Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Faezeh Daneshmand-Bahman; Ateke Goshvarpour
Volume 16, Issue 2 , September 2022, , Pages 115-131
Abstract
Anxiety disorders are one of the most common and debilitating mental disorders worldwide. On the other hand, since 2019, with the outbreak of Covid-19, anxiety has increased among people, especially the medical staff. Currently, anxiety is diagnosed (when the symptoms are severe enough) using a questionnaire ...
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Anxiety disorders are one of the most common and debilitating mental disorders worldwide. On the other hand, since 2019, with the outbreak of Covid-19, anxiety has increased among people, especially the medical staff. Currently, anxiety is diagnosed (when the symptoms are severe enough) using a questionnaire by a specialist. To resolve this shortcoming, researchers have recently paid attention to the use of brain signals. Consequently, the present study aimed to diagnose anxiety using brain signals. The novelty of this study is the use of the Chebyshev chaotic map for the first time in biological signal analysis. It used the DASPS database, which includes a 14-channel electroencephalogram (EEG) of 23 people (10 men and 13 women, with a mean age of 30 years). The self-assessment manikin scores were used to divide anxiety into two and four levels. First, the data were normalized. Then, the chaotic map was reconstructed and divided into 128 strips. The density of points in each of the strips was calculated. Two indicators were considered as features, (1) maximum density and (2) its corresponding sample. Finally, features were applied to Support Vector Machines (SVM) and k-Nearest Neighbors (K-NN) in 5 ways, (1) feature 1 of all channels, (2) feature1 mapping of all channels using principal component analysis (PCA), (3) feature 2 of all channels, (4) feature 2 mapping of all channels using PCA and (5) each feature - each channel separately. The results show a maximum accuracy of 93.75% for diagnosing two levels of anxiety and 96.15% for diagnosing four levels of anxiety. In addition, K-NN outperformed SVM. Accordingly, the proposed algorithm can be introduced as a suitable approach for diagnosing anxiety.
Bioelectromagnetics
Susan Kohzad; Bahram Bolouri; Farnaz Nikbakht; Zahra Kohzad
Volume 6, Issue 2 , June 2012, , Pages 107-111
Abstract
There is a growing public concern that the extremely low frequency (ELF) range of the environmental electromagnetic fields may have adverse biological effects. In this frequency range, 217Hz is the modulating signal being used in Global System of Mobile. This study investigated the possible effects of ...
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There is a growing public concern that the extremely low frequency (ELF) range of the environmental electromagnetic fields may have adverse biological effects. In this frequency range, 217Hz is the modulating signal being used in Global System of Mobile. This study investigated the possible effects of 217 Hz pulsed electromagnetic field on the anxiety and the cortisol level in rats. Twenty four male Wistar rat (200 - 250 g) were randomly grouped into test, sham and control. Using a pair of Helmholtz coil system, the test group was exposed to a uniform pulsed EMF of 200µT intensity for 4 h/day for 21 days. A similar procedure with no field was repeated for the sham group. All groups were tested in an `Elevated- plus` maze system. Then via the heart puncture scheme, the blood samples were collected. The serum cortisol levels were evaluated using ELISA method.The ANOVA test revealed no significant differences for the Elevated- plus maze test. Serum cortisol level was significantly higher in test group compared to the control group.These findings were in consistent with the work of others indicating that low frequency band of EMF might not have any effect on the anxiety but it increases the cortisol levels as a stress marker.
