Wavelets for QRS detection.

This paper examines the use of different wavelet functions for QRS complex detection in ECG. Wavelets provide time and frequency analysis simultaneously and offer flexibility with a number of wavelet functions with different properties available. This research has examined wavelet functions with different properties to determine the effects of orthogonality and time/frequency compactness of the wavelet on the ability to correctly detect the QRS. The error in detection (false negatives and positives) is the criterion for determining the efficacy of the wavelet function. The paper reports a significant reduction in error in detection of QRS complexes with mean error reduced to 0.75%. It also reports that wavelet functions that support symmetry and compactness provide better results.

Neural networks and wavelet decomposition for classification of surface electromyography.

To determine the status of a muscle, surface electromyography (SEMG) is a useful tool being non-invasive and easy to record. Clinicians are able to classify the signal visually but because of the large number of parameters of the signal, automatic classification becomes difficult. This paper reports our efforts at using wavelet transforms to process the signal before using neural networks for classification. We have found that by using specific wavelet functions and at specific levels of decomposition, the features of the signal correlating with muscle status were highlighted.