EEG Data acquisition and automatic seizure detection using wavelet transforms in the newborn EEG.

This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality. The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term newborn EEG signals acquisition is considerably more difficult than that of adults and children. This is because, the number of the electrodes attached to the skin is limited by the size of the head, the newborns EEGs vary from day to day, and the newborns are reluctant of being in the recording situation. Also, the movement of the newborn can create artifact in the recording and as a result strongly affect the electrical seizure recognition. Most of the existing methods for neonates are either time or frequency based, and, therefore, do not consider the non-stationarity nature of the EEG signal. Thus, notwithstanding the plethora of existing methods, this thesis applies the discrete wavelet transform (DWT) to account for the non-stationarity of the EEG signals. First, two methods for seizure detection in neonates are proposed. The detection schemes are based on observing the changing behaviour of a number of statistical quantities of the wavelet coefficients (WC) of the EEG signal at different scales. In the first method, the variance and mean of the WC are considered as a feature set to dassify the EEG data into seizure and non-seizure. The test results give an average seizure detection rate (SDR) of 97.4%. In the second method, the number of zero-crossings, and the average distance between adjacent extrema of the WC of certain scales are extracted to form a feature set. The test obtains an average SDR of 95.2%. The proposed feature sets are both simple to implement, have high detection rate and low false alarm rate. Then, in order to reduce the complexity of the proposed schemes, two optimising methods are used to reduce the number of selected features. First, the mutual information feature selection (MIFS) algorithm is applied to select the optimum feature subset. The results show that an optimal subset of 9 features, provides SDR of 94%. Compared to that of the full feature set, it is clear that the optimal feature set can significantly reduce the system complexity. The drawback of the MIFS algorithm is that it ignores the interaction between features. To overcome this drawback, an alternative algorithm, the mutual information evaluation function (MIEF) is then used. The MIEF evaluates a set of candidate features extracted from the WC to select an informative feature subset. This function is based on the measurement of the information gain and takes into consideration the interaction between features. The performance of the proposed features is evaluated and compared to that of the features obtained using the MIFS algorithm. The MIEF algorithm selected the optimal 10 features resulting an average SDR of 96.3%. It is also shown, an average SDR of 93.5% can be obtained with only 4 features when the MIEF algorithm is used. In comparison with results of the first two methods, it is shown that the optimal feature subsets improve the system performance and significantly reduce the system complexity for implementation purpose

Neonates' EEG spike detection using a time-frequency approach

This paper proposes a new time-frequency (TF) based approach for detecting spikes in newborns' EEG signals. Since the automated detection of spikes in the EEG is an important deal in the diagnosis of epilepsy and is a goal sought by many researchers. in the proposed method, first a TF representation of the EEG epoch is computed and then its average over all frequencies is used to distinguish between the EEG spikes and the background. The method is used in conjunction with a few time-frequency distributions (TFDs) to detect spikes in the EEG signals acquired from 5 neonates, with ages ranging from two days to two weeks. The obtained results show that the short-time Fourier transform (STFT) outperforms the other TFDs with the average detection rate of 96%

An optimal feature set for seizure detection systems for newborn EEG signals

A novel automated method is applied to Electroencephalogram (EEG) data to detect seizure events in newborns. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimizing technique based on mutual information feature selection (MIFS) is employed. This technique evaluates a set of candidate features extracted from the WCs to select an informative subset. This subset is used as an input to an artificial neural network (ANN) classifier. The classifier organizes the EEG signal into seizure or non-seizure activities. The training and test sets are obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The optimized results show an average seizure detection rate of 94%

Comparing two time-scale and time-frequency based methods in newborns' EEG seizure detection

In this research, two different approaches for detecting seizure patterns in newborns' Electroencephalogram (EEG) signals are compared. The first proposed approach is a time-frequency (TF) based method, in which, the discrimination between seizure and non-seizure states is based on the TF distance between the consequent segments in the EEG signal. Three different TF measures and three different reduced time-frequency distributions (TFD) are used in this study. The second proposed approach is a discrete wavelet transform (DWT) based method, in which, the detection scheme is based on observing the changing behavior of few statistical quantities of the wavelet coefficients (WCs) of the EEGs at various scales. These statistics form a feature set which is fed into an artificial neural network (ANN) classifier to organize the EEG signals into seizure and non-seizure activities. The proposed methods are tested on the EEG data acquired from three neonates with ages under two weeks. The empirical results validate the suitability of the two proposed methods in automated newborns' seizure detection. The results present an average seizure detection rate (SDR) of 96% and false alarm rate (FAR) of 5% using Kullback-Leibler measure which outperforms the other two distance measures and the DWT based method