Abstract
An automatic Uni- or Multi-modal Inteligent
Seizure Acquisition (UISA/MISA) system is highly applicable
for onset detection of epileptic seizures based on motion data.
The modalities used are surface electromyography (sEMG),
acceleration (ACC) and angular velocity (ANG). The new
proposed automatic algorithm on motion data is extracting
features as “log-sum” measures of discrete wavelet components.
Classification into the two groups “seizure” versus “nonseizure”
is made based on the support vector machine (SVM)
algorithm.
The algorithm performs with a sensitivity of 91-100%, a
median latency of 1 second and a specificity of 100% on
multi-modal data from five healthy subjects simulating seizures.
The uni-modal algorithm based on sEMG data from the
subjects and patients performs satisfactorily in some cases.
As expected, our results clearly show superiority of the multimodal
approach, as compared with the uni-modal one.
Original language | English |
---|---|
Title of host publication | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Publisher | IEEE |
Publication date | 2010 |
Pages | 3269-3272 |
ISBN (Print) | 978-1-4244-4123-5 |
DOIs | |
Publication status | Published - 2010 |
Event | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sept 2010 Conference number: 32 http://embc2010.embs.org/ |
Conference
Conference | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
---|---|
Number | 32 |
Country/Territory | Argentina |
City | Buenos Aires |
Period | 31/08/2010 → 04/09/2010 |
Internet address |
Series | I E E E Engineering in Medicine and Biology Society. Conference Proceedings |
---|---|
ISSN | 2375-7477 |