The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Individu boleh dikenal pasti melalui ciri yang diekstrak daripada elektrokardiogram (ECG). Walau bagaimanapun, debaran yang tidak teratur akibat tekanan atau senaman mengurangkan ketepatan pengenalan akibat herotan bentuk gelombang ECG. Dalam surat ini, kami mencadangkan skim pengenalan manusia berdasarkan spektrum frekuensi ECG, yang boleh berjaya mengekstrak ciri dan dengan itu mengenal pasti individu walaupun semasa bersenam. Untuk skema yang dicadangkan, kami menunjukkan kadar ketepatan 99.8% dalam eksperimen terkawal dengan subjek bersenam. Tahap ketepatan ini dicapai dengan menentukan ciri penting individu dengan pengelas hutan rawak. Di samping itu, keberkesanan skim yang dicadangkan disahkan menggunakan pangkalan data ECG yang tersedia untuk umum. Kami menunjukkan bahawa skim yang dicadangkan juga mencapai ketepatan yang tinggi dengan pangkalan data awam ini.
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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Salinan
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
Salinan
@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},}
Salinan
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 -