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Support Feature Machine for Classification of Abnormal Brain Activity
Published on Aug 14, 20076728 Views
In this study, a novel multidimensional time series classification technique, namely support feature machine (SFM), is proposed. SFM is inspired by the optimization model of support vector machine and
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Chapter list
Support Feature Machine for Classification of Abnormal Brain Activity 00:03
Agenda00:45
Objectives01:22
How Many People Having Epilepsy?02:12
Epilepsy and Seizures02:33
Intracranial EEG Acquisition03:12
Electroencephalogram (EEG)04:04
10-Second EEGs: Seizure Evolution04:48
Open Problems05:47
Data Transformation Using Chaos Theory06:17
Measure of Chaos07:08
Classification of Physiological States07:11
Support Vector Machine vs Support Feature Machine07:50
Nearest Neighbor for Time Series08:44
Similarity Measures09:40
Support Feature Machine10:23
Decision Rule: Basic Ideas11:24
Optimization Model I: Averaging12:04
Model I: Averaging Formulation13:09
Optimization Model II: Voting13:37
Decision Rule: Basic Ideas13:51
Model II: Voting Formulation13:55
Data Selection and Sampling14:04
Sensitivity and Specificity14:24
5-Fold Cross Validation Result14:28
DTW pt 115:18
DTW pt 215:23
Automated Seizure Prediction Paradigm15:24
Concluding Remarks15:40
Reference16:29
Acknowledgements16:32