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Background: Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability. Objective: This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA). Particularly, for analyzing OCTA images and measuring several indices describing microvascular morphology, vessel morphology, and FAZ morphology. Material and Methods: In this analytical study, we developed a toolbox or QOCTA using image processing algorithms provided in MATLAB. The software automatically determines FAZ and measures several parameters related to both size and shape of FAZ including area, perimeter, Feret's diameter circularity, axial ratio, roundness, and solidity. The microvascular structure is derived from the processed image to estimate the vessel density (VD). To assess the reliability of the software, three independent operators measured the mentioned parameters for the eyes of 21 subjects. The consistency of the values was assessed using the intraclass correlation coefficient (ICC) index. Results: Excellent consistency was observed between the measurements completed for the superficial layer, ICC >0.9. For the deep layer, good reliability in the measurements was achieved, ICC >0.7. Conclusion: The developed software is reliable; hence, it can facilitate quantitative OCTA, further statistical comparison in cohort OCTA studies, and can assist with obtaining deeper insights into retinal variations in various populations. [ABSTRACT FROM AUTHOR]

Background: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal. Objective: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal. Material and Methods: In this analytical study, we developed a hardwaresoftware system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal. Results: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2. Conclusion: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases. [ABSTRACT FROM AUTHOR]

Objectives. A posture maintained for a long period can be harmful to the health of office workers. This study aimed to estimate the recommended ergonomic duration for maintaining different sitting postures. Methods. Forty healthy male and female students participated in this experiment designed to measure perceived discomfort caused by maintaining common static sitting postures of office workers in a simple ergonomic set-up for 4 min. 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 were tabulated. The shortest holding time of a posture was obtained for the moderate neck flexion (1.61 min), and the longest holding time was obtained for a leg posture with 90° knee flexion (6.45 min). Conclusions. The recommended holding time in this study may help to assess the risk of musculoskeletal disorders (MSDs) in office workers and train the individuals involved in office tasks in proper sitting behavior. [ABSTRACT FROM AUTHOR]

Background: 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. Objective: 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. Material and Methods: 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. Results: The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%). Conclusion: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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 twenty videos of different single-task jobs. In the second phase, the DAT was designed using penpaper 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The emergence of a new variant of SARS-CoV-2 in the UK that is spreading more rapidly has raised great concerns not only in the UK but also whole Europe and other parts of the globe. The newly identified variant of SARS-CoV-2 that is reported to be more contagious has prompted many countries to ban travel to and from the UK. As of April 2, 2021, nearly 4.35 million confirmed cases of coronavirus (COVID-19) have been reported in the UK out of which more than 127,000 people have died. These numbers reveal a need for predictor models to assist with management, prevention, and treatment decisions. Here, we presented an Artificial Intelligence (AI) model to predict the death rate in various cities of the United Kingdom. Training and testing the model using the data available on the European data portal showed promising results with predicted R2 = 0.88. [ABSTRACT FROM AUTHOR]

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 the area under the curve 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. [ABSTRACT FROM AUTHOR]

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 performance of this system with Guy's stone score (GSS) and the Clinical Research Office of Endourological Society (CROES) nomogram. Patients and Methods: Data from 146 adult patients (87 males, 59%) who underwent PCNL at our institute were used. To validate the system, accuracy of the software for predicting each postoperative outcome was compared with the actual outcome. Similarly, preoperative data were analyzed with GSS and CROES nomograms to determine stone-free status as predicted by these nomograms. A receiver operating characteristic (ROC) curve was generated for each scoring system, and the area under the ROC curve (AUC) was calculated and used to assess the predictive performance of all three models. Results: Overall stone-free rate was 72.6% (106/146). Forty of 146 patients (27.4%) were scheduled for 42 ancillary procedures (extracorporeal shockwave lithotripsy [SWL] [n = 31] or repeat PCNL [n = 11]) to manage residual renal stones. Overall, the machine learning system predicted the PCNL outcomes with an accuracy ranging between 80% and 95.1%. For predicting the stone-free status, the AUC for the software (0.915) was significantly larger than the AUC for GSS (0.615) or CROES nomograms (0.621) (p < 0.001). Conclusion: At the internal institutional level, the machine learning-based software was a promising tool for recording, processing, and predicting outcomes after PCNL. Validation of this system against an external dataset is highly recommended before its widespread application. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the second cause of death among women. Ultrasound (US) imaging is the most common technique for diagnosing breast cancer; however, detecting breast lesions in US images is a difficult task, mainly, because it provides low-quality images. Consequently, identifying lesions in US images is still a challenging task and an open problem in US image processing. This study aims to develop an automated system for the identification of lesions in US images Method: We proposed an automatic method to assist radiologists in inspecting and analyzing US images in breast screening and diagnosing breast cancer. In contrast to previous research, this work focuses on fusing information extracted from different frames. The developed method consists of template matching, morphological features extraction, local binary patterns, fuzzy C-means clustering, region growing, and information fusion-based image segmentation technique. The performance of the system was evaluated using a database composed of 22 US videos where 10 breast US films were obtained from patients with breast lesions and 12 videos belonged to normal cases. Results: The sensitivity, specificity, and accuracy of the system in detecting frames with breast lesions were 95.7%, 97.1%, and 97.1%, respectively. The algorithm reduced the vibration of the physician's hands' while probing by assessing every 10 frames regardless of the results of the prior frame; hence, lowering the possibility of missing a lesion during an examination. Conclusion: The presented system outperforms several existing methods in correctly detecting breast lesions in a breast cancer screening test. Fusing information that exists in frames of a breast US film can help improve the identification of lesions (suspect regions) in a screening test. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

The shapes and sounds of isolated motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. These parameters can be analyzed qualitatively by an expert or quantitatively by using pattern recognition techniques. Due to the advantages of quantitative EMG method, developing robust automated MUAP classifiers have been explored and several systems have been developed for this purpose by now, but the accuracy of the existing methods is not high enough to be used in clinical environments. In this paper, a novel classification strategy based on ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture is proposed to determine the class label (myopathic, neuropathic, or normal) for a given MUAP. The developed system employs both time domain and time-frequency domain features of the MUAPs extracted from an EMG signal using an EMG signal decomposition system. Different classification strategies including single classifier and multiple classifiers with several subsets of features were investigated. Experimental results using a set of real EMG signals showed robust performance of multi-classifier methods proposed here. Of the methods studied, the multi-classifier that uses multiple features sets and a combination of both trainable and nontrainable fusion techniques to aggregate base classifiers showed the best performance with average accuracy of 97% which is significantly higher than the average accuracy of single SVM-based classifier system (i.e., 88%). [ABSTRACT FROM AUTHOR]

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 (Ac), assignment rate (Ar), correct classification rate (CCr), and the error in estimating the number of MUPTs represented in the set of detected MUPs (ENMUPTs) was evaluated using 32 simulated and 30 real EMG signals comprised of 3–11 and 3–15 MUPTs, respectively. The developed system, with average CCr of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CCr of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of ENMUPTs, the new system, with average ENMUPTs 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 ENMUPTs of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in Ac, Ar, and ENMUPTs 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. [ABSTRACT FROM AUTHOR]

Motor unit potential trains (MUPTs) extracted via electromyographic (EMG) signal decomposition can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid. In this paper, support vector machine (SVM)-based supervised classifiers are proposed to estimate the validity of extracted MUPTs. The classifiers use either the MU firing pattern or the MUP shape consistency of an MUPT, or both, to estimate its validity. The developed classifiers estimate the class label of an MUPT (i.e., valid/invalid) and a degree of support for the decision being made. A single SVM that estimates the validity of a given MUPT using extracted MU firing pattern and MUP shape features was investigated. In addition, the effectiveness of multiclassifier techniques which estimate the overall validity of a train by fusing the MU firing pattern and MUP shape validity of a given MUPT, determined separately by two distinct SVMs, was also investigated. Training based only on simulated data showed robust classification performance of the several multiclassifier methods when tested using both simulated and real test data. Of the methods studied, the multiclassifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance. Assuming 12.7% of extracted MUPTs are on average invalid, the estimated accuracy for this method in correctly categorizing MUPTs extracted during decomposition was 99.4% and 98.8% for simulated and real data, respectively. [ABSTRACT FROM PUBLISHER]

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model's performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition., (© 2024. The Author(s).)

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.

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.

Information regarding motor unit potentials (MUPs) and motor unit fi ring patterns during muscle contractions is useful for physiological investigation and clinical examinations either for the understanding of motor control or for the diagnosis of neuromuscular disorders. In order to obtain such information, composite electromyographic (EMG) signals are decomposed (i.e., resolved into their constituent motor unit potential trains [MUPTs]). The goals of automatic decomposition techniques are to create a MUPT for each motor unit that contributed significant MUPs to the original composite signal. Diagnosis can then be facilitated by decomposing a needle-detected EMG signal, extracting features of MUPTs, and finally analyzing the extracted features (i.e., quantitative electromyography). Herein, the concepts of EMG signals and EMG signal decomposition techniques are explained. The steps involved with the decomposition of an EMG signal and the methods developed for each step, along with their strengths and limitations, are discussed and compared. Finally, methods developed to evaluate decomposition algorithms and assess the validity of the obtained MUPTs are reviewed and evaluated.

Information regarding the morphology of motor unit potentials (MUPs) and motor unit firing patterns can be used to help diagnose, treat, and manage neuromuscular disorders. In a conventional electromyographic (EMG) examination, a clinician manually assesses the characteristics of needle-detected EMG signals across a number of distinct needle positions and forms an overall impression of the condition of the muscle. Such a subjective assessment is highly dependent on the skills and level of experience of the clinician, and is prone to a high error rate and operator bias. Quantitative methods have been developed to characterize MUP waveforms using statistical and probabilistic techniques that allow for greater objectivity and reproducibility in supporting the diagnostic process. In this review, quantitative EMG (QEMG) techniques ranging from simple reporting of numeric MUP values to interpreted muscle characterizations are presented and reviewed in terms of their clinical potential to improve status quo methods. QEMG techniques are also evaluated in terms of their suitability for use in a clinical decision support system based on previously established criteria. Aspects of prototype clinical decision support systems are then presented to illustrate some of the concepts of QEMG-based decision making.

A method using the gap statistic is proposed to evaluate the validity of a motor unit potential train (MUPT) in terms of motor unit potential (MUP) shape consistency. This algorithm determines whether the MUPs of a given MUPT are homogeneous in terms of their shapes or not. It also checks if there are gaps in the inter-discharge interval (IDI) train of the given MUPT. If the MUPs are not homogeneous or if there is a temporal gap in the MUPT, the given MUPT is split into valid trains. To overcome MUP shape variability caused by jitter or needle movement during signal detection, similar MUPTs are merged if the resulting merged train is a valid train. Experimental results using simulated EMG signals show that the accuracy of the developed method in determining valid MUPTs and invalid MUPTs correctly is 97.58% and 99.33% on average, respectively. This performance encourages the use of this method for automated validation of MUPTs.

A robust and fast method to assess the validity of a motor unit potential train (MUPT) obtained by decomposing a needle-detected EMG signal is proposed. This method determines whether a MUPT represents the firings of a single motor unit (MU) or the merged activity of more than one MU, and if is a single train it identifies whether the estimated levels of missed and false classification errors in the MUPT are acceptable. Two supervised classifiers, the Single/Merged classifier (SMC) and the Error Rate classifier (ERC), and a linear model for estimating the level of missed classification error have been developed for this objective. Experimental results using simulated data show that the accuracy of the SMC and the ERC in correctly categorizing a train is 99% and %84 respectively.