NDLI: Neural network approach for T-wave end detection: A comparison of architectures

Content Provider	IEEE Xplore Digital Library
Author	Suarez Leon, Alexander A. Molina, Danelia Matos Vazquez Seisdedos, Carlos R. Goovaerts, Griet Vandeput, Steven Van Huffel, Sabine
Copyright Year	2015
Description	Author affiliation: Electrical Engineering Faculty, Universidad de Oriente, Santiago de Cuba, Cuba (Suarez Leon, Alexander A.; Molina, Danelia Matos; Vazquez Seisdedos, Carlos R.) \|\| KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Belgium (Goovaerts, Griet; Vandeput, Steven; Van Huffel, Sabine)
Abstract	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.
Starting Page	589
Ending Page	592
File Size	556324
Page Count	4
File Format	PDF
ISBN	9781509006854
ISSN	2325887X
e-ISBN	9781509006847
DOI	10.1109/CIC.2015.7410979
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Creative Commons Attribution License 2.5 (CCAL)
Subject Keyword	Principal component analysis Training Standards Eigenvalues and eigenfunctions Bayes methods Heart Detection algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Science Cardiology and Cardiovascular Medicine

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