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On ultrasound classification of stroke risk factors from randomly chosen respondents using non-invasive multispectral ultrasonic brain measurements and adaptive profiles

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, we present a new brain diagnostic method based on a computer aided multispectral ultrasound diagnostics method (CAMUD). We explored the standard values of the relative time of flight (RIT), as well as the attenuation, ATN, of multispectral longitudinal ultrasound waves propagated non-invasively through the brains of a standard Caucasian volunteer population across different ages and genders. For the interpretation of the volunteers health questionnaire and ultrasound data we explored various clustering and classification algorithms, such as PCA and ANOVA. We showed that the RIT and ATN values provide very good estimators of possible physiological changes in the brain tissue and can differentiate the possible high-risk groups obtained by other groups and methods (Russo et al. [1]; Lloyd-Jones et al. [2]; Medscape [3]). Special attention should be given to the subgroup which included almost 39% of the volunteers. Respondents in this group have a significantly increased minimum ATN value (see Classification Trees). These values are strongly correlated with the identified risk of stroke factors being: age, increased alcohol consumption, cases of heart disease and stroke in the family as already shown by Rusco and as incorporated into Lloyd-Jones et al., ‘‘Heart Disease and Stroke Statistics – 2009 Update’’, by the American Heart Association (AHA) and American Stroke Association (ASA), as updated recently in the 2015 ‘‘Stroke Prevention Guidelines’’.
Twórcy
autor
  • SoNovum AG, Leipzig, Germany
autor
  • SoNovum AG, Leipzig, Germany
autor
  • SoNovum AG, Leipzig, Germany
  • MTZ Clinical Research, Warsaw, Poland
autor
  • Department of Cardiology and Internal Medicine, Military Institute of Medicine, Warsaw, Poland
autor
  • Sonomed Sp. Z o. o., Warsaw, Poland
Bibliografia
  • [1] Russo CA, Ho K, Elixhauser A. Hospital stays for circulatory diseases, 2004. Rockville, MD: Agency for Healthcare Research and Quality; February 2007.
  • [2] Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, et al. A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee, Heart disease and stroke statistics – 2009 update.
  • [3] Medscape. New stroke prevention guidelines: a quick and easy guide; January 2015.
  • [4] Mazur R. Dynamics of brain density in the acute phase of ischemic stroke. Udar Mózgu 2002;4(1):1–8.
  • [5] O'Donnel M, Jayess ET, Miller JG. Kramers–Kronig relationship between ultrasonic attenuation and phase velocity. J Acoust Soc Am 1981;69(March (3)):1.
  • [6] Wrobel M. Advanced ultrasonic interferometer and method of non-linear classification and identification of matter using the same, WO 2007/000047, June 28, 2005.
  • [7] Zoni-Berisso M, Lercari F, Carazza T, Domenicucci S. Clin Epidemiol 2014;6:213–9.
  • [8] Haeusler KG, Wilson D, Fiebach JB, Kirchhof P, Werring DJ. Brain MRI to personalise atrial fibrillation therapy: current evidence and perspectives. Heart 2014;100(18):1408–13. http://dx.doi.org/10.1136/heartjnl-2013-305151 [Epub 2014 June 20]
  • [9] Bang OY, Ovbiagele B, Kim JS. Evaluation of cryptogenic stroke with advanced diagnostic techniques. Stroke 2014;45:1186–94.
  • [10] Saito T, Kawamura Y, Tanabe Y, Asanome A, Takahashi K, Sawada J, et al. Cerebral microbleeds and asymptomatic cerebral infarctions in patients with atrial fibrillation. J Stroke Cerebrovasc Dis 2014;23(July (6)):1616–22.
  • [11] Fisher M. MRI screening for chronic anticoagulation in atrial fibrillation. Front Neurol 2013;4(Oct):137.
  • [12] Kobayashi A, Iguchi M, Shimizu S, Uchiyama S. Silent cerebral infarcts and cerebral white matter lesions in patients with nonvalvular atrial fibrillation. J Stroke Cerebrovasc Dis 2012;21:310–7.
  • [13] Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford MJ. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA 2001;285:2864–70.
  • [14] Tatu L, Moulin T, Bogousslavsky J, Duvernoy H. Arterial territories of human brain: brainstem and cerebellum. Neurology 1996;47(5):1125–35.
  • [15] Tatu L, Moulin T, Bogousslavsky J, Duvernoy H. Arterial territories of the human brain: cerebral hemispheres. Neurology 1998;50(6):1699–708.
  • [16] Jodlowski L. Pomiar fazy sygnalu harmonicznego z zastosowaniem niskich czestotliwosci próbkowania, Conference Materials, 27th Winter School on Molecular and Quantum Acoustics, Ustron 23–27/02/1998.
  • [17] Szostakowski M, Jodlowski L. Measurements of changes of phase velocity in a fluid using digital. 27th Winter School on Molecular and Quantum Acoustics, Ustron (Poland) February 1998.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e16dc1b8-8583-403e-b82e-1619aa29e830
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