Hybrid processing and time-frequency analysis of ECG signal

Area of Science:

• Biomedical Engineering
• Signal Processing
• Computational Biology

Background:

• Electrocardiogram (ECG) analysis is vital for diagnosing cardiac conditions.
• Detecting specific ECG waves like P-waves and f-waves is challenging, especially during arrhythmias such as atrial fibrillation (AF).
• Existing methods for ECG wave detection can be complex and computationally intensive.

Purpose of the Study:

• To develop a simple and efficient algorithm for detecting QRS-T complexes in ECG signals.
• To enable the identification of P-waves (normal sinus rhythm) and f-waves (atrial fibrillation).
• To analyze and differentiate ECG signals during normal rhythm and AF.

Main Methods:

• A novel approach combining histogram analysis and genetic algorithms (GA) for ECG signal processing.
• Utilizing signal processing techniques including power spectrum function, auto-correlation, and cross-correlation functions.
• Comparative analysis of ECG signals recorded during normal rhythm and AF.

Main Results:

• The proposed method efficiently detects QRS-T complexes, facilitating P-wave or f-wave identification.
• Significant differences were observed between ECG signals with and without AF when analyzed using signal processing techniques.
• The GA-based approach offers a straightforward yet effective means for ECG analysis.

Conclusions:

• The developed histogram and GA-based method provides an efficient way to detect QRS-T complexes and associated waves (P-wave/f-wave).
• This technique can help distinguish between normal sinus rhythm and atrial fibrillation.
• The study highlights the utility of signal processing and genetic algorithms in advancing ECG analysis.