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.