An algorithm deploying least squares error correction and median-based correction successfully reduced baseline drift in ECG signals to a near-zero value.
Baseline drift in ECG signal is the biggest hurdle in visualization of correct waveform and computerized detection of wave complexes based on threshold decision. The baseline drift may be linear, static, nonlinear or wavering. Reducing the baseline drift to a near zero value greatly helps in visually inspecting the morphology of the wave components as well as in computerized detection and delineation of the wave complexes. The algorithm is developed for computer implementation using Matlab. It deploys least squares error correction and correction based on overall median of individual single lead data, to reduce baseline drift. QRS complexes are then detected to find RR intervals of the waveform. Finally, median based correction is implemented in the RR interval and a drift free signal is achieved. This can help cardiologists significantly
Chouhan et al. (Thu,) conducted a other in ECG baseline drift. Algorithm for baseline drift removal was evaluated on Removal of baseline drift. An algorithm deploying least squares error correction and median-based correction successfully reduced baseline drift in ECG signals to a near-zero value.