Neural network approach for T-wave end detection: A comparison of architectures
In this paper, a new approach to the problem of detecting the end of the T wave (Te) on the electrocardiogram (ECG) using Multilayer Perceptron (MLP) neural networks is proposed and evaluated. The approach consists of a neural network acting as a regression function that estimates the Te location us...
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| creator | Suarez Leon, Alexander A. Matos Molina, Danelia Vazquez Seisdedos, Carlos R. Goovaerts, Griet Vandeput, Steven Van Huffel, Sabine |
| description | In this paper, a new approach to the problem of detecting the end of the T wave (Te) on the electrocardiogram (ECG) using Multilayer Perceptron (MLP) neural networks is proposed and evaluated. The approach consists of a neural network acting as a regression function that estimates the Te location using the samples between two consecutive R peaks. The input vectors were taken using three dimensional reduction methods (Discrete Cosine Transform, DCT, Principal Component Analysis, PCA and resampling, RES) over a window of 100 samples. For training, Bayesian regularization has been used. A total of 1536 neural networks were trained. The results show that PCA and DCT are more feasible than RES as dimension reduction methods. Finally, a brief comparison with other algorithms proposed in the literature is included. |
| doi_str_mv | 10.1109/CIC.2015.7410979 |
| format | Conference Proceeding |
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| fulltext | fulltext |
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| ispartof | 2015 Computing in Cardiology Conference (CinC), 2015, p.589-592 |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Algorithms Bayes methods Cardiology Computation Detection algorithms Eigenvalues and eigenfunctions Heart Mathematical analysis Neural networks Principal component analysis Reduction Three dimensional Training |
| title | Neural network approach for T-wave end detection: A comparison of architectures |
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