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How might we create a more realistic ECG Training?

Electrocardiography (ECG or EKG) is the process of recording the electrical activity of the heart over a period of time using electrodes placed on a patient’s body. These electrodes detect the tiny electrical changes on the skin that arise from the heart muscle depolarizing during each heartbeat. [1] It is necessary for the diagnosis and prompt initiation of therapy in patients with acute coronary syndromes (ACS) and is the most accurate means of diagnosing conduction disturbances and arrhythmias. [2]ECG is an irreplaceable diagnostic method in clinical practice. It offers great diagnostic value at minimal costs while being a relatively quick, painless and noninvasive process. The quality of the resulting graph is depending on the accurate placement of the electrodes on the patients' body and that the patient lies absolutely still to avoid any muscle contractions which may lead to distortions of the graph.The interpretation of ECGs is a highly complex topic which requires lots of training and experience. Although there has been plenty of research on the topic of automated interpretation and pattern recognition of ECGs by computer algorithms and neural networks, a reliable interpretation of complex ECGs cannot be guaranteed as of today. While the trend seems to favor automated ECG interpretation, a clear prediction when these technologies have saturated the market cannot be given. One reason for this, similar to autonomous vehicles, is the issue of where liability can be found when an incorrect diagnosis leads to harming of a patient. For the foreseeable future we will most likely rely on the skill and experience of humans to interpret ECGs. [1] https://en.wikipedia.org/wiki/Electrocardiography [2] The British Journal of Primary Care Nursing: Taking an ECG: Getting the best possible recording

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-125680
Date January 2016
CreatorsSiebert, Jost
PublisherUmeå universitet, Designhögskolan vid Umeå universitet
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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