EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach.

Objective: Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales.

Polymethyl Methacrylate-Based Smart Microfluidic Point-of-Care Testing of Prothrombin Time and International Normalized Ratio through Optical Detection.

The mechanical heart valve is a crucial solution for many patients. However, it cannot function on the state of blood as human tissue valves. Thus, people with mechanical valves are put under anticoagulant therapy.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.

Background: Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques.

The inputs to the outputs of nonlinear systems can be modeled using machine and deep learning approaches, among which artificial neural networks (ANNs) are a promising option. However, noisy signals affect ANN modeling negatively; hence, it is important to investigate these signals prior to the modeling. Herein, two customized and simple approaches, visual inspection and absolute correlation, are proposed to examine the relationship between the inputs and outputs of a nonlinear system.

Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals.

Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed.

Microfluidics-based device for the measurement of blood viscosity and its modeling based on shear rate, temperature, and heparin concentration.

Blood viscosity measurements are crucial for the diagnosis and understanding of a range of hematological and cardiovascular diseases. Such measurements are heavily used in monitoring patients during and after surgeries, which necessitates the development of a highly accurate viscometer that uses a minimal amount of blood. In this work, we have designed and implemented a microfluidic device that was used to measure fluid viscosity with a high accuracy using less than 10 μl of blood.

Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG.

Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims  to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system.

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

Background: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to distinguish only children with ASD from those normally developed without approaching their autistic features severity.

Objectives: Identifying potential differences between children with mild and sever ASD based on EEG analysis using empirical mode decomposition (EMD) and second-order difference plot (SODP) models, and determining the accuracy of such model outcome measures to distinguish ASD severity levels.

Methods: Resting-state EEG data recorded for 36 children, who divided equally into two matched groups of mild and sever ASD.

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise.

Anodic oxidation of slaughterhouse wastewater on boron-doped diamond: process variables effect.

A non-sacrificial boron-doped diamond electrode was prepared in the laboratory and used as a novel anode for electrochemical oxidation of poultry slaughterhouse wastewater. This wastewater poses environmental threats as it is characterized by a high content of recalcitrant organics. The influence of several process variables, applied current density, initial pH, supporting electrolyte nature, and concentration of electrocoagulant, on chemical oxygen demand (COD) removal, color removal, and turbidity removal was investigated.

Improvement of medical content in the curriculum of biomedical engineering based on assessment of students outcomes.

Background: Improvement of medical content in Biomedical Engineering curricula based on a qualitative assessment process or on a comparison with another high-standard program has been approached by a number of studies. However, the quantitative assessment tools have not been emphasized. The quantitative assessment tools can be more accurate and robust in cases of challenging multidisciplinary fields like that of Biomedical Engineering which includes biomedicine elements mixed with technology aspects.

Classification of Normal, Ictal and Inter-ictal EEG via Direct Quadrature and Random Forest Tree.

This paper presents an accurate nonlinear classification method that can help physicians diagnose seizure in electroencephalographic (EEG) signal characterized by a disturbance in temporal and spectral content. This is accomplished by applying four steps. First, different EEG signals containing healthy, ictal and seizure-free (inter-ictal) activities are decomposed by empirical mode decomposition method.

An alternative respiratory sounds classification system utilizing artificial neural networks.

Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills.

Methods: This work revolves around exploiting autocorrelation in the feature extraction stage.

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

Background: Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.

Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition.

The purpose of this study is to investigate the potential of the ensemble empirical mode decomposition (EEMD) to extract cardiogenic oscillations from inductive plethysmography signals in order to measure cardiac stroke volume. First, a simple cardio-respiratory model is used to simulate cardiac, respiratory, and cardio-respiratory signals. Second, application of empirical mode decomposition (EMD) to simulated cardio-respiratory signals demonstrates that the mode mixing phenomenon affects the extraction performance and hence also the cardiac stroke volume measurement.

A model of mechanical interactions between heart and lungs.

To study the mechanical interactions between heart, lungs and thorax, we propose a mathematical model combining a ventilatory neuromuscular model and a model of the cardiovascular system, as described by Smith et al. (Smith, Chase, Nokes, Shaw & Wake 2004 Med. Eng.

SAPHIR - a multi-scale, multi-resolution modeling environment targeting blood pressure regulation and fluid homeostasis.

We present progress on a comprehensive, modular, interactive modeling environment centered on overall regulation of blood pressure and body fluid homeostasis. We call the project SAPHIR, for "a Systems Approach for PHysiological Integration of Renal, cardiac, and respiratory functions". The project uses state-of-the-art multi-scale simulation methods.

Stroke volume estimation by thoracocardiography is better when glottis is closed.

Thoracocardiography approach pretends to non-invasively monitor stroke volume by inductive plethysmographic recording of ventricular volume curves by a transducer placed on the chest. The purpose of this study was to investigate the potential of thoracocardiography to estimate stroke volumes while apnea with open glottis. We hypothesized that, when glottis is open, stroke volumes would be better estimated if airways flow curves were taken into account.