title: Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse.
abstract: This study aimed to develop a new approach to detect intracranial pressure (ICP) slow waves based on morphological changes of ICP pulse waveforms. A recently proposed Morphological Clustering and Analysis of ICP Pulse (MOCAIP) algorithm was utilized to calculate a set of metrics that characterize ICP pulse morphology. A regularized linear quadratic classifier was used to test the hypothesis that classification between ICP slow wave and flat ICP could be achieved using features composed of mean values and dispersion of 24 MOCAIP metrics. To optimize the classification performance, three feature selection techniques (differential evolution, discriminant analysis and analysis of variance) were applied to find an optimal set of MOCAIP metrics under different criteria. In addition, we selected three sets of metrics common to those found by combination of two selection methods, to be used as classification features (differential evolution and analysis of variance, discriminant analysis and analysis of variance, and combination of differential evolution and discriminant analysis). To test the approach, a total of 276 selections of ICP recordings corresponding to two patterns without waves and containing slow waves were obtained from overnight ICP studies of 44 hydrocephalus patients performed at the UCLA Adult Hydrocephalus Center. Our results showed that the best classification performance of differentiation of slow waves from the ICP recording without slow waves was obtained using the combination of metrics common to both differential evolution and analysis of variance methods; achieving an accuracy of 89%, specificity 96%, and sensitivity 83%.
authors.affiliation: Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, CA 90095, Los Angeles, USA.
authors.forename: Magdalena
authors.initials: M
authors.lastname: Kasprowicz
authors.forename: Shadnaz
authors.initials: S
authors.lastname: Asgari
authors.forename: Marvin
authors.initials: M
authors.lastname: Bergsneider
authors.forename: Marek
authors.initials: M
authors.lastname: Czosnyka
authors.forename: Robert
authors.initials: R
authors.lastname: Hamilton
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D016000:Cluster Analysis; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D006849:Hydrocephalus / Q000503:physiopathology; D007427:Intracranial Pressure; D016014:Linear Models; D015141:Los Angeles; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010507:Periodicity; D017440:Photoperiod; D013997:Time Factors; D014495:Universities