Abstract
Electrocardiogram (ECG) contains crucial clinical information about the cardiac activities of the heart, however, such signal a part of being in large volume is often characterised by a low quality due to the noise and other artifacts. In order to correctly extract the important features from the ECG signal, first it needs to be preprocessed, denoised and normilised. Significant attention in the literature has been directed toward the ECG preprocessing, though there are ambiguity to which wavelet performs the best for ECG signal processing as well as which decomposition level should be used and how the baseline wander can be removed. Parameters of wavelets have been investigated but the lack of evidence for recommendations is not found. This research conducts a comprehensive study to identify some characteristics of optimal decomposition level and to identify the span that should be used. We have taken into consideration all available wavelets within the Matlab environment and tested it on a number of randomly chosen ECG signals. Results indicate that the decomposition level of 4 should be used and that the Biorthogonal wavelet bior3.9 performs the best for smoothing and baseline drift removal. Also, we concluded that the optimal value for span is 100, which guarantees the best baseline wander removal.
Highlights
Cardiovascular disease (CVD) is a disorder affecting both the vasculature and the heart muscle itself
It is obvious that with the increase of decomposition level the Signal-to-Noise Ratio (SNR) is lower as the signal is cleaned more, along with removing the noise some important features from the signal are removed
This is because with higher decomposition level signal is cleaned more, overcleaning of noise can remove some important features from the ECG signal, which are important for identification of arrhythmias
Summary
Cardiovascular disease (CVD) is a disorder affecting both the vasculature (i.e. hypertension) and the heart muscle itself. Cardiovascular disease remains the number one cause of mortality in the western world, responsible for more than 16 million deaths annually worldwide. 30% of all patients with CVD die from the disease. Regular visits to doctors and early detection from Electrocardiogram (ECG) is a critical step in the prevention of cardiovascular disease (Mendis et al, 2011). An ECG captures the electrical signal within the heart. Traditional ECG signal is displayed on a graph where the X-axis is Time and the Y-axis can be either Voltage (mV) or Amplitude (dB). An ECG graph can be used for analysis of long intervals, measuring the consistency of the beats and looking for proper depolarisation, repolarisation and any irregular beats (Braunwald, 1997)
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