Electrocardiogram Classification Using Reservoir Computing With Logistic Regression

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2015-05, Vol.19 (3), p.892-898
Main Authors: Escalona-Moran, Miguel Angel, Soriano, Miguel C., Fischer, Ingo, Mirasso, Claudio R.
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmias, Cardiac - diagnosis
Classification
Computation
Databases, Factual
Delays
Electrocardiography
Electrocardiography - methods
Heart beat
Humans
Instrumentation
Logistic Models
Preprocessing
Regression
Reservoirs
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Testing
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2014.2332001