Abstract: Electrocardiogram (ECG), a non-stationary signal, is extensively used as one of the important diagnostic tools for the detection of the health of a heart.  Comparison of overall ECG waveform pattern and shape enables doctors to diagnose possible diseases. Currently there is computer based analysis which employs certain signal processing to diagnose a patient based on ECG recording. The Electrocardiogram may contain various artefacts, noise and baseline wander when ECG is recorded which severely limits the utility of the recorded ECG and thus needs to be removed for better clinical evaluation.  Signal pre-processing helps us remove contaminants from the ECG signals. The baseline wander and other wideband noise are not suppressed by hardware equipments. Software schemes are more powerful and feasible for offline ECG signal processing.  Automatic detection of R peaks in a QRS complex is a fundamental requirement for automatic disease identification. Recently, numerous research and techniques have been developed for processing, detection of QRS complex, P and T waves of ECG signal. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for processing, QRS complex and P and T wave detection of ECG signals and make comparison among them.


Keywords: ECG, de-noising, pre-processing, feature extraction.