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OCCUPATIONAL APPLICATIONSErgonomic risk assessment is a key step in managing work-related musculoskeletal disorders. Diverse assessment methods exist, and errors may occur if inappropriate methods are selected. Understanding the level of knowledge, how to use methods, and exploring factors affecting erroneous usage of these methods, can provide useful information for health and safety regulatory authorities and decision-makers to identify problems and determine an action plan to eliminate them. We found that Iranian occupational health specialists have little knowledge about the types of pen-and-paper observational methods (OMs), and most of them use a limited number of these methods. Content analysis of interviews identified three main categories of influential factors and 12 subcategories. The main categories were educational, individual, and organizational factors. These results suggest the need for more effort to ensure that practitioners possess better knowledge and skills in the selection and application of pen-and-paper OMs.Background Ergonomic risk assessment is a key step in managing ergonomics in the workplace. Errors in selecting risk assessment methods may occur during risk assessment and could invalidate the reliability of the results obtained.Purpose The aims of the present study were to investigate Iranian occupational health specialists’ knowledge and application of pen-and-paper observational methods (OMs) for ergonomics assessment and to determine their influential factors.Methods We used a mixed-method design in two phases, combining quantitative and qualitative approaches. Quantitative data were collected from 237 occupational health specialists who were included in the study through purposive sampling of experts. In the qualitative phase, in-depth semi-structured interviews were conducted with participants from the first phase, and the factors associated with the selection and application of the pen-and-paper OMs were examined over a 6-month period.Results Participants were familiar with 13 out of 34 methods studied, and they had performed risk assessments with only nine of them. The most frequently used methods were RULA (95.4%), REBA (84.8%), QEC (65.4%), and ROSA (54.9%). Content analysis of interviews identified three main categories of influential factors and 12 subcategories. The main categories included educational, individual, and organizational factors.Conclusion Iranian occupational health specialists were found to have relatively limited knowledge about pen-and-paper OMs, and most of them use a limited number of these methods. These results suggest the need for more effort to ensure that practitioners possess better knowledge and skills in the selection and application of these methods. Creating stricter regulations regarding the qualifications of practitioners, modifying academic curriculum content, continuously monitoring the performance of practitioners, and holding retraining workshops in a purposeful manner could help minimize errors in selecting and applying pen-and-paper OMs.

Objectives. This study aimed to determine the types and frequency of pen-paper observational methods (OMs) used by Iranian practitioners and to identify their errors in selecting and implementing these methods. Methods. This was a systematic review and analytical study of papers in which the OMs had been used. Scientific databases were analyzed from September 1970 to September 2018. Errors were determined based on a list of wrong practices both in the selection and implementation of methods. Three ergonomists carried out the process of identifying errors independently. Results. The most frequently used methods were rapid upper limb assessment (RULA), quick exposure check (QEC) and rapid entire body assessment (REBA), respectively. Errors in selecting and implementing pen-paper OMs were 53.3 and 36.4%, respectively. Conclusions. Despite the abundant number of pen-paper OMs, Iranian practitioners use few of them. The high rate of errors can indicate a lack of knowledge and skills among practitioners for selecting and implementing OMs. The development of decision-making tools may help practitioners to select appropriate pen-paper OMs for assessing different types of tasks.

The COVID-19 pandemic has affected people globally; nowadays several countries are facing a major change in daily life due to universal quarantining, closed schools, social isolation, and shelter-in-place orders. In addition, this pandemic caused an economic crisis. International Labour Organization (ILO) reported that not only COVID-19 is a serious threat to public health but also the economic and social disruption of this crisis threatens the long-term livelihoods and wellbeing of millions of people. In this short paper, we introduce our hypothesis on using artificial intelligence (AI) technology to predict which employees are most vulnerable to the infections caused by the novel coronavirus (SARS-CoV-2). Such a system can be used in a wide variety of work places such as libraries, banks, drugstores, and hotel receptions to reduce the risk of severe infections in employees as well as to ensure the safety of labor force and the sustainability of businesses and jobs.

Purpose: To validate the output of a machine learning-based software as an intelligible interface for predicting multiple outcomes after percutaneous nephrolithotomy (PCNL). We compared the perform...

A significant error that may occur during ergonomic risk assessment and invalidate assessment reliability corresponds to technique selection. This study aimed to develop a new tool called the Decision Aid Tool (DAT) to reduce pen-paper observational technique selection errors.This quasi-experiment before-after study was performed in three phases. In the first phase, the participants' skills in technique selection were examined by showing them 20 videos of different single-task jobs. In the second phase, the DAT was designed using pen-paper observational techniques. Finally, in the third phase, 115 occupational health specialists included in the study through purposive sampling of experts evaluated the tool's efficacy.The results of the first phase showed that 62% of participants made an error in selecting the proper technique. The mean and standard deviation scores from the first and third phases were 11.4 ± 6.59 and 39.01 ±1.89, respectively. The mean scores increased significantly after using DAT, and 97.5% of participants could correctly select task techniques.The efficacy of DAT was confirmed in a quasi-experimental before-and-after study. Using DAT increases the participants' ability to choose the correct technique. The DAT can be functional for practitioners to select the pen-paper observational techniques correctly under the purpose of assessment, the body areas, and the characteristics of the task to be assessed.

Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas-oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (

Acquisition and analyzing the nerve action potentials is a fundamental part of studying the nervous system. A major step in such analysis is spike sorting. Here, we proposed a spike sorting method using the spectral clustering (SC) technique. Various features including raw samples, first and second derivatives (FD, SD), and principal component analysis (PCA), were used to represent spikes. PCA was also used to estimate the number of clusters. A new criterion separability index (SI) was proposed to quantitively measure the difficulty of extracting spike trains from a given signal. The results support our hypothesis about the good performance of spectral clustering on neural data, especially when using raw data samples as features that reduce the computational cost and assist with fast spike sorting. The results, also showed that the proposed index could disclose spike sorting difficulty, so it could be a useful complement to the spike sorting procedure as a classifier to decide which signal may need extra human correction. Such grouping might help with reducing both cost and processing time in analyzing large datasets.

Purpose: A posture maintained for a prolonged period can be harmful to the health of office workers. This study aimed to estimate the recommended ergonomic duration for maintaining different sitting postures.Materials and Methods: Forty healthy male and female students participated in this experiment designed to measure perceived discomforts caused by the maintained common static sitting postures of office workers in a simple ergonomic set up for 4 minutes. The Borg CR10 scale was given to the participants to assess the discomfort in different body parts, before and after each experiment. Based on the mean group discomfort level of 2, the recommended holding time of each posture was estimated.Results: The recommended holding time and its discomfort score for each studied posture was tabulated. The moderate neck flexion had the highest score of discomfort among different neck postures with recommended MHT of 1.61 minutes. The lowest discomfort score was obtained for neutral neck posture (recommended MHT = 2.37 min). The moderate forward trunk inclination (30°) showed the highest discomfort score with MHT of 1.78 minutes among different trunk postures. The lowest discomfort score was obtained for the backward trunk position (back supported) with recommended MHT of 5.92 minutes. Among the most common knee postures, while working at the workstation, the lowest discomfort score was obtained for the knee flexed about 90 degrees, with the recommended MHT of 5 minutes. The lower limbs position with crossing one leg over the other (leg crossing) received the highest discomfort score (recommended MHT = 1.88 min).Conclusions: The recommended holding time in this study may help assess the risk of MSDs in office work and training proper sitting behavior to individuals involved in office tasks.

Objectives. Common ergonomic office workstations are designed for a few optimum postures. Nonetheless, sitting is a dynamic activity and the ideal sitting posture is rarely maintained in practice. Therefore, the present study aimed to investigate the sitting behavior of office workers in an actual working environment using ergonomically adjusted workstations to examine whether they promote maintaining appropriate sitting postures. Methods. Sitting behaviors (frequency of postures and position changes in different body parts) were explored among 26 office workers during a 60-min sitting duration, using the posture recording and classification method developed by Graf et al. The rapid upper limb assessment (RULA) method was also used to assess postural load. Then, the results of the RULA method were compared with the results from investigating the sitting behavior of office workers. Results. Common ergonomic workstations were effective in eliminating some awkward postures. However, some important risk factors such as holding postures with an inappropriate lumbar spine curve (86% of the observations) and maintaining a posture for a long time (for 7-12 min) were observed in the participants' sitting behaviors, while they were neglected in the RULA method. Conclusions. The common ergonomic workstations could not guarantee the users' appropriate sitting behaviors.

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival.To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection.The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity97.2%, mean specificity96.4%, and average accuracy97.1%).Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically.

Background: Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings.Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices.Results: Overall, the developed models showed excellent performance with AUC around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are “Glasgow coma scale motor response,” “pupillary reactivity,” and “age.”Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set.

Cartilage engineering has the potential to overcome clinical deficiency in joint disorders. Decellularized extracellular matrix (dECM) has great biocompatibility and bioactivity and can be considered an appropriate natural scaffold for tissue engineering applications. Both insulin-like growth factor-1 (IGF-1) and mechanical compression stimulate the production of cartilage ECM, modulate mechanical properties, and gene expression. The current investigation aimed to fabricate a high-quality moldable artificial cartilage by exposing the chondrocytes in biomimicry conditions using cartilage dECM, IGF-1, and mechanical stimulations. In this study, an ad hoc bioreactor was designed to apply dynamic mechanical stimuli (10 % strain, 1 Hz) on chondrocyte-laden cartilage dECM-constructs with/without IGF-1 supplementation for 2 weeks, 3 h/day. Our data revealed that mechanical stimulation had no adverse effect on cell viability and proliferation. However, it elevated the expression of chondrogenic markers such as collagen type II (COL2A1), aggrecan (ACAN), and proteoglycan-4 (PRG-4), and reduced the expression of matrix metalloproteinase-3 (MMP-3). Mechanical stimulation also promoted higher newly formed glycosaminoglycan (GAG) and produced more aligned fibers that can be responsible for higher Young's modulus of the engineered construct. Even though IGF-1 demonstrated some extent of improvement in developing neocartilage, it was not as effective as mechanical stimulation. Neither IGF-1 nor compression elevated the collagen type I expression. Compression and IGF-1 showed a synergistic impact on boosting the level of COL2A1 but not the other factors. In conclusion, mechanical stimulation on moldable cartilage dECM can be considered a good technique to fabricate artificial cartilage with higher functionality.

Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improving MUP template estimation using the wavelet denoising technique. Material and Methods: In this analytical study, several MUP template estimators were developed by combining conventional estimation methods and wavelet denoising techniques. A MUP template was initially estimated using conventional methods such as mean, median, median-trimmed mean, or mode. Thereafter, it was post-processed using the wavelet denoising technique. Results: Evaluation results of the studied estimators using 40 simulated EMG signals with a true template for each constituent MUP train showed that augmented wavelet- based template estimation methods are more reliable than conventional methods. However, on average, wavelet denoising was not much effective. Around 40 MUPs of a MU is sufficient to estimate its MUP template. Conclusion: Although wavelet techniques are effective in EMG signal analysis, here wavelet denoising did not practically improve MUP template estimation. Considering computational simplicity and estimation error, the two methods median and median-trimmed mean are practical estimators that can provide a good estimation of a MUP template for a MU when approximately 40 MUPs are available. Nevertheless, the baseline noise level in the MUP templates estimated using the median-trimmed mean method is slightly lower than that in the templates estimated using the median method.

We presented an artificial intelligence-based model to predict annual effective dose (AED) value of health workers. Potential factors affecting AED and the results of annual blood tests were collected from 91 radiation workers. Filter-based feature selection strategy revealed that the eight factors plate, red cell distribution width (RDW), educational degree, nonacademic course in radiation protection (hour), working hours per month, department and the number of procedures done per year and work in radiology department or not (0,1) were the most important predictors for AED. The prediction model was developed using a multilayer perceptron neural network and these prediction parameters as inputs. The model provided favorable accuracy in predicting AED value while a regression model did not. There was a strong linear relationship between the predicted AED values and the measured doses (R-value =0.89 for training samples and 0.86 for testing samples). These results are promising and show that artificial neural networks can be used to improve/facilitate dose estimation process.

Background: Scoliosis is the most common type of spinal deformity. A universal and standard method for evaluating scoliosis is Cobb angle measurement, but several studies have shown that there is intra- and inter- observer variation in measuring cobb angle manually. Objective: Develop a computer- assisted system to decrease operator-dependent errors in Cobb angle measurement. Methods: The spinal cord in the given x-ray image of the spine is highlighted using contract-stretching technique. The overall structural curvature of the spine is determined by a semi-automatic algorithm aided by the operator. Once the morphologic curve of the spine is determined, in the last step the cobb-angle is estimated by calculating the angle between two normal lines to the spinal curve at the inflection points of the curve. Results: Evaluation results of the developed algorithms using 14 radiographs of patients (4 - 40 years old) with cobb angle ranges from 34 - 82 degrees, revealed that the developed algorithm accurately estimated cobb angle. Statistical analysis showed that average angle values estimated using the developed method and that provided by experts are statistically equal. The correlation coefficient between the angle values estimated using the developed algorithm and those provided by the expert is 0.81. Conclusion: Compared with previous algorithms, the developed system is easy to use, less operator-dependent, accurate, and reliable. The obtained results are promising and show that the developed computer-based system could be used to quantify scoliosis by measuring Cobb angle. Citation: Safari A, Parsaei H, Zamani A, Pourabbas B. A Semi-Automatic Algorithm for Estimating Cobb Angle. J Biomed Phys Eng. 2019;9(3):317-326. https://doi.org/10.31661/jbpe.v9i3Jun.730.

A decision support system (DSS) was developed to predict postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL). The system can serve as a promising tool to provide counseling before an operation. The overall procedure includes data collection and prediction model development. Pre/postoperative variables of 254 patients were collected. For feature vector, we used 26 variables from three categories including patient history variables, kidney stone parameters, and laboratory data. The prediction model was developed using machine learning techniques, which includes dimensionality reduction and supervised classification. A novel method based on the combination of sequential forward selection and Fisher's discriminant analysis was developed to reduce the dimensionality of the feature space and to improve the performance of the system. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running leave-one-patient-out cross-validation approach on the dataset. The system provided favorable accuracy (94.8%) in predicting the outcome of a treatment procedure. The system also correctly estimated 85.2% of the cases that required stent placement after the removal of a stone. In predicting whether the patient might require a blood transfusion during the surgery or not, the system predicted 95.0% of the cases correctly. The results are promising and show that the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.

Background : Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs. Objective: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG. Material and Methods: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too. Results : Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error

This study aims to develop a semi-automatic system for brain tumor segmentation in 3D MR images. For a given image, noise was corrected using SUSAN algorithm first. A specific region of interest (ROI) that contains tumor was identified and then the intensity non-uniformity in ROI was corrected via the histogram normalization and intensity scaling. Each voxel in ROI was presented using 22 features and then was categorized as tumor or non-tumor by a multiple-classifier system. T1- and T2-weighted images and fluid-attenuated inversion recovery (FLAIR) were examined. The system performance in terms of Dice index (DI), sensitivity (SE) and specificity (SP) was evaluated using 150 simulated and 30 real images from the BraTS 2012 database. The results showed that the presented system with an average DI > 0.85, SE > 0.90, and SP > 0.98 for simulated data and DI > 0.80, SE > 0.84, and SP > 0.98 for real data might be used for accurate extraction of the brain tumors. Moreover, this system is 6 times faster than a similar system that processes the whole image. In comparison with two state-of-the-art tumor segmentation methods, our system improved DI (e.g., by 0.31 for low-grade tumors) and outperformed these algorithms. Considering the costs of imaging procedures, tumor identification accuracy and computation times, the proposed system that augmented general pathological information about tumors and used only 4 features of FLAIR images can be suggested as a brain tumor segmentation system for clinical applications.

Feature extraction is an important step of resolving an electromyographic (EMG) signal into its component motor unit potential trains, commonly known as EMG decomposition. Until now, different features have been used to represent motor unit potentials (MUPs) and improve decomposition processing time and accuracy, but a major limitation is that no systematic comparison of these features exists. In an EMG decomposition system, like any pattern recognition system, the features used for representing MUPs play an important role in the overall performance of the system. A cross comparison of the feature extraction methods used in EMG signal decomposition can assist in choosing the best features for representing MUPs and ultimately may improve EMG decomposition results. This paper presents a survey and cross comparison of these feature extraction methods. Decomposability index, classification accuracy of a ${k}$ -nearest neighbors classifier, and class-feature mutual information were employed for evaluating the discriminative power of various feature extraction techniques commonly used in the literature including time domain, morphological, frequency domain, and discrete wavelets. In terms of data, 45 simulated and 82 real EMG signals were used. Results showed that among time domain features, the first derivative of time samples exhibit the best separability. For morphological features, slope analysis provided the most discriminative power. Discrete Fourier transform coefficients offered the best separability among frequency domain features. However, neither morphological nor frequency domain techniques outperformed time domain features. The detail 4 coefficients in a discrete wavelets decomposition exceeded in evaluation measures when compared with other feature extraction techniques. Using principal component analysis slightly improved the results, but it is time consuming. Overall, considering computation time and discriminative ability, the first derivative of time samples might be efficient in representing MUPs in EMG decomposition and there is no need for sophisticated feature extraction methods.

Two systems are presented for segmentation of vertebrae in a 3D computed tomography (CT) image. The first method extracts seven features from each voxel and uses a multi-layer perceptron neural network (MLPNN) to classify the voxel as vertebrae or background. In the second method, the segmentation is completed in two steps: first, a newly developed adaptive pulse coupled neural network (APCNN) directly applied to a given image segments vertebrae, then the result is refined using a median filter. In the developed APCNN, the values for the user-defined parameters of the pulse coupled neural networks (PCNN) are adaptively adjusted for each image individually, instead of using one value for all images as in conventional PCNN. The performance of both systems in terms of Dice index (DI) was evaluated and compared against the state-of-the-art segmentation methods using seventeen clinical and standard CT images. Overall, both systems demonstrated statistically similar and promising performance with average DI > 95%. Compared to existing PCNN-based segmentation algorithms, the accuracy of the proposed APCNN improved by 29.3% on average. The developed APCNN-based system is more accurate than MLPNN-based system and existing PCNN-based algorithms in segmentation of vertebrae with blurred and weak boundaries and in the images contaminated by salt- and- pepper noise. In terms of computation time, the APCNN-based system is 16 times faster than the MLPNN-based system. Consequently, the presented APCNN-based algorithm is both accurate and fast and could be used in clinical environment for segmentation of vertebrae in 3D CT images.

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time-frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique-namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

A system to resolve an intramuscular electromyographic (EMG) signal into its component motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters of motor units (MUs), such as their motor unit potential (MUP) templates and mean firing rates, are of interest. The system filters an EMG signal, detects MUPs, and clusters and classifies the detected MUPs into MUPTs. Clustering is partially based on the K-means algorithm, and the supervised classification is implemented using a certainty-based algorithm. Both clustering and supervised classification algorithms use MUP shape and MU firing pattern information along with signal dependent assignment criteria to obtain robust performance across a variety of EMG signals. During classification, the validity of extracted MUPTs are determined using several supervised classifiers; invalid trains are corrected and the assignment threshold for each train is adjusted based on the estimated validity (i.e., adaptive classification). Performance of the developed system in terms of accuracy (A(c)), assignment rate (A(r)), correct classification rate (CC(r)) , and the error in estimating the number of MUPTs represented in the set of detected MUPs (E(NMUPTs)) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CC(r) of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CC(r) of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of E(NMUPTs), the new system, with average E(NMUPTs) of 0.3 and 0.2 for simulated and real data respectively, was better able to estimate the number of MUPTs represented in a set of detected MUPs than the previous system, with average E(NMUPTs) of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in A(c), A(r), and E(NMUPTs) for the newly developed system were lower than for the previous system, which demonstrates that the new system can successfully adjust the assignment criteria based on the characteristics of a given signal to achieve robust performance across a wide variety of EMG signals, which is of paramount importance for successfully promoting the clinical application of EMG signal decomposition techniques.

Background: Cardiac output (CO) is the total volume of blood pumped by the heart per minute and is a function of heart rate and stroke volume. CO is one of the most important parameters for monitoring cardiac function, estimating global oxygen delivery and understanding the causes of high blood pressure. Hence, measuring CO has always been a matter of interest to researchers and clinicians. Several methods have been developed for this purpose, but a majority of them are either invasive, too expensive or need special expertise and experience. Besides, they are not usually risk free and have consequences. Objective: Here, a semi-invasive system was designed and developed for continuous CO measurement via analyzing and processing arterial pulse waves. Results: Quantitative evaluation of developed CO estimation system was performed using 7 signals. It showed that it has an acceptable average error of (6.5%) in estimating CO. In addition, this system has the ability to consistently estimate this parameter and to provide a CO versus time curve that assists in tracking changes of CO. Moreover, the system provides such curve for systolic blood pressure, diastolic blood pressure, average blood pressure, heart rate and stroke volume. Conclusion: Evaluation of the results showed that the developed system is capable of accurately estimating CO. The curves which the system provides for important parameters may be valuable in monitoring hemodynamic status of high-risk surgical patients and critically ill patients in Intensive Care Units (ICU). Therefore, it could be a suitable system for monitoring hemodynamic status of critically ill patients.