[Wavelet analysis for electrocardiogram variation of arrhythmia patients]

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Context:

• Electrocardiograms (ECGs) are crucial for diagnosing cardiac conditions.
• Traditional ECG analysis may not capture subtle changes associated with arrhythmias.
• Age-related variations in ECG parameters require careful consideration.

Purpose:

• To introduce wavelet analysis as a novel method for quantifying QRS complex energy proportion in ECGs.
• To investigate the influence of age and arrhythmia on QRS complex energy distribution.
• To evaluate the potential of wavelet-derived features for diagnosing sinus arrhythmia.

Summary:

• Wavelet transform, specifically using the Mexican-Hat mother wavelet, was applied to ECG data from young, elderly, and arrhythmia patient groups.
• Analysis focused on the energy proportion of QRS complexes across multiple scales.
• Results indicated no significant age-related changes in QRS energy proportion but a marked decrease in arrhythmia patients compared to healthy controls near 17Hz.

Impact:

• The energy proportion of QRS complexes, calculated via wavelet analysis, serves as a sensitive feature index for identifying sinus arrhythmia.
• This non-invasive technique offers a potential tool for objective arrhythmia diagnosis.
• Findings suggest wavelet analysis can distinguish between healthy individuals and those with sinus arrhythmia, independent of age.