A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning
| Autor: | Hani Sabbour, Akram Alomainy, Ali Rizwan, Ahmed Zoha, Qammer H. Abbasi, Muhammad Imran, Ameena Saad Al-Sumaiti, Ismail Ben Mabrouk |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Computer science Biomedical Engineering Wearable computer 02 engineering and technology 030204 cardiovascular system & hematology Machine learning computer.software_genre Timely diagnosis Machine Learning Electrocardiography 03 medical and health sciences 0302 clinical medicine Low energy Atrial Fibrillation 0202 electrical engineering electronic engineering information engineering medicine Humans Diagnosis Computer-Assisted business.industry Cardiac arrhythmia Signal Processing Computer-Assisted 020206 networking & telecommunications Atrial fibrillation medicine.disease Female State (computer science) Artificial intelligence business computer |
| Zdroj: | IEEE Reviews in Biomedical Engineering. 14:219-239 |
| ISSN: | 1941-1189 1937-3333 |
| Popis: | Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. In this paper, state-of-the-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF. |
| Databáze: | OpenAIRE |
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