TY - GEN
T1 - Pediatric Seizure Forecasting using Nonlinear Features and Gaussian Mixture Hidden Markov Models on Scalp EEG Signals
AU - Solorzano-Espindola, Carlos Emiliano
AU - Tovar-Corona, Blanca
AU - Anzueto-Rios, Alvaro
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/13
Y1 - 2018/11/13
N2 - Seizure forecasting systems have been studied in recent years for improving the quality of life for patients with epilepsy and gain further understanding about seizures. A common approach for this is the study of electroencephalography (EEG) recordings, using signal processing techniques and, more recently, machine learning algorithms. A four-stage system is developed for patient-specific seizure prediction; consisting of pre-processing, dimensionality reduction, feature extraction and classification between interictal and preictal EEG signals. A hybrid method using principal component analysis (PCA) and independent component analysis (ICA) is applied for dimensionality reduction. Nonlinear features are selected for the analysis and characterization of the signals. A Hidden Markov Model (HMM) with Gaussian mixture emissions is trained for each type of signal and evaluated as a classifier. A sensitivity of 0.95 and a specificity of 0.86 were achieved.
AB - Seizure forecasting systems have been studied in recent years for improving the quality of life for patients with epilepsy and gain further understanding about seizures. A common approach for this is the study of electroencephalography (EEG) recordings, using signal processing techniques and, more recently, machine learning algorithms. A four-stage system is developed for patient-specific seizure prediction; consisting of pre-processing, dimensionality reduction, feature extraction and classification between interictal and preictal EEG signals. A hybrid method using principal component analysis (PCA) and independent component analysis (ICA) is applied for dimensionality reduction. Nonlinear features are selected for the analysis and characterization of the signals. A Hidden Markov Model (HMM) with Gaussian mixture emissions is trained for each type of signal and evaluated as a classifier. A sensitivity of 0.95 and a specificity of 0.86 were achieved.
KW - Electroencephalography
KW - Entropy
KW - Forecasting
KW - Markov
KW - Seizure
UR - http://www.scopus.com/inward/record.url?scp=85058481967&partnerID=8YFLogxK
U2 - 10.1109/ICEEE.2018.8533947
DO - 10.1109/ICEEE.2018.8533947
M3 - Contribución a la conferencia
AN - SCOPUS:85058481967
T3 - 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018
BT - 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018
Y2 - 5 September 2018 through 7 September 2018
ER -