Shannon's Energy Based Algorithm in ECG Signal Processing

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Accurate QRS complex detection in electrocardiograms (ECG) is crucial for diagnosing heart conditions.
• Existing methods may face challenges with complex ECG signals.
• The Physikalisch-Technische Bundesanstalt (PTB) database offers valuable ECG data for research.

Purpose of the Study:

• To develop and evaluate a new method for QRS complex detection in 12-lead ECG signals using Shannon energy (SE).
• To assess the algorithm's performance on a diverse dataset including healthy individuals and patients with heart disease.

Main Methods:

• Computation of Shannon energy (SE) from ECG signals.
• Envelope creation of SE using a defined threshold.
• Identification of signal peaks for QRS complex detection.
• Validation using the PTB Diagnostic ECG Database (PTBDB).

Main Results:

• The Shannon energy (SE) method demonstrated high performance metrics.
• Sensitivity: 99.924%
• Detection Error Rate (DER): 0.155%
• Positive Predictivity (+P): 99.922%
• Classification Accuracy (Acc): 99.846%.

Conclusions:

• The proposed Shannon energy (SE) based algorithm is highly effective for QRS complex detection in ECG signals.
• This method offers a reliable tool for the diagnosis of heart diseases.
• The algorithm's performance indicates its potential for clinical application.