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

Aiyun Chen,Jin He,Hao Wang,Qijun Huang,Wenhan Liu,Sheng Chang,Yidan Zhang,Mengxin Zhang

doi:10.3390/s20144003

Abstract

As one of the important components of electrocardiogram (ECG) signals, QRS signal represents the basic characteristics of ECG signals. The detection of QRS waves is also an essential step for ECG signal analysis. In order to further meet the clinical needs for the accuracy and real-time detection of QRS waves, a simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection is proposed. The exponential transform (ET) and proportional-derivative (PD) control-based adaptive threshold are designed to detect QRS-complex. The proposed ET can effectively narrow the magnitude difference of QRS peaks, and the PD control-based method can adaptively adjust the current threshold for QRS detection according to thresholds of previous two windows and predefined minimal threshold. The ECG signals from MIT-BIH databases are used to evaluate the performance of the proposed algorithm. The overall sensitivity, positive predictivity, and accuracy for QRS detection are 99.90%, 99.92%, and 99.82%, respectively. It is also implemented on Altera Cyclone V 5CSEMA5F31C6 Field Programmable Gate Array (FPGA). The time consumed for a 30-min ECG record is approximately 1.3 s. It indicates that the proposed algorithm can be used for wearable heart rate monitoring and automatic ECG analysis.

Highlights

Electrocardiogram (ECG) plays an important role in the diagnosis of cardiac diseases
This paper presents a simple, fast, hardware-friendly, real-time QRS detection algorithm that can be lightweight
A simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection is proposed in this paper

Summary

Introduction

Electrocardiogram (ECG) plays an important role in the diagnosis of cardiac diseases. To analyze massive ECG data from long-time monitoring system for cardiac condition, such as 24 h Holter (or even longer), it is a heavy and tedious burden for the physicians. To alarm ECG abnormalities in time, the real-time monitoring of the heart condition and automatic analysis of ECG signal is of great significance. The performance of an automatic ECG analysis system relies much on the features extracted from fiducial points such as P, Q, R, S, T, of which the QRS-complex stands out for its large amplitude and sharp slope. QRS-complex detection has served as the fundamental step for the automatic detection of other ECG fiducial points and further analysis. The morphology of ECG varies greatly from person to person, even in different time for the same individual. ECG is generally contaminated with various noises, such as power line interference, electrode contact noise, motion artifact, muscle contraction, and baseline wander

Methods

Results

Discussion

Conclusion