Identification of QRS complexes in 12-lead electrocardiogram

Mehta, S. S.; Lingayat, N. S.

doi:10.1016/j.eswa.2007.10.007

Cited by 32 publications

(21 citation statements)

References 25 publications

(24 reference statements)

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“…(12) Table 4 shows that detection accuracy of the proposed method (99.9) is slightly better than the method proposed in Bolton and Westphal [4] and Mehta and Lingayat [35]. Se (99.91%) and PP (100%) of the proposed method is comparable to the best method [4] among the methods reported in Table 5.…”

Section: Detection Accuracysupporting

confidence: 65%

“…In a Tele-Cardiology system for compression, transmission and analysis of ECG signal, this combined unit could be a better choice. Detection accuracy (%) Support Vector Machine (SVM) [35] 99.75 Length and Energy Transformation [37] 99.60 Time Recursive Prediction [38] 99.00 Bottom up approach [39] 98.49 Mathematical Morphology [40] 99.38 Hybrid Complex Wavelet [41] 99.7 Hilbert Transform [4] 99.87 Proposed 99.90 Identification of various ECG characteristic points, extraction of time domain features, height measurement of various peaks by a method based on first derivative, Hilbert transform, lead wise variable threshold and slope reversal is proposed in this paper. The method does not include much mathematical and computational complexity.…”

Section: Discussionmentioning

confidence: 99%

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ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach

Mukhopadhyay

Mitra

2012

Journal of Medical Engineering & Technology

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An accurate and reliable ECG feature extraction algorithm is presented in this paper. ECG samples are de-noised and its first derivative and Hilbert transform are computed. Sample having maximum amplitude in the transformed domain is found out and those samples having amplitudes within a lead wise specified threshold of that maximum are marked. In the original signal, where these marked samples undergo slope reversals are spotted as R-peak. On the left and right side of the R-peak, slope reversals are identified as Q and S peak, respectively. QRS onset-offset points, T and P waves are also detected. ECG baseline modulation correction is done after detecting characteristics points. The algorithm offers a good level of Sensitivity, Positive Predictivity and accuracy of R peak detection. Each wave and segment duration and each peak height is measured. Measurement errors of extracted ECG features are calculated. The algorithm is implemented on MATLAB 7.1 environment.

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Section: Detection Accuracysupporting

confidence: 65%

Section: Discussionmentioning

confidence: 99%

ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach

Mukhopadhyay

Mitra

2012

Journal of Medical Engineering & Technology

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“…Mehta and Lingayat (2007a) describe a method for the detection of QRS using SVM. Mehta and Lingayat (2007b) proposed signal entropy based method using SVM for the detection of QRS complexes (cardiac beat) in the single lead ECG.…”

Section: Introductionmentioning

confidence: 99%

An integrated intelligent computing method for the detection and interpretation of ECG based cardiac diseases

Pandey

Mishra

2010

IJKESDP

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Intelligent computing system and knowledge-based system have been widely used in the diagnosis and classification of ECG based diseases. Several detection methods of ECG parameters for a particular disease have also been reported in the literature. But little effort has been made by researchers to combine both. In this work, an integrated model of rule base system for generating cases and ANN methods for matching cases in the case base reasoning model for the interpretation and diagnosis of sinus disturbances (SD) is developed. The SD is hierarchically structured in terms of their physio-psycho parameters and ECG based parameters. Cumulative confidence factor (CCF) is computed at different nodes of hierarchy. The SD considered are sinus arrest, sinus bradycardia, sinus tachycardia and sinus arrhythmia. MIT/BIH ECG database is used in the simulation study. The basic objective of this work is to enhance the computational effort with certain level of efficiency and accuracy.

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“…For instance, in the area of the ECG signal Chawla et al [3,4], Milanesi et al [5] and Taigang-Clifford et al [6] proposed statistical-based methodologies for the ECG pre-processing (including noise and motion artifact removal) and detection of this signal is a major event. Other innovative methods by Mehta et al [7], Chouhan et al [8] and Mehta-Lingayat [9][10][11] elaborated some efficient algorithms for the aim of ECG signal P, QRS and T waves detection as well as their robust delineation (segmentation) based on Support Vector Machine as the discrimination method and signal information-based measure as the feature of the detection-delineation process. As some other proposed methods, the algorithms based on mathematical models [12], Hilbert transform and the first derivative [13][14][15][16], multiple higher order moments [17], second order derivative [18], wavelet transform and the filter banks [19][20][21][22], combination of signal derivatives and multi-resolution digital filters with non-parametric detection algorithms [23][24][25][26], soft computing (Neuro-fuzzy, genetic algorithm) [27], Hidden Markov Models (HMM) application [28] can be mentioned as the recent studies.…”

Section: Introductionmentioning

confidence: 99%

Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

Homaeinezhad

Atyabi

Deneshvar

et al. 2011

Numer Methods Biomed Eng

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SUMMARYThe aim of this study is to describe a new false-alarm probability (FAP) bounded unified framework for segmentation of the phonocardiogram (PCG) signal sounds registered by an electronic stethoscope board. To meet this end, first the original PCG signal is pre-processed by application of an appropriate bandpass finite-duration impulse response (FIR) filter and then by implementation of à trous discrete wavelet transform (DWT) to the filtered signal for extracting several dyadic scales. Then, after choosing a proper scale, a fixed sample size sliding window is moved on the selected scale and in each slide, six feature vectors namely summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. Next, using an adaptive smoothing filter (ASF), the obtained metric is modulated and freed from the fast fluctuations occurring in the vicinity of events onset and offset locations which consequently results in enhancement of edge detection accuracy. Later, histogram parameters of the filtered DS metric are used for the regulation of the -level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. To assess the performance quality of the proposed PCG segmentation algorithm, the method was applied to all 85 records of Nursing Student Heart Sounds database including stenosis, insufficiency, regurgitation, gallop, septal defect, sound split, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. The method was also applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as a result, no false positive or false negative errors were detected. High robustness against measurement noises of the electronic stethoscopes, acceptable detection-segmentation accuracy of PCG events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameter dependency on the acquisition sampling frequency can be mentioned as important merits and capabilities of the proposed PCAGI-based PCG events detection-segmentation algorithm.

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Identification of QRS complexes in 12-lead electrocardiogram

Cited by 32 publications

References 25 publications

ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach

ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach

An integrated intelligent computing method for the detection and interpretation of ECG based cardiac diseases

Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

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