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

Abstract

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.

Citation

Liaqat, S., Dashtipour, K., Zahid, A., Arshad, K., Ullah Jan, S., Assaleh, K., & Ramzan, N. (2021). A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration. Frontiers in Communications and Networks, 2, Article 679502. https://doi.org/10.3389/frcmn.2021.679502

• 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
• DOI: https://doi.org/10.3389/frcmn.2021.679502
• Keywords: atrial fibrillation, skin hydration, machine learning and deep learning, healthcare, machine learning
• Public URL: http://researchrepository.napier.ac.uk/Output/2937056