A hybrid wavelet based K-means clustering approach to detect intracranial hypertension

Autor:	Matyas Kenez, Parisa Naraei, Alireza Sadeghian
Rok vydání:	2017
Předmět:	0301 basic medicine 03 medical and health sciences 030104 developmental biology Wavelet Computer science k-means clustering Unsupervised learning Data mining computer.software_genre Hybrid approach Cluster analysis computer Intracranial pressure
Zdroj:	IHTC
DOI:	10.1109/ihtc.2017.8058190
Popis:	Intracranial pressure (ICP) refers to the pressure within the skull and is known to have significant importance in revealing the compliance state of the brain. Due to the invasive nature of the ICP measurement, many researches have attempted noninvasive approaches of colletcing ICP information with limited clinical applications. In this paper, a wavelet based K-means clustering analysis has been conducted to detect the patterns of physiological signals and study their changes. The analysis has been performed on 20 patients with traumatic brain injuries and the results show that the hybrid approach is a viable method to detect the patterns in an unsupervised manner.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0f0bd01f617b5aa79532aca1daf26763 https://doi.org/10.1109/ihtc.2017.8058190 Zobrazit plný text záznamu