A PD control-based QRS detection algorithm for wearable ECG applications.

Area of Science:

• Biomedical Engineering
• Cardiovascular Technology
• Signal Processing

Background:

• Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular conditions.
• Arrhythmia presents challenges in accurate QRS complex detection due to irregular wave magnitudes and intervals.
• Existing QRS detection algorithms struggle with missed detections or false positives in cases of bradycardia or pauses.

Purpose of the Study:

• To develop and evaluate a novel QRS detection algorithm for wearable ECG devices.
• To improve the accuracy of QRS detection in the presence of arrhythmia using proportional-derivative (PD) control.
• To ensure reliable real-time ECG monitoring for mobile applications.

Main Methods:

• Implementation of a QRS detection algorithm utilizing proportional-derivative (PD) control.
• Application of the algorithm to ECG data from 78 patients with diverse cardiovascular diseases.
• Performance evaluation based on sensitivity and positive predictive value.

Main Results:

• The proposed PD control algorithm achieved high overall sensitivity (99.28%) and positive predictive value (99.26%).
• The algorithm effectively avoids missing small QRS waves following large ones.
• It also prevents false detection of noise during long intervals between QRS complexes (e.g., bradycardia, pauses).

Conclusions:

• The PD control-based QRS detection algorithm demonstrates robust performance for wearable ECG applications.
• Its low computational complexity makes it suitable for real-time mobile ECG monitoring systems.
• This algorithm offers a reliable solution for accurate arrhythmia detection.