Key result
A general framework utilizing machine learning was presented to address limitations in conventional electrocardiography monitoring systems and improve cardiovascular disease detection.
Why the study?
Conventional ECG monitoring systems have several limitations, highlighting the need to adopt new technology such as machine learning to improve monitoring and medical applications.
This review highlights the limitations of conventional ECG monitoring and proposes a machine learning-based framework to enhance its diagnostic capabilities.
Proposed ML ECG framework should not change practice; leaves open need for prospective trials to assess real-world utility.
As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.
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Khairuddin et al. (2019) conducted a review in Cardiovascular disease. Machine learning vs. Conventional ECG monitoring systems was evaluated. A general framework utilizing machine learning was presented to address limitations in conventional electrocardiography monitoring systems and improve cardiovascular disease detection.
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