ECG Signal Analysis: Different Approaches

ECG Signal Analysis: Different Approaches

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-7 Number-5                          
Year of Publication : 2014
Authors :  S. Thulasi Prasad , S. Varadarajan
  10.14445/22315381/IJETT-V7P275

citation 

S. Thulasi Prasad , S. Varadarajan, Article : ECG Signal Analysis: Different Approaches, International Journal of Engineering Trends and Technology(IJETT), 7(5),212-216, published by seventh sense research group

Abstract

In recent years scientists and engineers are facing several challenges in solving biomedical problems and making Digital Signal Processing as an essential and effective pedagogical approach to solve a problem of detecting selected arrhythmia conditions from a patient’s electrocardiograph (ECG) signals. The detection of QRS complex has many clinical applications as it marks the beginning of the left ventricular contraction. A lot of possible heart malfunctions such as cardiac arrhythmias, transient ischemic episodes and silent myocardial ischemia or failures will be slow while monitoring of ECG signal in real-time during normal activity. Introducing an efficient method for arrhythmia detection can be very useful for better conceptual understanding of signal processing. In this paper, we discussed two methods to clean ECG signal corrupted by noise and to extract required parameters for detecting arrhythmia condition. One method is Hilbert Transform method and another method is Filter Bank method. These methods involve using filter techniques, algorithms of finding peaks & valleys, local maxima & minima etc, for determining R peaks, R-R intervals and QRS complexes.

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Keywords:
ECG, QRS, Arrhythmia, SA node, AV node, Filter Bank, Adaptive LMS filter, Downsampling, MATLAB.