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Paper Abstract and Keywords
Presentation 2022-03-04 09:30
Classification and discriminability of heart rate variability indices in monkeys and humans using machine learning
Itaru Kaneko, Daisuke Hirahara (Tohoku Univ.), Junichiro Hayano (Nagoya City Univ.), Emi Yuda (Tohoku Univ.) MBE2021-100
Abstract (in Japanese) (See Japanese page) 
(in English) In the field of biometrics, the use of physical characteristics to identify individuals has become a familiar technology in recent years. However, it has not been clarified whether information obtained from unregistered human bio-signals, such as ECG waveforms, can be used to identify individuals. Therefore, in this study, we evaluated discriminant analysis by comparing human and monkey ECGs using machine learning. For the ECG waveforms, 10 ECGs of newborns were randomly selected from the ALLSTAR (Allostatic State Mapping by Ambulatory ECG Repository) database (Japan). For monkey ECGs, ECG data from macaque monkeys (Macaca fascicularis) were calculated. T-SNE (T-Distributed Stochastic Neighbor Embedding) was used for machine learning, and the heart rate variability (HRV) indices calculated for each minute were compared. The heart rate variability indices were calculated using four time-domain parameters: heart rate (HR), mean value of RR intervals (Mean), standard deviation of RR intervals (SDNN), and root mean square of differences between successive adjacent RR intervals (RMSSD), and four frequency-domain parameters: calculated total power (TP), very low frequency domain (VLF), low frequency domain (LF), high frequency domain (HF), and LF/HF. Heart rate variability index of each data was extracted and visualized by dimensionality reduction to two dimensions using t-SNE, and the results showed that the ECG of the monkey could not be detected.
Keyword (in Japanese) (See Japanese page) 
(in English) Heart Rate Variability (HRV) / Biometrics / Electrocardiogram (ECG) / Machine Learning / T-SNE / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 389, MBE2021-100, pp. 55-55, March 2022.
Paper # MBE2021-100 
Date of Issue 2022-02-23 (MBE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MBE2021-100

Conference Information
Committee MBE NC  
Conference Date 2022-03-02 - 2022-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MBE 
Conference Code 2022-03-MBE-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification and discriminability of heart rate variability indices in monkeys and humans using machine learning 
Sub Title (in English)  
Keyword(1) Heart Rate Variability (HRV)  
Keyword(2) Biometrics  
Keyword(3) Electrocardiogram (ECG)  
Keyword(4) Machine Learning  
Keyword(5) T-SNE  
Keyword(6)  
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1st Author's Name Itaru Kaneko  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Daisuke Hirahara  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Junichiro Hayano  
3rd Author's Affiliation Nagoya City University (Nagoya City Univ.)
4th Author's Name Emi Yuda  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2022-03-04 09:30:00 
Presentation Time 25 minutes 
Registration for MBE 
Paper # MBE2021-100 
Volume (vol) vol.121 
Number (no) no.389 
Page p.55 
#Pages 1 
Date of Issue 2022-02-23 (MBE) 

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