Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/16347
Author(s): | Roza, V. C. C. de Almeida, A. M. Postolache, O. A. |
Date: | 2017 |
Title: | Design of an artificial neural network and feature extraction to identify arrhythmias from ECG |
Pages: | 391-396 |
ISBN: | 978-1-5090-2984-6 |
DOI (Digital Object Identifier): | 10.1109/MeMeA.2017.7985908 |
Keywords: | Electrocardiography Feature extraction Training Artificial neural networks Neurons Heart rate Diseases |
Abstract: | This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSR-complex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | IT-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
MEMEA_2017.pdf | Pós-print | 881,2 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.