Epilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.
In this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.
Medical laboratory accreditation becomes a trend to be trustable for diagnosis of diseases. It is always performed at regular intervals to assure competence of quality management systems (QMS) based on pre-defined standards. However, few attempts were carried out to assess the quality level of medical laboratory services. Moreover, there is no realistic study that classifies and makes analyses of laboratory performance based on a computational model. The purpose of this study was to develop an integrated system for medical laboratory accreditation that assesses QMS against ISO 15189. In addition, a deep analysis of factors that sustain accreditation was presented. The system started with establishing a core matrix that maps QMS elements with ISO 15189 clauses. Through this map, a questionnaire was developed to measure the performance. Therefore, score indices were calculated for the QMS. A fuzzy logic model was designed based on the calculated scores to classify medical laboratories according to their tendency for accreditation. Further, in case of failure of accreditation, cause-and-effect root analysis was done to realize the causes. Finally, cloud computing principles were employed to launch a web application in order to facilitate user interface with the proposed system. In verification, the system has been tested using a dataset of 12 medical laboratories in Egypt. Results have proved system robustness and consistency. Thus, the system is considered as a self-assessment tool that demonstrates points of weakness and strength.
The aim of this study was to develop and compare techniques to increase the prediction accuracy of patient mortality and organ dysfunction in the Intensive Care Units (hereinafter ICU) of hospitals. Patient mortality was estimated with two models of artificial neural network (ANN)-backpropagation (BP) and simplified acute physiology score (SAPS). Organ dysfunction was predicted by coupled ANN self-organizing map (SOM) and logistic organ dysfunction score (LODS) method on the basis of patient conditions. Input dataset consisted of 36 features recorded for 4,000 patients in the ICU. An integrated response surface methodology (RSM) and genetic algorithm (GA) was developed to achieve the best topology of the ANN-BP model. Although mortality prediction of the best ANN-BP (MSE = 0.0036, AUC = 0.83, R2 = 0.81) was more accurate than that of the SAPS score model (MSE = 0.0056, AUC = 0.82, R2 = 0.78), the execution time of the former (=45 min) was longer than that of the latter (=20 min). Therefore, the principal component analysis (PCA) was used to reduce the input feature dimensions, which, in turn, reduced the execution time up to 50%. Data reduction also helped to increase the network accuracy up to 90%. The likelihood of organ dysfunction determined by coupled ANN and scoring method technique can be much more efficient than the LODS model alone because the SOM could successfully classify the patients in 64 classes. The primary patient classification plays a major role in increasing the efficiency of an estimator.
The electroencephalogram (EEG) induced by steady-state visual evoked potential (SSVEP) will contain background noise. Most existing research on this problem uses signal-processing methods to enhance the EEG. The purpose of this paper is to explore another method that can be used to enhance the EEG. We creatively combined motion stimuli with light-flashing stimuli and designed a paradigm in which motion and light-flashing simultaneously will stimulate with the same frequency; this is called multi-source co-frequency stimulus. To avoid the direct stimulus of light-flashing in the human eye and ensure that the composite paradigm provided adequate comfort, the light-flashing pattern was presented in a ring form and the motion stimulus was presented in the center of that ring. Our hypothesis is that when the motion and the light-flashing are simultaneously stimulated with the same frequency, the EEG they induce will be superimposed in some way, and this will enhance the EEG. The multi-source co-frequency stimulus was found to achieve a higher signal-to-noise ratio (SNR), better accuracy, and a higher information transmission rate (ITR) than single stimulus. The experimental results showed that it is feasible to use the method proposed in this study to enhance the EEG.
Tools for the numerical prediction of haemodynamics in multi-disciplinary integrated heart simulations have to be based on computational models that can be solved with low computational effort and still provide physiological flow characteristics. In this context the mitral valve model is important since it strongly influences the flow kinematics, especially during the diastolic phase. In contrast to a 3D valve, a vastly simplified valve model in form of a simple diode is known to be unable to reproduce the characteristic vortex formation and unable to promote a proper ventricular washout. In the present study, an adaptation of the widely used simplest modelling approach for the mitral valve is employed and compared to a physiologically inspired 3D valve within the same ventricular geometry. The adapted approach shows enhanced vortex formation and an improved ventricular washout in comparison to the diode type model. It further shows a high potential in reproducing the main flow characteristics and related particle residence times generated by a 3D valve.
The use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors’ utilization; (3) foot pose and trajectory estimation methods. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors’ utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.
The goal of this in vitro study was to determine the insertion torque/time integral for three implant systems. Bone level implants (n = 10; BLT – Straumann Bone Level Tapered 4.1 mm × 12 mm, V3 – MIS V3 3.9 mm × 11.5 mm, ASTRA – Dentsply-Sirona ASTRA TX 4.0 mm × 13 mm) were placed in polyurethane foam material consisting of a trabecular and a cortical layer applying protocols for medium quality bone. Besides measuring maximum insertion torque and primary implant stability using resonance frequency analysis (RFA), torque time curves recorded during insertion were used for calculating insertion torque/time integrals. Statistical analysis was based on ANOVA, Tukey’s honest differences test and Pearson product moment correlation (α = 0.05). Significantly greater mean maximum insertion torque (59.9 ± 4.94 Ncm) and mean maximum insertion torque/time integral (961.64 ± 54.07 Ncm∗s) were recorded for BLT implants (p < 0.01). V3 showed significantly higher mean maximum insertion torque as compared to ASTRA (p < 0.01), but significantly lower insertion torque/time integral (p < 0.01). Primary implant stability did not differ significantly among groups. Only a single weak (r = 0.61) but significant correlation could be established between maximum insertion torque and insertion torque/time integral (p < 0.01) when all data from all three implant groups were pooled. Implant design (length, thread pitch) seems to affect insertion torque/time integral more than maximum insertion torque.
Spasticity is one of the major problems that arise in different neurological diseases and seriously affect the quality of human life. Research on the understanding of mechanism of spasticity remains as important as the studies on the spasticity therapy and rehabilitation. In this study, the spasticity mechanism which develops concerning the upper motor neuron lesions is investigated by modelling “Patella tendon reflex triggered patella pendulum”. The mathematical model based on the pendulum phenomenon is developed by solving the curve-fitting problem as finding the curve that best fits a set of data points. Electrophysiological and dynamic measurement data were taken from 76 spastic subjects and 20 healthy participants. The mathematical model is determined by the morphological properties of the goniometric variations. The results denote that the mathematical model containing two clinically relevant parameters –frequency component of the damped oscillatory motion defined as “f0” with the maximum angle of the reflex defined as “a0” ensures to distinguish spasticity from healthy subjects.
The phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.
We analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.
In response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.
The VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.