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Identification of Exercising Individuals Based on Features Extracted from ECG Frequency Spectrums

Tatsuya NOBUNAGA, Toshiaki WATANABE, Hiroya TANAKA

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Summary :

Individuals can be identified by features extracted from an electrocardiogram (ECG). However, irregular palpitations due to stress or exercise decrease the identification accuracy due to distortion of the ECG waveforms. In this letter, we propose a human identification scheme based on the frequency spectrums of an ECG, which can successfully extract features and thus identify individuals even while exercising. For the proposed scheme, we demonstrate an accuracy rate of 99.8% in a controlled experiment with exercising subjects. This level of accuracy is achieved by determining the significant features of individuals with a random forest classifier. In addition, the effectiveness of the proposed scheme is verified using a publicly available ECG database. We show that the proposed scheme also achieves a high accuracy with this public database.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.7 pp.1151-1155
Publication Date
2018/07/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E101.A.1151
Type of Manuscript
LETTER
Category
Biometrics

Authors

Tatsuya NOBUNAGA
  Toyota Central Research and Development Laboratories, Inc.
Toshiaki WATANABE
  Toyota Central Research and Development Laboratories, Inc.
Hiroya TANAKA
  Toyota Central Research and Development Laboratories, Inc.

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