Literature DB >> 32069949

Early Detection of Atrial Fibrillation Based on ECG Signals.

Nuzhat Ahmed1, Yong Zhu2.   

Abstract

Atrial fibrillation, often called AF is considered to be the most common type of cardiac arrhythmia, which is a major healthcare challenge. Early detection of AF and the appropriate treatment is crucial if the symptoms seem to be consistent and persistent. This research work focused on the development of a heart monitoring system which could be considered as a feasible solution in early detection of potential AF in real time. The objective was to bridge the gap in the market for a low-cost, at home use, noninvasive heart health monitoring system specifically designed to periodically monitor heart health in subjects with AF disorder concerns. The main characteristic of AF disorder is the considerably higher heartbeat and the varying period between observed R waves in electrocardiogram (ECG) signals. This proposed research was conducted to develop a low cost and easy to use device that measures and analyzes the heartbeat variations, varying time period between successive R peaks of the ECG signal and compares the result with the normal heart rate and RR intervals. Upon exceeding the threshold values, this device creates an alert to notify about the possible AF detection. The prototype for this research consisted of a Bitalino ECG sensor and electrodes, an Arduino microcontroller, and a simple circuit. The data was acquired and analyzed using the Arduino software in real time. The prototype was used to analyze healthy ECG data and using the MIT-BIH database the real AF patient data was analyzed, and reasonable threshold values were found, which yielded a reasonable success rate of AF detection.

Entities:  

Keywords:  ECG signal; atrial fibrillation (AF); biosensor; heart health monitoring; low-cost device

Year:  2020        PMID: 32069949     DOI: 10.3390/bioengineering7010016

Source DB:  PubMed          Journal:  Bioengineering (Basel)        ISSN: 2306-5354


  4 in total

1.  Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning.

Authors:  Jie Yang; Jinfeng Li; Kun Lan; Anruo Wei; Han Wang; Shigao Huang; Simon Fong
Journal:  Bioengineering (Basel)       Date:  2022-06-22

2.  Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs.

Authors:  Hyeonjeong Lee; Miyoung Shin
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

3.  Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals.

Authors:  Alfonso Maria Ponsiglione; Francesco Amato; Maria Romano
Journal:  Bioengineering (Basel)       Date:  2021-12-28

4.  AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.

Authors:  Bambang Tutuko; Siti Nurmaini; Alexander Edo Tondas; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ria Esafri; Firdaus Firdaus; Ade Iriani Sapitri
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-14       Impact factor: 2.796

  4 in total

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