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Results: 1 to 20 of 124

1.

Comparing a supervised and an unsupervised classification method for burst detection in neonatal EEG.

Löfhede J, Degerman J, Löfgren N, Thordstein M, Flisberg A, Kjellmer I, Lindecrantz K.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:3836-9. doi: 10.1109/IEMBS.2008.4650046.

PMID:
19163549
[PubMed - indexed for MEDLINE]
2.

Classification of burst and suppression in the neonatal electroencephalogram.

Löfhede J, Löfgren N, Thordstein M, Flisberg A, Kjellmer I, Lindecrantz K.

J Neural Eng. 2008 Dec;5(4):402-10. doi: 10.1088/1741-2560/5/4/005. Epub 2008 Oct 29.

PMID:
18971517
[PubMed - indexed for MEDLINE]
3.

Comparison of three methods for classifying burst and suppression in the EEG of post asphyctic newborns.

Löfhede J, Löfgren N, Thordstein M, Flisberg A, Kjellmer I, Lindecrantz K.

Conf Proc IEEE Eng Med Biol Soc. 2007;2007:5136-9.

PMID:
18003162
[PubMed - indexed for MEDLINE]
4.

Detection of bursts in the EEG of post asphyctic newborns.

Löfhede J, Löfgren N, Thordstein M, Flisberg A, Kjellmer I, Lindecrantz K.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:2179-82.

PMID:
17946094
[PubMed - indexed for MEDLINE]
5.

Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines.

Derya Ubeyli E.

Comput Biol Med. 2008 Jan;38(1):14-22. Epub 2007 Jul 24.

PMID:
17651716
[PubMed - indexed for MEDLINE]
6.

Multi-channel EEG based neonatal seizure detection.

Greene BR, Reilly RB, Boylan G, de Chazal P, Connolly S.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:4679-84.

PMID:
17945851
[PubMed - indexed for MEDLINE]
7.

An SVM-based system and its performance for detection of seizures in neonates.

Temko A, Thomas E, Boylan G, Marnane W, Lightbody G.

Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2643-6. doi: 10.1109/IEMBS.2009.5332807.

PMID:
19963774
[PubMed - indexed for MEDLINE]
8.

Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals.

Ubeyli ED.

Neural Netw. 2008 Nov;21(9):1410-7. doi: 10.1016/j.neunet.2008.08.005. Epub 2008 Sep 6.

PMID:
18815008
[PubMed - indexed for MEDLINE]
9.

Effect of finite sample size on feature selection and classification: a simulation study.

Way TW, Sahiner B, Hadjiiski LM, Chan HP.

Med Phys. 2010 Feb;37(2):907-20.

PMID:
20229900
[PubMed - indexed for MEDLINE]
Free PMC Article
10.

Comparison of filtering and classification techniques of electroencephalography for brain-computer interface.

Renfrew M, Cheng R, Daly JJ, Cavusoglu M.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:2634-7. doi: 10.1109/IEMBS.2008.4649741.

PMID:
19163244
[PubMed - indexed for MEDLINE]
11.

A new approach to discriminative HMM training for pathological voice classification.

Sarria-Paja M, Castellanos-Dominguez G, Delgado-Trejos E.

Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4674-7. doi: 10.1109/IEMBS.2010.5626408.

PMID:
21096005
[PubMed - indexed for MEDLINE]
12.

Multiclass support vector machines for EEG-signals classification.

Güler I, Ubeyli ED.

IEEE Trans Inf Technol Biomed. 2007 Mar;11(2):117-26.

PMID:
17390982
[PubMed - indexed for MEDLINE]
13.

Detection of artifacts from high energy bursts in neonatal EEG.

Bhattacharyya S, Biswas A, Mukherjee J, Majumdar AK, Majumdar B, Mukherjee S, Singh AK.

Comput Biol Med. 2013 Nov;43(11):1804-14. doi: 10.1016/j.compbiomed.2013.07.031. Epub 2013 Aug 22.

PMID:
24209926
[PubMed - indexed for MEDLINE]
14.

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

Acharya UR, Sree SV, Chattopadhyay S, Yu W, Ang PC.

Int J Neural Syst. 2011 Jun;21(3):199-211. doi: 10.1142/S0129065711002808.

PMID:
21656923
[PubMed - indexed for MEDLINE]
15.

Automatic identification of epileptic and background EEG signals using frequency domain parameters.

Faust O, Acharya UR, Min LC, Sputh BH.

Int J Neural Syst. 2010 Apr;20(2):159-76.

PMID:
20411598
[PubMed - indexed for MEDLINE]
16.

Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait.

Kamruzzaman J, Begg RK.

IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 1):2479-90.

PMID:
17153205
[PubMed - indexed for MEDLINE]
17.

EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures.

Temko A, Nadeu C, Marnane W, Boylan G, Lightbody G.

IEEE Trans Inf Technol Biomed. 2011 Nov;15(6):839-47. doi: 10.1109/TITB.2011.2159805. Epub 2011 Jun 16.

PMID:
21690018
[PubMed - indexed for MEDLINE]
Free PMC Article
18.

[EEG signal classification based on EMD and SVM].

Li S, Zhou W, Cai D, Liu K, Zhao J.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Oct;28(5):891-4. Chinese.

PMID:
22097250
[PubMed - indexed for MEDLINE]
19.

Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification.

Diez PF, Mut V, Laciar E, Torres A, Avila E.

Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2579-82. doi: 10.1109/IEMBS.2009.5335278.

PMID:
19965216
[PubMed - indexed for MEDLINE]
20.

Hidden Markov models used for the offline classification of EEG data.

Obermaier B, Guger C, Pfurtscheller G.

Biomed Tech (Berl). 1999 Jun;44(6):158-62.

PMID:
10427911
[PubMed - indexed for MEDLINE]
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