Optimization of ECG Peaks (Amplitude and Duration) in Predicting ECG Abnormality using Artificial Neural Network
| Autor: | Nik Ghazali Nik Daud, Khairol Amali Bin Ahmad, F.R. Hashim, Ja rsquo afar Adnan, A. S. N. Mokhtar, Amir F Rashidi |
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| Rok vydání: | 2017 |
| Předmět: |
Multidisciplinary
Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation 020206 networking & telecommunications Pattern recognition 02 engineering and technology ECG abnormality Bayesian interpretation of regularization Backpropagation Levenberg–Marquardt algorithm Amplitude Duration (music) Multilayer perceptron 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Indian Journal of Science and Technology. 10:1-5 |
| ISSN: | 0974-5645 0974-6846 |
| DOI: | 10.17485/ijst/2017/v10i12/112970 |
| Popis: | Artificial Neural Networks (ANN) adapted from neuron concept's, generally applied in various applications especially the fields of biomedical engineering. ANN techniques have been applied in order to provide educated solutions to assist in decision making for the medical purpose. The study was conducted for the purpose of determining the suitability and implementation of ANN to detect ECG abnormalities by using six features from ECG signal, both amplitude and duration of P, QRS and T peaks and used as input vector for ANN. In this study, Multilayer Perceptron (MLP) network is trained by using three different training/learning algorithms. The network is trained by using Bayesian Regularization (BR) algorithm has provided the highest accuracy performance (93.19%), followed by Levenberg Marquardt (LM) (92.88%) and Backpropagation (BP) (88.63%). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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