TY - JOUR
T1 - Random ensemble learning for EEG classification
AU - Hosseini, Mohammad Parsa
AU - Pompili, Dario
AU - Elisevich, Kost
AU - Soltanian-Zadeh, Hamid
N1 - Publisher Copyright: © 2017 Elsevier B.V.
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
KW - Brain–computer interface
KW - Computational neuroscience
KW - Distributed computing system
KW - Electroencephalogram
KW - Ensemble learning
KW - Epileptic seizure detection
UR - http://www.scopus.com/inward/record.url?scp=85039865976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039865976&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.artmed.2017.12.004
DO - https://doi.org/10.1016/j.artmed.2017.12.004
M3 - Article
C2 - 29306539
SN - 0933-3657
VL - 84
SP - 146
EP - 158
JO - Artificial Intelligence In Medicine
JF - Artificial Intelligence In Medicine
ER -