Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/10008031,
  title    = {Wavelet-Based ECG Signal Analysis and Classification},
  author    = {Madina Hamiane and  May Hashim Ali},
  country   = {Bahrain},
  institution={Ahlia University},
  abstract  = {This paper presents the processing and analysis of ECG signals. The study is based on wavelet transform and uses exclusively the MATLAB environment. This study includes removing Baseline wander and further de-noising through wavelet transform and metrics such as signal-to noise ratio (SNR), Peak signal-to-noise ratio (PSNR) and the mean squared error (MSE) are used to assess the efficiency of the de-noising techniques. Feature extraction is subsequently performed whereby signal features such as heart rate, rise and fall levels are extracted and the QRS complex was detected which helped in classifying the ECG signal. The classification is the last step in the analysis of the ECG signals and it is shown that these are successfully classified as Normal rhythm or Abnormal rhythm.  The final result proved the adequacy of using wavelet transform for the analysis of ECG signals.
},
    journal   = {International Journal of Computer, Electrical, Automation, Control and Information Engineering},  volume    = {11},
  number    = {7},
  year      = {2017},
  pages     = {895 - 909},
  ee        = {http://waset.org/publications/10008031},
  url       = {http://waset.org/Publications?p=127},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 127, 2017},
}