Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Medical Devices

Background:

• Cardiac pacemakers are crucial for monitoring and managing heart rates, especially in bradycardia.
• Accurate electrocardiogram (ECG) detection, particularly the QRS complex, is vital for pacemaker function.
• Digital pacemakers offer improved efficiency, accuracy, and power consumption.

Purpose of the Study:

• To propose a joint algorithm for QRS complex detection and ECG data compression in digital pacemakers.
• To enhance the accuracy and efficiency of ECG signal analysis within implantable cardiac devices.
• To reduce hardware costs and improve data management for pacemaker technology.

Main Methods:

• A novel algorithm combining biorthogonal wavelet transform and run-length encoding (RLE) for ECG analysis.
• Implementation of a modified demand-based filter bank architecture for wavelet transform.
• Utilization of linear phase structures for lowpass and highpass filters to reduce hardware cost.
• R-peak detection by comparing denoised ECG signals against a threshold value.

Main Results:

• Achieved high sensitivity (99.75%) and positive predictivity (99.98%) for R-peak detection using the MIT-BIH arrhythmia database.
• Demonstrated a low data error rate (DER) of 0.002 for the R-peak detector.
• Obtained a high compression ratio (CR) of 17.1 for detected ECG data using RLE.
• Reduced hardware cost by approximately 50% through optimized filter design.

Conclusions:

• The proposed joint algorithm effectively detects QRS complexes and compresses ECG data for digital pacemakers.
• The method offers superior performance in accuracy and data reduction compared to existing techniques.
• This advancement contributes to more efficient, cost-effective, and reliable cardiac pacemaker technology.