Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features -variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients-were able to significantly capture the seizures. An improvement of 4.77-13.51% in the Bayesian error from the baseline was obtained.
Balamuthia mandrillaris is one of the 4 amebas in fresh water and soil that cause diseases in humans. Granulomatous amebic encephalitis (GAE), caused by B. mandrillaris, is a rare but life-threatening condition. A 4-year-old, previously healthy, Thai girl presented with progressive headache and ataxia for over a month. Neuroimaging studies showed an infiltrative mass at the right cerebellar hemisphere mimicking a malignant cerebellar tumor. The pathological finding after total mass removal revealed severe necrotizing inflammation, with presence of scattered amebic trophozoites. Cerebrospinal fluid (CSF) obtained from lumbar puncture showed evidence of non-specific inflammation without identifiable organisms. A combination of pentamidine, sulfasalazine, fluconazole, and clarithromycin had been initiated promptly before PCR confirmed the diagnosis of Balamuthia amebic encephalitis (BAE). The patient showed initial improvement after the surgery and combined medical treatment, but gradually deteriorated and died of multiple organ failure within 46 days upon admission despite early diagnosis and treatment. In addition to the case, 10 survivors of BAE reported in the PubMed database were briefly reviewed in an attempt to identify the possible factors leading to survival of the patients diagnosed with this rare disease.
This paper aims to apply machine learning techniques to an automated epileptic seizure detection using EEG signals to help neurologists in a time-consuming diagnostic process. We employ two approaches based on convolution neural networks (CNNs) and artificial neural networks (ANNs) to provide a probability of seizure occurrence in a windowed EEG recording of 18 channels. In order to extract relevant features based on time, frequency, and time-frequency domains for these networks, we consider an improvement of the Bayesian error rate from a baseline. Features of which the improvement rates are higher than the significant level are considered. These dominant features extracted from all EEG channels are concatenated as the input for ANN with 7 hidden layers, while the input of CNN is taken as raw multi-channel EEG signals. Using multi-concept of deep CNN in image processing, we exploit 2D-filter decomposition to handle the signal in spatial and temporal domains. Our experiments based on CHB-MIT Scalp EEG Database showed that both ANN and CNN were able to perform with the overall accuracy of up to 99.07% and F1-score of up to 77.04%. ANN with dominant features is more capable of detecting seizure events than CNN whereas CNN requiring no feature extraction is slightly better than ANN in classification accuracy.
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