Detection of Atrial Fibrillation Using a Machine Learning Approach
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis....
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doaj-006fa5df5c42482d8a45982768c042be2020-11-27T08:08:30ZengMDPI AGInformation2078-24892020-11-011154954910.3390/info11120549Detection of Atrial Fibrillation Using a Machine Learning ApproachSidrah Liaqat0Kia Dashtipour1Adnan Zahid2Khaled Assaleh3Kamran Arshad4Naeem Ramzan5School of Engineering and Computing, University of the West of Scotland, Glasgow G72 0LH, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKFaculty of Engineering and IT, Ajman University, Ajman 346, UAEFaculty of Engineering and IT, Ajman University, Ajman 346, UAESchool of Engineering and Computing, University of the West of Scotland, Glasgow G72 0LH, UKThe atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.https://www.mdpi.com/2078-2489/11/12/549atrial fibrillationmachine learningcardiovasculardeep learninghealthcare |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sidrah Liaqat Kia Dashtipour Adnan Zahid Khaled Assaleh Kamran Arshad Naeem Ramzan |
spellingShingle |
Sidrah Liaqat Kia Dashtipour Adnan Zahid Khaled Assaleh Kamran Arshad Naeem Ramzan Detection of Atrial Fibrillation Using a Machine Learning Approach Information atrial fibrillation machine learning cardiovascular deep learning healthcare |
author_facet |
Sidrah Liaqat Kia Dashtipour Adnan Zahid Khaled Assaleh Kamran Arshad Naeem Ramzan |
author_sort |
Sidrah Liaqat |
title |
Detection of Atrial Fibrillation Using a Machine Learning Approach |
title_short |
Detection of Atrial Fibrillation Using a Machine Learning Approach |
title_full |
Detection of Atrial Fibrillation Using a Machine Learning Approach |
title_fullStr |
Detection of Atrial Fibrillation Using a Machine Learning Approach |
title_full_unstemmed |
Detection of Atrial Fibrillation Using a Machine Learning Approach |
title_sort |
detection of atrial fibrillation using a machine learning approach |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2020-11-01 |
description |
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate. |
topic |
atrial fibrillation machine learning cardiovascular deep learning healthcare |
url |
https://www.mdpi.com/2078-2489/11/12/549 |
work_keys_str_mv |
AT sidrahliaqat detectionofatrialfibrillationusingamachinelearningapproach AT kiadashtipour detectionofatrialfibrillationusingamachinelearningapproach AT adnanzahid detectionofatrialfibrillationusingamachinelearningapproach AT khaledassaleh detectionofatrialfibrillationusingamachinelearningapproach AT kamranarshad detectionofatrialfibrillationusingamachinelearningapproach AT naeemramzan detectionofatrialfibrillationusingamachinelearningapproach |
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1724413657089048576 |