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Abstract: Clinicians primarily rely on the electroencephalogram (EEG) to diagnose subtle and non-convulsive seizures, but EEG interpretation is laborious, and can delay treatment for hours. We demonstrate a method for automating seizure detection. We first transform the EEG waveforms into spectrographic images (n=282), then extract salient descriptors via a computer vision technique, and finally train support vector machines (SVM) on these descriptors with 10-fold cross validation. The SVM achieved 98% accuracy (AUC of ROC curve) in seizure detection

Learning Objective 1: To understand that EEGs can be transformed into spectrographic images, and are then amenable to computer vision and machine learning classification techniques.

Learning Objective 2: To recognize that spectrographic images, and images in general, can be quantitatively and robustly described in higher dimensions using feature extraction techniques.

Learning Objective 3: To recognize that support vector machines are a useful statistical learning method that can accurately classify images based on their descriptors.

Learning Objective 4: To apprepriate the need for automated EEG seizure detection at the bedside.

Authors:

Peter Yan (Presenter)
Weill Cornell Medical College

Fei Wang, Weill Cornell Medical College
Ramin Zabih, Cornell Tech
Zachary Grinspan, Weill Cornell Medical College

Presentation Materials:

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