TY - GEN
T1 - Noise-Removal from Spectrally-Similar Signals Using Reservoir Computing for MCG Monitoring
AU - Sakib, Sadman
AU - Fouda, Mostafa M.
AU - Al-Mahdawi, Muftah
AU - Mohsen, Attayeb
AU - Oogane, Mikihiko
AU - Ando, Yasuo
AU - Fadlullah, Zubair Md
N1 - Funding Information:
ACKNOWLEDGMENTS This work was partially supported by the Center for Science and Innovation in Spintronics (Core Research Cluster), Center for Spintronics Research Network, Tohoku University, the S-Innovation program, Japan Science and Technology Agency (JST), and by JSPS KAKENHI Grant Number JP19K15429. In addition, parts of this paper were made possible by NPRP grant NPRP13S-0205-200270 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Internet of Things (IoT)
KW - medical analytics
KW - noise
KW - reservoir computing
KW - Smart health
KW - spintronic sensor
UR - http://www.scopus.com/inward/record.url?scp=85115702083&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115702083&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500993
DO - 10.1109/ICC42927.2021.9500993
M3 - Conference contribution
AN - SCOPUS:85115702083
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications, ICC 2021
Y2 - 14 June 2021 through 23 June 2021
ER -