Deep Learning Models for Magnetic Cardiography Edge Sensors Implementing Noise Processing and Diagnostics

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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Remote health monitoring has become a necessity due to reduced healthcare access resulting from pandemic lockdowns and the increasing aging population. Electrocardiography (ECG) is the standard for cardiac monitoring and arrhythmia identification, but it is inconvenient for long-time remote monitoring. Recently, Magnetocardiography (MCG) sensors that operate at room temperature became available based on spintronic sensors. However, MCG analysis is affected by the low-frequency noise present at the sensors. In this paper, we present an artificial intelligence (AI)-aided multi-model pipeline combining two AI architectures, defined as model-M1 and model-M2, targeted for ultra-edge Internet of Things (IoT) sensors to simulate arrhythmia detection. Model-M1 is a denoising preprocessor based on a sliding-window assisted deep-learning (DL) model. We investigate various methods to achieve high accuracy with lightweight computation. Model-M2 is a lightweight DL model that analyzes denoised ECG output from model-M1 to identify arrhythmia. We use multiple publicly available clinically annotated datasets to evaluate our proposal. We find that denoising by model-M1 retains the features, which assist the model-M2 in achieving high classification accuracy, compared to using a conventional moving average filter. This AI pipeline architecture is promising for privacy-preserving ultra-edge medical sensing devices.

Original languageEnglish
Pages (from-to)2656-2668
Number of pages13
JournalIEEE Access
Publication statusPublished - 2022
Externally publishedYes


  • Internet of Things (IoT)
  • Remote health monitoring
  • arrhythmia
  • convolutional neural network (CNN)
  • deep learning (DL)
  • electrocardiogram (ECG)
  • magnetocardiography (MCG)
  • medical analytics
  • spintronic sensor

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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