LOW COMPLEXITY ALGORITHM FOR EXTRACTION OF ECG FIDUCIAL POINTS

An automated algorithm for detection of fiducial points from the electrocardiogram (ECG) and identification of its various morphologies is proposed in this work. The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in computationally constrained devices, thus the complexity of the processing algorithms should remain at a minimum level. Under this context, a discrete wavelet transform (DWT) with the Haar function as mother wavelet, is used as principal analysis method in this paper. From the modulus - maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time - domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time - and frequency domain signal processing. Conventionally suc h ECG signals are acquired by ECG acquisition devices and those devices generate a printout of the lead outputs. A cardiologist analyzes the data for checking the abnormality or normalcy of the signal. But in recent times, automatic ECG processing has been of tremendous focus. The main point of concern is how to develop a system for extracting the features from ECG signal so that these features can be used for Automatic Diseases Diagnosis. In this Article We discuss a technique for extracting features fro m ECG signal and further analyze for ‘QRS’, ‘P - R’& ‘S - T’ intervals in appropriate time.