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| Presentation | 2023-07-13 13:50 A GAN-Based Data Augmentation Approach to Improve Heart Rate Range Classification via Doppler Radar Danyuan Yu, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.)  SeMI2023-31 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this work, a novel data augmentation framework is proposed for improving heart rate (HR) range classification using Doppler radar signals generated from electrocardiogram (ECG) signals. The proposed framework utilizes an attention-based Wasserstein Generative Adversarial Networks-Gradient penalty (aWGAN-GP) to generate synthetic Doppler radar signals, which are then used to train a support vector machine (SVM) for classifying HR ranges (i.e., into low, normal, and high categories). Additionally, to address the imbalanced class distribution of ECG samples, a simple augmentation method for ECG signals is proposed, which is used to synthesize new Doppler radar signals for training the SVM. Our experiments show that the proposed method yields more accurate morphology of Doppler radar signals in terms of root mean squared error (RMSE) and Pearson's correlation coefficient (PCC). Moreover, our method also achieves better diversity in the generated Doppler radar signals, as demonstrated by the lowest relative Fréchet inception distance (rFID). Finally, our method successfully relieves the data imbalance problem and achieves improved HR range classification performance in terms of accuracy, F1-score, and recall. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | GAN / data augmentation / electrocardiogram (ECG) / heart rate (HR) / Doppler radar / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 110, SeMI2023-31, pp. 46-51, July 2023. | |
| Paper # | SeMI2023-31 | |
| Date of Issue | 2023-07-05 (SeMI) | |
| 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 | SeMI2023-31 |
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| Conference Information | |
| Committee | SeMI RCS RCC NS SR |
| Conference Date | 2023-07-12 - 2023-07-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Osaka University Nakanoshima Center + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Distributed Wireless Network, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2023-07-SeMI-RCS-RCC-NS-SR |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A GAN-Based Data Augmentation Approach to Improve Heart Rate Range Classification via Doppler Radar |
| Sub Title (in English) | |
| Keyword(1) | GAN |
| Keyword(2) | data augmentation |
| Keyword(3) | electrocardiogram (ECG) |
| Keyword(4) | heart rate (HR) |
| Keyword(5) | Doppler radar |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Danyuan Yu |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Mondher Bouazizi |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-07-13 13:50:00 |
| Presentation Time | 25 minutes |
| Registration for | SeMI |
| Paper # | SeMI2023-31 |
| Volume (vol) | vol.123 |
| Number (no) | no.110 |
| Page | pp.46-51 |
| #Pages | 6 |
| Date of Issue | 2023-07-05 (SeMI) |
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