Advertisement

Feature Extraction by Quick Reduction Algorithm: Assessing the Neurovascular Pattern of Migraine Sufferers from NIRS Signals

  • Samanta Rosati
  • Gabriella Balestra
  • Filippo MolinariEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)

Abstract

A migraine is a neurological disorder that can be caused by many factors, including genetic mutations, life-style, cardiac defects, endocrine pathologies, and neurovascular impairments. In addition to these health problems, an association between some types of migraines and increased cardiovascular risk has emerged in the past 10 years. Moreover, researchers have demonstrated an association between migraines and impaired cerebrovascular reactivity. It is possible to observe carbon dioxide dysregulation in some migraineurs, while others show a markedly decreased vasomotor reactivity to external stimuli. Therefore, the assessment of the cerebrovascular pattern of migraineurs is important both for the onset of a personalized therapy and for follow-up care. Near-infrared spectroscopy is a widely used tool for the non-invasive monitoring of brain oxygenation. It can be used to track hemodynamic changes during external stimulation (i.e. vaso-active maneuvers such as hypercapnia or hyperventilation). Unfortunately, near-infrared spectroscopy (NIRS) signals acquired during vaso-active maneuvers are non-stationary and require a time–frequency processing approach. To fully describe the cerebrovascular patterns of migraineurs, we extracted several parameters from the NIRS signals. Using these parameters, we compiled a dataset in which complexity was very high and the clinical/physiological information was impossible to track.

Keywords

Feature Selection Feature Selection Method Migraine With Aura Cerebral Autoregulation Conditional Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Kruit MC, van Buchem MA, Hofman PA, Bakkers JT, Terwindt GM, Ferrari MD, Launer LJ (2004) Migraine as a risk factor for subclinical brain lesions. JAMA 291(4):427–434CrossRefGoogle Scholar
  2. 2.
    Scher AI, Terwindt GM, Picavet HS, Verschuren WM, Ferrari MD, Launer JL (2005) Cardiovascular risk factors and migraine: the GEM population-based study. Neurology 64(4):614–620CrossRefGoogle Scholar
  3. 3.
    Tietjen GE (2009) Migraine as a systemic vasculopathy. Cephalalgia 29(9):987–996CrossRefGoogle Scholar
  4. 4.
    Liboni W, Molinari F, Allais G, Mana O, Negri E, Grippi G, Benedetto C, D’Andrea G, Bussone G (2007) Why do we need NIRS in migraine? Neurol Sci 28:S222–S224CrossRefGoogle Scholar
  5. 5.
    Nowak A, Kacinski M (2009) Transcranial Doppler evaluation in migraineurs. Neurol Neurochir Pol 43(2):162–172Google Scholar
  6. 6.
    Vernieri F, Tibuzzi F, Pasqualetti P, Altamura C, Palazzo P, Rossini PM, Silvestrini M (2008) Increased cerebral vasomotor reactivity in migraine with aura: an autoregulation disorder? a transcranial Doppler and near-infrared spectroscopy study. Cephalalgia 28(7):689–695CrossRefGoogle Scholar
  7. 7.
    Molinari F, Liboni W, Grippi G, Negri E (2006) Relationship between oxygen supply and cerebral blood flow assessed by transcranial Doppler and near-infrared spectroscopy in healthy subjects during breath-holding. J Neuroeng Rehabil 3:16CrossRefGoogle Scholar
  8. 8.
    Silvestrini M, Baruffaldi R, Bartolini M, Vernieri F, Lanciotti C, Matteis M, Troisi E, Provinciali L (2004) Basilar and middle cerebral artery reactivity in patients with migraine. Headache 44(1):29–34CrossRefGoogle Scholar
  9. 9.
    Watanabe Y, Tanaka H, Dan I, Sakurai K, Kimoto K, Takashima R, Hirata K (2011) Monitoring cortical hemodynamic changes after sumatriptan injection during migraine attack by near-infrared spectroscopy. Neurosci Res 69(1):60–66CrossRefGoogle Scholar
  10. 10.
    Viola S, Viola P, Litterio P, Buongarzone MP, Fiorelli L (2010) Pathophysiology of migraine attack with prolonged aura revealed by transcranial Doppler and near infrared spectroscopy. Neurol Sci 31(1):S165–S166CrossRefGoogle Scholar
  11. 11.
    Diener HC, Kurth T, Dodick D (2007) Patent foramen ovale and migraine. Curr Pain Headache Rep 11(3):236–240CrossRefGoogle Scholar
  12. 12.
    Liboni W, Molinari F, Allais GB, Mana O, Negri E, D’Andrea G, Bussone G, Benedetto C (2008) Patent foramen ovale detected by near-infrared spectroscopy in patients suffering from migraine with aura. Neurol Sci 29(1):S182–S185CrossRefGoogle Scholar
  13. 13.
    Rothrock JF (2008) Patent foramen ovale (PFO) and migraine. Headache 48(7):1153CrossRefGoogle Scholar
  14. 14.
    Giustetto P, Liboni W, Mana O, Allais G, Benedetto C, Molinari F (2010) Joint metabonomic and instrumental analysis for the classification of migraine patients with 677-MTHFR mutations. Open Med Inform J 4:23–30Google Scholar
  15. 15.
    Obrig H, Neufang M, Wenzel R, Kohl M, Steinbrink J, Einhaupl K, Villringer A (2000) Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. Neuroimage 12(6):623–639CrossRefGoogle Scholar
  16. 16.
    Sliwka U, Harscher S, Diehl RR, van Schayck R, Niesen WD, Weiller C (2001) Spontaneous oscillations in cerebral blood flow velocity give evidence of different autonomic dysfunctions in various types of headache. Headache 41(2):157–163CrossRefGoogle Scholar
  17. 17.
    Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502CrossRefGoogle Scholar
  18. 18.
    Su CT, Yang CH (2008) Feature selection for the SVM: an application to hypertension diagnosis. Expert Syst Appl 34:754–763CrossRefGoogle Scholar
  19. 19.
    Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37CrossRefGoogle Scholar
  20. 20.
    Duncan A, Meek JH, Clemence M, Elwell CE, Tyszczuk L, Cope M, Delpy D (1995) Optical pathlength measurements on adult head, calf and forearm and the head of the new-born infant using phase resolved optical spectroscopy. Phys Med Biol 40(2):295–304CrossRefGoogle Scholar
  21. 21.
    Leung TS, Tachtsidis I, Smith M, Delpy DT, Elwell CE (2006) Measurement of the absolute optical properties and cerebral blood volume of the adult human head with hybrid differential and spatially resolved spectroscopy. Phys Med Biol 51(3):703–717CrossRefGoogle Scholar
  22. 22.
    Okada E, Firbank M, Schweiger M, Arridge SR, Cope M, Delpy DT (1997) Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. Appl Opt 36(1):21–31CrossRefGoogle Scholar
  23. 23.
    Headache Classification Committee (1988) Classification and diagnostic criteria for headache disorders, cranial neuralgias and facial pain. Headache Classification Committee of the International Headache Society. Cephalalgia 8:1–96CrossRefGoogle Scholar
  24. 24.
    Cohen L (1989) Time-frequency distributions—a review. Proc IEEE 77(7):941–981CrossRefGoogle Scholar
  25. 25.
    Molinari F, Rosati S, Liboni W, Negri E, Mana O, Allais G, Benedetto C (2010) Time-Frequency Characterization of Cerebral Hemodynamics of Migraine Sufferers as As-sessed by NIRS Signals. EURASIP J Adv Sig Proc. doi: 10.1155/2010/459213 Google Scholar
  26. 26.
    Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11(5):341–356MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Jensen R, Shen Q (2008) Computational intelligence and feature selection: rough and fuzzy approaches. Wiley, HobokenCrossRefGoogle Scholar
  28. 28.
    Moradi H, Grzymala-Busse JW, Roberts JA (1998) Entropy of english text: experi-ments with humans and a machine learning system based on rough sets. Inf Sci 104:31–47CrossRefGoogle Scholar
  29. 29.
    Feng L, Wang GY, Li XX (2010) Knowledge acquisition in vague objective information systems based on rough sets. Expert Syst 27(2):129–142CrossRefGoogle Scholar
  30. 30.
    Matsumoto Y, Watada J (2009) Knowledge acquisition from time series data through rough sets analysis. IJICIC 5:4885–4897Google Scholar
  31. 31.
    Greco S, Matarazzo B, Slowinski R (2001) Rough sets theory for multicriteria decision analysis. Eur J Oper Res 129:1–47MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Pawlak Z, Sowinski R (1994) Rough set approach to multi-attribute decision analysis. Eur J Oper Res 72(3):443–459CrossRefzbMATHGoogle Scholar
  33. 33.
    Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24(6):833–849CrossRefzbMATHGoogle Scholar
  34. 34.
    Tsumoto S (1998) Automated extraction of medical expert system rules from clinical databases based on rough set theory. Info Sci 112:67–84CrossRefGoogle Scholar
  35. 35.
    Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput J 9(1):1–12CrossRefGoogle Scholar
  36. 36.
    Shen Q, Chouchoulas A (2000) Modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Eng Appl Artif Intel 13(3):263–278CrossRefGoogle Scholar
  37. 37.
    Chen Y, Miao D, Wang R (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recogn Lett 31:226–233CrossRefGoogle Scholar
  38. 38.
    Chen Y, Miao D, Wang R, Wu K (2011) A rough set approach to feature selection based on power set tree. Knowl -Based Syst 24:275–281CrossRefGoogle Scholar
  39. 39.
    Liboni W, Molinari F, Allais G, Mana O, Negri E, Bussone G, D’Andrea G, Benedetto C (2009) Spectral changes of near-infrared spectroscopy signals in migraineurs with aura reveal an impaired carbon dioxide-regulatory mechanism. Neurol Sci 30(1):S105–S107CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Samanta Rosati
    • 1
  • Gabriella Balestra
    • 1
  • Filippo Molinari
    • 1
    Email author
  1. 1.Biolab, Department of Electronics and TelecommunicationsPolitecnico di TorinoTorinoItaly

Personalised recommendations