A fuzzy clustering neural network architecture for classification of ECG arrhythmias

Accurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers in biology and medicine Jg. 36; H. 4; S. 376 - 388
Hauptverfasser: Özbay, Yüksel, Ceylan, Rahime, Karlik, Bekir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.04.2006
Schlagworte:
Adult
Algorithms
Arrhythmia
Arrhythmias, Cardiac - classification
Clustering
Computer applications
Diagnosis
ECG
Electrocardiograms
Electrocardiography
Female
Fuzzy c-means
Fuzzy clustering
Fuzzy Logic
Fuzzy set theory
Health care
Humans
Male
Medicine
Multilayer perceptron
Neural network
Neural networks
Neural Networks (Computer)
Pattern recognition
Pattern Recognition, Automated
Fuzzy clustering
Arrhythmia
ECG
Multilayer perceptron
Neural network
Pattern recognition
Fuzzy c-means
ISSN:0010-4825, 1879-0534
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Accurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy clustering NN architecture (FCNN) for early diagnosis. The ECG signals are taken from MIT-BIH ECG database, which are used to classify 10 different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75 ± 19.06 ). The test results suggest that a new proposed FCNN architecture can generalize better than ordinary MLP architecture and also learn better and faster. The advantage of proposed structure is a result of decreasing the number of segments by grouping similar segments in training data with fuzzy c-means clustering.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2005.01.006