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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    • "Differential operation (90-degree differential phase shifter or 90-degree phase difference generator) is a basic mathematical operation and has been widely used in the signal analysis and processing, especially in the detection and extraction of signal singular points. For example, differentiation forms the basis of many QRS complex detection algorithms [1] [2]. Since it is basically a high-pass filter, the derivative amplifies the high-frequency characteristics of the QRS complex while attenuating the lower frequencies, the P and T waves. "
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    ABSTRACT: A fractional 90° phase-shift filtering technique is investigated based on the double-sided Grünwald-Letnikov differintegrator. Thanks to the Grünwald-Letnikov definition of fractional calculus, first the left and the right-sided Grünwald-Letnikov differintegrators are presented, which are generalised magnitude-and-phase modulations. Then, parallel a left-sided Grünwald-Letnikov differintegrator with a right-sided Grünwald-Letnikov differintegrator to obtain a double-sided Grünwald-Letnikov differintegrator, which is essentially a fractional 90° phase-shift filter with the capability of noise immunity. A double-sided symmetrical convolution mask is constructed to implement the proposed fractional 90° phase shifter. Finally, a singularity detection example is illustrated to demonstrate that the proposed double-sided Grünwald-Letnikov differintegrator is forward 90°, which is the same as the conventional first-order differential, and possesses good noise immunity that can be tuned by adjusting the differintegral order.
    IET Signal Processing 06/2015; 9(4). DOI:10.1049/iet-spr.2014.0062 · 0.91 Impact Factor
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    • "The QRS detection module can be performed prior to the preprocessing of heartbeats. Many methods are available for QRS detection with accuracy rates greater than 99.5% [34-36]. "
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    ABSTRACT: Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice.
    BioMedical Engineering OnLine 06/2014; 13(1):90. DOI:10.1186/1475-925X-13-90 · 1.43 Impact Factor
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    • "Extraction of the heartbeats first requires QRS wave detection. Currently, many methods are available for QRS detection with accuracy rates greater than 99.5% [33-35]. The focus of this study was on heartbeat classification, not on QRS detection. "
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    ABSTRACT: Background Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). Methods This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. Results The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. Conclusions A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.
    BioMedical Engineering OnLine 06/2014; 13(1):72. DOI:10.1186/1475-925X-13-72 · 1.43 Impact Factor
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