Time Delay Estimation: A New Insight Into the Woody's Method

Cabasson, Aline; Meste, Olivier

doi:10.1109/lsp.2008.2001558

Cited by 42 publications

(45 citation statements)

References 17 publications

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“…The 2DSW algorithm is able to account for complex changes in the morphology of the ECG waveform and was shown to track QT changes with high precision while being robust against signal artefacts [11,12]. Briefly, the algorithm first identifies QRS complexes and uses these fiducial points to generate an averaged template beat, employing an improved version of Woody's time delay estimation method [13]. The next points of interest such as Q and T end are marked on the template in a semi-automated manner.…”

Section: Beat-to-beat Qt Interval Extractionmentioning

confidence: 99%

Entropy Analysis of RR and QT Interval Variability during Orthostatic and Mental Stress in Healthy Subjects

Baumert

Czippelová

Ganesan

et al. 2014

Entropy

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Autonomic activity affects beat-to-beat variability of heart rate and QT interval. The aim of this study was to explore whether entropy measures are suitable to detect changes in neural outflow to the heart elicited by two different stress paradigms. We recorded short-term ECG in 11 normal subjects during an experimental protocol that involved head-up tilt and mental arithmetic stress and computed sample entropy, cross-sample entropy and causal interactions based on conditional entropy from RR and QT interval time series. Head-up tilt resulted in a significant reduction in sample entropy of RR intervals and cross-sample entropy, while mental arithmetic stress resulted in a significant reduction in coupling directed from RR to QT. In conclusion, measures of entropy are suitable to detect changes in neural outflow to the heart and decoupling of repolarisation variability from heart rate variability elicited by orthostatic or mental arithmetic stress.

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Section: Beat-to-beat Qt Interval Extractionmentioning

confidence: 99%

Entropy Analysis of RR and QT Interval Variability during Orthostatic and Mental Stress in Healthy Subjects

Baumert

Czippelová

Ganesan

et al. 2014

Entropy

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show abstract

“…1-According to (9) and (10), we should have small normalized recontruction error. This condition is not enough because if we increase R and N , naturally the reconstruction error will be decreased.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, we align the spike matrices using improved version of Woody's method proposed in [9] and stack them in 3-way data tensor T with dimensions E × T × K (Fig. 2).…”

Section: Pre-processingmentioning

confidence: 99%

Characterizing absence epileptic seizures from depth cortical measurements

Akhavan

Phlypo

Soltanian-Zadeh

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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In this paper, we are going to localize the onset regions and investigate the dynamics of absence epileptic seizures using local field potential recording by depth electrode. We assume that there are some hidden states (under Markovian model) during the seizure and each spike of the seizure is generated when one of the states is activated. Each state is considered as the linear superposition of a few epileptic activities (substates) and each epileptic activity is described by two characteristics: 1) the spatial topography and 2) the temporal representation. Experimental results demonstrate the generality of the proposed model in characterizing absence epileptic seizures.

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“…We impose that v 1 [K]=v 3 [K]=0which implies, given the relations (10), that C 1 = C 2 . So, the continuity for n = K is ensured.…”

Section: B Model Using Piecewise Linear Function (M P )mentioning

confidence: 99%

“…When the signal under consideration is unknown, i.e., the P wave in our case, Woody's method [9] is a good candidate that belongs to the cross-correlation family. Besides, the Improved Woody method outperforms significantly Woody's technique [10]. Moreover, for TDE problems, several other techniques have been used mainly working in the frequency domain [11]- [13].…”

mentioning

confidence: 98%

Quantifying the PR Interval Pattern During Dynamic Exercise and Recovery

Cabasson

Meste

Blain

et al. 2009

IEEE Trans. Biomed. Eng.

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Abstract-We present a novel analysis tool for time delay estimation in electrocardiographic signal processing. This tool enhances PR interval estimation (index of the atrioventricular conduction time) by limiting the distortion effect of the T wave overlapping the P wave at high heart rates. Our approach consists of modeling the T wave, canceling its influence, and finally estimating the PR intervals during exercise and recovery with the proposed generalized Woody method. Different models of the T wave are presented and compared in a statistical summary that quantitatively justifies the improvements introduced by this study. Among the different models tested, we found that a piecewise linear function significantly reduces the T wave-induced bias in the estimation process. Combining this modeling with the proposed time delay estimation method leads to accurate PR interval estimation. Using this method on real ECGs recorded during exercise and its recovery, we found: 1) that the slopes of PR interval series in the early recovery phase are dependent on the subjects' training status (average of the slopes for sedentary men = 0.11 ms/s, and for athlete men = 0.28 ms/s), and 2) an hysteresis phenomenon exists in the relation PR/RR intervals when data from exercise and recovery are compared.

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Time Delay Estimation: A New Insight Into the Woody's Method

Cited by 42 publications

References 17 publications

Entropy Analysis of RR and QT Interval Variability during Orthostatic and Mental Stress in Healthy Subjects

Entropy Analysis of RR and QT Interval Variability during Orthostatic and Mental Stress in Healthy Subjects

Characterizing absence epileptic seizures from depth cortical measurements

Quantifying the PR Interval Pattern During Dynamic Exercise and Recovery

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