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

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)2656-2668
ページ数13
ジャーナルIEEE Access
10
DOI
出版ステータスPublished - 2022
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

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