Abstract
This paper presents a reliable QRS detection algorithm to detect and classify Electrocardiogram (ECG) waveform abnormalities by extracting features such as heart rate and duration of QRS complex. As R peak detection is the pivotal step in automatic electrocardiogram analysis, various mathematical operations like clipping, differentiation and squaring are carried out in the preprocessing stage to enhance the section containing the QRS complex. Thresholding is performed to detect the R peaks. In order to improve the accuracy of the algorithm search back technique is implemented to determine the missing R peaks and heart beat. Once the position of R peak is detected, positions of Q and S are determined using two different search intervals to take into account the anomalous conditions as well. The heart rate and QRS width are then computed and compared with the normal values to determine the degree and type of abnormality. MIT-BIH database is used to evaluate this algorithm. The algorithm gives sensitivity of 99.34% and positive predictivity of 96.79%. In the proposed algorithm complicated mathematical operations like Fourier Transform, Hilbert Transform or crosscorrelation are not computed, hence is convenient to realize.
Seema Khadirnaikar
Research Scholar
My research interests include application of supervised and unupervised machine learning techniques to precision medicine.