Electrocardiogram classification using reservoir computing with logistic regression.

Miguel Angel Escalona-Morán, Miguel C Soriano, Ingo Fischer, Claudio R Mirasso
Author Information

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.

MeSH Term

Arrhythmias, Cardiac
Databases, Factual
Electrocardiography
Humans
Logistic Models
Sensitivity and Specificity
Signal Processing, Computer-Assisted

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