A robust wavelet-based multi-lead electrocardiogram delineation algorithm

Ghaffari, Ali; Homaeinezhad, Mohammad Reza; Akraminia, M; Atarod, Mohammad; Daevaeiha, M. M.

doi:10.1016/j.medengphy.2009.07.017

Cited by 104 publications

(107 citation statements)

References 18 publications

Supporting

Mentioning

103

Contrasting

Unclassified

Order By: Relevance

“…Considering that the manual annotation process of T-wave peaks and ends for QTDB was performed having in sight each pair of available leads, we choose for each point the channel with less error, as recommended by Martínez et al [15] and Zhang et al [11]. [18] 99.87 99.80 0.3 ± 4.1 0.8 ± 10.7 J. P. Martínez et al [15] 99.77 97.79 0.2 ± 13.9 -1.6 ± 18.1 Vila et al [9] 92.6 N/R -12 ± 23.4 0.8 ± 30.3 Zhang et al [11] N/R N/R N/A 0.31 ± 17.43…”

Section: B Results Over Qt Databasementioning

confidence: 99%

“…Furthermore, according to Murray et al [10], manual measurement of QT interval exhibits considerable inter and intra-observer variability. Various automatic methods have been proposed for both detection and segmentation of Twave, based on: trapezium's area approach [4], accumulative area approach [11], mathematical models of the ECG [9,14], discrete wavelet-based estimators using the derivative of a smoothing function as the prototype wavelet [15], singular value decomposition (SVD) of multiple lead ECG signals [12], ECG curve length transform [13], phasor transformed ECG and instantaneous phase variation [17], other approaches based on Wavelet transform [18,19], pattern recognition techniques [22], among other methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model

Madeiro

Nicolson

Cortez

et al. 2013

Medical Engineering & Physics

View full text Add to dashboard Cite

-This paper presents an innovative approach for T-wave peak detection and subsequent T-wave end location in 12-lead paced ECG signals based on a mathematical model of a skewed Gaussian function. Following the stage of QRS segmentation, we establish search windows using a number of the earliest intervals between each QRS offset and subsequent QRS onset. Then, we compute a template based on a Gaussian-function, modified by a mathematical procedure to insert asymmetry, which models the Twave. Cross-correlation and an approach based on the computation of Trapezium's area are used to locate, respectively, the peak and end point of each T-wave throughout the whole raw ECG signal. For evaluating purposes, we used a database of high resolution 12-lead paced ECG signals, recorded from patients with ischemic cardiomyopathy (ICM) in the University Hospitals of Leicester NHS Trust, UK, and the well-known QT database. The average T-wave detection rates, sensitivity and positive predictivity, were both equal to 99.12%, for the first database, and, respectively, equal to 99.32% and 99.47%, for QT database. The average time errors computed for T-wave peak and T-wave end locations were, respectively, -0.38±7.12ms and -3.70±15.46ms, for the first database, and 1.40±8.99ms and 2.83±15.27ms, for QT database. The results demonstrate the accuracy, consistency and robustness of the proposed method for a wide variety of T-wave morphologies studied.Index Terms -T-wave peak, T-wave end, T-wave modelling, skewed Gaussian function, cross-correlation, paced electrocardiogram (paced ECG).

show abstract

Section: B Results Over Qt Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model

Madeiro

Nicolson

Cortez

et al. 2013

Medical Engineering & Physics

View full text Add to dashboard Cite

show abstract

“…So far, variant methods for ECG de-noising and R-wave detection have been proposed, including approaches of derivatives [10,11], digital filters [12][13][14][15], wavelet transform(WT) [1,[16][17][18][19][20][21][22][23][24][25][26], artificial neural network (ANN) [27,28], support vector machine (SVM) [29], k-means [30], empirical mode decomposition (EMD) [31], geometrical matching [32][33][34], combined threshold method [35,36], phase space method [37], Hilbert Transform method [38], and mixed approach [39,40]. Almost all of the methods listed above have some limitations.…”

Section: Introductionmentioning

confidence: 99%

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Rui

Wang

et al. 2017

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

show abstract

“…18,20 Therefore, parameterization and detection of the ECG signal events using a reliable algorithm is the first stage in the computer analysis of the ECG signal. Numerous approaches have yet been developed for the aim of detection of the ECG events including mathematical models, 28 Hilbert transform, and the first derivative, 1,12 second-order derivative, 21 wavelet transform and the filter banks, 9,10,19 soft computing (Neuro-fuzzy, genetic algorithm), 14 Hidden Markov Models (HMM) application, 5 etc. The performance of QRS detection algorithms can easily be verified using the standard databases such as MIT-BIH Arrhythmia Database 26 ; however, validation of a proposed algorithms for the detection-delineation of P and T-waves has turned to a difficult problem due to the lack of a gold standard as universal reference.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

et al. 2010

Self Cite

View full text Add to dashboard Cite

In this study, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s). In the MHOM measure, important geometric characteristics such as maximum value to minimum value ratio, area, extent of smoothness or being impulsive and distribution skewness degree (asymmetry), occult. In the proposed method, first three leads of high resolution 24-h holter data are extracted and preprocessed using Discrete Wavelet Transform (DWT). Next, a sample to sample sliding window is applied to preprocessed sequence and in each slid, mean value, variance, skewness, and kurtosis of the excerpted segment are superimposed called MHOM. The MHOM metric is then used as decision statistic to detect and delineate ECG events. To show advantages of the presented method, it is applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.95% and P+ = 99.94% are obtained for the detection of QRS complexes, with the average maximum delineation error of 6.1, 4.1, and 6.5 ms for P-wave, QRS complex, and T-wave, respectively showing marginal improvement of detectiondelineation performance. In the next step, the proposed method is applied to DAY hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC, and Premature Atrial Complex-PAC) and average values of Se = 99.97% and P+ = 99.95% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process, reliable robustness against strong noise, artifacts, and probable severe arrhythmia(s) of high resolution holter data can be mentioned as important merits and capabilities of the proposed algorithm.

show abstract

A robust wavelet-based multi-lead electrocardiogram delineation algorithm

Cited by 104 publications

References 18 publications

New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model

New approach for T-wave peak detection and T-wave end location in 12-lead paced ECG signals based on a mathematical model

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric

Contact Info

Product

Resources

About