A Comparison of Baseline Removal Algorithms for Electrocardiogram (ECG) Based Automated Diagnosis of Coronory Heart Disease

This paper presents a comparison of different approaches for performing baseline removal in the electrocardiogram (ECG) signal for use in an ECG based decision support system for diagnosis of coronary heart disease. Our implementations of seven different algorithms for removal of baseline from the ECG signal have been compared which include methods based on use of linear Digital filters, Adaptive filters, Multiresolution analysis and Curve fitting or polynomial based approaches. The comparison was carried out using manual ST Segment level annotations in different ST segment deviation episodes from the European Society of Cardiology (ESC) ST-T database. Results indicate that the use of Wavelet Adaptive Filter for baseline removal produces ST segment levels which are the closest to those annotated by the human expert.