Design and Implementation of an Optimized ECG Signal Preprocessing Model for Improved Diagnostic Performance

Resumen

The study of electrocardiogram (ECG) signal is a very important element of cardiovascular disease diagnosis. Nevertheless, when observed, ECG signals are polluted with noise and artifacts that restrict proper interpretation and thus affect diagnostic model performance. In this study, the researcher will pay attention to the development and optimization of a powerful preprocessing process of ECG data to enhance its signal quality and the effectiveness of further analysis. The pipeline uses noise processing methods with high noise reduction capabilities such as wavelet denoising and empirical mode decomposition (EMD) in addition to reliable segmentation processes using QRS complex detection algorithms. The feature extraction exploits both the time and frequency domain features with a favour on those which prove robust against noise and artifacts. The efficiency of the optimized pipeline is computed through a benchmark ECG data, and compared with existing methodologies. The findings indicate an appreciable enhancement in signal-to-noise ratio (SNR) and decrease in the misclassification rates in later diagnostic models. This development will help in creating better and precise diagnostic tools using ECG, hopefully improving the quality of patient care and eliminating false diagnosis. The enhanced preprocessing pipeline can clear the way to enhanced, more reliable diagnostic algorithms, as well as allows to take advantage of the ECG data in large epidemiological studies. The resultant optimized pipeline forms a resource of use to researchers and clinicians dealing with ECG data as the newly optimized pipeline adds reliability and efficiency to ECG-based diagnostics.