Electrocardiogram (ECG) Outline Exploration And Classification Through The Range of CWT (Cross Wavelet Transform)

The electrical activity of the heart is measured by an electrocardiogram (ECG), and any variation from the typical rhythm indicates a potentially harmful change in the pathophysiological state of the organ. The cardiologists have a good understanding of the format and structure of a normal, healthy electrocardiogram (ECG). They examine the clinical abnormality that is present in the ECG by visually inspecting the morphology of the signal using their years of experience. This is done with the electrocardiogram. It is vital to select an appropriate representation for the signal itself before we can move forward with developing a system that is capable of performing automatic ECG analysis. The raw time series can be used as a sufficient description of the data that has to be analyzed, and this is the way that is the simplest to utilize. Despite the fact that this method is enticing owing to the inherent simplicity it possesses and the ease with which it can be implemented, it is plagued by a number of significant shortcomings. This study work presents a classification methodology for screening patients with antero septal myocardial infarction that is constructed utilizing the closest neighbour (NN) method (ASMI). The amplitude of the QRS and the height of the T wave, both of which are diagnostically relevant parameters, are retrieved here from leads V1-V4. For the purpose of merging the effects of the four leads, a score value approach is utilized. The NN classifier uses both the Euclidian and the Mahalanobis distance metrics in its calculations. It was discovered that the latter provides a better level of performance.