hamid reza marateb

Diabetes mellitus (DM) is a chronic disease that affects public health. The prediction of blood glucose concentration (BGC) is essential to improve the therapy of type 1 DM (T1DM).

Having considered the risk of hyper‑ and hypo‑glycemia, we provide a new hybrid modeling approach for BGC prediction based on a dynamic wavelet neural network (WNN) model, including a heuristic input selection. The proposed models include a hybrid dynamic WNN (HDWNN) and a hybrid dynamic fuzzy WNN (HDFWNN). These wavelet‑based networks are designed based on dominant wavelets selected by the genetic algorithm‑orthogonal least square method. Furthermore, the HDFWNN model structure is improved using fuzzy rule induction, an important innovation in the fuzzy wavelet modeling. The proposed networks are tested on real data from 12 T1DM patients and also simulated data from 33 virtual patients with an UVa/Padova simulator, an approved simulator by the US Food and Drug Administration.

A comparison study is performed in terms of new glucose‑based assessment metrics, such as gFIT, glucose‑weighted form of ESODn (gESODn), and glucose‑weighted R2 (gR2). For real patients’ data, the values of the mentioned indices are accomplished as gFIT = 0.97 ± 0.01, gESODn = 1.18 ± 0.38, and gR2 = 0.88 ± 0.07. HDFWNN, HDWNN and jump NN method showed the prediction error (root mean square error [RMSE]) of 11.23 ± 2.77 mg/dl, 10.79 ± 3.86 mg/dl and 16.45 ± 4.33 mg/dl, respectively.

Furthermore, the generalized estimating equation and post hoc tests show that proposed models perform better compared with other proposed methods

Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications. In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes. It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set. Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.

In this study, we aimed to determine comprehensive maternal characteristics associated with birth weight using Bayesian modeling.
A total of 526 participants were included in this prospective study. Nutritional status, supplement consumption during the pregnancy, demographic and socioeconomic characteristics, anthropometric measures, physical activity, and pregnancy outcomes were considered as e?ective variables on the birth weight. Bayesian approach of complex statistical models using Markov chain Monte Carlo approach was used for modeling the data considering the real distribution of the response variable.
Tere was strong positive correlation between infant birth weight and the maternal intake of Vitamin C, folic acid, Vitamin B3, Vitamin A, selenium, calcium, iron, phosphorus, potassium, magnesium as micronutrients, and fber and protein as macronutrients based on the 95% high posterior density regions for parameters in the Bayesian model. None of the maternal characteristics had statistical association with birth weight.
Higher maternal macro? and micro?nutrient intake during pregnancy was associated with a lower risk of delivering low birth weight infants. Tese fndings support recommendations to expand intake of nutrients during pregnancy to high level.

To investigate the associations of genetic polymorphism with high-density lipoprotein-cholesterol (HDL-C) levels in Iranian adolescents.
This multicentre study was conducted on 10 - 18 year-old students from 27 provinces in Iran. Logic regression approach was used to determine the main effects and interactions of polymorphisms related to HDL-C levels.
The rs708272 polymorphism was significantly related to HDL-C levels. Moreover, rs708272 increased HDL-C levels and had a protective effect on HDL-C. The interaction of rs2230808 and rs5880 polymorphisms as well as the interaction of rs320 and rs708272 polymorphisms were associated with lower HDL-C levels. Furthermore, the interaction of rs320 and rs1801177 polymorphisms was associated with lower HDL-C levels.
We found that not only single SNPs, but also interactions of several SNPs affect HDL-C levels. Given the high prevalence of low HDL-C in Middle Eastern populations, further genetic studies are required for detailed analysis.

This paper is a meta‑analysis of the published data from in vitro studies to evaluate whether spontaneous apoptosis might be influenced by extremely low frequency (ELF) magnetic fields (MFs).

A comprehensive scientific literature search in electronic databases was conducted and studies covering the period 2000–2010 were selected. Then, published studies involving the desired topic were retrieved. The inclusion criteria were percentage of apoptosis in the cells exposed to 50–60 Hz ELF‑MFs. The statistical analysis was performed by comprehensive meta‑analysis version 2.

The summary measure of association (95% confidence interval) for all 18 effect estimated from 8 studies was 1.18 (1.15, 1.20). Heterogeneity among studies was found. There was no evidence of publication bias for the association between exposure to MF and apoptosis risk.

Our meta‑analysis provided conclusive data that ELF‑MFs can increase apoptosis in cancer and normal cells. Furthermore, there is a possibly individual intensity and time range with maximum created effect according to window effect.

Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal. Neurofeedback usually provides the audio and or video feedback. Positive or negative feedback is produced for desirable or undesirable brain activities, respectively. In this review, we provided clinical and technical information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha, beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages (unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential, functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback i.e. treatment of attention deficit hyperactivity disorder, anxiety, depression, epilepsy, insomnia, drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum disorders and so on as well as other applications such as pain management, and the improvement of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies have been conducted on the neurofeedback therapy and its effectiveness on the treatment ofmany diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it is a non-invasive procedure, its validity has been questioned in terms of conclusive scientific evidence. For example, it is expensive, time-consuming and its benefits are not long-lasting. Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is known as a complementary and alternative treatment of many brain dysfunctions. However, current research does not support conclusive results about its efficacy.

Nowadays, computer-aided diagnosis systems are widely used in medicine. These systems could assist medical doctors in early correct diagnosis of diseases. The performance of such systems must be correctly assessed. In this paper, the performance criteria of these diagnosis systems are taken into account.
The diagnosis error of such systems was estimated based on the gold standard data using measures such as Sensitivity, Specificity, Accuracy, Precision, Area Under curve ROC (receiver operating characteristic), F-measure, Matthews correlation coefficient , etc. The advantages and disadvantages of those criteria were also discussed. Then, the performance of two Coronary Artery Disease diagnosis systems was assessed. The statistically significant superior method was identified using the McNemar’s test.
Findings: Since the analyzed dataset was balanced, the overall performance of the diagnosis methods was assessed using the accuracy measure. The accuracy of the methods was 84% and 86%, respectively. The entire systems were not reliable since Type I error (Alpha) was not less than 0.05. However, the second system had acceptable statistical power (>80%). The diagnosis performance of those systems was “very good” (0.8 The performance of the diagnosis systems must be assessed using the proper methods and criteria. Using different suitable performance measures, it is possible to assess the diagnosis performance of such systems in details.

Walking is one of the most widely used movements affecting life quality. Therefore, the study of factors affecting human gait has always been an important issue. Walking speed, as a physical perturbation, affects the quality of human walking. The purpose of this study is to estimate the effects of walking speed on the short-time gait parameters. Thirty-two healthy subjects (mean SD, age: 27.56 ± 20.4 years; body height: 158.19 ± 20.83 cm; body weight: 54.89 ± 20.59 kg; gender: 59% female) participated in this study. Kinetic, kinematic and electromyographic data were recorded at the following five walking speed categories: very slow, slow, medium, fast and very fast. The effect of speed on spatio-temporal parameters, muscle synergy space, walking smoothness, representation of joints displacement and the correlation between lower limb displacement and also correlation between muscles activation patterns were studied. Having being used physical perturbation, 46 predictors were extracted from one gait cycle information, some of which were proposed for the first time in the literature for example size of muscle synergy, minimum angular jerk, lower limb contributions and skewness, kurtosis and curvature of joints movements . Using muscle synergies showed that increasing walking speed leads to increase the size of synergy space. It could be concluded that central nervous system tries to adopt more organaized strategy for recruiting muscles and remaining stable at fast speeds. Our results showed that, speed plays a crucial role in human gait characteristic. We can investigate our methods among more subjects and also patients with gait disorders. We can evaluate other indices like gait stability based on short-term data recording.

In current years, the application of biopotential signals has received a lot of attention in literature. One of these signals is an electromyogram (EMG) generated by active muscles. Surface EMG (sEMG) signal is recorded over the skin, as the representative of the muscle activity. Since its amplitude can be as low as 50 µV, it is sensitive to undesirable noise signals such as power‑line interferences. This study aims at designing a battery‑powered portable four channel sEMG signal acquisition system. The performance of the proposed system was assessed in terms of the input voltage and current noise, noise distribution, and synchronization and input noise level among different channels. The results indicated that the designed system had several inbuilt operational merits such as low referred to input noise (lower than 0.56 µV between 8 Hz and 1000 Hz), considerable elimination of power‑line interference and satisfactory recorded signal quality in terms of signal‑to‑noise ratio. The muscle conduction velocity was also estimated using the proposed system on the brachial biceps muscle during isometric contraction. The estimated values were in then normal ranges. In addition, the system included a modular configuration to increase the number of recording channels up to 96.

Birth weight is probably the most important factor affecting neonatal mortality and morbidity. Compared with normal weight infants, low-birth-weight (LBW) infants may be more at risk for many health problems. The prediction of low birth weight is important as it may cause mental and physical health problems in childhood and adulthood. We assessed a computer-aided diagnosis system to classify infants to low or normal birth weight categories. In the present study, the association between the low birth weight and the intake of about 40 types of macro- and micronutrients during the first (1st Tr), second (2nd Tr.) and third (3rd Tr.) trimesters was assessed based on demographic and reproductive characteristics, physical activity and nutrients intake in pregnant women. The dataset used in this study contained 526 pregnant women with 95 input features. The used classifiers were k-Nearest Neighbors (kNN), Probabilistic Neural Network (PNN), and two Adaptive Neuro-Fuzzy Classifiers (ANFC-SCG: Scaled Conjugate Gradient, ANFC-LHs: Linguistic Hedges). Also, sequential feature selection (FS) was applied on the low birth weight risk factors to reduce the feature space. The accuracy of the classifiers kNN, PNN, ANFC-SCG and ANFC-LHs were 48%, 50%, 50% and 50% without feature selection and 93%, 83%, 80% and 83% with feature selection, respectively. Among the tested classifiers, the statistical power and type I error (α) of the best configuration (FS-kNN; k = 3) were 96% and 0.10 in the Leave-One-Out validation framework, showing that the proposed diagnosis system is clinically reliable. Also, using Leave-One-Out cross-validation, the guarding against Type III error was granted.

Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (?) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were “age and ST/heart rate slope categories,” “exercise-induced angina status,” fluoroscopy, and thallium-201 stress scintigraphy results. The proposed method showed “substantial agreement” with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.

Obesity represents one of the most important nutritional problems worldwide.Obesity in childhood can cause variety of health issues such as orthopedic, neurological, pulmonary and gastroenterological disorders in the future, although no side effects were reported from malignant obesity during childhood. In this paper, we presented a computer-aided diagnosis system to predict the obesity based on input features obtained from the life style and other factors of the subjects. The total number of 9795 subjects (49.17% boy) aged 6 to 18 years taken from the CASPIAN IV study participated in this study. The input parameters of the proposed system were taken from the dietary habit, physical activity, family history, social economic status, and other features. Then, the obesity was predicted using the data mining and artificial intelligence techniques. Feature Selection (FS) methods were also used to improve the performance of the proposed system. The performance of the diagnosis system was assessed based on the hold-out validation framework. The performance of the classifications method has been validated by hold-out cross validation. Among the different classification techniques tested, SVM with FS showed the best performance. The accuracy and precision of this method were 63.3% and 83.7%, respectively. Some features such as age, physical activities, birth feeding and family history of diabetes mellitus detected as the most effectivefactors with obesity in both gender. Designing of an intelligentdiagnosis system with the input parameter such as life-style, socioeconomic status and genetic information can help predict obesity in children to modify their life-style to improve theirqualityof life in the future. A web-based version of this intelligent system can easily provide the obesity prediction facilities for the families at home.

A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet‑based speech coding strategy for cochlear implants, which gives a novel speech processor. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down‑sample the output at each level. The speech data used for the current study consists of 30 consonants, sampled at 16 ksps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short‑time objective intelligibility (STOI) measure and segmental signal‑to‑noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter‑bank. The statistical analysis revealed that the UWT‑based N‑of‑M strategy significantly improved the MOS, STOI and segmental SNR (P < 0.001) compared with what obtained with the IIR filter‑bank based strategies. The advantage of UWPT is that it is shift‑invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter‑bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter‑banks

The purpose of this study was to estimate the torque from high‑density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate‑to‑high isometric elbow flexion‑extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro‑fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro‑fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black‑box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG‑Torque modeling in clinical applications.

selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinalscale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.