A robust approach towards epileptic seizure detection

In this paper we present the application of ensemble learning to epileptic seizure detection problem. We propose a robust learning framework to mitigate class imbalance in large CHB-MIT (982 hrs) scalp EEG dataset. The algorithm being used is RUSBoost which is a hybrid data sampling and boosting technique designed especially for skewed classes. The data that is being used in this study has severe class imbalance, with average representation of 0.38% of seizure class to that of 99.62% of non-seizure class. The proposed approach shows the power of RUSBoost in terms of robustness and generalization. We compared our method with the most successful Support Vector Machine (SVM) based approach and report competitive results of 97% seizure detection accuracy, mean detection delay of 2.7s and false detection rate of 0.08 seizure/hr. We also report fast training times of just under three minutes on average for average training data of 21 hrs.

[1]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2]  J. Gotman,et al.  An automatic warning system for epileptic seizures recorded on intracerebral EEGs , 2005, Clinical Neurophysiology.

[3]  Ali H. Shoeb,et al.  Patient-specific seizure onset detection , 2004, Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[5]  Eric Panken,et al.  A micropower support vector machine based seizure detection architecture for embedded medical devices , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[7]  Naveen Verma,et al.  A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System , 2010, IEEE Journal of Solid-State Circuits.

[8]  Brian Litt,et al.  One-Class Novelty Detection for Seizure Analysis from Intracranial EEG , 2006, J. Mach. Learn. Res..

[9]  Yann LeCun,et al.  Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.

[10]  Pramod P. Khargonekar,et al.  Fast SVM training using approximate extreme points , 2013, J. Mach. Learn. Res..

[11]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[12]  Ali H. Shoeb,et al.  Application of Machine Learning To Epileptic Seizure Detection , 2010, ICML.