Literature DB >> 12356494

Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels.

Inan Güler1, Firat Hardalaç, Memduh Kaymaz.   

Abstract

In this work, transcranial Doppler signals recorded from the temporal region of the brain on 35 patients were transferred to a personal computer by using a 16-bit sound card. Fast Fourier transform and adaptive auto regressive-moving average (A-ARMA) methods were applied to transcranial Doppler frequencies obtained from the middle cerebral artery in the temporal region. Spectral analyses were obtained to compare both methods for medical diagnoses. The sonograms obtained using A-ARMA method give better results for spectral resolution than the FFT method. The sonograms of A-ARMA method offer net envelope and better imaging, so that the determination of blood flow and brain pressure can be calculated more accurately. All diseases show higher resistance to flow than controls with no difference between males and females. Whereas values between disease classes differed, resistance within each class was remarkably constant.

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Year:  2002        PMID: 12356494     DOI: 10.1016/s0010-4825(02)00036-7

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  4 in total

1.  Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels.

Authors:  Firat Hardalaç
Journal:  J Med Syst       Date:  2008-04       Impact factor: 4.460

2.  A survey on application of quantitative methods on analysis of brain parameters changing with temperature.

Authors:  Ayşe Demirhan; Memduh Kaymaz; Raşit Ahıska; Inan Güler
Journal:  J Med Syst       Date:  2009-06-09       Impact factor: 4.460

3.  Classification of MCA stenosis in diabetes by MLP and RBF neural network.

Authors:  Uyman Ergün; Necaattin Barýpçý; Ahmet Tevfik Ozan; Selami Serhatlýoğlu; Erkin Oğur; Firat Hardalaç; Inan Güler
Journal:  J Med Syst       Date:  2004-10       Impact factor: 4.460

4.  A new method for diagnosis of cirrhosis disease: complex-valued artificial neural network.

Authors:  Yüksel Ozbay
Journal:  J Med Syst       Date:  2008-10       Impact factor: 4.460

  4 in total

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