QRS complex detection in ECG signal for wearable devices

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Continuous electrocardiogram (ECG) monitoring is crucial for cardiac patients, especially those with arrhythmias.
• Wearable heart monitoring systems require computationally efficient and accurate QRS complex detection algorithms.
• Existing QRS detection methods may lack the necessary efficiency or accuracy for long-term wearable applications.

Purpose of the Study:

• To develop and evaluate a novel QRS complex detection algorithm.
• To enhance the accuracy and computational efficiency of R-peak detection for wearable ECG devices.
• To provide a robust solution for continuous cardiac monitoring in arrhythmia patients.

Main Methods:

• A dual slope technique is employed, analyzing two slopes on either side of the R peak.
• A steepness-measuring variable is developed.
• An adjustable R-R interval based window and adaptive thresholding are utilized for R-peak detection.

Main Results:

• The algorithm achieved a 99.16% detection rate on the MIT/BIH arrhythmia database.
• High sensitivity (0.9935) and positive predictivity (0.9981) were recorded.
• Performance was favorably compared against two established R-peak detection algorithms.

Conclusions:

• The proposed dual slope QRS detection algorithm is computationally efficient and accurate.
• It is well-suited for real-time QRS detection in wearable ECG applications.
• This method offers a significant improvement for continuous cardiac monitoring of patients with arrhythmias.