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 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.

[1]  Senén Barro,et al.  A new approach for TU complex characterization , 2000, IEEE Transactions on Biomedical Engineering.

[2]  Raúl Alcaraz,et al.  Application of the phasor transform for automatic delineation of single-lead ECG fiducial points , 2010, Physiological measurement.

[3]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[4]  Qinghua Zhang,et al.  An Algorithm for Robust and Efficient Location of T-Wave Ends in Electrocardiograms , 2006, IEEE Transactions on Biomedical Engineering.

[5]  Qinghua Zhang,et al.  An algorithm for QRS onset and offset detection in single lead electrocardiogram records , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Aldebaro Klautau,et al.  New approach for T-wave end detection on electrocardiogram: Performance in noisy conditions , 2011, Biomedical engineering online.

[7]  P. C. Cortez,et al.  New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model. , 2013, Medical engineering & physics.

[8]  Pablo Laguna,et al.  A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG , 1997, Computers in Cardiology 1997.

[9]  M R Homaeinezhad,et al.  A robust wavelet-based multi-lead Electrocardiogram delineation algorithm. , 2009, Medical engineering & physics.

[10]  Suzanne Kieffer,et al.  Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry , 2011, Biomedical engineering online.

[11]  Jean-Yves Tourneret,et al.  P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler , 2010, IEEE Transactions on Biomedical Engineering.