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A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy.

Aiyun Chen1, Yidan Zhang1, Mengxin Zhang1

  • 1School of Physics and Technology, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|July 26, 2020
PubMed
Summary
This summary is machine-generated.

A new algorithm for electrocardiogram (ECG) QRS wave detection offers high accuracy and real-time performance. This simple, hardware-friendly method is ideal for wearable devices and automatic ECG analysis.

Keywords:
PD-controlQRS detectionelectrocardiogram (ECG)exponential transform (ET)real-time

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • QRS wave detection is crucial for electrocardiogram (ECG) signal analysis.
  • Accurate and real-time QRS detection is essential for clinical applications.
  • Existing methods may face challenges in accuracy and real-time processing.

Purpose of the Study:

  • To develop a simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection.
  • To improve the accuracy and efficiency of QRS complex identification in ECG signals.
  • To meet the increasing clinical demands for advanced ECG analysis.

Main Methods:

  • Proposed an algorithm utilizing exponential transform (ET) to narrow QRS peak magnitude differences.
  • Implemented a proportional-derivative (PD) control-based adaptive threshold for dynamic threshold adjustment.
  • Evaluated algorithm performance using ECG signals from the MIT-BIH databases.

Main Results:

  • Achieved high performance metrics: 99.90% sensitivity, 99.92% positive predictivity, and 99.82% accuracy for QRS detection.
  • Demonstrated real-time processing capability with approximately 1.3 seconds for a 30-minute ECG record on an FPGA.
  • The algorithm is hardware-friendly and suitable for resource-constrained environments.

Conclusions:

  • The proposed ET and PD control-based algorithm provides a robust solution for accurate and real-time QRS detection.
  • The algorithm's efficiency and accuracy make it suitable for wearable heart rate monitoring.
  • This method facilitates automatic ECG analysis, potentially improving diagnostic capabilities.