EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier

Emotion recognition by artificial intelligence (AI) is a challenging task. A wide variety of research has been done, which demonstrated the utility of audio, imagery, and electroencephalography (EEG) data for automatic emotion recognition. This paper presents a new automated emotion recognition framework, which utilizes electroencephalography (EEG) signals. The proposed method is lightweight, and it consists of four major phases, which include: a reprocessing phase, a feature extraction phase, a feature dimension reduction phase, and a classification phase. A discrete wavelet transforms (DWT) based noise reduction method, which is hereby named multi scale principal component analysis (MSPCA), is utilized during the pre-processing phase, where a Symlets-4 filter is utilized for noise reduction. A tunable Q wavelet transform (TQWT) is utilized as feature extractor. Six different statistical methods are used for dimension reduction. In the classification step, rotation forest ensemble (RFE) classifier is utilized with different classification algorithms such as k-Nearest Neighbor (k-NN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and four different types of the decision tree (DT) algorithms. The proposed framework achieves over 93 % classification accuracy with RFE + SVM. The results clearly show that the proposed TQWT and RFE based emotion recognition framework is an effective approach for emotion recognition using EEG signals.</p

Default-Mode-Like Network Activation in Awake Rodents

During wakefulness and in absence of performing tasks or sensory processing, the default-mode network (DMN), an intrinsic central nervous system (CNS) network, is in an active state. Non-human primate and human CNS imaging studies have identified the DMN in these two species. Clinical imaging studies have shown that the pattern of activity within the DMN is often modulated in various disease states (e.g., Alzheimer's, schizophrenia or chronic pain). However, whether the DMN exists in awake rodents has not been characterized. The current data provides evidence that awake rodents also possess ‘DMN-like’ functional connectivity, but only subsequent to habituation to what is initially a novel magnetic resonance imaging (MRI) environment as well as physical restraint. Specifically, the habituation process spanned across four separate scanning sessions (Day 2, 4, 6 and 8). At Day 8, significant (p<0.05) functional connectivity was observed amongst structures such as the anterior cingulate (seed region), retrosplenial, parietal, and hippocampal cortices. Prior to habituation (Day 2), functional connectivity was only detected (p<0.05) amongst CNS structures known to mediate anxiety (i.e., anterior cingulate (seed region), posterior hypothalamic area, amygdala and parabracial nucleus). In relating functional connectivity between cingulate-default-mode and cingulate-anxiety structures across Days 2-8, a significant inverse relationship (r = −0.65, p = 0.0004) was observed between these two functional interactions such that increased cingulate-DMN connectivity corresponded to decreased cingulate anxiety network connectivity. This investigation demonstrates that the cingulate is an important component of both the rodent DMN-like and anxiety networks

Conversion of failed vertical banded gastroplasty to open adjustable gastric banding

Background: Occasionally, patients with failed vertical banded gastroplasty (VBG) present for secondary treatment. We performed the reoperations using adjustable gastric banding (AGB) technique

An Entropy-Based Evaluation Method For Knowledge Bases Of Medical Information Systems

In this paper we introduce a method to develop knowledge bases for medical decision support systems, with a focus on evaluating such knowledge bases. Departing from earlier efforts with concept maps, we developed an ontological-semantic knowledge base and evaluated its information content using the metrics we have developed, and then compared the results to the UMLS backbone knowledge base. The evaluation method developed uses information entropy of concepts, but in contrast to previous approaches normalizes it against the number of relations to evaluate the information density of knowledge bases of varying sizes. A detailed description of the knowledge base development and evaluation is discussed using the underlying algorithms, and the results of experimentation of the methods are explained. The main evaluation results show that the normalized metric provides a balanced method for assessment and that our knowledge base is strong, despite having fewer relationships, is more information-dense, and hence more useful. The key contributions in the area of developing expert systems detailed in this paper include: (a) introduction of a normalized entropy-based evaluation technique to evaluate knowledge bases using graph theory, (b) results of the experimentation of the use of this technique on existing knowledge bases

Zinc and copper status in acute pancreatitis - An experimental study

Metal ions are required as active components of several proteins, including pancreatic enzymes, and they can play important roles in the etiopathogenesis of acute pancreatitis. In the present study, we measured the concentrations of zinc (Zn) and copper (Cu) in both serum and pancreatic tissue, as markers of trace element status in an experiental acute pancreatitis model. Twenty-four male Wistar rats were divided into two groups: the experimental group (N=24) and the control group (N=10). Acute pancreatitis was induced by injection of 48% ethyl alcohol into the common biliary duct. The animals were sacrificed 24 h later to detect the concentrations of Zn and Cu. There was no significant difference in tissue Zn and Cu concentrations between control and experimental groups (p<0.05). However, in the acute pancreatitis group, serum Zn and Cu levels were very significantly lower (p<0.001 and p<0.0001, respectively). In conclusuion, these findings suggested that altered mineral metabolism in serum and pancreatic tissue may have contributed to the pathophysiology of acute pancreatitis

Additional file 1: of Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

Transverse images and MNI coordinates for identified brain regions. (DOCX 336 kb

Functional Connectivity in the Anxiety Network in Non-habituated, Awake Rats.

<p><b>A-B.</b> Functional connectivity amongst the anterior cingulate cortex and brain regions implicated in anxiety was significantly greater during the initial scanning session at Day 2. All statistical maps are superimposed upon an in-house, high-resolution rat anatomical template. <b>B.</b> Functional connectivity at Day 2 and Day 8 is quantified between the cingulate and anteroventral thalamus, and also with the posterior hypothalamic area. The ANOVA test yielded insignificant results for the cingulate-anteroventral thalamus (F_3,22 = 2.49, p = 0.087) and cingulate-posterior hypothalamic area (F_3,22 = 1.37, p = 0.27) functional interactions. Two-tailed, t-test did reveal significant differences particularly between Day 2 and Day 8 (cingulate-anteroventral thalamus- <b>Day 4</b>: t₁₀ = 1.91, p = 0.09; <b>Day 6</b>: t₁₂ = 1.31, p = 0.22; <b>Day 8</b>: t₁₂ = 2.26, p = 0.043). (cingulate-posterior hypothalamic area- <b>Day 4</b>: t₁₀ = 1.07, p = 0.30; <b>Day 6</b>: t₁₂ = 0.52, p = 0.61; <b>Day 8</b>: t₁₂ = 2.74, p = 0.018). All error bars represent s.e.m. Day 2 (N = 7); Day 4 (N = 5); Day 6 (N = 7); Day 8 (N = 7). # p<0.05 <b>C.</b> A significant inverse correlation was detected between the cingulate-default mode and cingulate-anxiety network structures. One statistical outlier (Z = 3.76) was detected using Grubb's test for outlier detection, and not included in correlation analysis. For N = 26, Z-critical  =  2.84 (p<0.05).</p