Using wavelet transforms for ECG characterization. An on-line digital signal processing system

Sahambi, J. S.; Tandon, Sanjeev; Bhatt, Rajendra

doi:10.1109/51.566158

Cited by 313 publications

(100 citation statements)

References 10 publications

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“…First K. Shafqat, Second S. K. Pal, Third S. Kumari and Fourth P. A. Kyriacou, Senior Member, IEEE T [5]. First derivative of the Gaussian smoothing function was used as the mother wavelet.…”

Section: Changes In Heart Rate Variability In Patients Under Local Anmentioning

confidence: 99%

Changes in Heart Rate Variability in Patients under Local Anesthesia

Shafqat

Pal

Kumari

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Section: Changes In Heart Rate Variability In Patients Under Local Anmentioning

confidence: 99%

Changes in Heart Rate Variability in Patients under Local Anesthesia

Shafqat

Pal

Kumari

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…In this pathology, P wave and QRS-T generation are independent 9,24 . Therefore, P-wave detectors based on relative positions between R and P waves cannot be used 13,25,27 . Esophageal or intracavitarial electrodes have been used to obtain an isolated atrial activity that allows the P wave 11 to be detected.…”

Section: Figure 1 Electrocardiographic Signal Showing Type III Avb mentioning

confidence: 99%

“…P wave detectors usually use the relationship between the P wave and the QRS complex during normal cardiac rhythm 13,25,27 , applying a search window to the left side of the QRS complex. However, since atrial and ventricular activities are not related in type III AVB, the P and the R waves are not related, either.…”

Section: Proposed Algorithmmentioning

confidence: 99%

P and R Wave Detection in Complete Congenital Atrioventricular Block

et al. 2008

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Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using non-invasive electrocardiographic surface leads.The proposed algorithm is divided into three stages. In the first stage, the R waves located by a QRS detector are used to generate the RR series and time references for the other stages of the algorithm. In the second stage, the ventricular activity (QT segment) is removed by using an adaptive filter that obtains an averaged pattern of the QT segment. In the third stage, a new P wave detector is applied to the residual signal obtained after QT cancellation in order to detect P wave locations and get the PP time series.Eight Holter records from patients with congenital type III AVB were used to verify the proposed algorithm. Although further improvements should be made to improve the algorithm's performance, the results obtained show high average values of sensitivity (90.52 %) and positive prediction (90.98%). KeywordsComplete congenital atrioventricular block, P and R wave detection, QRS, adaptive impulse correlated filter, SNEO.2 2

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“…Contrary to our approach, other WT-based approaches conducted MMA on multiple-resolution levels. This multiple level MMA, coupled with the use of computationally intensive mother wavelets can yield very accurate results for ECG delineation [27], [34]. However we will demonstrate in our analysis (see Section IV) that the computational complexity, in terms of the arithmetic operations required, by the state of the art WT-delineator [34], which sets the basis of of DWTbased ECG feature extraction, far exceeds the computational complexity of the proposed algorithm, even after including the TDM step.…”

Section: A Choice Of the Wavelet Functionmentioning

confidence: 99%

A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications

Mazomenos

Biswas

Acharyya

et al. 2013

IEEE J. Biomed. Health Inform.

153

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Abstract-This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2.423N + 214 additions and 1.093N + 12 multiplications for N ≤ 861 or 2.553N + 102 additions and 1.093N + 10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.

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Using wavelet transforms for ECG characterization. An on-line digital signal processing system

Cited by 313 publications

References 10 publications

Changes in Heart Rate Variability in Patients under Local Anesthesia

Changes in Heart Rate Variability in Patients under Local Anesthesia

P and R Wave Detection in Complete Congenital Atrioventricular Block

A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications

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