Detection of T-Waves in 12-Lead Electrocardiogram

Ghanekar, Umesh; Singh, Awadhesh Kumar; Pandey, Rajoo

doi:10.1109/iccima.2007.25

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…These methods are based on derivative-based algorithms (Holsinger et al 1971), filtering approaches (digital filters (Yu et al 1985), adaptive filters (Soria et al 1998)), mathematical transformations (wavelet (Li C et al 1995, Martinez J P et al 2004, Dumont et al 2010, filter banks (Afonso et al 1999), phasor transform (Martinez A et al 2010)), classification methods (neural network approaches (Hu et al 1993), support vector machine (SVM) (Mehta et al 2008), fuzzy C-means algorithm (Mehta et al 2009)), hidden Markov models (HMM) (Coast et al 1990, Hughes et al 2004a, Hughes et al 2006, Andreao et al 2006a, Andreao et al 2006b, automated method (Christov et al 2007) and mathematical morphology methods (Sun et al 2005). Adaptive filters, wavelet transform, SVM, mathematical morphology methods, HMM and Partially Collapsed Gibbs Sampler (PCGS) (Lin et al 2010, Lin et al 2011a have also been used for P-and T-wave delineation.…”

Section: Introductionmentioning

confidence: 99%

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

Akhbari¹,

Shamsollahi²,

Jutten³

et al. 2016

Physiol. Meas.

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Abstract. In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 msec and 22 msec, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods.

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Section: Introductionmentioning

confidence: 99%

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

Akhbari¹,

Shamsollahi²,

Jutten³

et al. 2016

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…It is observed that sensitivity and specificity values for P and T detection are moderate because it is difficult to detect them as compared to QRS complex features irrespective of the approach or algorithm used. Reliable detection of P and T wave is more difficult than QRS complex detection for several reasons including low amplitudes, low signal-to-noise ratio, amplitude and morphological variability and possible overlapping of the P wave with the QRS complex [6].…”

Section: Resultsmentioning

confidence: 99%

Electrocardiogram Feature Extraction and Pattern Recognition Using a Novel Windowing Algorithm

Umer¹,

Bhatti²,

Tariq³

et al. 2014

ABB

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Windows are based on varying R-R intervals. It has been tested on ECG simulator data and also on different records of the MIT-BIH arrhythmia database, producing satisfactory results. ECG timing intervals are also required for monitoring the cardiac condition of patients. Hence after feature detections ECG timing intervals like the PR interval, QRS duration, the QT interval, the QT corrected interval and Vent Rate are efficiently calculated using proposed Formulae.

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“…The results show that the sensitivity for the peaks of the P, QRS and T waves is higher than 98% and the standard deviation of the mean error for each delineated point is below the tolerances of the Common Standards for Electrocardiography (CSE) committee [35] (s < 2σ CSE , known as "loose criterion"), except for the onset of the P wave, for which this value is 3 ms above. The end of the T wave has the highest mean error but below the tolerance, the proper delineation of this point is a well-known challenge, even for cardiologists and different studies have been developed discussing this issue [36].…”

Section: Journal Of L a T E X Class Files Vol 6 No 1 January 2007mentioning

confidence: 99%

A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems

Bote

Recas

Rincon³

et al. 2018

IEEE J. Biomed. Health Inform.

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Abstract-This work presents a new modular and lowcomplexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform realtime delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in run time to a wide range of modes and sampling rates, from a Ultra-low power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete High-accuracy delineation mode in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography (CSE) committee in the High-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultra-low-power 8-MHz TI MSP430 series microcontroller ranges from 0.2 to 8.5% according to the mode used.

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Detection of T-Waves in 12-Lead Electrocardiogram

Cited by 14 publications

References 15 publications

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

Electrocardiogram Feature Extraction and Pattern Recognition Using a Novel Windowing Algorithm

A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems

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