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Feature Selection for Heart Rate Variability Based Biometric Recognition Using Genetic Algorithm

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Intelligent Systems Technologies and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 384))

Abstract

Heart Rate Variability (HRV) is a prominent property of heart, so far utilized by medical community for diagnostic and prognostic purpose. There was an early attempt to employ HRV for biometric recognition purpose however due to lack of information, the methodologies applied, features used, and results obtained are not available for reference and comparison. In this article we attempt to utilize HRV for biometric purpose, and subsequently obtained 101 most commonly used HRV features. These features have been identified in the guidelines framed by the especially constituted taskforce of European Society of Cardiology and North American Society of Pacing and Electrophysiology for standardization of HRV related studies. Biometric recognition system depends basically on some strongly discriminative elements in a feature vector for accurately distinguishing individuals. The large feature vector of 101 features in addition to the useful ones, may definitely have irrelevant and redundant features. Therefore features selection becomes a crucial step before classification is attempted and feature selection from a large feature sets, cannot be done arbitrarily. The main intention of this article is to identify prominent features of HRV data that can be employed in biometric recognition. For this purpose we applied Genetic Algorithm (GA) which utilizes adaptive search techniques and have documented significant improvement on variety of search problems. GA proposed 15 prominent features out of 101. Performance analysis with the identified features is presented along with the recognition rate.

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Authors and Affiliations

  1. Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, 431001, Maharashtra, India

    Nazneen Akhter, Siddharth Dabhade, Nagsen Bansod & Karbhari Kale

Authors
  1. Nazneen Akhter
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  2. Siddharth Dabhade
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  3. Nagsen Bansod
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  4. Karbhari Kale
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Corresponding author

Correspondence to Nazneen Akhter .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’ Informazione (DINFO) , Universita degli Studi di Firenze, Firenze, Italy

    Stefano Berretti

  2. School of CS/IT, Indian Institute of Information Tech. and Management – Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Indian Institute of Management Rohtak, Rohtak, Haryana, India

    Praveen Ranjan Srivastava

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Akhter, N., Dabhade, S., Bansod, N., Kale, K. (2016). Feature Selection for Heart Rate Variability Based Biometric Recognition Using Genetic Algorithm. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-23036-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23035-1

  • Online ISBN: 978-3-319-23036-8

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