QRS Detection Based on Improved Adaptive Threshold.

Area of Science:

• Biomedical Engineering
• Cardiology
• Signal Processing

Background:

• Cardiovascular disease is a leading global cause of mortality.
• Accurate electrocardiogram (ECG) analysis, particularly QRS complex detection, is crucial for timely diagnosis.
• Existing threshold algorithms offer speed and low memory usage, suitable for mobile ECG systems, but require improved detection rates.

Purpose of the Study:

• To develop an improved adaptive threshold algorithm for enhanced QRS complex detection in ECG signals.
• To increase the accuracy and reliability of automated ECG analysis for cardiovascular disease screening.
• To provide diagnostic aid tools for healthcare professionals.

Main Methods:

• The proposed algorithm involves three main stages: signal preprocessing, peak finding, and adaptive threshold QRS detection.
• The algorithm was evaluated using the MIT-BIH Arrhythmia database.
• Performance was compared against two other established QRS detection algorithms.

Main Results:

• The improved adaptive threshold algorithm achieved a QRS detection rate of 99.41%.
• High sensitivity (Se) of 99.72% and specificity (Sp) of 99.69% were recorded.
• The algorithm demonstrated superiority over two other compared methods.

Conclusions:

• The developed adaptive threshold algorithm significantly enhances QRS detection accuracy for ECG analysis.
• The algorithm's performance supports its application in portable, wearable, and wireless ECG systems for improved cardiovascular diagnostics.
• Additional outputs, including suspicious abnormal area identification and RR-Lorenz plots, offer valuable diagnostic aid for clinicians.