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A Sorting Fuzzy Min-Max Model in an Embedded System for Atrial Fibrillation Detection

Published:06 October 2022Publication History
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Abstract

Atrial fibrillation detection (AFD) has attracted much attention in the field of embedded systems. In this study, we propose a sorting fuzzy min-max (SFMM) model, and then develop an SFMM-based embedded system for AF detection. The proposed SFMM model is essentially enhanced the fuzzy min-max (FMM) model that have been successfully applied in many classification fields. In comparison with the typical FMM model, the proposed SFMM model can overcome the limitation of the input order problem encountered in the typical FMM model. The embedded system consists of a control chip and an analog-digital conversion (ADC) chip. The STM32F407 chip is used as the control chip and the ADS1292 chip, which has a high common-mode rejection ratio (CMRR), is used as the ADC chip. A series of machine learning benchmarks are included to evaluate the performance of the SFMM model. Experimental results on AF data further demonstrate the effectiveness of the SFMM-based embedded system.

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 2s
        June 2022
        383 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/3561949
        • Editor:
        • Abdulmotaleb El Saddik
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Publication History

        • Published: 6 October 2022
        • Online AM: 5 August 2022
        • Accepted: 27 July 2022
        • Revised: 11 July 2022
        • Received: 31 October 2021
        Published in tomm Volume 18, Issue 2s

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