Dragoljub Gajic

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices and (c) classification by quadratic classifiers. The proposed methodology was applied on EEG data sets that belong to three subject groups: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval and (c) epileptic subjects during a seizure. An overall classification accuracy of 99% was achieved. The results confirmed that the proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.

Optimization of technological procedure for amygdalin isolation from plum seeds (Pruni domesticae semen)

The process of amygdalin extraction from plum seeds was optimized using central composite design (CCD) and multilayer perceptron (MLP). The effect of time, ethanol concentration, solid-to-liquid ratio, and temperature on the amygdalin content in the extracts was estimated using both mathematical models. The MLP 4-3-1 with exponential function in hidden layer and linear function in output layer was used for describing the extraction process. MLP model was more superior compared with CCD model due to better prediction ability. According to MLP model, the suggested optimal conditions are: time of 120 min, 100% (v/v) ethanol, solid-to liquid ratio of 1:25 (m/v) and temperature of 34.4°C. The predicted value of amygdalin content in the dried extract (25.42 g per 100 g) at these conditions was experimentally confirmed (25.30 g per 100 g of dried extract). Amygdalin (>90%) was isolated from the complex extraction mixture and structurally characterized by FT-IR, UV, and MS methods.

Modelling and Optimization of Methylene Blue Adsorption from Aqueous Solution Using Bentonite Clay

Abstract Dyes are widely used in many process industries such as textiles, food, paper, cosmetics, plastics and rubbers. The adsorption process of dye from industrial wastewaters is an ideal alternative than other expensive treatment options. For the removal of methylene blue from aqueous solutions the different adsorbents were used such as wheat shells, kaolin, activated carbon, activated carbon from oil palm wood and bamboo, Indian Rosewood sawdust, natural zeolite and perlite. The aim of this paper was to model and optimize the adsorption process of methylene blue from aqueous solutions at room temperature using bentonite clay as the adsorbent. The central composite design was used as the suitable mathematical approach for investigation the interactions between process variables. The contact time (4.8 – 55.2 min), initial dye concentration (16.6 – 33.4xa0mgL −1 ), and adsorbent concentration (1,113.7 – 12,886.3xa0mgL − 1 ) was consider as the independent variables, while the percentage of a dsorbed methylene blue was selected as the adequate response. The reduced second order polynomial model was successfully applied for fitting the experimental data. The optimal conditions were obtained using the numerical optimization. The predicted optimal value of adsorbed methylene blue was in agreement with the experimentally obtained value that clearly showed us both applicability and reliability of the numerical optimization applied in this particular case study.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings.