Continuous Detection of Cerebral Vasodilatation and Vasoconstriction Using Intracranial Pulse Morphological Template Matching

• Published in: PloS one Vol. 7; no. 11; p. e50795
• Main Authors: Asgari, Shadnaz, Gonzalez, Nestor, Subudhi, Andrew W., Hamilton, Robert, Vespa, Paul, Bergsneider, Marvin, Roach, Robert C., Hu, Xiao
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.11.2012; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Analysis; Bioengineering; Biology; Blood flow; Brain - blood supply; Carbon dioxide; Cerebral blood flow; Cerebrovascular Circulation; Clustering; Computer engineering; Computer Science; Electrocardiography; Emergency medical care; Engineering; Female; Flow velocity; Hemodynamics; Humans; Hyperventilation; Mathematics; Medicine; Middle Aged; Morphology; Neurosurgery; Pulse Wave Analysis - methods; Rebreathing; Skull; Template matching; Training; Ultrasound; Vascular diseases; Vasoconstriction; Vasodilation; Young Adult; Los Angeles California; California; United States > US; Colorado
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0050795

Although accurate and continuous assessment of cerebral vasculature status is highly desirable for managing cerebral vascular diseases, no such method exists for current clinical practice. The present work introduces a novel method for real-time detection of cerebral vasodilatation and vasoconstriction using pulse morphological template matching. Templates consisting of morphological metrics of cerebral blood flow velocity (CBFV) pulse, measured at middle cerebral artery using Transcranial Doppler, are obtained by applying a morphological clustering and analysis of intracranial pulse algorithm to the data collected during induced vasodilatation and vasoconstriction in a controlled setting. These templates were then employed to define a vasodilatation index (VDI) and a vasoconstriction index (VCI) for any inquiry data segment as the percentage of the metrics demonstrating a trend consistent with those obtained from the training dataset. The validation of the proposed method on a dataset of CBFV signals of 27 healthy subjects, collected with a similar protocol as that of training dataset, during hyperventilation (and CO₂ rebreathing tests) shows a sensitivity of 92% (and 82%) for detection of vasodilatation (and vasoconstriction) and the specificity of 90% (and 92%), respectively. Moreover, the proposed method of detection of vasodilatation (vasoconstriction) is capable of rejecting all the cases associated with vasoconstriction (vasodilatation) and outperforms other two conventional techniques by at least 7% for vasodilatation and 19% for vasoconstriction.