Paper Info
Title
Noise-Removal from Spectrally-Similar Signals Using Reservoir Computing for MCG Monitoring
Abstract
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.
Year
DOI
Venue
2021
10.1109/ICC42927.2021.9500993
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
Keywords
DocType
ISSN
Smart health, Internet of Things (IoT), reservoir computing, noise, spintronic sensor, medical analytics
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Sadman Sakib133.10
Mostafa M. Fouda200.68
Muftah Al-Mahdawi300.34
Attayeb Mohsen400.68
Mikihiko Oogane501.69
Y Ando6175.47
Zubair Md. Fadlullah775645.47