Regression analysis for peak designation in pulsatile pressure signals.
Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently develope...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 9; pp. 967 - 978 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104907037&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104907037 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2009 vid: 47 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104907037 NLM19578916 2010382762 10.1007/s11517-009-0505-5 NLM19578916 PMC2734262 104907037 ppf: 967 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Regression analysis for peak designation in pulsatile pressure signals. aug: au: Scalzo F Xu P Asgari S Bergsneider M Hu X Scalzo, Fabien Xu, Peng Asgari, Shadnaz Bergsneider, Marvin Hu, Xiao affil: Department of Neurosurgery, Geffen School of Medicine, University of California, Los Angeles, USA sug: subj: Algorithms Evaluation Intracranial Pressure Evaluation Monitoring, Physiologic Methods Brain Injuries Comparative Studies Human Hydrocephalus Regression ab: Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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