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A new method of electrodes placement to improve QRS detection in real-time stress ECG acquisition

This paper introduces animproved QRS complex detecting method using dynamic threshold algorithm combined with a new method of electrodes placement to minimize baseline drift and different types of noise in real-time ECG acquisition with moving patients. | TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 18, SOÁ K4- 2015 A new method of electrodes placement to improve QRS detection in real-time stress ECG acquisition Le Cao Dang Nguyen Hoang Tan Phan Hoai Nam Thai Minh Quoc Ho Chi Minh city University of Technology, VNU-HCM (Manuscript Received on August 01st, 2015, Manuscript Revised August 27th, 2015) ABSTRACT: In dynamic threshold method to detect QRS complex from ECG signal, especially in real-time application, there are two main issues: baseline drift and noise. This paper introduces an improved QRS complex detecting method using dynamic threshold algorithm combined with a new method of electrodes placement to minimize baseline drift and different types of noise in real-time ECG acquisition with moving patients. Our method proved to be more effective in detecting QRS complex with less error due to minimized baseline drift and noise in original ECG signal. . Key words: WB lead, QRS complex detection algorithm, dynamic threshold, ECG. 1. INTRODUCTION As a vital sign, heart rate is one of the most important biological parameters of living human body. In ECG data, QRS complex has a particular waveform with high R-peak amplitude in compared with baseline signal. Due to its special property, QRS complex is often used as triggering signal to register heart beats and calculate heart rate. In practice, real-time ECG processing is an sophisticated procedure because ECG signal amplitude changes continuously and unstably due to body movements such as breathing, muscle contraction etc. Pan and Tompkins [1] developed an algorithm that automatically changes threshold and parameter in each heart cycle to adapt continuous real-time changing of ECG. signal. The accuracy of dynamic threshold algorithm relies mainly on how it changes its threshold. Xue et al. [2] developed an adaptive filter algorithm based on artificial neural network to detect QRS complex. Dotsinsky and Stoyanov [3] introduced heuristic algorithm for .