Electroencephalogram (EEG)-based emotion recognition is a rapidly growing field. This paper presents a research study on identifying the changes caused by human emotions in brain signals through EEG records and further examines the availability of chaotic analysis for emotion prediction applications. We designed an efficient presentation including 30 pictures from International Affective Picture System (IAPS) which could stimulate the emotions of happiness, sadness and fear. A group of 20 persons consisting of 12 males and 8 females took place in our research study as subjects voluntarily. In order to acquire the EEG signals under picture induction environment, a total of 30 pictures that might reveal the feelings of happiness, sadness and fear were shown to the volunteer subjects that participated in the study voluntarily. Data acquired from EEG records were analyzed by using chaotic analysis through MATLAB program and two different attributes consisting of "Largest Lyapunov Exponent (LLE)" and "Correlation Dimension" were obtained. The mean values of each attribute that obtained through chaotic analysis were compared by using independent t-test and dependent t-test in SPSS program with 95% confidence interval and a P value of p<;0.05. The independent samples t-test was used to compare the mean values of each attribute relevant to two unrelated groups (Females-Males) and Dependent samples t-test was used to test whether the mean values of two related observations (two observations for per subject) significantly differs from the hypothesized value. Our study shows that chaotic analysis promises hope for future studies.

Bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ) are serious mental disorders that affect millions of people worldwide. Due to the overlapping clinical symptoms, early and accurate diagnosis of these disorders is difficult. This reduces patients’ quality of life, increases healthcare costs, and negatively impacts the treatment process. This study proposes an objective approach to support clinician decisions based on electroencephalography (EEG) signals and deep learning models. EEG signals recorded from 106 patients were divided into three groups: patients with SZ ( $n=43$ ), BD ( $n=30$ ), and MDD ( $n=33$ ). Feature extraction from the EEG signals was conducted in two different experiments. In the first experiment, preprocessed EEG data were directly applied to the input of deep learning models for automatic feature extraction. In the second experiment, short-time Fourier transform (STFT) was applied to the EEG signals, and the obtained features were used as input for classification. In order to evaluate the classification performance of the experiments, six different deep learning models, including unidirectional, bidirectional, and hybrid architectures, were used: gated recurrent unit (GRU), bidirectional GRU (BiGRU), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), hybrid LSTM-GRU, and BiLSTM-BiGRU models. The experimental results showed that time-frequency domain features were more discriminatory than automatically extracted features. The BiLSTM-BiGRU hybrid model using the STFT features reached the highest classification performance with 94.55% accuracy, 0.944 recall, 0.972 specificity, 0.946 precision, and 0.945 F1-score. Our findings demonstrated the potential of deep learning models using time-frequency domain features as input in multi-class classification of mental disorders.

Determining the blood glucose level is important for the prevention and treatment of diabetes mellitus. We developed a sensor system using Quartz Crystal Microbalance (QCM) to determine the blood glucose level from human blood serum. This study consists of two experimental stages: artificial glucose/pure water solution tests and human blood serum tests. In the first stage of the study, the QCM sensor with the highest performance was identified using artificial glucose solution concentrations. In the second stage of the study, human blood serum measurements were performed using QCM to determine blood glucose levels. QCM sensors were coated with phthalocyanines (Pcs) by jet spray method. The blood glucose values of 96 volunteers, which ranged from 71 mg/dL to 329 mg/dL, were recorded. As a result of the study, human glucose values were determined with an average error of 3.25%.