Challenges with Machine Learning for Microwave Breast Tumor detection

In this paper, challenges of combining machine learning techniques with near-field microwave probes for breast tumor detectionis presented. The concept of using microwaves imaging (MI) modality for breast tumors detection is based on the electrical propertiescontrast between normal and tumors breast tissues. MI utilizedmicrowave signals to illuminate the breast tissues using near fieldprobes placed at different locations surrounding the breast. Thebackscattered microwaves signals are then received by the sameprobes. Diagnosis breast tumor is done by estimating the variations in the response of the reflection coefficient of the probe. Machine learning techniques are applied to accentuate the variancein the sensor’s responses for both healthy and tumorous cases.The main challenge of using the machine learning technique withnear-field microwave probes for breast tumor detection is to find asuitable combination of features and classifiers which discriminatesbetween the normal and abnormal breast

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM

Non-invasive Glucose Monitoring using Microwave Sensor with Machine Learning

Diabetes is a chronic condition that occurs when the levels of glucose are high in the blood because the body cannot produce any or enough insulin or use insulin effectively. According to the International Diabetes Federation (IDF), 537 million people are currently living with diabetes. It is very important for people with diabetes to regularly check their blood glucose levels to keep track of any increase or decrease in these levels, and adjust the amount of medication accordingly. This process is called Continuous Glucose Monitoring (CGM). CGM techniques can be categorized based on invasiveness as invasive, minimally invasive, and non-invasive. In the non-invasive there is no need for any blood sample extraction or any implantation of electrodes in the body. First, a review of the non-invasive CGM techniques in the last ten years is conducted in order to understand the current status of the CGM and highlight the challenges that face every technique in order to come up with a better solution. The techniques used for non-invasive CGM can be classified into six major categories: optical, microwave, thermal, transdermal, hybrid and other. In order to overcome the shortcomings of the invasive and minimally-invasive methods of CGM, such as pain, discomfort, and risk of infection, non-invasive CGM is needed. However, due to the multiple challenges such as accuracy, usability and applicability, contemporary non-invasive glucose monitors are still not sufficiently reliable. In this thesis, a non-invasive glucose monitoring system is developed using microwave sensor with machine learning techniques. The system has two parts: hardware, which is the microwave sensor, and software, which is the machine learning algorithms. The physical sensor is microwaves-based using inexpensive printed circuit board technology. Electrically-small dipole and another spiral microwave sensor were designed and used in this thesis taking into account different factors like frequency range, penetration and safety of the human. Machine learning techniques were used to select the most distinguish features in order to predict the actual glucose level in the human. Different feature engineering types were used to extract the discriminate features that will be inputted to different regression algorithms to predict the glucose levels. The main idea of the thesis is based on studying dielectric properties (permittivity and conductivity) of the human body tissues in order to find a relation with the corresponding glucose level in those tissues. This is done using CST simulation along with experiments. Experimental results on aqueous solutions (water-glucose solutions) used as a proof of concept and to check the ability of the microwave sensors to detect the different concentrations of these simple water- glucose solutions. In simulation, a hand model system was designed with different tissues/layers to simulate the effects of the microwave sensor with respect to changing in dielectric properties (permittivity and conductivity) of those tissues/layers. Different systems (corresponding to different hand layers/tissues) were trained and tested using cross validation, and the Root Mean Square Error (RMSE) were acceptable

Single trial EEG patterns for the prediction of individual differences in fluid intelligence

Assessing a person’s intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual’s fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.</p

Risk Factors Of Preterm Birth of Neonates Attended Al- Mukalla Maternity and Childhood Hospital ,Yemen

The preterm birth continues to be the leading cause of prenatal morbidity and mortality. Neonates born preterm are known to have a certain added risk of death, disease, and disability.The aim of the present study is to identify risk factors associated with preterm birthof neonatesinAl-Mukalla Maternity and Childhood Hospital (MCH).Retrospectivecase-control study at a ratio of 1 1 was conducted in the neonatal unit of Pediatric Ward from October 2012 to October 2013, cases and controls data were collected from medical records.A total of 104 cases and 104 controls were included in the study. The results showed severalrisk factorssignificantly associated with preterm birth of neonateswhich are:bad obstetric history (BOH) ,with p value 0 .014, present maternal diseases including hypertension, pre or/and - eclampsia, urinary tract infection and genital infection(p value= 0.003, 0.002, 0.045, 0.002 respectively) as well aspresentof twins and antipartum hemorrhage (p value= 0 .000, 0.028respectively).We Concluded that the most common risk factors of preterm birth of neonate were BOH, Maternal diseases in current pregnancy as well as present of twins and antipartum hemorrhage. It is necessary to improve prenatal care for pregnant women which may decrease the potential of preterm birth of neonates

Single trial EEG patterns for the prediction of individual differences in fluid intelligence

Assessing a person’s intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual’s fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.</p

Mortality and Morbidity Among Preterm Neonates Admitted To Al-Mukalla Maternity and Childhood Hospital, Yemen

Globally, preterm birth is a major clinical problem associated with significant mortality and morbidity in the perinatal, neonatal, and childhood periods and even in adulthood. The study aimed to evaluate the mortality and morbidity of preterm neonates admitted to Maternity and Childhood Hospital (MCH) in Al-Mukalla. A retrospective case-control study at a ratio of 1 1 was conducted in the neonatal unit of pediatric ward from October 2012 to October 2013. The study included records of 104 preterm neonates as cases and equal numbers of full term neonates as control. The results showed high mortality rate (22.6%) among the neonates. The main causes of death among cases were respiratory disorders 62.5% and Sepsis 35% while in controls were birth asphyxia 42.9% and congenital anomalies 42.9%. There were statistically significant association between neonatal mortality rate and body weight and gestational age of the neonate (p value = 0 .000 and 0.001 respectively). Regarding morbidity, there were statistically significant association between cases and controls in the following complications: respiratory distress, apnea, feeding problems, jaundice (p value = 0.016, 0.000, 0.014, 0.020 respectively) As well as (hypoglycemia, sepsis, gastrointestinal bleeding and Birth asphyxia ( (p value = 0.006, 0.000, 0.010 and 0.006 respectively). The mortality rate was high among preterm infants. We concluded that urgent improvement is needed in prenatal and neonatal care to reduce death and complications