Biometric identification of cardiosynchronous waveforms utilizing person specific continuous and discrete wavelet transform features

• Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2012; pp. 4311 - 4314
• Main Authors: Bhagavatula, C., Venugopalan, S., Blue, R., Friedman, R., Griofa, M. O., Savvides, M., Vijaya Kumar, B. V. K.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2012
• Subjects: Algorithms; Biometry - methods; Cardiography, Impedance - methods; Conductometry - methods; Continuous wavelet transforms; Diagnosis, Computer-Assisted - methods; Discrete wavelet transforms; Feature extraction; Heart - physiology; Heart beat; Heart Function Tests - methods; Humans; Reproducibility of Results; Sensitivity and Specificity; Spectrogram; Wavelet Analysis
• ISBN: 1424441196; 9781424441198
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2012.6346920

In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the wavelet space were done using the Euclidean distance measure. In the latter case, the signal was decomposed at various levels using different wavelet bases, in order to extract both low frequency and high frequency components. Comparisons at each decomposition level were performed using the same distance measure as before. The data set used consists of four subjects, each with a 15 minute RFII recording. The various data samples for our experiments, corresponding to a single heart beat duration, were extracted from these recordings. We achieve identification rates of up to 99% using the CWT approach and rates of up to 100% using the DWT approach. While the small size of the dataset limits the interpretation of these results, further work with larger datasets is expected to develop better algorithms for subject identification.