JOURNAL ARTICLE

Heart rate events classification via explainable fuzzy logic systems

Anis Jannah DahalanTajul Rosli RazakMohammad Hafiz IsmailShukor Sanim Mohd FauziRay Adderley JM Gining

Year: 2021 Journal:   IAES International Journal of Artificial Intelligence Vol: 10 (4)Pages: 1036-1036   Publisher: Institute of Advanced Engineering and Science (IAES)

Abstract

<span lang="EN-US">As people, we have no way of knowing whether our heart rate is considered normal or not. The strength and quality of our pulses will deteriorate as we get older. As a result, this may indicate a heart attack or another illness that requires immediate attention. The main goal of this paper is to define the heart rate stage using fuzzy logic systems (FLSs). In practice, however, designing or developing fuzzy logic systems is extremely difficult. To achieve this aim, we suggested a solution that involves: i) classifying the medical expert's criterion for signs of heart rate; ii) developing an explainable fuzzy logic system for heart rate measurement; and iii) evaluating the proposed system with human experts. In addition, the aim of this research was to provide an explainable fuzzy system that people could use to self-monitor heart rate levels and determine their health status. As a result, it is hoped that this research would provide insight into how to improve the development of fuzzy logic systems, especially in the field of medical applications.</span>

Keywords:
Fuzzy logic Computer science Field (mathematics) Artificial intelligence Fuzzy control system Machine learning Data mining Mathematics

Metrics

8
Cited By
1.42
FWCI (Field Weighted Citation Impact)
31
Refs
0.81
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Heart Rate Variability and Autonomic Control
Health Sciences →  Medicine →  Cardiology and Cardiovascular Medicine
Non-Invasive Vital Sign Monitoring
Physical Sciences →  Engineering →  Biomedical Engineering
EEG and Brain-Computer Interfaces
Life Sciences →  Neuroscience →  Cognitive Neuroscience

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