An adaptive QRS detection algorithm for ultra-long-term ECG recordings.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Mobile, ultra-long-term ECG monitoring faces challenges in accurate QRS complex detection.
• Issues include high heart rate, signal amplitude variations, and signal quality degradation due to motion, noise, and electrode placement.

Purpose of the Study:

• To propose a revised QRS detection algorithm that overcomes common challenges in ECG monitoring.
• To enhance the accuracy and robustness of QRS detection for mobile and long-term applications.

Main Methods:

• Modified a state-of-the-art QRS detection algorithm with two key improvements.
• Implemented local amplitude estimation and an adaptive mechanism for heart rate changes.
• Validated against a benchmark algorithm using diverse ECG datasets, including 14-day recordings.

Main Results:

• The proposed algorithm achieved 99.90% sensitivity and 99.73% positive predictive value on ultra-long-term ECG recordings.
• Outperformed the state-of-the-art algorithm on the same dataset (99.30% sensitivity, 99.68% PPV).
• Demonstrated high numerical efficiency, analyzing a 14-day recording in approximately 157 seconds.

Conclusions:

• A novel QRS detection algorithm has been developed.
• The algorithm's efficiency and accuracy are suitable for mobile health, ultra-long-term, and pathological ECG analysis.
• It is also effective for batch processing of large ECG databases.