Algorithm and System Design for Epileptic Seizure Prediction
Date Issued
2012
Date
2012
Author(s)
Chang, Nai-Fu
Abstract
Epilepsy is a chronic neurological disorder that affects around 50 million people worldwide. For patients with medically intractable epilepsy, it is the sudden, unforeseen way in which seizures occur that represents one of the most disabling aspects of the disease. Apart from the risk of serious injury, there is often an intense feeling of helplessness that has a strong impact on the everyday life of a patient. A method capable of predicting the occurrence of seizures could significantly improve the therapeutic possibilities and thereby the quality of life for epilepsy patients. However, a fundamental issue in prediction problem that has not been properly resolved is variation issue, especially in light of the many confounding variables such as medications, fluctuating patient state, seizure heterogeneity, and the inherently stochastic nature of these events.
In the thesis, we proposed an online-retraining scheme to deal with variation issue. The performance of the method is evaluated on Electrocorticogram(ECoG) recording from Freiburg database as well as long-term scalp EEG recording from CHB-MIT EEG Database and National Taiwan University Hospital. The algorithm shows 69.7% sensitivity and 54.5% sensitivity in ECoG and scalp EEG databases, while improving the sensitivity of on-line training method by 37.9% and 22.7% in ECoG database and EEG database respectively. Fixed channel selection method and adaptive channel selection method are proposed to boost the sensitivity up to 78.8% and
77.3% in ECoG database as well as EEG database, and reduce the number of channels required to only three or four rather than several or tens. Band selection shows that the patterns of high gamma band and high beta band are more effective on the seizure prediction problem to classify inter-ictal and pre-ictal states.
Considering feasibility, convenience and flexibility, we proposed a seizure prediction system, consisting of 3-channel EEG dry sensors, wireless connection and seizure prediction engine, to warn the users the upcoming of seizure onset. A seizure detection module is added into the system to support automatic seizure onset marking. Besides, the system retains large flexibility for users. The location of key channels, the supposed length of prediction horizon and the suitable retraining period for online retraining scheme are all could be changed easily in accordance to patients condition.
In the appendix, we introduced another work about design and implementation of on-line Empirical Mode Decomposition (EMD) biomedical microprocessor for Hilbert-Huang transform (HHT). On-chip implementation of HHT has great impact to analyze the non-linear and non-stationary biomedical signals on wearable or implantable sensors for the real-time applications. EMD is the key component for the HHT processor. In tradition, EMD is usually performed after the collection of a large window of signals, and the long latency may not be feasible for the real-time applications. In this work, the architecture of on-line EMD for biomedical signals is proposed. The on-line interpolation method with data reuse as well as component and iteration loop decomposition is applied to obtain low latency and low hardware cost. The first chip of EMD processor is fabricated in UMC 90nm LL process and consumes 57.3 μW.
Subjects
Epilepsy
Seizure Prediction
Type
thesis
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