A real-time QRS detector based on discrete wavelet transform and cubic spline interpolation

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Accurate QRS detection is crucial for electrocardiogram (ECG) analysis.
• Existing methods often struggle with robustness and precision, particularly in real-world applications.
• Challenges include signal noise and the need for real-time processing.

Purpose of the Study:

• To develop and validate a real-time QRS detection algorithm for portable health monitoring.
• To improve the robustness and accuracy of QRS detection in noisy ECG signals.
• To integrate the algorithm into a Portable Health Monitor System (PHMS).

Main Methods:

• Utilized discrete wavelet transform (DWT) and Cubic Spline Interpolation as preprocessing steps.
• Implemented an improved dynamic weights adjusting strategy for enhanced detection robustness.
• Employed a peak detector and adaptive threshold detector for precise R-point identification.

Main Results:

• Achieved high performance metrics on the MIT-BIH arrhythmia database: 99.75% sensitivity and 99.83% positive prediction.
• Demonstrated robust performance in real-time processing of noisy signals within the PHMS.
• Reported a total root mean square error of 16.03 ms for time accuracy.

Conclusions:

• The proposed wavelet and spline-based QRS detector offers a robust and accurate solution for real-time ECG analysis.
• The algorithm's performance is suitable for practical application in portable health monitoring systems.
• The method effectively addresses challenges posed by signal noise and ensures reliable R-point detection.