Automation of ECG heart beat detection using Morphological filtering and Daubechies wavelet transform

摘要

The most specific diagnostic test for heart diseases is the Electrocardiogram (ECG). ECG is a graphical representation of the electrical activity of the heart. Analysis of an ECG signal starts with the detection of QRS complex. Detection of QRS complex is a difficult task as the signal is frequently corrupted by powerline interference, baseline drift, motion artifact and electromyographic interference. Therefore, reliable and accurate detection of QRS complex is gaining momentum nowadays. A novel QRS detection algorithm based on Mathematical Morphological (MM) filtering and Daubechies wavelet transform has been developed in this work. MM uses its hybrid opening-closing operations for impulsive noise suppression and baseline wander removal. Daubechies3 WT is used for signal analysis since it has a shape similar to the ECG signal. R peak is extracted as a first in the feature extraction since it is having highest amplitude, followed by Q peak and S peak extraction. Heart beat rate was calculated from the R-R peak interval. From the heart rate and R-R peak interval the diagnosis of the cardiac ailments is done.