Spike pattern recognition by supervised classification in low dimensional embedding space.

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Spike pattern recognition by supervised classification in low dimensional embedding space.

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2016 nî lūn-bûn

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2016年の論文

@ja

2016年論文

@yue

2016年論文

@zh-hant

2016年論文

@zh-hk

2016年論文

@zh-mo

2016年論文

@zh-tw

2016年论文

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2016年论文

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2016年论文

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Spike pattern recognition by s ...... w dimensional embedding space.

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Spike pattern recognition by s ...... w dimensional embedding space.

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Spike pattern recognition by s ...... w dimensional embedding space.

@en

P1476

Spike pattern recognition by s ...... w dimensional embedding space.

@en

P2093

Iosif Mporas

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Kyriakos Garganis

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Vasileios Megalooikonomou

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P2860

A new mathematical approach based on orthogonal operators for the detection of interictal spikes in epileptogenic data.

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.

Spike detection: a review and comparison of algorithms.

Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data.

Computerized epileptiform transient detection in the scalp electroencephalogram: obstacles to progress and the example of computerized ECG interpretation.

A study on fuzzy C-means clustering-based systems in automatic spike detection.

Wavelet analysis of EEG for three-dimensional mapping of epileptic events.

Interictal spikes and epileptogenesis.

One-class classification of temporal EEG patterns for K-complex extraction.

A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram.

P2888

s40708-016-0044-4

P304

73-83

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P31

scholarly article

P356

10.1007/S40708-016-0044-4

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P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Evangelia Zacharaki

P577

2016-03-16T00:00:00Z

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P5875

298905749

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P698

27747608

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P932

4883172

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