Noise-Removal from Spectrally-Similar Signals Using Reservoir Computing for MCG Monitoring

Sadman Sakib, Mostafa M. Fouda, Muftah Al-Mahdawi, Attayeb Mohsen, Mikihiko Oogane, Yasuo Ando, Zubair Md Fadlullah

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Continuous low-rate monitoring is an important IoT application, which requires high-fidelity in observing signals with low frequency. However, most sensors exhibit noise that is inversely-proportional to spectral frequency (1/f noise). Because both the relevant signal and noise share the same spectral properties, standard linear filtering techniques cannot be used. We are looking into a special application for remote healthcare of the magnetic field sensing of cardiac activity, magnetocardiography (MCG). For such an application, we need to develop a noise separation method, that is also resource-efficient. Previously, we demonstrated AI-based removal of 1/f noise in MCG by a convolutional neural network coupled with gated recurrent units. However, it needs a large amount of data for training, requiring significant training time and computational power. In this work, we employ reservoir computing (RC) for noise-removal, while being conservative in computing resources.

本文言語English
ホスト出版物のタイトルICC 2021 - IEEE International Conference on Communications, Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728171227
DOI
出版ステータスPublished - 2021 6月
外部発表はい
イベント2021 IEEE International Conference on Communications, ICC 2021 - Virtual, Online, Canada
継続期間: 2021 6月 142021 6月 23

出版物シリーズ

名前IEEE International Conference on Communications
ISSN(印刷版)1550-3607

Conference

Conference2021 IEEE International Conference on Communications, ICC 2021
国/地域Canada
CityVirtual, Online
Period21/6/1421/6/23

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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