Design of an optimum Poincaré plane for extracting meaningful samples from EEG signals.

Biosignals are considered as important sources of data for diagnosing and detecting abnormalities, and modeling dynamics in the body. These signals are usually analyzed using features taken from time and frequency domain. In theory' these dynamics can also be analyzed utilizing Poincaré plane that intersects system's trajectory. However' selecting an appropriate Poincaré plane is a crucial part of extracting best Poincaré samples. There is no unique way to choose a Poincaré plane' because it is highly dependent to the system dynamics. In this study, a new algorithm is introduced that automatically selects an optimum Poincaré plane able to transfer maximum information from EEG time series to a set of Poincaré samples. In this algorithm' EEG time series are first embedded; then a parametric Poincaré plane is designed and finally the parameters of the plane are optimized using genetic algorithm. The presented algorithm is tested on EEG signals and the optimum Poincaré plane is obtained with more than 99% data information transferred. Results are compared with some typical method of creating Poinare samples and showed that the transferred information using with this method is higher. The generated samples can be used for feature extraction and further analysis.

Prediction of epileptic seizures from EEG using analysis of ictal rules on Poincaré plane.

BACKGROUND AND OBJECTIVE: Epilepsy is a neurological disorder that causes recurrent and abrupt seizures which makes the patients insecure. Predicting seizures can reduce the burdens of this disorder. METHODS: A new approach in seizure prediction is presented that includes a novel technique in feature extraction from EEG. The algorithm firsts creates an embedding space from EEG time series. Then it takes samples with most of the information using an optimized and data specific Poincare plane. In order to quantify small dynamics on the Poincare plane, based on the order of locations of Poincaré samples in the sequence, 64 fuzzy rules in each channel are defined. Features are extracted based on the frequency distribution of these fuzzy rules in each minute. Then features with higher variance are selected as ictal features and again reduced using PCA. Finally, in order to evaluate how these innovative features can increase the performance of the seizure prediction algorithm, the transition from interictal to preictal state is scored utilizing SVM. RESULTS: The algorithm is tested on 460 h of EEG from 19 patients of Freiburg dataset who had at least 3 seizures. Considering maximum Seizure Prediction Horizon of 42 minutes, average sensitivity was 91.8 - 96.6% and average false prediction rate was 0.05 - 0.08/h. CONCLUSIONS: The presented algorithm shows a better performance and more robustness compare to most of existing methods, and shows power in extracting optimal features from EEG.

Quantitative and qualitative determination of dimethyl phthalate and N, N-diethyl-m-toluamide in repellents commercial formulations by high performance thin layer chromatography.

In this study HPTLC was used for simultaneous quantitative and qualitative determination of N, N-diethyl meta toluamide (DEET) and dimethyl phthalate (DMP), which are the main elements and active ingredients in current chemical repellents. Some defined amounts of commercial form of 3 repellents included trench pomade, stick insect repellent (SIR), which is containing 33% of DEET and DMP60 (dimethyl phthalate 60%) dissolved in ethyl acetate solvent, separately. The method employed TLC aluminum plate precoated with silica gel plates (SiO4) 60F245 as the stationary phase. The solvent system consisted of benzene-diethyl etherhexane (5:3:2, v/v/v) as mobile phase. The multiple level method used for spotting. Densitometric analysis of repellents was carried out using TLC scanner 3 and CATS4 software in the absorption/reflection mode at 230 nm. According to the results, the type and amount of active ingredients in DMP60 lotion was 61.8 g (SE = +/-1.6) per 100 cc and in SIR, 31.3 g (SE = +/-0.8) diethyl meta toluamide per 100 g of repellents raw materials. Also the active ingredients in trench pomade were determined as a combination of DMP and DEET by rates of 5.5 g (SE = +/-0.2) and 25 g (SE = +/-l) per 100 g repellents commercial formulations, respectively. In this study, the value of Rf for DMP and DEET was calculated 0.71 +/- 0.2 and 0.32 +/- 0.2, respectively. HPTLC is a suitable method to quantitatively and quantitatively determine repellents which have DMP and DEET active ingredients. Since most of commercial chemical repellents have this active ingredient, adjusting and setting HPTLC up can be important.