Paper Abstract and Keywords |
Presentation |
2018-12-06 11:20
Hardware implementation of ECG signals outlier detector trained by Sparse Robust Deep Autoencoder Naoto Soga, Shimpei Sato, Hiroki Nakahara (Titech) RECONF2018-42 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Current ECG outlier detection is rule-based, there are many false positives, and it is necessary to study a new outlier detection method. As a method of detecting an outlier, autoencoder which is one of the models of a neural network is known. In this research, we trained autoencoder using Robust Deep Autoencoder (RDA) which can train an autoencoder without training data. We made the weighting parameters sparse and we reduced the weighting parameters. As a result, about 95% of the weighting parameters were reduced while keeping the F1 score down to about 0.84%. In this research, we also implemented the autoencoder trained by RDA on the FPGA. By adjusting the bit width and the way of storing the weighting parameters, it was possible to reduce the capacity of the weighting parameters by about 95.6% compared to before applying a sparse network to autoencoder. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
outlier detection / autoencoder / a sparse network / FPGA / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 340, RECONF2018-42, pp. 45-50, Dec. 2018. |
Paper # |
RECONF2018-42 |
Date of Issue |
2018-11-28 (RECONF) |
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) |
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RECONF2018-42 |
Conference Information |
Committee |
VLD DC CPSY RECONF CPM ICD IE IPSJ-SLDM |
Conference Date |
2018-12-05 - 2018-12-07 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Satellite Campus Hiroshima |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Design Gaia 2018 -New Field of VLSI Design- |
Paper Information |
Registration To |
RECONF |
Conference Code |
2018-12-VLD-DC-CPSY-RECONF-CPM-ICD-IE-SLDM-EMB-ARC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Hardware implementation of ECG signals outlier detector trained by Sparse Robust Deep Autoencoder |
Sub Title (in English) |
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outlier detection |
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autoencoder |
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a sparse network |
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FPGA |
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1st Author's Name |
Naoto Soga |
1st Author's Affiliation |
Tokyo Institute of Technology (Titech) |
2nd Author's Name |
Shimpei Sato |
2nd Author's Affiliation |
Tokyo Institute of Technology (Titech) |
3rd Author's Name |
Hiroki Nakahara |
3rd Author's Affiliation |
Tokyo Institute of Technology (Titech) |
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Speaker |
Author-1 |
Date Time |
2018-12-06 11:20:00 |
Presentation Time |
25 minutes |
Registration for |
RECONF |
Paper # |
RECONF2018-42 |
Volume (vol) |
vol.118 |
Number (no) |
no.340 |
Page |
pp.45-50 |
#Pages |
6 |
Date of Issue |
2018-11-28 (RECONF) |
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