Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems.

Computer Methods and Programs in Biomedicine
Paolo Massimo BuscemaFrancesca Della Torre

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, having been recognized as a true cardiovascular epidemic. In this paper, a new methodology for Computer Aided Diagnosis of AF based on a special kind of artificial adaptive systems has been developed. Following the extraction of data from the PhysioNet repository, a new dataset composed of the R/R distances of 73 patients was created. To avoid redundancy, the training set was created by randomly selecting 50% of the subjects from the entire sample, thus making a choice by patient and not by record. The remaining 50% of subjects were randomly split by records in testing and prediction sets. The original ECG data has been transformed according to the following four orders of abstraction: a) sequence of R/R intervals; b) composition of ECG data into a moving window; c) training of different machine learning systems to abstract the function governing the AF; d) fuzzy transformation of Machine learning estimations. In this paper, in parallel with the classic method of windowing, we propose a variant based on a system of progressive moving averages. The best performing machine learning, Supervised Contractive Map (SVCm), reached an overall mean accur...Continue Reading

Citations

Nov 27, 2020·BMC Medical Informatics and Decision Making·Yusong HuPeizhe Li
Jun 7, 2021·Computer Methods and Programs in Biomedicine·Jader Giraldo-GuzmánMiguel Urina-Triana
Sep 4, 2021·Computer Methods and Programs in Biomedicine·Hongpo ZhangZongmin Wang

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