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.
Amir Babaoghli; Hadi Soltanizadeh
Volume 13, Issue 3 , October 2019, , Pages 235-246
Abstract
Diseases associated with the retina and macula of the eye, causing permanent loss of vision or a great deal of loss of vision in people, leads to a decrease in the quality of life and a lot of problems in daily life. For this reason, the timely and correct identification of these diseases and disorders ...
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Diseases associated with the retina and macula of the eye, causing permanent loss of vision or a great deal of loss of vision in people, leads to a decrease in the quality of life and a lot of problems in daily life. For this reason, the timely and correct identification of these diseases and disorders has become important. The optical coherence tomography imaging method provides high precision in imaging and good information about the depth of the retina. This imaging technique is a great help in the accurate identification of macular-related diseases. Age-related macular degeneration is one of the most common retinal diseases. The purpose of this study is to design and implement a system that is reliable, fast and can detect the age-related macular degeneration by using optical coherence tomography image processing accurately and quickly. In these studies, histograms of orientational gradients and principal component analysis for extraction of features and AdaBoost ensemble classification method have been used to classify the data. The database used includes 269 patients and 115 healthy people. All three indicators of accuracy, sensitivity and specificity of the implemented system were measured at 100%.
Bioelectrics
Amir Soleymankhani; Vahid Shalchyan
Volume 12, Issue 2 , September 2018, , Pages 85-96
Abstract
The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity ...
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The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity of single neurons. Based on the structural properties of the neuron, such as its dendritic tree, and the distance and direction of it relative to the electrode, it can be claimed that the form of its spike waveform is unique and constant. However, spike sorting under low signal-to-noise ratio (SNR) conditions is always accompanied with challenges. A spike sorting algorithm usually consists of three sections including the spike detection, feature extraction, and classification. In this paper, a method based on optimization of continuous wavelet coefficients is presented which is effective in low SNR values. In the proposed method, after the calculation of the parameterized wavelet coefficients, using the Euclidean distance and the area under the receiver operator characteristic curve, the best parameters were chosen to increase the separation of the features, so that a suitable scale was first found with the Euclidean distance criterion and then the translation parameter was obtained with the second criterion. In this research k-means algorithm was used for the clustering as a simple but efficient method. For evaluation, three simulated data sets were made in 9 different SNRs with a modeled background noise. The obtained results from simulated data showed that the optimization of parameters in continuous wavelet transform using the proposed algorithm could effectively improve the spike sorting performance compared to principal component analysis method.
