[Research on the detection algorithm of electrocardiogram characteristic wave based on energy segmentation and...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Accurate electrocardiogram (ECG) wave detection is crucial for cardiovascular disease and heart rate variability analysis.
• Existing methods struggle with low accuracy and poor real-time performance, especially during patient motion.

Purpose of the Study:

• To develop a robust ECG detection algorithm addressing accuracy and real-time challenges in motion states.
• To improve the efficiency of ECG signal analysis for clinical applications.

Main Methods:

• A novel algorithm combining segmentation energy and stationary wavelet transform (SWT).
• QRS complex detection via segmented signal energy and moving average.
• P wave and T wave localization using SWT's fifth component after QRS amplitude zeroing.

Main Results:

• High accuracy in detecting QRS complex across various motion states.
• Real-time performance demonstrated by detecting a 30-minute ECG record in 0.22 seconds.
• Achieved over 95% accuracy for P wave and T wave detection following QRS complex identification.

Conclusions:

• The proposed algorithm significantly enhances ECG signal detection efficiency and real-time capabilities.
• This method offers a new approach for real-time ECG signal classification and cardiovascular disease diagnosis.