Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved

Classification of epilepsy using computational intelligence techniques

AbstractThis paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network (ANN) and support vector machines (SVMs) combined with supervised learning algorithms, and k-means clustering (k-MC) combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One (OvO), One Vs All (OvA) and Binary Decision Tree (BDT), are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour (k-NN) and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise

Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients

Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury

Micro-Satellite Attitude Determination with Only a Single Horizon Sensor

Through using measurement from only a single horizon sensor, this paper presented a quaternion-based 3-axis attitude determination method, which can be implemented on board micro-satellites and applied over a whole orbital period. Firstly, a description of attitude representation on the quaternion is given. Secondly, a detailed modeling formulation with nadir vector and measurement equations on attitude estimation system is demonstrated. Afterwards, a correction is made to eliminate the estimation error resulted from Earth’s oblateness, and able to further improve the accuracy of the attitude determination algorithm. Finally, a six degree-of-freedom closed-loop simulation is used to validate the accuracy of the attitude determination method given in this paper

Micro-Satellite Attitude Determination with Only a Single Horizon Sensor

Through using measurement from only a single horizon sensor, this paper presented a quaternion-based 3-axis attitude determination method, which can be implemented on board micro-satellites and applied over a whole orbital period. Firstly, a description of attitude representation on the quaternion is given. Secondly, a detailed modeling formulation with nadir vector and measurement equations on attitude estimation system is demonstrated. Afterwards, a correction is made to eliminate the estimation error resulted from Earth’s oblateness, and able to further improve the accuracy of the attitude determination algorithm. Finally, a six degree-of-freedom closed-loop simulation is used to validate the accuracy of the attitude determination method given in this paper