2019/11/08 by Attayeb Mohsen, Muftah Al-Mahdawi, Mohsen, Attayeb +11
Computer Science · Engineering · Medicine · Neuroscience · Physics and Astronomy · #Applied Physics (physics.app-ph) #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #physics.app-ph
paper · pdf · doi:10.48550/arxiv.1911.03127
Presented at IEEE International Conference on Communications 2020
openalex publication_date 2019/11/08 · arxiv created 2020/06/10 · arxiv updated 2020/06/11 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
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.