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.
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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Tatsuya NOBUNAGA, Toshiaki WATANABE, Hiroya TANAKA, "Identification of Exercising Individuals Based on Features Extracted from ECG Frequency Spectrums" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 7, pp. 1151-1155, July 2018, doi: 10.1587/transfun.E101.A.1151.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1151/_p
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@ARTICLE{e101-a_7_1151,
author={Tatsuya NOBUNAGA, Toshiaki WATANABE, Hiroya TANAKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Identification of Exercising Individuals Based on Features Extracted from ECG Frequency Spectrums},
year={2018},
volume={E101-A},
number={7},
pages={1151-1155},
abstract={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.},
keywords={},
doi={10.1587/transfun.E101.A.1151},
ISSN={1745-1337},
month={July},}
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TY - JOUR
TI - Identification of Exercising Individuals Based on Features Extracted from ECG Frequency Spectrums
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1151
EP - 1155
AU - Tatsuya NOBUNAGA
AU - Toshiaki WATANABE
AU - Hiroya TANAKA
PY - 2018
DO - 10.1587/transfun.E101.A.1151
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2018
AB - 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.
ER -