Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | Frontiers in Physiology |
| MU Faculty or unit | |
| Citation | |
| web | https://www.frontiersin.org/articles/10.3389/fphys.2022.867033/full |
| Doi | http://dx.doi.org/10.3389/fphys.2022.867033 |
| Keywords | electrocardiogram; ECG recording; animal model; deep learning; ECG analysis; artificial intelligence; isolated heart; arrhythmia classification |
| Description | Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology. |
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