Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing

SIGNAL PROCESSING FOR RAMAN SPECTRA FOR DISEASE DETECTION

Raman Spectroscopy enables in-depth study into the molecular structure of solid, liquid and gasses from its scattering spectrum. As such, the spectrum could offer a biochemical fingerprint to identify unknown molecules. Surface Enhanced Raman Spectroscopy (SERS) amplifies the weak Raman signal by 10+3 to 10+7 times, revolutionary making the method appealing to the research community. SERS has been proven useful for disease detection from a medium such as a cell, serum, urine, plasma, saliva, tears. The spectra displayed are noisy and complicated by the presence of other molecules, besides the targeted one. Moreover, the difference between the infected and controlled samples is far too minute for detection by the naked human eyes. Hence, signal processing techniques are found crucial to single out fingerprint of the target molecule from biological spectra. Our work here examines signal processing techniques attempted on SERS spectra for disease detection, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression Analysis (LRA). It is found that PCA-LDA is the most popular (45%), ensued by PCA-ANN (33%) and SVM (22%). PCA-SVM yields the highest in accuracy (99.9%), followed by PCA-ANN (98%) and LRA (97%). PCA-LDA and SVM score the highest in both sensitivity-specificity.Keywords: Raman Spectra, Surface Enhanced Raman Spectroscopy (SERS), Neural Network (NN), Support Vector Machine (SVM), Logistic Regression Analysis (LRA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)

Fabrication effects on polysilicon-based microcantilever piezoresistivity for biological sensing application

In principle, adsorption of biological molecules on a functionalized surface of a microfabricated cantilever will cause a surface stress and consequently the cantilever bending. In this work, four different type of polysilicon-based piezoresistive microcantilever sensors were designed to increase the sensitivity of the microcantilevers sensor because the forces involved is very small. The design and optimization was performed by using finite element analysis to maximize the relative resistance changes of the piezoresistors as a function of the cantilever vertical displacements. The resistivity of the piezoresistivity microcantilevers was analyzed before and after dicing process. The maximum resistance changes were systematically investigated by varying the piezoresistor length. The results show that although the thickness of piezoresistor was the same at 0.5 μm the resistance value was varied