[An Improved Cubic Spline Interpolation Method for Removing Electrocardiogram Baseline Drift]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Apr;33(2):227-231. doi: 10.7507/1001-5515.20160040.
[Article in Chinese]

Abstract

The selection of fiducial points has an important effect on electrocardiogram(ECG)denoise with cubic spline interpolation.An improved cubic spline interpolation algorithm for suppressing ECG baseline drift is presented in this paper.Firstly the first order derivative of original ECG signal is calculated,and the maximum and minimum points of each beat are obtained,which are treated as the position of fiducial points.And then the original ECG is fed into a high pass filter with 1.5Hz cutoff frequency.The difference between the original and the filtered ECG at the fiducial points is taken as the amplitude of the fiducial points.Then cubic spline interpolation curve fitting is used to the fiducial points,and the fitting curve is the baseline drift curve.For the two simulated case test,the correlation coefficients between the fitting curve by the presented algorithm and the simulated curve were increased by 0.242and0.13 compared with that from traditional cubic spline interpolation algorithm.And for the case of clinical baseline drift data,the average correlation coefficient from the presented algorithm achieved 0.972.

MeSH terms

  • Algorithms*
  • Computer Simulation
  • Electrocardiography*
  • Humans
  • Signal Processing, Computer-Assisted*