Article

QRS complexes detection for ECG signal: The Difference Operation Method

Department of Electrical Engineering, National Central University, Jhongli 320, Taiwan, ROC.
Computer Methods and Programs in Biomedicine (Impact Factor: 1.9). 10/2008; 91(3):245-54. DOI: 10.1016/j.cmpb.2008.04.006
Source: PubMed

ABSTRACT This paper proposes a simple and reliable method termed the Difference Operation Method (DOM) to detect the QRS complex of an electrocardiogram (ECG) signal. The proposed DOM includes two stages. The first stage is to find the point R by applying the difference equation operation to an ECG signal. The second stage looks for the points Q and S based on the point R to find the QRS complex. From the QRS complex, the T wave and P wave can be obtained by the existing methods. Some records (QRS complex and T and P waves) of ECG signals in MIT-BIH arrhythmia database is tested to show the DOM has a much more precise detection rate and faster speed than other methods.

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    • "In general, the design of R peak detector has to be robust and adaptive in order to obtain excellent R peal detection with low error rate, good sensitivity with higher accuracies [14]. However, quality of database may also be responsible for giving poor detection accuracy since there is too much effect of noise and abnormalities in the ECG signal [15]. Many researchers have reported through several approaches to perfectly detect the number of heart beats from the ECG signals through QRS detection [16] [17]. "
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    • "Pan-Tompkins; the other is wavelet transforms [2], and with the developing of wavelet, the second generation wavelet appears. Besides, there are other several methods: [3] proposed " Difference Operation Method (DOM) " for detecting the QRS complex. The paper [4] "
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    ABSTRACT: This paper presents a wearable physiological parameters monitoring device real time monitoring electrocardiograph (ECG), respiration, blood oxygen saturation, blood pressure, motion state and temperature continuously, online analysis and displaying the result in personal computer. Especially, Improved Pan-Tompkins Algorithm was embedded into wearable physiological parameters monitoring device to detect R peak, and further compute heart rate. Based upon the analysis of QRS frequency, the slope and the threshold decision, the improved algorithm can reliably recognize R peak of QRS complexes. Compared to the traditional Pan-Tompkins, there are three improvements. First one is accurately calculating heart rate even in a slow-moving state; another is wider sampling rate-500 and 1000 sampling rate or any other sampling rate; the last one is effectively avoiding the finite word-length effect during calculation in float type. The improved Pan-Tompkins algorithm makes ECG measurement more accurate and more flexible. Based on 20 volunteers' experimental tests, the fully-integration system with improved Pan-Tompkins Algorithm can accurately monitor the real-time R peak and other physiological indicators in a calm status, even in slow-moving status the system works well.
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    • "Fig. 1 A normal ECG waveform Automatic detection of ECG features depends mainly on the accurate detection of R-peak. R-peak detection using slope-amplitude analysis [2], digital filters [3] [4] [5] [6], difference operation method [7] and transformed domains [8] were the first few developed methods. Artificial Neural Networks [9] [10], Genetic Algorithm [11], Hidden Markov Model [12] and Support Vector Machines [13], Shannon energy envelope (SEE) estimator[14] were also used for the QRS detection. "
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    ABSTRACT: Cardiovascular system study using ECG signals have evolved tremendously in the domain of electronics and signal processing. However, there are certain floating challenges unresolved in the analysis and detection of abnormal performances of cardiovascular system. As the medical field is moving towards more automated and intelligent systems, wrong detection or wrong interpretations of ECG waveform of abnormal conditions can be quite fatal. Since the PQRST signals vary their positions randomly, the process of locating, identifying and classifying each feature can be cumbersome and it is prone to errors. Here we present an automated scheme using adaptive wavelet to detect prominent R-peak with extreme accuracy and algorithmically tag and mark the coexisting peaks P, Q, S, and T with almost same accuracy. The adaptive wavelet approach used in this scheme is capable of detecting R-peak in ECG with 99.99% accuracy along with the rest of the waveforms.
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