A new neural network with adaptive activation function for classification of ECG Arrhythmias
Küçük Resim Yok
Tarih
2007
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER-VERLAG BERLIN
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multilayered perception (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
Açıklama
11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks -- SEP 12-14, 2007 -- Vietri sul Mare, ITALY
Anahtar Kelimeler
ANN, adaptive activation function, classification, ECG, arrhythmia
Kaynak
KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT I, PROCEEDINGS
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
4692
