Recognition of Signs of Diabetes in Electric Cardiac Signals by Neural Network Analysis
Biomedical Engineering, ISSN: 1573-8256, Vol: 55, Issue: 1, Page: 73-77
2021
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Article Description
Diabetes mellitus is one of the commonest chronic diseases. Modern diagnostic tools and novel glucose monitoring devices may significantly improve the patient’s prognosis. This work proposes a new approach to the diagnosis of diabetes using electrocardiography. The approach is based on neural network analysis of ECG databases and detection of diabetes-specific changes in the cardiac cycle.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85105710627&origin=inward; http://dx.doi.org/10.1007/s10527-021-10074-2; https://link.springer.com/10.1007/s10527-021-10074-2; https://link.springer.com/content/pdf/10.1007/s10527-021-10074-2.pdf; https://link.springer.com/article/10.1007/s10527-021-10074-2/fulltext.html; https://dx.doi.org/10.1007/s10527-021-10074-2; https://link.springer.com/article/10.1007/s10527-021-10074-2
Springer Science and Business Media LLC
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