Classification of ECG waveforms using a novel neural network

It is important to detect and display waveforms on the ECG recordings fast and automatically. In this study, an artificial neural network trained by genetic algorithms (NeTGA) is proposed for ECG waveform detection. After the R peak of the QRS complex is detected, a window containing an ECG period is formed around the R peak. The significant frequency components of the signal in the window are used to form the feature vectors. NeTGA, grow and learn (GAL), multi-layer perceptron (MLP), and Kohonen networks are comparatively investigated to detect seven different ECG waveforms. It is observed that the proposed network results in better classification performance with less number of nodes.

[1]  Z Dokur,et al.  Detection of ECG waveforms by neural networks. , 1997, Medical engineering & physics.

[2]  A. I. Ethem Alpaydin.  Neural models of incremental supervised and unsupervised learning , 1990 .

[3]  B. H. Blott,et al.  Review of neural network applications in medical imaging and signal processing , 1992, Medical and Biological Engineering and Computing.

[4]  T. Ölmez.  Classification of ECG waveforms by using RCE neural network and genetic algorithms , 1997 .

[5]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[6]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[7]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.