Cognitive assessments for the early diagnosis of dementia after stroke

The early detection of poststroke dementia (PSD) is important for medical practitioners to customize patient treatment programs based on cognitive consequences and disease severity progression. The aim is to diagnose and detect brain degenerative disorders as early as possible to help stroke survivors obtain early treatment benefits before significant mental impairment occurs. Neuropsychological assessments are widely used to assess cognitive decline following a stroke diagnosis. This study reviews the function of the available neuropsychological assessments in the early detection of PSD, particularly vascular dementia (VaD). The review starts from cognitive impairment and dementia prevalence, followed by PSD types and the cognitive spectrum. Finally, the most usable neuropsychological assessments to detect VaD were identified. This study was performed through a PubMed and ScienceDirect database search spanning the last 10 years with the following keywords: “post-stroke”; “dementia”; “neuro-psychological”; and “assessments”. This study focuses on assessing VaD patients on the basis of their stroke risk factors and cognitive function within the first 3 months after stroke onset. The search strategy yielded 535 articles. After application of inclusion and exclusion criteria, only five articles were considered. A manual search was performed and yielded 14 articles. Twelve articles were included in the study design and seven articles were associated with early dementia detection. This review may provide a means to identify the role of neuropsychological assessments as early PSD detection tests

Review on support vector machine (SVM) classifier for human emotion pattern recognition from EEG signals

This study reviewed the strategy in pattern classification for human emotion recognition system based on Support Vector Machine (SVM) classifier on Electroencephalography (EEG) signal. SVM has been widely used as a classifier and has been reported as having minimum error and produce accurate classification. However, the accuracy is influenced by many factors such as the electrode placement, equipment used, preprocessing techniques and selection of feature extraction methods. There are many types of SVM classifier such as SVM via Radial Basis Function (RBF), Linear Support Vector Machine (LSVM) and Multiclass Least Squares Support Vector Machine (MC-LS-SVM). SVM via RBF states the average accuracy rate of 92.73, 85.41, 93.80 and 67.40% using different features extraction method, respectively. The accuracy using LSVM and MC-LS-SVM classifier are 91.04 and 77.15%, respectively. Although, the accuracy rate influenced by many factors in the experimental works, SVM always shows their function as a great classifier. This study will discuss and summarize a few related works of EEG signals in classifying human emotion using SVM classifier

Cognitive impairment and memory dysfunction after a stroke diagnosis: a post-stroke memory assessment

Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors’ quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented

Hardware Implementation of 32-Bit High-Speed Direct Digital Frequency Synthesizer

The design and implementation of a high-speed direct digital frequency synthesizer are presented. A modified Brent-Kung parallel adder is combined with pipelining technique to improve the speed of the system. A gated clock technique is proposed to reduce the number of registers in the phase accumulator design. The quarter wave symmetry technique is used to store only one quarter of the sine wave. The ROM lookup table (LUT) is partitioned into three 4-bit sub-ROMs based on angular decomposition technique and trigonometric identity. Exploiting the advantages of sine-cosine symmetrical attributes together with XOR logic gates, one sub-ROM block can be removed from the design. These techniques, compressed the ROM into 368 bits. The ROM compressed ratio is 534.2 : 1, with only two adders, two multipliers, and XOR-gates with high frequency resolution of 0.029 Hz. These techniques make the direct digital frequency synthesizer an attractive candidate for wireless communication applications

An Integrated Hybrid Energy Harvester for Autonomous Wireless Sensor Network Nodes

Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN) NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH) is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH) is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling

Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task

The aim of this pilot study was to select the most similar mother wavelet function and the most efficient threshold in order to use with wavelet basis function for the human brain electrical activity during working memory task. A 60 seconds was recorded from the scalp using the Electroencephalography (EEG). 19 electrodes were placed over different sites on the scalp where analyzed for one control subject and one post-stroke patients in the first week of his stroke onset. In this study, forty-five mother wavelet basis functions from orthogonal families with four thresholding methods were used. The selection of mother wavelet functions like Daubechies (db), symlet (sym) and coiflet (coif) and the thresholding methods these are sqtwolog, rigrsure, heursure and minimax are to check mother wavelet functions similarity with the recorded EEG signals during working memory task. The test have been done using four evaluating criteria, namely signal to noise ratio (SNR), peak signal to noise ratio (PSNR) mean square error (MSE) and crosscorelation method (xcorr). Symlet mother wavelet of order 9 (sym9) is the most compatible for all the 19 channels for both EEG datasets that selected to be examined and the best results have been obtained by using the rigrsure thresholding method

EEG markers for early detection and characterization of vascular dementia during working memory tasks

The aim of the this study was to reveal markers using spectral entropy (SpecEn), sample entropy (SampEn) and Hurst Exponent (H) from the electroencephalography (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. EEG artifacts were removed using independent component analysis technique and wavelet technique. With ANOVA (p < 0.05), SpecEn was used to test the hypothesis of slowing the EEG signal down in both VaD and MCI compared to control subjects, whereas the SampEn and H features were used to test the hypothesis that the irregularity and complexity in both VaD and MCI were reduced in comparison with control subjects. SampEn and H results in reducing the complexity in VaD and MCI patients. Therefore, SampEn could be the EEG marker that associated with VaD detection whereas H could be the marker for stroke-related MCI identification. EEG could be as a valuable marker for inspecting the background activity in the identification of patients with VaD and stroke-related MCI

Cognitive assessments for the early diagnosis of dementia after stroke

The early detection of poststroke dementia (PSD) is important for medical practitioners to customize patient treatment programs based on cognitive consequences and disease severity progression. The aim is to diagnose and detect brain degenerative disorders as early as possible to help stroke survivors obtain early treatment benefits before significant mental impairment occurs. Neuropsychological assessments are widely used to assess cognitive decline following a stroke diagnosis. This study reviews the function of the available neuropsychological assessments in the early detection of PSD, particularly vascular dementia (VaD). The review starts from cognitive impairment and dementia prevalence, followed by PSD types and the cognitive spectrum. Finally, the most usable neuropsychological assessments to detect VaD were identified. This study was performed through a PubMed and ScienceDirect database search spanning the last 10 years with the following keywords: “post-stroke”; “dementia”; “neuro-psychological”; and “assessments”. This study focuses on assessing VaD patients on the basis of their stroke risk factors and cognitive function within the first 3 months after stroke onset. The search strategy yielded 535 articles. After application of inclusion and exclusion criteria, only five articles were considered. A manual search was performed and yielded 14 articles. Twelve articles were included in the study design and seven articles were associated with early dementia detection. This review may provide a means to identify the role of neuropsychological assessments as early PSD detection tests

Optical Interconnect Waveguide in Electronic Circuit

The increasing demand in silicon nano-photonics has encouraged many researchers to put more efforts to explore the feasibility of using optics in the communication medium in order to replace the conventional electrical interconnects (EIs). In this paper, we proposed a SOI- based waveguide in the optical interconnect (OI) link at an operating frequency of 1550nm to work as an interconnection path in a circuit. The performance capability of the OI link was tested using a two-stage CE amplifier to work as the interconnection path from the 1st stage to the 2nd stage amplifier. In term of optical performances, the optical waveguide interconnect managed to achieve a single mode condition for a TE mode and fulfill the receiver sensitivity of a photodiode. While, in term of electrical performance, a two-stage CE amplifier is able to produce a high gain, a wide bandwidth and high slew rate. The proposed implementation of the OIs waveguide is succesfully enhance the performance of the two-stage CE amplifier as well as the analog electronic circuit applications