Abstract
Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.
Highlights
50 million people live with epilepsy worldwide [2]
Epilepsy is a neurological disorder which can occur at any age, currently has no cure, and is characterized by seizures that can happen without noticeable warning [3]
Expanding on previous work [1], where the authors employed a mixed-filter approach to perform seizure state estimation using two EEG channels, we propose that the removal of the self-imposed channel count restriction may lead to increases in accuracy and robustness of the state-estimation and be more applicable to non-focal type seizures [7]
Summary
In the US alone, the National Institutes of Health spends over $150 million each year on epilepsy research. This accounts for roughly 82% of research that is not coming from industry sources [3]. Epilepsy is a neurological disorder which can occur at any age, currently has no cure, and is characterized by seizures that can happen without noticeable warning [3]. This can lead to other health problems such as brain injury from falling, psychiatric conditions, and a reduction in quality of life [4], [5]. A focal seizure is one that is triggered by a localized portion of the brain, while a general seizure can be triggered in multiple parts of the brain [7]
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