AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)


As we are about to embark upon the highly hyped 'Society 5.0', powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings. On the merits of low power consumption, portability, and non-intrusiveness, there are no suitable IoT solutions that can provide information comparable to the conventional Electrocardiography (ECG). In this paper, we propose an IoT device utilizing a spintronic-technology-based ultra-sensitive Magnetic Tunnel Junction (MTJ) sensor that measures the magnetic fields produced by cardio-vascular electromagnetic activity, i.e. Magentocardiography (MCG). We treat the low-frequency noise generated by the sensor, which is also a challenge for most other sensors dealing with low-frequency bio-magnetic signals. Instead of relying on generic signal processing techniques such as moving average, we employ deep-learning training on bio-magnetic signals. Using an existing dataset of ECG records, MCG signals are synthesized. A unique deep learning model, composed of a one-dimensional convolution layer, Gated Recurrent Unit (GRU) layer, and a fully-connected neural layer, is trained using the labeled data moving through a striding window, which is able to smartly capture and eliminate the noise features. Simulation results are reported to evaluate the effectiveness of the proposed method that demonstrates encouraging performance.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150895
Publication statusPublished - 2020 Jun
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: 2020 Jun 72020 Jun 11

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2020 IEEE International Conference on Communications, ICC 2020


  • ECG
  • GRU
  • IoT
  • MCG
  • Smart health
  • convolution
  • deep learning
  • medical analytics
  • noise
  • spintronic sensor


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