Paper Info
Title
A new neural network with adaptive activation function for classification of ECG arrhythmias
Abstract
This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multilayered perceptron (MLP) with backpropagation training algorithm, and a new neural network with adaptive activation function (AAFNN) for classification of ECG arrhythmias. The ECG signals are taken from MIT-BIH ECG database, which are used to classify ten 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 10 patients (7 male and 3 female, average age is 33.8±16.4). The results show that neural network with adaptive activation function is more suitable for biomedical data like as ECG in the classification problems and training speed is much faster than neural network with fixed sigmoid activation function.
Year
DOI
Venue
2007
10.1007/978-3-540-74819-9_1
KES (1)
Keywords
Field
DocType
atrial fibrillation,mit-bih ecg database,ecg signal,neural network,experimental ecg record,ecg arrhythmias,adaptive activation function,new neural network,well-known neural network architecture,atrial flutter,backpropagation algorithm,multi layer perceptron,activation function,classification,backpropagation
Left bundle branch block,Pattern recognition,Activation function,Computer science,Right bundle branch block,Artificial intelligence,Sinus bradycardia,Backpropagation,Artificial neural network,Perceptron,Atrial flutter,Machine learning
Conference
Volume
ISSN
Citations 
4692
0302-9743
3
PageRank 
References 
Authors
0.40
12
2
Name
Order
Citations
PageRank
Gülay Tezel11047.40
Yüksel Ozbay231217.03