An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet

An Electrocardiogram (ECG) signal describes the electrical
activity of the heart recorded by electrodes placed on the
surface of human body. It summarizes an important electrical
activity used for the primary diagnosis of heart abnormalities
such as Tachycardia, Bradycardia, Normalcy, Regularity and
Heart Rate Variation. The most clinically useful information
of the ECG signal is found in the time intervals between its
consecutive waves and amplitudes defined by its features. In
this paper, an ECG feature extraction algorithm based on
Daubechies Wavelet Transform is presented. DB4 Wavelet is
selected due to the similarity of its scaling function to the
shape of the ECG signal. R peaks detection is the core of this
algorithm’s feature extraction. All other primary peaks are
extracted with respect to the location of R peaks through
creating windows proportional to their normal intervals. The
proposed extraction algorithm is evaluated on MIT-BIH
Arrhythmia Database. Experimental results indicate that the
algorithm can successfully detect and extract all the primary
features with a deviation error of less than 10%.