Order selection in Doppler blood flow signal spectral analysis using autoregressive modelling

Kałużyński, K.

doi:10.1007/bf02442176

Cited by 42 publications

(13 citation statements)

References 9 publications

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“…13 Among several methods of estimation of the AR model parameters (Yule Walker equation, Burg algorithm, least squares algorithm), the Burg method was used in this study. The Burg AR method is based on the minimization of the forward and backward prediction errors and estimation of the reflection coefficient.…”

Section: Spectral Analysismentioning

confidence: 99%

Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

et al. 2007

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Carotid Artery Doppler Signals were recorded from 114 subjects, 60 of whom had Atherosclerosis disease while the rest were healthy controls. Diagnosis of Atherosclerosis from Carotid Artery Doppler Signals was conducted using Fuzzy weighted pre-processing and Least Square Support Vector Machine (LSSVM). First, in order to determine the LSSVM inputs, spectral analysis of Carotid Artery Doppler Signals was performed via Autoregressive (AR) modeling. Then, fuzzy weighted pre-processing based is proposed expert system, applied to inputs obtained from spectral analysis of Carotid Artery Doppler Signals. LSSVM was used to detect Atherosclerosis from Carotid Artery Doppler Signals. All data set were obtained from Carotid Artery Doppler Signals of healthy subjects and subjects suffering from Atherosclerosis disease. The employed expert system has achieved 100% classification accuracy using a 10-fold Cross Validation (CV) method.

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Section: Spectral Analysismentioning

confidence: 99%

Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

et al. 2007

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“…One of the significant issues for employing the AR method is the selection of model degree. Many researchers studied on this problem and the Akaike Information Criteria (AIC) introduced by Akaike is used most commonly in the literature (Kaluzynski, 1989). The selection of AIC model degree is in the form of making the below statement minimum.…”

Section: Analysis Of Surface Emg Signalsmentioning

confidence: 99%

“… wp 2 decreases with the increase of the AR model degree and therefore, ln wp 2 decreases as well. That being said, 2p/N increases too with the increase of p. In this case, a minimum value should be designated for p (Kay, 1988;Kaluzynski, 1989). The p value calculated via the AIC criteria was obtained as 12.…”

Section: Analysis Of Surface Emg Signalsmentioning

confidence: 99%

EMG PSD Measures in Orthodontic Appliances

Okkesim¹,

Uysal²,

Baysal³

et al. 2012

EMG Methods for Evaluating Muscle and Nerve Function

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“…Several criteria were introduced (see [1, p. 1396] and a number of attached references) to provide objective basis for the model order selection. However, subjective judgement is still required for selection of the adequate order (see, for example, [5]). In this work, we propose a model order selection that uses results on restoration of AR structure of buried signals.…”

Section: Introductionmentioning

confidence: 99%

Recovering of autoregressive spectral estimates of signals buried in noise

Bey

2007

SIViP

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In this paper, we propose a noise modeling that does not destroy AR structure of buried signals in noise independently of its nature (white or colored, Gaussian or not) and its variance. Expression of perturbed AR coefficients is derived and proposed restoration does not use any a-priori information on the nature of noise and its variance. It is shown that AR coefficients are closer to nominal ones (noise-free) in the presence of noise for lower frequency contents with respect to the sampling frequency of corresponding continuous-time processes from which samples are taken for AR estimation. For unknown frequency contents, denoising of AR coefficients is obtained by decreasing the time interval separating samples used by AR estimation. A model order selection adapted to degraded signal-to-noise ratios is proposed. Performances of the proposed recovering of original AR spectra are demonstrated via signals buried in white and colored noise. Observed results are in accordance with the developed theory.

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Order selection in Doppler blood flow signal spectral analysis using autoregressive modelling

Cited by 42 publications

References 9 publications

Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM)

EMG PSD Measures in Orthodontic Appliances

Recovering of autoregressive spectral estimates of signals buried in noise

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