1. Electrocardiogram classification using reservoir computing with logistic regression.
- Author
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Escalona-Morán MA, Soriano MC, Fischer I, and Mirasso CR
- Subjects
- Databases, Factual, Humans, Logistic Models, Sensitivity and Specificity, Arrhythmias, Cardiac diagnosis, Electrocardiography methods, Signal Processing, Computer-Assisted
- Abstract
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.
- Published
- 2015
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