Original articleNeural networks for classification of ECG ST-T segments
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Overview of featurization techniques used in traditional versus emerging deep learning-based algorithms for automated interpretation of the 12-lead ECG
2021, Journal of ElectrocardiologyCitation Excerpt :At the final stage in the interpretation pipeline illustrated in Fig. 1 the harvested features are interrogated and a diagnostic statement is produced. Many methods have been reported to facilitate this stage, these include statistical techniques, the application of human or machine defined rules, and, in some cases, more exotic machine learning and AI based methods [11–14]. Specifically, in the case of the latter, researchers have previously reported the use of neural networks in the decision stage [12–14].
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1998, International Journal of Medical InformaticsComputer treason: Intraobserver variabilty of an electocardiographic computer system
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1996, Artificial Intelligence in MedicinePossibilities of using neural networks for ECG classification
1996, Journal of Electrocardiology
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Dr. Ednebrandt was supported during his research work in Glasgow in part by grants from the Swedish Medical Research Council and from the Faculty of Medicine, University of Lund, Sweden.
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