IMPROVING ECG DIAGNOSIS ACCURACY THROUGH COMPUTATIONAL ANALYSIS OF P-WAVES USING DIGITIZER SOFTWARE

Abstract

This experimental study utilized secondary data derived from ECG images representing five distinct P-wave conditions: Normal, Absent, Inverted, Left atrial enlargement, and Right atrial enlargement. The analysis of these data involved a series of six processing stages, beginning with denoising and digitization using Origin Pro Software to ensure clarity and precision of the images. Following this, both manual and automated point selection techniques were employed to extract key features for further analysis. Key metrics, including the amplitude, time, and angles for each P-wave, were then examined. These were crucial in identifying significant differences between normal and abnormal P-waves, thus contributing to a deeper understanding of the variations in the ECG patterns. In addition to these analyses, the study introduced two novel diagnostic scoring models, named after the researcher as Maaz’s scoring criteria, which were based on a combination of P-wave time, amplitude, and angle measurements. These scoring models present a new and promising approach to improving the accuracy and reliability of ECG diagnostics by offering a comprehensive framework for evaluating P-wave abnormalities. The findings of this research provide valuable insights into the diagnostic process, contributing to the development of more robust and reliable methods for detecting abnormal P-wave patterns and advancing the overall field of electrocardiography.