DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
• Real-time detection of ECG features is essential for continuous monitoring and early intervention.
• Existing algorithms may struggle with noise, baseline drift, and artifacts, impacting accuracy.

Purpose of the Study:

• To develop a real-time algorithm for accurate ECG characteristic detection using wavelet transforms (WTs).
• To distinguish vital ECG components (QRS complexes, P and T waves) from various signal interferences.
• To evaluate the algorithm's performance and compare it with existing methods.

Main Methods:

• Implementation of a wavelet transform (WT) based algorithm optimized for real-time processing.
• Deployment on a Digital Signal Processor (DSP) with a 50 MHz clock frequency.
• Performance evaluation and accuracy assessment using the standard MIT/BIH arrhythmia database.

Main Results:

• The developed WT algorithm successfully distinguishes QRS complexes, P waves, and T waves from noise and artifacts.
• The algorithm demonstrates high accuracy, correctly detecting 99.7% of QRS complexes in the MIT/BIH database.
• Comparative analysis indicates competitive or superior performance compared to a C language implementation.

Conclusions:

• The real-time WT algorithm offers a robust and accurate solution for ECG characteristic detection.
• Its high detection rate and ability to handle artifacts make it suitable for clinical applications and wearable devices.
• The DSP implementation ensures efficient processing for real-time ECG monitoring systems.