Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering from PD. In this study, an EEG-based deep learning model was proposed for the automatic detection of PD to support experts. The EEG dataset comprises signals recorded by the University of Iowa from 14 PD patients and 14 healthy controls. First of all, the power spectral density values (PSDs) ​​of the frequencies between 1 and 49 Hz of the EEG signals were calculated separately using periodogram, welch, and multitaper spectral analysis methods. 49 feature vectors were extracted for each of the three different experiments. Then, the performances of support vector machine, random forest, k-nearest neighbor, and bidirectional long-short-term memory (BiLSTM) algorithms were compared using the PSDs feature vectors. After the comparison, the model integrating welch spectral analysis and the BiLSTM algorithm showed the highest performance as a result of the experiments. The deep learning model achieved satisfactory performance with 0.965 specificity, 0.994 sensitivity, 0.964 precision, 0.978 f1-score, 0.958 Matthews correlation coefficient, and 97.92% accuracy. The study is a promising attempt to detect PD from EEG signals and it also provides evidence that deep learning algorithms are more effective than machine learning algorithms for EEG signal analysis.

Phubbing, alienation, digital game addiction, independent self-construal, and interdependent self- construal among high school students: a path analysis / Ningufoneo, alienación, adicción a los juegos digitales, autoconstrucción independiente y autoconstrucción interdependiente entre estudiantes de secundaria: un análisis de trayectoria

In this study, the aim is to analyze the relationships between phubbing, alienation, digital game addiction, independent self-construal, and interdependent self-construal among high school students. The sample of the study consists of 1,932 students studying in different high schools in Turkey who were selected by the stratified random sampling method, considering the grade levels and gender variables. The students completed surveys regarding self-construal, digital game addiction, alienation, and phubbing. The data obtained were analyzed by path analysis, one of the structural equation modeling methods. In the research, nine hypotheses were developed for the proposed model based on theoretical knowledge. As a result of the analysis, eight hypotheses were supported, and one was unsupported. According to the findings, digital game addiction had a significant impact on alienation and phubbing; also, alienation had a considerable impact on phubbing. The model explained 16% of the variance (R2= .16) of phubbing, directly and indirectly. This means that the exogenous variables have a moderate level of influence on the endogenous variable. Moreover, alienation had a maximum degree of effect on phubbing.

El objetivo de este estudio es analizar las relaciones entre ningufoneo, alienación, adicción a los juegos digitales, autoconstrucción independiente y autoconstrucción interdependiente entre estudiantes de secundaria. La muestra consta de 1.932 estudiantes de diferentes escuelas secundarias de Turquía, que fueron seleccionados por el método de muestreo aleatorio estratificado considerando los niveles de grado y el sexo. Los estudiantes completaron encuestas sobre autoconstrucción, adicción a los juegos digitales, alienación y ningufoneo. Los datos obtenidos se analizaron mediante análisis de trayectoria, uno de los métodos de modelado de ecuaciones estructurales. En la investigación se desarrollaron nueve hipótesis para el modelo propuesto a partir de conocimientos teóricos. Como resultado del análisis se confirmaron ocho hipótesis y una no se confirmó. Según los hallazgos, la adicción a los juegos digitales tuvo un impacto significativo en la alienación y el ningufoneo; además, la alienación tuvo un impacto considerable en el ningufoneo. El modelo explicó el 16% de la varianza (R2= 0,16) de ningufoneo, directa e indirectamente. Esto significa que las variables exógenas tienen un nivel moderado de influencia sobre la variable endógena. Además, la alienación tuvo un grado máximo de efecto sobre el ningufoneo.