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: 1.56). 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.

0 0
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.
    Sensors 01/2013; 13(1):813-28. · 1.95 Impact Factor
  • [show abstract] [hide abstract]
    ABSTRACT: QRS complex detection usually provides the fundamentals to automated electrocardiogram (ECG) analysis. In this paper, a new approach of QRS complex detection without the stage of noise suppression was developed and evaluated, which was based on the combination of two techniques: matched filtering and triangle character analysis. Firstly, a template of QRS complex was selected automatically by the triangle character in ECG, and then it was time-reversed after removing its direct current component. Secondly, matched filtering was implemented at low computational cost by finite impulse response, which further enhanced QRS complex and attenuated non-QRS regions containing P-wave, T-wave and various noise components. Subsequently, triangle structure-based threshold decision was processed to detect QRS complexes. And RR intervals and triangle structures were further analyzed for the reduction of false-positive and false-negative detections. Finally, the performance of the proposed algorithm was tested on all 48 records of the MIT-BIH Arrhythmia Database. The results demonstrated that the detection rate reached 99.62 %, the sensitivity got 99.78 %, and the positive prediction was 99.85 %. In addition, the proposed method was able to identify QRS complexes reliably even under the condition of poor signal quality.
    Australasian physical & engineering sciences in medicine / supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine 07/2012; 35(3):341-56. · 0.89 Impact Factor
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.
    The Scientific World Journal 01/2014; 2014:182938. · 1.73 Impact Factor


Available from

Yun-Chi Yeh