A review and comparison of the state-of-the-art techniques for atrial fibrillation detection and skin hydration

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article number679502
Number of pages9
JournalFrontiers in Communications and Networks
Volume2
DOIs
Publication statusPublished - 15 Jul 2021

Keywords

  • atrial fibrillation
  • skin hydration
  • machine learning and deep learning
  • healthcare
  • machine learning

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