This paper proposes a method to remove the noise in the ECG (Electrocardiogram) signals using Legendre moments. Noise is removed in the reconstructed ECG signals when lower order Legendre moments are used. RMSE (Root Mean Square Error) is used as the distortion measure for the reconstructed ECG signals. With sampling rate of 256 Hz and number of moments used is 13% of the data in each interval, experimental results show that reconstruction of ECG signal using Legendre moments can produce a smoother signal without noise while maintaining signal quality that is acceptable to cardiologist.
The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.