A Powerful Algorithm to aid Cardiac Arrhythmia Diagnosis

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• The Electrocardiogram (ECG) records the heart's electrical activity, with P, QRS, and T waves indicating cardiac status.
• Accurate interpretation of ECG signals is crucial for diagnosing various heart conditions, including arrhythmias.
• Existing methods may require enhancement for robust wave identification and measurement.

Purpose of the Study:

• To develop a powerful algorithm for aiding cardiac diagnosis using ECG signals.
• To improve the accuracy of identifying and measuring key ECG waves (P, QRS, T).
• To facilitate the diagnosis of cardiac arrhythmias through advanced signal processing.

Main Methods:

• Utilized a deterministic approach based on decision trees for ECG analysis.
• Implemented a combiner linear adaptive filter for signal-to-noise enhancement.
• Employed a derivative approach for precise identification and measurement of P, QRS, and T waves.

Main Results:

• The algorithm demonstrated highly satisfactory performance on both simulated and real ECG signals.
• Successful identification and measurement of ECG waves were achieved post-enhancement.
• The approach proved effective in aiding the diagnosis of cardiac arrhythmias like junctional escapes and blocks.

Conclusions:

• The developed algorithm offers a powerful tool for cardiac diagnosis.
• Signal enhancement and precise wave analysis are key to improving arrhythmia detection.
• This method shows significant promise for clinical application in ECG interpretation.