Methodology For Detection of QRS Pattern Using Secondary Wavelets
This work addresses a domain-specific problem in healthcare signal processing, offering an incremental improvement for detecting abnormalities in ECG data.
The paper tackles the problem of detecting QRS patterns in ECG signals by introducing a method to create a generalized adapted wavelet based on anomaly samples, and it successfully locates the R peak in noisy ECG signals.
Applications of wavelet transform to the field of health care signals have paved the way for implementing revolutionary approaches in detecting the presence of certain abnormalities in human health patterns. There were extensive studies carried out using primary wavelets in various signals like Electrocardiogram (ECG), sonogram etc. with a certain amount of success. On the other hand analysis using secondary wavelets which inherits the characteristics of a set of variations available in signals like ECG can be a promise to detect diseases with ease. Here a method to create a generalized adapted wavelet is presented which contains the information of QRS pattern collected from an anomaly sample space. The method has been tested and found to be successful in locating the position of R peak in noise embedded ECG signal.