Neonatal Seizure Detection Using Blind Adaptive Fusion

Abstract

Seizure is the result of excessive electrical discharges of neurons, which usually develops synchronously and happens suddenly in the central nervous system. Clinically, it is difficult for physician to identify neonatal seizures visually, while EEG seizures can be recognized by the trained experts. Usually, in NICUs, EEG monitoring systems are used instead of the expensive on-site supervision. However, it is time-consuming to review an overnight recording, which motivates the researchers to develop automated seizure detection algorithms.Although, there are few detection algorithms existed in the literature, it is difficult to evaluate these mathematical model based algorithms since their performances vary significantly on different data sets. By extending our previous results on multichannel information fusion, we propose a distributed detection system consisting of the existing detectors and a fusion center to detect the seizure activities in the newborn EEG. The advantage of our technique is that it does not require any prior knowledge of the hypotheses or the detector performances, which are often unknown in real applications. Therefore, this proposed technique has the potential to improve the performances of the existing neonatal seizure detectors.In this thesis, we first review two newborn EEG models, one of which is used to generate neonatal EEG signals. The synthetic data is used later for testing purpose. IIIWe also review three existing algorithms and implement them to work as the local detectors. Then, we introduce the fusion algorithms applied in the fusion center for two different scenarios: large sample size and small sample size. We finally provide some numerical results to show the applicability, effectiveness, and the adaptability of the blind algorithms in the seizure detection problem. We also provide the testing results obtained using the synthetic to show the improvement of the detection system.