AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application
AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application
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用于心磁图应用的基于自旋电子的物联网传感器的人工智能辅助噪声处理
DOI：
10.1109/icc40277.2020.9148617
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Fadlullah Zubair Md
中科院分区：
文献类型：
--
作者：
Mohsen Attayeb;Al-Mahdawi Muftah;Fouda Mostafa M.;Oogane Mikihiko;Ando Yasuo;Fadlullah Zubair Md
随着我们即将进入由物联网（IoT）驱动的高度炒作的“社会5.0”，传统的监测人类心脏信号以跟踪心血管状况的方法具有挑战性，特别是在远程医疗环境中。在低功耗、便携性和非侵入性的优点上，没有合适的物联网解决方案可以提供与传统心电图（ECG）相当的信息。在本文中，我们提出了一种利用基于自旋电子技术的超灵敏磁性隧道结（MTJ）传感器的物联网设备，该传感器可测量心血管电磁活动产生的磁场，即磁共振心动图（MCG）。我们处理传感器产生的低频噪声，这也是大多数其他传感器处理低频生物磁信号的挑战。我们不依赖移动平均等通用信号处理技术，而是对生物磁信号进行深度学习训练。使用ECG记录的现有数据集，合成MCG信号。一个独特的深度学习模型，由一维卷积层，门控递归单元（GRU）层和全连接神经层组成，使用通过跨越窗口移动的标记数据进行训练，能够智能地捕获和消除噪声特征。仿真结果报告，以评估所提出的方法，表现出令人鼓舞的性能的有效性。
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.
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影响因子：
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