Automated Indicator of Atrial Fibrillations Risk Using Machine Learning

Almazrouei, Majed; Al-Rajab, Murad

doi:10.1109/csci54926.2021.00254

Cited by 1 publication

(2 citation statements)

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“…Table 1 lists the most common machine learning methods used in the field of AF [112]. In summary, several publications were analyzed to find the most prevalent algorithms and to showcase the results of each way of implementation.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The proposed model is utilized as a general classification technique for ECG data and accurate detection of different disorders. Numerous algorithms have been developed and a lot of work has already been done for classifying ECG data in the recent literature for arrhythmia detection [112]. Data pre-processing, feature extraction, and classification are three processes in the general classification method.…”

Section: Introductionmentioning

confidence: 99%

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A model to enhance the atrial fibrillations’ risk detection using deep learning

Almazrouei¹,

Al-Rajab²

2022

PEN

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Atrial fibrillation (AF) is a complex arrhythmia linked to a variety of common cardiovascular illnesses and conventional cardiovascular risk factors. Although awareness and improved detection of AF have improved over the last decade as the incidence and prevalence of AF has increased, current trends in using machine learning approaches to diagnose AF are still lacking in precision. To determine the true nature of the Electrocardiography (ECG) signal segments, a Convolutional Neural Network (CNN) model was employed to discover hidden information. Fully Connected (FC) layers were then utilized to categorize the ECG data segments as normal or abnormal. The suggested algorithm's findings were compared to state-of-the-art arrhythmia identification algorithms in the literature for the MIT-BIH ECG database. The methodology proved not only to yield high classification performance (98.5%) but also low processing computational advantage where the CNN was the most accurate algorithm used for atrial fibrillation detection hence. To conclude the findings of the research, a model was prepared to test the accuracy of the most common ML algorithms used for AF detection. After comparing the results of the experiment, it was clear that CNN algorithm is the best approach compared to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%