Application of Machine Learning To Epileptic Seizure Detection

We present and evaluate a machine learning approach to constructing patient-specific classifiers that detect the onset of an epileptic seizure through analysis of the scalp EEG, a non-invasive measure of the brain's electrical activity. This problem is challenging because the brain's electrical activity is composed of numerous classes with overlapping characteristics. The key steps involved in realizing a high performance algorithm included shaping the problem into an appropriate machine learning framework, and identifying the features critical to separating seizure from other types of brain activity. When trained on 2 or more seizures per patient and tested on 916 hours of continuous EEG from 24 patients, our algorithm detected 96% of 173 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period. We also provide information about how to download the CHB-MIT database, which contains the data used in th...

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Ali H. Shoeb, John V. Guttag
• Keywords: Electrical Activity, False Detections, ICML 2010, Machine Learning, Patient-specific Classifiers