype vector and associated to the available subjects. On its side, the proposed time-domain feature extraction method is a semi-fiducial procedure, where the well-known Pan–Tompkins algorithm is first used to detect the R wave peaks of the QRS complexes, and then fixed-width time segments are selected for further dimensionality reduction and feature extraction. The resulting combined methods are efficient, robust, scalable and attain excellent results (with up-to 98.6% sensitivity) on all the subjects of the Physikalisch-Technische Bundesanstalt (PTB) database, regardless of their pathological or healthy status. Additionally, we also show how the existing Auto Correlation/Discrete Cosine Transform (AC/DCT)-based non-fiducial feature extraction method can be integrated within our framework, allowing us to attain up to 90.6% sensitivity on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Since this database is much noisier and has a much lower sampling rate (360 Hz instead of 1000 Hz), we claim that this is a very good result. • We propose a novel efficient framework for ECG-based biometric identification. • A clustering-based classifier is used to reduce the computational/storage cost. • Hierarchical agglomerative clustering (HAC) is used to build the clusters. • A novel semi-fiducial time-domain feature extraction method is proposed. • Statistical analysis of P-QRS-T complexes is performed on MIT-BIH arrhythmia and PTB. [ABSTRACT FROM AUTHOR]