The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia

Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions

Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis

Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA−WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and kNN, respectively, in classifying VaD, stroke-related MCI, and control patients, respectively. Therefore, EEG could be a reliable index for inspecting concise markers that are sensitive to VaD and stroke-related MCI patients compared to control healthy subjects

Between honour and dignity : Kurdish literary and cinema narratives and their attempt to rethink identity and resistance

Based on Kwame A. Appiah’s take on the moral revolution as well as on the concept of moral imagination, this chapter analyses selected Kurdish literary and cinema narratives. It shows that while the traditional honour relied on courage and faithfulness, the understanding of dignity developed by modern narratives recognises the value of life and elevates love for human life as a guiding moral principle. This process engages many traditional motives which have been updated according to the needs of modern society. The chapter also links postcolonial approaches with the recent studies on dehumanisation and suggests that reading Kurdish narratives through the lens of moral imagination can assist in overcoming the entrenched dehumanisation of the Kurds

The nicotinic α6 subunit gene determines variability in chronic pain sensitivity via cross-inhibition of P2X2/3 receptors

International audienceChronic pain is a highly prevalent and poorly managed human health problem. We used microarray-based expression genomics in 25 inbred mouse strains to identify dorsal root ganglion (DRG)-expressed genetic contributors to mechanical allodynia, a prominent symptom of chronic pain. We identified expression levels of Chrna6, which encodes the α6 subunit of the nicotinic acetylcholine receptor (nAChR), as highly associated with allodynia. We confirmed the importance of α6* (α6-containing) nAChRs by analyzing both gain- and loss-of-function mutants. We find that mechanical allodynia associated with neuropathic and inflammatory injuries is significantly altered in α6* mutants, and that α6* but not α4* nicotinic receptors are absolutely required for peripheral and/or spinal nicotine analgesia. Furthermore, we show that Chrna6's role in analgesia is at least partially due to direct interaction and cross-inhibition of α6* nAChRs with P2X2/3 receptors in DRG nociceptors. Finally, we establish the relevance of our results to humans by the observation of genetic association in patients suffering from chronic postsurgical and temporomandibular pain