Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach

2020 ◽  
Vol 30 (07) ◽  
pp. 2050029
Author(s):  
Andrés Ortiz ◽  
Francisco J. Martinez-Murcia ◽  
Juan L. Luque ◽  
Almudena Giménez ◽  
Roberto Morales-Ortega ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Detection System ◽  
High Sensitivity ◽  
Evaluation Procedure ◽  
Auditory Stimuli ◽  
Behavioral Variables ◽  
Specific Frequency ◽  
Specific Difficulty ◽  
Nonspeech Stimulus ◽  
The Brain

Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD. The results corroborate that nonspeech stimulus modulated at specific frequencies related to the sampling processes developed in the brain to capture rhymes, syllables and phonemes produces effects in specific frequency bands that differentiate between controls and DD subjects. The proposed method showed relatively high sensitivity above 0.6, and up to 0.9 in some of the experiments.

Comparison of Cytocidal Activities of L-DOPA and Dopamine in S. cerevisiae and C. glabrata

2020 ◽  
Vol 16 (1) ◽  
pp. 90-93
Author(s):  
Carmen E. Iriarte ◽  
Ian G. Macreadie
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopaminergic Neurons ◽  
Candida Glabrata ◽  
High Sensitivity ◽  
Time Dependent ◽  
Colony Forming Units ◽  
Dependent Cell ◽  
The Brain

Background: Parkinson's Disease results from a loss of dopaminergic neurons, and reduced levels of the neurotransmitter dopamine. Parkinson's Disease treatments involve increasing dopamine levels through administration of L-DOPA, which can cross the blood brain barrier and be converted to dopamine in the brain. The toxicity of dopamine has previously studied but there has been little study of L-DOPA toxicity. Methods: We have compared the toxicity of dopamine and L-DOPA in the yeasts, Saccharomyces cerevisiae and Candida glabrata by cell viability assays, measuring colony forming units. Results: L-DOPA and dopamine caused time-dependent cell killing in Candida glabrata while only dopamine caused such effects in Saccharomyces cerevisiae. The toxicity of L-DOPA is much lower than dopamine. Conclusion: Candida glabrata exhibits high sensitivity to L-DOPA and may have advantages for studying the cytotoxicity of L-DOPA.

scholarly journals Optical Imaging of Beta-Amyloid Plaques in Alzheimer’s Disease

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 255
Author(s):  
Ziyi Luo ◽  
Hao Xu ◽  
Liwei Liu ◽  
Tymish Y. Ohulchanskyy ◽  
Junle Qu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Optical Imaging ◽  
Imaging Techniques ◽  
High Sensitivity ◽  
Pathological Feature ◽  
Promising Tool ◽  
Abnormal Accumulation ◽  
Accumulation Mechanism ◽  
The Brain

Alzheimer’s disease (AD) is a multifactorial, irreversible, and incurable neurodegenerative disease. The main pathological feature of AD is the deposition of misfolded β-amyloid protein (Aβ) plaques in the brain. The abnormal accumulation of Aβ plaques leads to the loss of some neuron functions, further causing the neuron entanglement and the corresponding functional damage, which has a great impact on memory and cognitive functions. Hence, studying the accumulation mechanism of Aβ in the brain and its effect on other tissues is of great significance for the early diagnosis of AD. The current clinical studies of Aβ accumulation mainly rely on medical imaging techniques, which have some deficiencies in sensitivity and specificity. Optical imaging has recently become a research hotspot in the medical field and clinical applications, manifesting noninvasiveness, high sensitivity, absence of ionizing radiation, high contrast, and spatial resolution. Moreover, it is now emerging as a promising tool for the diagnosis and study of Aβ buildup. This review focuses on the application of the optical imaging technique for the determination of Aβ plaques in AD research. In addition, recent advances and key operational applications are discussed.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

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