Abstract
Electrocardiography, technique, which is an essential tool in the diagnosis of heart disease, as well as other organs, is used by doctors for over 100 years. It is used to measure electrical activity of the heart as a function of time and present it in digital or analogue form. Whilst the standard 12 lead ECG is the basic clinical method of heart diagnosis it has its drawbacks. Measuring all 12 leads is often difficult and impractical, most of all it restricts patient movement. In 1988, Gordon Dower developed a system of quasi-orthogonal lead called EASI, which uses only 5 electrodes in order to register standard 12 lead ECG signals. The main goal of this work is to present a new tool using machine learning algorithms which transforms electrocardiographic signals (ECG) performed by EASI into a standard 12-channel ECG, which therefore could be an ideal tool for diagnose of NCDs.
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Oleksy, W., Budzianowski, Z., Tkacz, E., Garbacik, M. (2018). New Diagnostic Tool for Patients Suffering from Noncommunicable Diseases (NCDs). In: Rocha, Á., Guarda, T. (eds) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-319-73450-7_45
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DOI: https://doi.org/10.1007/978-3-319-73450-7_45
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