Individual identification via electrocardiogram analysis

Abstract

BackgroundDuring last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research.MethodsWe searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents.Results100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %.ConclusionsBiometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

“…(2) Collectability, that is an ECG signal can be easily measured compared to other biological signals [6]. (3) Uniqueness, which implies that an ECG signal is unique (4) Permanence, which means it is permanent and finally (5) Performance, which is similar to biometric system, it is secure, efficient and accurate [7,8].…”

Textile Concentric Ring Electrodes: Influence of Position and Electrode Size on Cardiac Activity Monitoring

“…The normalized amplitude is relevant since it provides information on the ability to analyze the morphology of each wave for clinical diagnosis. Parameters related to normalized amplitudes (or amplitude ratios) of the cardiac waves have been extensively used in the literature for different aims, ranging from biometric recognition systems to developing new diagnostic algorithms [28,29]. In the present work, peak-to-peak amplitudes of P and T waves were normalized to those of the whole average beat, that is, QRS in almost all cases.…”

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

“…In [27,28], the authors showed how IPI-based values can be employed as cryptographic keys. In addition, ECG biometrics is a growing field in which some approaches are based on characteristics’ points (including R-peaks and IPIs) [29,30]. …”