Enhanced Techniques for Cardiac Signal Processing in Continuous Daily Monitoring Systems

For continuous cardiac assessment, electrocardiographic (ECG) monitoring in wearable and ambulatory settings is becoming more and more important.However, it is still susceptible to a number of noise sources, including baseline drift, motion artifacts, electromagnetic interference (EMI), and muscle activity (EMG).P-waves, the QRS complex, and ST segments are examples of diagnostically important characteristics that may be obscured by these interferences.The non-adaptive, phasedistorting, and morphology-blurring characteristics of classical filters frequently lead to their failure.To address this, we suggest a dual-filter adaptive denoising pipeline that combines a locally adaptive Wiener filter with a modified Savitzky-Golay filter.Using statistical variance estimations and spectral flatness criteria, our method dynamically adjusts to spectral features and signal shape, preserving important cardiac fingerprints while reducing noise.In-depth MATLAB simulations show increases in signal-to-noise ratio (SNR) of 6-8 dB and a 47.5% decrease in root mean square error (RMSE), while preserving over 90% QRS fidelity across 14 different noise circumstances.Theoretical advances in signal processing and real-time cardiac diagnostics are connected in this work.