Higher order statistics and ECG arrhythmia classification

Alliche, A.; Mokrani, Karim

doi:10.1109/isspit.2003.1341202

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…The following indices were estimated: recurrence rate (RcRt), determinism (DET), Shannon entropy (Entr) and, L mean . Details about the procedure for recursive quantification analysis can be found in [2][3][4][5][6]. Briefly, in RQA, the following vectors were created:…”

Section: Hrv Analysismentioning

confidence: 99%

“…The nonlinear nature of both electrocardiograms (ECG) and heart rate variability (HRV) is widely documented [1]. This study explains several attempts to introduce indices purportedly characterizing nonlinear heart dynamics [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Cardiovascular signals seem to emerge from an intricate combination of periodic, quasi-periodic, linear and nonlinear, Gaussian and non-Gaussian, stochastic and deterministic interactions [4][5][6]. Accordingly, the implementation of methods based on considering some of these interactions while ignoring the contribution of others can lead to deceptive interpretations.…”

Section: Introductionmentioning

confidence: 99%

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Introducing BisQ, A Bicoherence-Based Nonlinear Index to Explore the Heart Rhythm

Hernández-Caceres

González-Fernández

Ontivero-Ortega

et al. 2020

MCA

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Nonlinear frequency coupling is assessed with bispectral measures, such as bicoherence. In this study, BisQ, a new bicoherence-derived index, is proposed for assessing nonlinear processes in cardiac regulation. To find BisQ, 110 ten-minute ECG traces obtained from 55 participants were initially studied. Via bispectral analysis, a bicoherence matrix (BC) was obtained from each trace (0.06 to 1.8 Hz with a resolution of 0.01 Hz). Each frequency pair in BC was tested for correlation with the HRV recurrent quantification analysis (RQA) index Lmean, obtained from tachograms from the same ECG trace. BisQ is the result of adding BC values corresponding to the three frequency pairs exhibiting the highest correlation with Lmean. BisQ values were estimated for different groups of subjects: healthy persons, persons with arrhythmia, persons with epilepsy, and preterm neonates. ECG traces from persons with arrhythmia showed no significant differences in BisQ values respect to healthy persons, while persons with epilepsy and neonates showed higher BisQ values (p < 0.05; Mann-Whitney U-test). BisQ reflects nonlinear interactions at the level of sinus-and atrial-ventricular nodes, and most likely cardiorespiratory coupling as well. We expect that BisQ will allow for further exploration of cardiac nonlinear dynamics, complementing available HRV indices.

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Section: Hrv Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Introducing BisQ, A Bicoherence-Based Nonlinear Index to Explore the Heart Rhythm

Hernández-Caceres

González-Fernández

Ontivero-Ortega

et al. 2020

MCA

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“…The power spectrum only provides information about the distribution of the signal's power among its individual frequency components and does not provide any information on the phase relations between them. Hence most often higher order spectral analysis is used in the analysis of nonlinear signals like EEG [22], ECG [23] as well as for pattern recognition [24], analysis of harmonic random processes [25], geophysical processes [26], turbines, astrophysical processes [27] etc. The first higher order spectrum is the bispectrum which is the Fourier transform of third-order correlation of the signal (power spectrum is Fourier Transform of the second order correlation).…”

Section: Introductionmentioning

confidence: 99%

Higher order spectral analysis of ECG signals

Kotriwar¹,

Kachhara²,

Harikrishnan³

et al. 2018

Preprint

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Higher Order Spectral (HOS) analysis is often applied effectively to analyze many bio-medical signals to detect nonlinear and non-Gaussian processes. One of the most basic HOS methods is the bispectral estimation, which extracts the degree of quadratic phase coupling between individual frequency components of a nonlinear signal. Most of the studies in this direction as applied to ECG signals are on the conventional, long duration (up to 24 hours) Heart Rate Variability (HRV) data. We report results of our studies on short duration ECG data of 60 seconds using power spectral and bispectral parameters. We analyze 60 healthy cases and 60 cases of patients diagnosed with four different heart diseases, Bundle Branch Block, Cardiomyopathy, Dysrhythmia and Myocardial Infarction. From the power spectra of these data sets we observe that the pulse frequency around 1 Hz has maximum power for all normal ECG data while in all disease cases, the power in the pulse frequency is suppressed and gets distributed among higher frequencies. The bicoherence indices computed show that the pulse frequency has strong quadratic phase coupling with a large number of higher frequencies in healthy cases indicating nonlinearity in the underlying dynamical processes. The loss or decrease of the phase coupling with pulse frequency is a clear indicator of abnormal conditions. In specific cases, bicoherence studies coupled with spectral filter, suggest ECG for Myocardial Infarction has noisy components while in Dysrhythmia, power is mostly at high frequencies with strong quadratic coupling indicating much more irregularity and complexity than normal ECG signals. In addition to serving as indicators suggestive of abnormal conditions of the heart, the detailed analysis presented can lead to a wholistic understanding of normal heart dynamics and its variations during onset of diseases.

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