HeartWave: Systematic Enhancement of ECG Signals for Accurate Diagnostics

This paper presents a comprehensive approach to ECG signal processing, detailing the sequential steps of data acquisition, preprocessing, and analysis. Beginning with wavelet-based denoising, the pipeline effectively reduces noise by 85–90% while preserving critical features. The implementation of band-pass and notch filtering ensures enhanced signal clarity, crucial for accurate downstream analysis. R-peak detection, facilitated by dynamic thresholding, achieved an accuracy of 90–95%, enabling the reliable computation of HRV metrics, including Mean RR Interval, SDNN, RMSSD, and PNN50, with accuracies ranging from 90–93%. Visualizations such as spectrogram analysis provide in-depth frequency-domain insights, augmenting the robustness of the methodology. Designed for scalability, the system demonstrates efficiency in handling large datasets and potential for real-time applications. Future advancements aim to incorporate adaptive algorithms, machine learning for arrhythmia detection, and multi-lead ECG analysis, paving the way for enhanced diagnostic capabilities and integration into wearable health monitoring systems.