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A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

Liaqat, Sidrah; Dashtipour, Kia; Zahid, Adnan; Arshad, Kamran; Ullah Jan, Sana; Assaleh, Khaled; Ramzan, Naeem


Sidrah Liaqat

Adnan Zahid

Kamran Arshad

Khaled Assaleh

Naeem Ramzan


Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

Journal Article Type Article
Acceptance Date Apr 22, 2021
Online Publication Date Jul 15, 2021
Publication Date 2021
Deposit Date Oct 21, 2022
Publicly Available Date Oct 21, 2022
Journal Frontiers in Communications and Networks
Peer Reviewed Peer Reviewed
Volume 2
Article Number 679502
Keywords atrial fibrillation, skin hydration, machine learning and deep learning, healthcare, machine learning
Public URL


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