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Heart-Based Biometrics and Possible Use of Heart Rate Variability in Biometric Recognition Systems

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 395))

Abstract

Heart rate variability (HRV) is an intrinsic property of heart and active research domain of the medical research community since last two decades. But in biometrics it is still in its infancy. This article is intended to present the state of art into heart-based biometrics and also explore the possibility of using HRV in biometric recognition systems. Subsequently, we designed hardware and software for data collection and also developed software for HRV analysis in Matlab, which generates 101 HRV Parameters (Features) using various HRV analysis techniques like statistical, spectral, geometrical, etc., which are commonly used and recommended for HRV analysis. All these features have their relative significance in medical interpretations and analysis, but among these 101 features reliable features that can be useful for biometric recognition were unknown; therefore feature selection becomes a necessary step. We used five different wrapper algorithms for feature selection, and obtained 10 reliable features out of 101. Using the proposed 10 HRV features, we used KNN for classification of subjects. The classification test gave us encouraging results with 82.22 % recognition rate.

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Acknowledgments

This work was carried out in Multimodal System Development laboratory established under UGC’s SAP scheme, SAP (II) DRS Phase-I F. No. 3-42/2009 & SAP (II) DRS Phase-II F. No.4-15/2015. This work was also supported by UGC under One Time Research Grant F. No. 4-10/2010 (BSR) & 19-132/2014 (BSR). The authors also acknowledge UGC for providing BSR fellowship.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), India

    Nazneen Akhter, Sumegh Tharewal & K. V. Kale

  2. M.G.M’s G. Y. Pathrikar College of C.S & I.T, Aurangabad, India

    Vijay Kale & Ashish Bhalerao

Authors
  1. Nazneen Akhter
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  2. Sumegh Tharewal
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  3. Vijay Kale
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  4. Ashish Bhalerao
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  5. K. V. Kale
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Corresponding author

Correspondence to Nazneen Akhter .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Università Ca' Foscari, Venice, Italy

    Agostino Cortesi

  3. Bialystok University of Technology Faculty of Computer Science, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

Appendix A

Appendix A

Poincare map features

1

2

• SD1

• SD2

• Standard deviations of the distances of the R–R (I) to the lines

• Y = x and y = –x + 2R–Rm, where R–Rm is the mean of all R–R (I)

• SD1 related to the

• Fast beat-to-beat variability in the data, while SD2

• Describes the long-term variability of R–R (I)

Statistical features

No.

Name

Description

1

SDNN

Standard deviation of all normal–normal intervals

2

RMSSD

Root mean square of successive differences

3

NN50

It’s a count of the number of adjacent pairs differing by more than 50 ms

4

pNN50

(%) NN50 count divided by total intervals

5

MeanRRI

Mean of normal–normal interval

6

MeanHR

Mean heart rate

7

Max

Maximum interval duration in a particular RRI

8

Min

Minimum interval duration

9

Mean

Mean of the whole RRI sequence

10

Median

Median of the RRI sequence

11

SDHR

Standard deviation of heart rate

Spectral features

No.

Name

Description

1

aVLF

Absolute value in very low-frequency spectrum

2

aLF

Absolute value in low-frequency Spectrum

3

aHF

Absolute value in high-frequency Spectrum

4

aTotal

Total absolute value

5

pVLF

Power % of very low frequency in PSD

6

pLF

Power % of low frequency in PSD

7

pHF

Power % of high Frequency in PSD

8

nLF

Low frequency in normalized Unit

9

nHF

High frequency in normalized Unit

10

LFHF

LF to HF Ratio

11

peakVLF

Peak value in very low frequency

12

peakLF

Peak value in low frequency

13

peakHF

Peak value in high frequency

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Akhter, N., Tharewal, S., Kale, V., Bhalerao, A., Kale, K.V. (2016). Heart-Based Biometrics and Possible Use of Heart Rate Variability in Biometric Recognition Systems. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2650-5_2

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  • DOI: https://doi.org/10.1007/978-81-322-2650-5_2

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