An adaptive backpropagation neural network for arrhythmia classification using R-R interval signal

Title:

An adaptive backpropagation neural network for arrhythmia classification using R-R interval signal

Creator:

Asl, Babak Mohammadzadeh, Sharafat, Ahmad R., and Setarehdan, Kamaledin S.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:449aa78e-912a-48f2-9b54-1777ea02a039
uuid:449aa78e-912a-48f2-9b54-1777ea02a039
doi:10.14311/NNW.2012.22.033

Subject:

Adaptive-learning-rate neural networks, arrhythmia classification, non-linear analysis, and R-R interval signal

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Automatic detection and classification of cardiac arrhythmias with high accuracy and by using as little information as possible is highly useful in Holter monitoring of the high risk patients and in telemedicine applications where the amount of information which must be transmitted is an important issue. To this end, we have used an adaptive-learning-rate neural network for automatic classification of four types of cardiac arrhythmia. In doing so, we have employed a mix of linear, nonlinear, and chaotic features of the R-R interval signal to significantly reduce the required information needed for analysis, and substantially improve the accuracy, as compared to existing systems (both ECG-based and R-R interval-based). For normal sinus rhythm (NSR), premature ventricular contraction (PVC), ventricular fibrillation (VF), and atrial fibrillation (AF), the discrimination accuracies of 99.59%, 99.32%, 99.73%, and 98.69% were obtained, respectively on the MIT-BIH database, which are superior to all existing classifiers.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:6

Harvested from:

CDK

Metadata only:

false