PMID- 24048343
OWN - NLM
STAT- MEDLINE
DCOM- 20140227
LR  - 20211021
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 13
IP  - 9
DP  - 2013 Sep 17
TI  - Model-based spike detection of epileptic EEG data.
PG  - 12536-47
LID - 10.3390/s130912536 [doi]
AB  - Accurate automatic spike detection is highly beneficial to clinical assessment of 
      epileptic electroencephalogram (EEG) data. In this paper, a new two-stage 
      approach is proposed for epileptic spike detection. First, the k-point nonlinear 
      energy operator (k-NEO) is adopted to detect all possible spike candidates, then 
      a newly proposed spike model with slow wave features is applied to these 
      candidates for spike classification. Experimental results show that the proposed 
      system, using the AdaBoost classifier, outperforms the conventional method in 
      both two- and three-class EEG pattern classification problems. The proposed 
      system not only achieves better accuracy for spike detection, but also provides 
      new ability to differentiate between spikes and spikes with slow waves. Though 
      spikes with slow waves occur frequently in epileptic EEGs, they are not used in 
      conventional spike detection. Identifying spikes with slow waves allows the 
      proposed system to have better capability for assisting clinical neurologists in 
      routine EEG examinations and epileptic diagnosis.
FAU - Liu, Yung-Chun
AU  - Liu YC
AD  - Department of Computer Science and Information Engineering, National Cheng Kung 
      University, No. 1, University Road, Tainan City 701, Taiwan. 
      ynsun@mail.ncku.edu.tw.
FAU - Lin, Chou-Ching K
AU  - Lin CC
FAU - Tsai, Jing-Jane
AU  - Tsai JJ
FAU - Sun, Yung-Nien
AU  - Sun YN
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20130917
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Action Potentials
MH  - Algorithms
MH  - Brain/*physiopathology
MH  - Computer Simulation
MH  - Diagnosis, Computer-Assisted/*methods
MH  - Electroencephalography/*methods
MH  - Epilepsy/*diagnosis/*physiopathology
MH  - Humans
MH  - *Models, Neurological
MH  - Pattern Recognition, Automated/methods
MH  - Reproducibility of Results
MH  - Sensitivity and Specificity
PMC - PMC3821325
EDAT- 2013/09/21 06:00
MHDA- 2014/02/28 06:00
PMCR- 2013/09/01
CRDT- 2013/09/20 06:00
PHST- 2013/06/17 00:00 [received]
PHST- 2013/09/06 00:00 [revised]
PHST- 2013/09/13 00:00 [accepted]
PHST- 2013/09/20 06:00 [entrez]
PHST- 2013/09/21 06:00 [pubmed]
PHST- 2014/02/28 06:00 [medline]
PHST- 2013/09/01 00:00 [pmc-release]
AID - s130912536 [pii]
AID - sensors-13-12536 [pii]
AID - 10.3390/s130912536 [doi]
PST - epublish
SO  - Sensors (Basel). 2013 Sep 17;13(9):12536-47. doi: 10.3390/s130912536.