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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    • "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.
    Full-text · Article · Jun 2015 · IET Signal Processing
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    • "If their position is different and amplitude of Q 1 is greater than Q 2 , then the position of Q 1 is the position of Q or vice versa. If the position of S 1 and S 2 are same, their position is the position of S. Otherwise, if 21 is the amplitude of point S i , i = 1, 2 (Fig. 1) [36]. To evaluate the classification probability of healthy, arrhythmia and ischemia using LDA and decision tree, 100% accurately detected QRS complexes (without QT complex inversion) of 108 episodes are selected.Fig. 1 Filtering and QRS complex detection for MITDB data # 117 (10 s data for better visualization) The performance of the methodologies is evaluated by the sensitivity (Se) and the specificity (Sp). "
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    ABSTRACT: Two of the most common cardiovascular diseases are myocardial ischemia and cardiac arrhythmias. Using the frequency domain features of QRS complex (i.e., frequency of the maximum peak in power spectrum and total average power) the proposed approach analyzes classification probability for these diseases by implementing Linear Discriminant Analysis (LDA) and Decision Tree. Moreover the classification probability is visualized using Naive Bayes classification algorithm. The methodology includes the QRS complex detection technique which is mainly comprises of three stages: Stage-1-baseline drifts and noise cancellation using Moving Average Filter (MAF) and Stationary Wavelet Transform (SWT); Stage-2-R-peaks localization using threshold based windowed filter: Stage-3-Q and S inflection points detection using search interval method. To perform uniform classification probability analysis, the proposed methodology is evaluated with 108 selected episodes which show 100% accuracy in QRS complex detection. The 108 episodes includes 36 lengthy ECG recordings from FANTASIA database (healthy subjects), MIT-BIH Arrhythmia database (arrhythmic subjects) and Long-Term ST database (ischemic subjects) respectively. Moreover, the energy surface distribution of segmented QRS complex is analysed with Short-Term Fourier Transform (STFT) which transforms time domain information of the complex into time-frequency domain.
    Full-text · Article · Jan 2015
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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.
    Full-text · Article · Jun 2014 · BioMedical Engineering OnLine
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