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

Tatsuya NOBUNAGA  Toshiaki WATANABE  Hiroya TANAKA  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E101-A   No.7   pp.1151-1155
Publication Date: 2018/07/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E101.A.1151
Type of Manuscript: LETTER
Category: Biometrics
biometrics,  electrocardiogram,  human identification,  spectrum,  

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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.