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arXiv · 1911.03127

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
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Abstract

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 ultra-sensitive sensor that measures the magnetic fields produced by cardio-vascular electrical activity, i.e. Magentocardiography (MCG). After that, we treat the low-frequency noise generated by the sensors, 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 averaging or filtering, we employ deep-learning training on bio-magnetic signals. Using an existing dataset of ECG records, MCG labels are synthetically constructed. A unique deep learning structure composed of combined Convolutional Neural Network (CNN) with Gated Recurrent Unit (GRU) 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.

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Attayeb Mohsen, Muftah Al-Mahdawi, Mostafa M. Fouda, Mikihiko Oogane, Yasuo Ando, Zubair Md Fadlullah. 2019-11-08. AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application. https://arxiv.org/abs/1911.03127

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