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

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Machine Learning in Healthcare Informatics

Part of the book series: Intelligent Systems Reference Library ((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.

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Authors and Affiliations

  1. Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

    Samanta Rosati, Gabriella Balestra & Filippo Molinari

Authors
  1. Samanta Rosati
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  2. Gabriella Balestra
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  3. Filippo Molinari
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Correspondence to Filippo Molinari .

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Editors and Affiliations

  1. Department of Computer Science, Louisiana Tech University, Ruston, Louisiana, USA

    Sumeet Dua

  2. Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA

    Prerna Dua

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Rosati, S., Balestra, G., Molinari, F. (2014). Feature Extraction by Quick Reduction Algorithm: Assessing the Neurovascular Pattern of Migraine Sufferers from NIRS Signals. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-40017-9_13

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  • Print ISBN: 978-3-642-40016-2

  • Online ISBN: 978-3-642-40017-9

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