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| 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 |
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| 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 |
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| 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) | |
| Keyword(7) | |
| Keyword(8) | |
| 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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