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Título: | Electrocardiogram Classification Using Reservoir Computing With Logistic Regression |
Autor: | Escalona-Morán, M. CSIC; Soriano, Miguel C. ; Fischer, Ingo CSIC ORCID ; Mirasso, Claudio R. CSIC ORCID | Fecha de publicación: | may-2015 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | IEEE Journal of Biomedical and Health Informatics 19(3): 892-898 (2015) | Resumen: | An adapted state-of-the-art method of processing information known as Reservoir Computing is used to show its utility on the open and time-consuming problem of heartbeat classification. The MIT-BIH arrhythmia database is used following the guidelines of the Association for the Advancement of Medical Instrumentation. Our approach requires a computationally inexpensive preprocessing of the electrocardiographic signal leading to a fast algorithm and approaching a real-time classification solution. Our multiclass classification results indicate an average specificity of 97.75% with an average accuracy of 98.43%. Sensitivity and positive predicted value show an average of 84.83% and 88.75%, respectively, what makes our approach significant for its use in a clinical context. | Versión del editor: | http://dx.doi.org/10.1109/JBHI.2014.2332001 | URI: | http://hdl.handle.net/10261/133959 | DOI: | 10.1109/JBHI.2014.2332001 | Identificadores: | issn: 2168-2194 |
Aparece en las colecciones: | (IFISC) Artículos |
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