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  • Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering
  • MF - Makale Koleksiyonu | Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering
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  •   DSpace@Işık
  • 1- Fakülteler | Faculties
  • Mühendislik Fakültesi / Faculty of Engineering
  • Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering
  • MF - Makale Koleksiyonu | Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering
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Biometric identification using fingertip electrocardiogram signals

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Date

2018-07

Author

Güven, Gökhan
Gürkan, Hakan
Güz, Ümit

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Citation

Güven, G., Gürkan, H. & Güz, Ü. (2018). Biometric identification using fingertip electrocardiogram signals. Signal, Image and Video Processing, 12(5), 933-940. doi:10.1007/s11760-018-1238-4

Abstract

In this research work, we present a newly fingertip electrocardiogram (ECG) data acquisition device capable of recording the lead-1 ECG signal through the right- and left-hand thumb fingers. The proposed device is high-sensitive, dry-contact, portable, user-friendly, inexpensive, and does not require using conventional components which are cumbersome and irritating such as wet adhesive Ag/AgCl electrodes. One of the other advantages of this device is to make it possible to record and use the lead-1 ECG signal easily in any condition and anywhere incorporating with any platform to use for advanced applications such as biometric recognition and clinical diagnostics. Furthermore, we proposed a biometric identification method based on combining autocorrelation and discrete cosine transform-based features, cepstral features, and QRS beat information. The proposed method was evaluated on three fingertip ECG signal databases recorded by utilizing the proposed device. The experimental results demonstrate that the proposed biometric identification method achieves person recognition rate values of 100% (30 out of 30), 100% (45 out of 45), and 98.33% (59 out of 60) for 30, 45, and 60 subjects, respectively.

Source

Signal, Image and Video Processing

Volume

12

Issue

5

URI

https://hdl.handle.net/11729/1410
http://dx.doi.org/10.1007/s11760-018-1238-4

Collections

  • MF - Makale Koleksiyonu | Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering [179]
  • Scopus İndeksli Makale Koleksiyonu [885]
  • WoS İndeksli Makale Koleksiyonu [893]

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    In this research work, we propose a one-dimensional Convolutional Neural Network (CNN) based biometric identification system that combines speech and ECG modalities. The aim is to find an effective identification strategy ...
  • A novel human identification system based on electrocardiogram features 

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    In this work, we present a novel biometric authentication approach based on combination of AC/DCT features, MFCC features, and QRS beat information of the ECG signals. The proposed approach is tested on a subset of 30 ...
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    In this work, we present a novel biometric authentication approach based on combination of AC/DCT features, MFCC features, and QRS beat information of the ECG signals. The proposed approach is tested on a subset of 30 ...



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