title: Electrocardiogram classification using reservoir computing with logistic regression.
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.
authors: Miguel Angel
authors: MA
authors: Escalona-Morán
authors: Miguel C
authors: MC
authors: Soriano
authors: Ingo
authors: I
authors: Fischer
authors: Claudio R
authors: CR
authors: Mirasso
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted