Showing 4 results for Data Analysis
Z. Tabanfar , S.m. Firoozabadi , Z. Khodakarami, Z. Shankayi ,
Volume 9, Issue 4 (12-2018)
Abstract
Aims: Electroencephalogram (EEG) is an important clinical test for the diagnosis of many brain diseases. The aim of this study was the analysis of electroencephalogram data during rest in patients with brain tumor.
Materials and Methods: In the present analytic observational study, EEG data of 44 patients with brain tumor (tumoral group) and 31 healthy subjects (healthy group) during rest were used. After preprocessing, the linear temporal features, linear spectral features of different frequency bands, and non-linear features of fractal dimension and entropy were extracted. Then, the distinction between healthy and tumoral groups based on extracted features was investigated, using the Davis-Bouldin statistic method, linear discriminant analysis (LDA) and nonlinear K-Nearest Neighbor (KNN) classification.
Findings: There was no significant difference between the the fractal kutz dimension and the waveform length of the two healthy and tumoral groups. Among other features, the sample entropy with a significant reduction in the tumoral group made the most distinction between the two groups (0.69 for the healthy group and 0.53 for the tumoral group). The highest classification accuracy of the two groups was 84%, using the sample entropy and KNN classification.
Conclusion: EEG signals have the potential to distinct the patients with brain tumor and healthy subjects. Nonlinear entropy features with more adaptation to the nonlinear nature of the brain shows a higher accuracy in the representation of the tumoral group. The less entropy of the tumoral group indicates less complexity in the brain processing of this group than the healthy group.
Volume 14, Issue 3 (5-2023)
Abstract
Dyadic Data Analysis (DDA) has been suggested, in the existing literature, to be used to explore interpersonal variables which have long been conventionally investigated in isolation. DDA is effective in analyzing procedures that go on among dyads in studies of family relations, partnership, teacher-student affairs, and many other interpersonal relationships. Illustrative examples come from psychological, behavioral, and sociological studies that help develop the researcher's ability to investigate relationship processes, model and test for the effects of actors, partners, and relationships, and control for the statistical inter-dependence which can be conceived between partners. The present paper first reviews the distinctive features of DDA and the potential advantages it can have for language studies. Also, it shows how longitudinal DDA is strongly needed in the investigation of L2 affective variables in the Second Language Acquisition (SLA) domain to longitudinally explore the dynamic and developmental nature of language learners’ affective factors. Finally, it goes on with making suggestions for a future line of inquiry using this innovative analytic procedure and ends with several conclusive remarks about this analytical framework which is compatible with the complexity of dynamic systems theory (CDST).
Volume 20, Issue 2 (4-2013)
Abstract
An upward trend in the divorce rate in Iran in recent years has attracted officials, researchers and sociologists towards investigating causes and factors contributing such a social menace. Based on the statistics published by the Statistics Center of Iran (SCI), the divorce rate has gone up from 1.5 in 1000 cases in 1996 to 2.3 in 1999 and 3.1 in 2006. Results of previous studies show that factors such as age and educational differences between husband and wife, women’s employment, addiction and lack of moral principles have been the most important causes of the divorce. This study, however, focusing on the socio-economic status of the divorcee in Iran, picks up a different view from that of other studies conducted in this field. This article also tries to identify the relationships between the rules applicable to personal and employment variables among divorced people using exploratory spatial data analysis (ESDA) techniques. The sample data used in this study include 6400 divorcee from the total divorced population (of 392075) in the county according to the 2006 census; those who have declared themselves without marriage partner due to divorce. The sample includes both male and female. Results show that the main characteristics of divorced women were their employment and level of education which were statistically significant in metropolitan regions where there is a rise in the employment and education level of women. On the contrary, low education, unemployment, and place of work have been significant factors among divorced men.
Volume 27, Issue 1 (5-2024)
Abstract
Background: topological data analysis (TDA) in neural network and its advantages over traditional graph theory methods by capturing higher-order relationships and complex structures within the brain examined in this research. TDA provides insights into cognitive processes by analyzing multi-scale interactions among neural activities and is increasingly utilized in both brain science and psychological research.
Methods: The methodology explores neural data from various sources, including multi-electrode arrays (MEAs), to study neural ensemble behaviors and connectivity. Additionally, it critiques existing methods like Granger causality analysis (GCA) for their limitations in interpreting neural data.
Results: According to our findings, the number of spiking activities and active channels rise from the 10th to the 60th day in vitro (DIV). Burst activities peaked between 30 and 50 DIV, while the firing rate in active channels continued to increase until 30 DIV. Furthermore, the average burst length exhibited a consistent rise until 50 DIV. However, the percentage of spikes involved in burst activities displayed a non-monotonic pattern, initially declining until 30 DIV and rising again in subsequent days. The fluctuations in average spike amplitudes can be attributed to factors such as the distance between neurons and electrodes, as well as the ongoing neuronal plasticity and migration.
Conclusion: In summary, TCA provides qualitative insights into network status based on quantitative metrics and established thresholds. While we focused on primary neuronal cells derived from rat cortices, MEA technology is versatile enough to monitor the developmental stages of any neuronal type, including those derived from human sources
