Variational Bayesian learning for Gaussian mixture HMM in seizure prediction based on long term EEG of epileptic rats

Abstract

Epilepsy is a common neurological disorder characterized by abnormal excessive or synchronous neural activity in brain. In this study, we develop an unsupervised learning for seizure prediction. Extracting wavelet features of brain electroencephalogram (EEG), we propose a Hidden Markov Model (HMM) with a mixture of Gaussian observation model as an unsupervised learning setting for seizure prediction, where the seizure predictions are derived from the posterior distributions over the hidden states in the HMM. By using the Variational Bayesian (VB) method instead of the Maximum Likelihood estimation, which is the method commonly used for training HMMs, we overcome data overfltting and make it possible to compare models with different model orders by means of the variational free energy. VB learning also improves results in terms of convergence speed and achieved performance. The proposed method was evaluated using 20h of labeled EEG recordings from 7 epileptic rats with total number of 350 seizures. Our method obtained a high sensitivity of 90.7% and a specificity of 88.9% with early detection of 1.3s, which makes it more reliable than ML estimation with a sensitivity of 82.1% and a specificity of 86.2% and late detection of 4s.