Random ensemble learning for EEG classification

Document Type

Article

Publication Date

1-1-2018

Publication Title

Artificial intelligence in medicine

Abstract

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility.

Medical Subject Headings

Automation; Brain; Brain Mapping; Brain Waves; Cloud Computing; Electrocorticography; Electroencephalography; False Negative Reactions; False Positive Reactions; Humans; Neural Networks (Computer); Predictive Value of Tests; Reproducibility of Results; Seizures; Signal Processing, Computer-Assisted; Support Vector Machine; Time Factors; Wavelet Analysis

PubMed ID

29306539

Volume

84

First Page

146

Last Page

158

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