Autism Spectrum Disorder Detection Based on Wavelet Transform of BOLD fMRI Signals using Pre-trained Convolution Neural Network

Autism spectrum disorder (ASD) is a mental disorder and the main problem in ASD treatment has no definite cure, and one possible option is to control its symptoms. Conventional ASD assessment using questionnaires may not be accurate and required evaluation of trained experts. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported for ASD detection. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. This study aims to present several convolutional neural networks as tools for ASD detection based on temporal dynamic features classification and improve the ASD prediction results. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as GoogLenet, DenseNet201, ResNet18, and ResNet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbours (KNN). The best prediction results are 85.9% accuracy achieved by extracted the features from DenseNet201 network and classified these features by KNN classifier. Comparison with previous studies, has indicated the good  potential of the proposed model for diagnosis of  ASD cases. From another perspective, the presented method can be applied for analysis of rs-fMRI data on other type of brain disorders

AUTOMATIC ARRHYTHMIA DETECTION ALGORITHM USING STATISTICAL AND AUTOREGRESSIVE MODEL FEATURES

Human heart healthiness is one of the major components to determine a person’s overall healthiness. Automatic arrhythmia detection is important in a remote area where there is a lack of experienced cardiologists. In this work, an automatic arrhythmia detection algorithm is developed using statistical and autoregressive features to assist medical officers in the diagnosis of arrhythmia diseases. Basic statistical components namely mean, energy, standard deviation, mean absolute deviation, fractal, inter-quartile range and min/max value, were calculated. Alongside with statistical features, 10th order auto-regressive model parameters are used as input features to support vector machine (SVM). All features are calculated using an electrocardiogram (ECG) signals windowed into per beat manner. The proposed algorithm is able to classify normal ECG beat and five types of abnormal ECG beat; paced beat, right & left bundle branch block beat, premature ventricular contraction beat and aberrated atrial premature beat. By using SVM with quadratic and cubic kernel function, the proposed algorithm achieved the best accuracy of 95.8%

Influence of Supervisory Style on Supervision Outcomes among Undergraduate Trainee Counsellors

The research aimed to identify the influence of supervisory styles on supervision outcome among undergraduate trainee counsellors. This research was quantitative with correlational research design in order to identify the influence and relationship between independent and dependent variables among trainee counsellors. 100 respondents from two (2) public universities in Malaysia were recruited. Stratified random sampling technique was utilized to select the respondent and proportional stratification was used to determine the sample size of each stratum. Supervisory Styles Inventory (SSI) and Supervisory Satisfaction Questionnaire (SSQ) was the instrument used in this study. The System Approach to Supervision (SAS) Model become underlying theory in this research. The results of the study were analysed by using Pearson’s product-moment Coefficients and Multiple Regression. Based on the findings, the supervisory styles showed that there was significant relationship with supervision outcome (r= 0.49, p< 0.05). Three of supervisory styles which were attractive (r= 0.48, p< 0.05), interpersonally sensitive (r= 0.48, p< 0.05) and task-oriented (r= 0.42, p< 0.05). The supervisory styles also showed there was significant influence with supervision outcome. Among the three supervisory styles, attractive and interpersonally sensitive was the most influence on supervision outcome (R2=0.23, Adjusted R2=0.22, F(1, 98) = 29.05, p<0.05). The findings of this study perhaps could expand knowledge and understanding on the individual differences to supervision field. Supervisors could examine and reflect upon their styles based on theoretical framework provided and can restructure the styles. Lastly, it also can improve the quality and effectiveness of supervision for both supervisor and supervisee

Segmentation of Prostate in MRI Images Using Depth Separable Convolution Operations

International audienc

Drowsiness Detection Using Ocular Indices from EEG Signal

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models

Simulation of Electromagnetic Generator as Biomechanical Energy Harvester

Portable electronic devices are dependent on batteries as the ultimate source of power. Irrefutably, batteries only have a limited operating period as they need to be regularly replaced or recharged. In many situations, the power grid infrastructure is not easily accessible to recharge the batteries and the recharging duration is also not convenient for the user to wait. Enhancement of a reliable electronic system by preventing power interruptions in remote areas is essential. Similarly, modern medical instruments and implant devices need reliable, almost maintenance-free power to ensure they are able to operate in all situations without any power interruptions. In this paper, the small-sized electromagnetic generator was designed to produce higher power by utilizing the knee angle transition involved during the walking phase as the input rotary force. The proposed generator design was investigated through COMSOL Multiphysics simulation. The achieved output RMS power was in the range of 3.31 W to 14.95 W based on the RPM range between 360 RPM to 800 RPM

Simulation of Electromagnetic Generator as Biomechanical Energy Harvester

Portable electronic devices are dependent on batteries as the ultimate source of power. Irrefutably, batteries only have a limited operating period as they need to be regularly replaced or recharged. In many situations, the power grid infrastructure is not easily accessible to recharge the batteries and the recharging duration is also not convenient for the user to wait. Enhancement of a reliable electronic system by preventing power interruptions in remote areas is essential. Similarly, modern medical instruments and implant devices need reliable, almost maintenance-free power to ensure they are able to operate in all situations without any power interruptions. In this paper, the small-sized electromagnetic generator was designed to produce higher power by utilizing the knee angle transition involved during the walking phase as the input rotary force. The proposed generator design was investigated through COMSOL Multiphysics simulation. The achieved output RMS power was in the range of 3.31 W to 14.95 W based on the RPM range between 360 RPM to 800 RPM

Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer&rsquo;s Disease

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain&rsquo;s neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer&rsquo;s patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 &times; 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer&rsquo;s vs. normal controls. The nonfractal-based approach provides a good representation of the brain&rsquo;s neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively