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projects:year5:16.02

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16.02 - Patient Specific Framework for Biomedical Signal Management

Project - Summary

Objectives Bioelectric measurements convey information about the function of the underlying organ or tissue. Despite the difference and complexity of the patterns in the cells' electrical activity, they may superficially appear to have a same phenotype. Therefore, the study of such signals is an interesting choice of medical experts for diagnosis and treatment. Identifying the underlying dynamics of physiological events using bioelectric signals is crucial to understand their functions. This provides discriminant characteristics for detecting the interest event from the background activity.

In this project, our focus is to use Electroencephalography (EEF) recordings to detect seizure events automatically based on nonlinear dynamics. This is a hot topic in epilepsy research community since diagnosis is still preformed by visual inspection. The tedious screening of long-term EEG recordings gives rise to the importance of automatic seizure detection methods. Thus, we propose a seizure detection system. For validating the proposed method, we use two publicly available datasets (CHB-MIT [1] [2] and University Hospital of Bonn dataset [3]).

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Moncef Gabbouj PI moncef.gabbouj@tuni.fi +358 (400) 736613 Tampere University
Serkan Kiranyaz Co-PI Not available Not available Tampere University
Morteza Zabihi Student Not available Not available Tampere University

Project - Impact and Uses/Benefits

The expected impacts and exploitation of the project's results are listed as follows:

1. Scientific area:

  • Contributing to better understanding of the mechanism of the physiological event such as epileptic seizure. This is beneficial for diagnosis, prediction, treatment, and research purposes.

2. Technology area:

  • Unordered List ItemInnovative, accurate and cost effective algorithm for seizure detection devices and epilepsy medical environments. Specifically, improving the accuracy of long-term EEG screening.

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
16.03_year_5_poster.pptx7.4 MiB2019/08/22 13:21
16.10_year_5_poster.pptx3.5 MiB2019/08/22 13:21
16.10_year_5_presentation.pdf689.0 KiB2019/08/22 13:21
16.06_year_5_presentation.pdf655.8 KiB2019/08/22 13:21
16.09_year_5_poster.pptx464.5 KiB2019/08/22 13:21
16.09_year_5_presentation.pdf405.1 KiB2019/08/22 13:21
16.07_year_5_presentation.pdf388.6 KiB2019/08/22 13:21
16.08_year_5_presentation.pdf678.1 KiB2019/08/22 13:21
16.08_year_5_poster.pptx764.8 KiB2019/08/22 13:21
16.01_year_5_presentation.pdf467.1 KiB2019/08/22 13:21
16.04_year_5_poster.pdf556.2 KiB2019/08/22 13:21
16.02_year_5_presentation.pdf540.7 KiB2019/08/22 13:21
16.01_year_5_poster.pdf291.8 KiB2019/08/22 13:21
16.02_year_5_poster.pdf317.3 KiB2019/08/22 13:21
16.04_year_5_presentation.pdf132.9 KiB2019/08/22 13:21
16.05_year_5_final_report.pdf1.3 MiB2019/08/22 10:47
16.10_year_5_final_report.pdf1.8 MiB2019/08/22 10:47
16.04_year_5_final_report.pdf1.6 MiB2019/08/22 10:47
16.03_year_5_final_report.pdf2.7 MiB2019/08/22 10:47
16.08_year_5_final_report.pdf2.3 MiB2019/08/22 10:47
16.06_year_5_final_report.pdf1.5 MiB2019/08/22 10:47
16.07_year_5_final_report.pdf2.0 MiB2019/08/22 10:47
16.09_year_5_final_report.pdf2.2 MiB2019/08/22 10:47
16.02_year_5_final_report.pdf2.1 MiB2019/08/22 10:47
16.01_year_5_final_report.pdf832.7 KiB2019/08/22 10:47
16.10_fig1.jpg190.8 KiB2019/08/21 10:33
projects/year5/16.02.1566488671.txt.gz · Last modified: 2019/08/22 10:44 by sally.johnson