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.
Highlights
The main causes of the rise in the number of deaths in the European Union (EU) and the United States (US) can be attributed to a growing rate of heart diseases
This study provides a survey of the state-of-the-art Atrial fibrillation (AF) detection techniques based on machine learning to signify the need for interdisciplinary research in the field
The advantages and disadvantages of different AF detection schemes and skin hydration level measurement approach in the literature are highlighted that to provide a basis for further improvements research
Summary
The main causes of the rise in the number of deaths in the European Union (EU) and the United States (US) can be attributed to a growing rate of heart diseases. We present an analysis along with a comparison of machine learning and deep learning based approaches for detection of these skin hydration and AF using ECG signals (Gogate et al, 2017b; Adeel et al, 2019b). This study provides a survey of the state-of-the-art AF detection techniques based on machine learning to signify the need for interdisciplinary research in the field. The advantages and disadvantages of different AF detection schemes and skin hydration level measurement approach in the literature are highlighted that to provide a basis for further improvements research. This section further presents a review of the machine learning-based AF detection methods.
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