Performance of ROC Curves and LVQ Neural Networks in Detection of Middle Cerebral Artery Spasm

  • Miroslaw Swiercz
  • Zenon Mariak
  • Jaroslaw Krejza
Part of the Advances in Soft Computing book series (AINSC, volume 19)


Transcranial color-coded Doppler ultrasonography (TCCD) is used by radiologists for detection of middle cerebral artery spasm. In this study, TCCD examination was performed in 100 patients, refereed by neurosurgeons for arterial angiography. Angiographic images were used to establish the four-grade scale of spasm, which was combined with TCCD blood flow measurements. The overall performance of TCCD in detection of MCA narrowing was estimated by means of Receiver Operating Characteristics (ROC). The ROC analysis proved very high efficiency (over 90%) of TCCD for detection of moderate-to-severe spasm. The multi-parameter classification of TCCD data was also performed by LVQ networks. LVQ networks showed very good accuracy (80–95%), not only for binary outcome, but also for classification of TCCD data in a multi-grade scale of spasm.


Receiver Operating Characteristic Middle Cerebral Artery Receiver Operating Characteristic Receiver Operating Characteristic Curve Learn Vector Quantization 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Miroslaw Swiercz
    • 1
  • Zenon Mariak
    • 2
  • Jaroslaw Krejza
    • 3
  1. 1.Faculty of Electrical EngineeringBialystok Technical UniversityBialystokPoland
  2. 2.Department of NeurosurgeryMedical Academy BialystokBialystokPoland
  3. 3.Department of RadiologyMedical Academy BialystokBialystokPoland

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