Grow and Learn (GAL) and Kohonen artificial neural networks were comparatively investigated for the detection of four different ECG waveforms using DFT spectrum features.
Artificial neural networks, specifically GAL and Kohonen networks, can be utilized to detect ECG waveforms based on DFT spectrum features.
In this study, ECG waveform detection was performed by using artificial neural networks (ANNs). Initially, the R peak of the QRS complex is detected, and then feature vectors are formed by using the amplitudes of the significant frequency components of the DFT spectrum. Grow and Learn (GAL) and Kohonen networks are comparatively investigated to detect four different ECG waveforms. The comparative performance results of GAL and Kohonen networks are reported.
Dokur et al. (Wed,) conducted a other in ECG waveform detection. Artificial neural networks (Grow and Learn and Kohonen networks) was evaluated on Detection of four different ECG waveforms. Grow and Learn (GAL) and Kohonen artificial neural networks were comparatively investigated for the detection of four different ECG waveforms using DFT spectrum features.