Digitization of ECG Records Using Signal Extraction Techniques

Heart failure is a leading cause of sudden unexpected death, especially among middle-aged and elderly people. Analog electrocardiogram (ECG) signals are conventionally used in medical settings, but their graphic printouts can be cumbersome to manage. Digitizing ECG signals offers a solution that allows for easier data storage, analysis and transmission. However, the process introduces quantization errors that must be addressed for accurate signal restoration. This study employs biomedical signal processing techniques in MATLAB to digitize analog ECG signals, analyze quantification errors, and restore the signals to their original form. Parameters such as filter types, sample rates, and quantization levels are carefully chosen to optimize signal fidelity. Multiple iterations are performed to refine the model and achieve a precision error close to zero. The proposed model presents a viable solution to digitize analog ECG signals and analyze quantification errors. The model’s utility for analyzing pre-recorded ECG data suggests potential applications in clinical settings for heart health assessment and monitoring. Further validation and refinement may improve its effectiveness in real-world applications.