Next Article in Journal
Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues
Previous Article in Journal
A Vision-Based Automated Guided Vehicle System with Marker Recognition for Indoor Use
Sensors 2013, 13(8), 10074-10086; doi:10.3390/s130810074

Stroke Damage Detection Using Classification Trees on Electrical Bioimpedance Cerebral Spectroscopy Measurements

1,* , 1,2
1 School of Technology and Health, Royal Institute of Technology, Alfred Nobels Allé 10, Huddinge SE-141 52, Sweden 2 School of Engineering, University of Boras, Allégatan 1, Boras SE-501 90, Sweden 3 Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg SE-405 30, Sweden 4 Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Hälsovägen 7, Stockholm SE-141 57, Sweden
* Author to whom correspondence should be addressed.
Received: 18 June 2013 / Revised: 31 July 2013 / Accepted: 5 August 2013 / Published: 7 August 2013
(This article belongs to the Section Biosensors)
View Full-Text   |   Download PDF [635 KB, uploaded 21 June 2014]   |   Browse Figures


After cancer and cardio-vascular disease, stroke is the third greatest cause of death worldwide. Given the limitations of the current imaging technologies used for stroke diagnosis, the need for portable non-invasive and less expensive diagnostic tools is crucial. Previous studies have suggested that electrical bioimpedance (EBI) measurements from the head might contain useful clinical information related to changes produced in the cerebral tissue after the onset of stroke. In this study, we recorded 720 EBI Spectroscopy (EBIS) measurements from two different head regions of 18 hemispheres of nine subjects. Three of these subjects had suffered a unilateral haemorrhagic stroke. A number of features based on structural and intrinsic frequency-dependent properties of the cerebral tissue were extracted. These features were then fed into a classification tree. The results show that a full classification of damaged and undamaged cerebral tissue was achieved after three hierarchical classification steps. Lastly, the performance of the classification tree was assessed using Leave-One-Out Cross Validation (LOO-CV). Despite the fact that the results of this study are limited to a small database, and the observations obtained must be verified further with a larger cohort of patients, these findings confirm that EBI measurements contain useful information for   assessing on the health of brain tissue after stroke and supports the hypothesis that classification features based on Cole parameters, spectral information and the geometry of EBIS measurements are useful to differentiate between healthy and stroke damaged brain tissue.
Keywords: stroke; electrical bioimpedance spectroscopy; classification tree; cole parameters stroke; electrical bioimpedance spectroscopy; classification tree; cole parameters
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

Atefi, S.R.; Seoane, F.; Thorlin, T.; Lindecrantz, K. Stroke Damage Detection Using Classification Trees on Electrical Bioimpedance Cerebral Spectroscopy Measurements. Sensors 2013, 13, 10074-10086.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


Cited By

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert