Analysis of ECG Signal Processing and Filtering Algorithms

Electrocardiography (ECG) is a common technique for recording the electrical activity of human heart. Accurate computer analysis of ECG signal is challenging as it is exceedingly prone to high frequency noise and various other artifacts due to its low amplitude. In remote heath care systems, computer based high level understanding of ECG signals is performed using advanced machine learning algorithms. The accuracy of these algorithms relies on the Signal-to-Noise-Ratio (SNR) of the input ECG signal. In this paper, we analyse various methods for removing the high frequency noise components from the ECG signal and evaluate the performance of several adaptive filtering algorithms. The result suggest that the Normalized Least Mean Square (NLMS) algorithm achieves high SNR and Sign LMS is computationally efficient.

@article{Zia-ul-Haque2019,
  title     = {Analysis of ECG Signal Processing and Filtering Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {3},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Zia-ul-Haque and Rizwan Qureshi and Mehmood Nawaz and Faheem Yar Khuhawar and Nazish Tunio and Muhammad Uzair},
  doi       = {10.14569/IJACSA.2019.0100370},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100370}
}