Showing papers in "Medical & Biological Engineering & Computing in 2014"
TL;DR: It is advocated that the clinical diagnosis of “impingement syndrome” be eliminated as it is no more informative than the diagnosis of "anterior shoulder pain” and the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging.
Abstract: "Impingement syndrome" is a common diagnostic label for patients presenting with shoulder pain. Historically, it was believed to be due to compression of the rotator cuff tendons beneath the acromion. It has become evident that "impingement syndrome" is not likely an isolated condition that can be easily diagnosed with clinical tests or most successfully treated surgically. Rather, it is likely a complex of conditions involving a combination of intrinsic and extrinsic factors. A mechanical impingement phenomenon as an etiologic mechanism of rotator cuff disease may be distinct from the broad diagnostic label of "impingement syndrome". Acknowledging the concepts of mechanical impingement and movement-related impairments may better suit the diagnostic and interventional continuum as they support the existence of potentially modifiable impairments within the conservative treatment paradigm. Therefore, it is advocated that the clinical diagnosis of "impingement syndrome" be eliminated as it is no more informative than the diagnosis of "anterior shoulder pain". While both terms are ambiguous, the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging and may prevent potentially inappropriate surgical interventions. We further recommend investigation of mechanical impingement and movement patterns as potential mechanisms for the development of shoulder pain, but clearly distinguished from a clinical diagnostic label of "impingement syndrome". For shoulder researchers, we recommend investigations of homogenous patient groups with accurately defined specific pathologies, or with subgrouping or classification based on specific movement deviations. Diagnostic labels based on the movement system may allow more effective subgrouping of patients to guide treatment strategies.
87 citations
TL;DR: A novel approach toward EEG-driven position control of a robot arm is proposed by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position.
Abstract: The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8 % and 44 s, respectively, and the average rate of reaching the target is 95 %. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.
80 citations
TL;DR: The present review study proposes and discusses the methods and systems introduced so far in the literature for performing automated or semi-automated segmentation in ultrasound images or videos of the CCA, and proposes the best performing method that can be used for the segmentation of the IMC and the atherosclerotic carotid plaque in ultrasound pictures and videos.
Abstract: The determination of the wall thickness (intima-media thickness (IMT)), the delineation of the ath- erosclerotic carotid plaque, the measurement of the diam- eter in the common carotid artery (CCA), as well as the grading of its stenosis are important for the evaluation of the atherosclerosis disease. All these measurements are also considered to be significant markers for the clinical evalu- ation of the risk of stroke. A number of CCA segmentation techniques have been proposed in the last few years either for the segmentation of the intima-media complex (IMC), the lumen of the CCA, or for the atherosclerotic carotid plaque from ultrasound images or videos of the CCA. The present review study proposes and discusses the methods and systems introduced so far in the literature for perform- ing automated or semi-automated segmentation in ultra- sound images or videos of the CCA. These are based on edge detection, active contours, level sets, dynamic pro- gramming, local statistics, Hough transform, statistical modeling, neural networks, and an integration of the above methods. Furthermore, the performance of these systems is evaluated and discussed based on various evaluation met- rics. We finally propose the best performing method that can be used for the segmentation of the IMC and the ather- osclerotic carotid plaque in ultrasound images and videos. We end the present review study with a discussion of the different image and video CCA segmentation techniques, future perspectives, and further extension of these tech- niques to ultrasound video segmentation and wall tracking
70 citations
TL;DR: Investigation of the effect of precordial electrodes displacement on morphology of the ECG signal in a group of 60 patients with diagnosed cardiac disease found lead V2 was the most sensitive to displacement errors, followed by leads V3, V1, and V4, for which the direction of electrodes displacement plays a key role.
Abstract: Inaccurate electrode placement and differences in inter-individual human anatomies can lead to misinterpretation of ECG examination. The aim of the study was to investigate the effect of precordial electrodes displacement on morphology of the ECG signal in a group of 60 patients with diagnosed cardiac disease. Shapes of ECG signals recorded from precordial leads were compared with signals interpolated at the points located at a distance up to 5 cm from lead location. Shape differences of the QRS and ST-T-U complexes were quantified using the distribution function method, correlation coefficient, root-mean-square error (RMSE), and normalized RMSE. The relative variability (RV) index was calculated to quantify inter-individual variability. ECG morphology changes were prominent in all shape parameters beyond 2 cm distance to precordial leads. Lead V2 was the most sensitive to displacement errors, followed by leads V3, V1, and V4, for which the direction of electrodes displacement plays a key role. No visible changes in ECG morphology were observed in leads V5 and V6, only scaling effect of signal amplitude. The RV ranged from 0.639 to 0.989. Distortions in ECG tracings increase with the distance from precordial lead, which are specific to chosen electrode, direction of displacement, and for ECG segment selected for calculations.
69 citations
TL;DR: A nonlinear least squares fitting is applied to extract the double-dispersion Cole impedance parameters from simulated magnitude response datasets without requiring the direct impedance data or phase information.
Abstract: In the field of bioimpedance measurements, the Cole impedance model is widely used for characterizing biological tissues and biochemical materials. In this work, a nonlinear least squares fitting is applied to extract the double-dispersion Cole impedance parameters from simulated magnitude response datasets without requiring the direct impedance data or phase information. The technique is applied to extract the impedance parameters from MATLAB simulated noisy magnitude datasets showing less than 1.2 % relative error when 60 dB SNR Gaussian white noise is present. This extraction is verified experimentally using apples as the Cole impedances showing less than 3 % relative error between simulated responses (using the extracted impedance parameters) and the experimental results over the entire dataset.
64 citations
TL;DR: A cortical surface pattern (CSP) combining the cortical thickness with curvatures is designed, which constructs an accurate human age estimation model with relevance vector regression and gets a remarkably high accuracy and a significantly high sensitivity/specificity.
Abstract: Brain development and healthy aging have been proved to follow a specific pattern, which, in turn, can be applied to help doctors diagnose mental diseases. In this paper, we design a cortical surface pattern (CSP) combining the cortical thickness with curvatures, which constructs an accurate human age estimation model with relevance vector regression. We test our model with two public databases. One is the IXI database (360 healthy subjects aging from 20 to 82 years old were selected), and the other is the INDI database (303 subjects aging from 7 to 22 years old were selected). The results show that our model can achieve as small as 4.57 years deviation in the IXI database and 1.38 years deviation in the INDI database. Furthermore, we employ this surface pattern to age groups classification and get a remarkably high accuracy (97.77 %) and a significantly high sensitivity/specificity (97.30/98.10 %). These results suggest that our designed CSP combining thickness with curvatures is stable and sensitive to brain development, and it is much more powerful than voxel-based morphometry used in previous methods for age estimation.
55 citations
TL;DR: An effective image segmentation method for the IMT measurement in an automatic way is proposed, which is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task.
Abstract: Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness.
54 citations
TL;DR: A lower risk of arterial vascular injury for calcified plaque, while higher risk of plaque ruptures for cellular and hypocellular plaques is suggested.
Abstract: The stresses induced on plaque wall during stent implantation inside a stenotic artery are associated with plaque rupture. The stresses in the plaque–artery–stent structure appear to be distinctly different for different plaque types in terms of both distribution and magnitude. In this study, a nonlinear finite element simulation was executed to analyze the influence of plaque composition (calcified, cellular, and hypocellular) on plaque, artery layers (intima, media, and adventitia), and stent stresses during implantation of a balloon expandable coronary stent into a stenosed artery. The atherosclerotic artery was assumed to consist of a plaque and normal arterial tissues on its outer side. The results revealed a significant influence of plaque types on the maximum stresses induced within plaque wall and artery layers during stenting, but not when calculating maximum stress on stent. The stress on stiffer calcified plaque wall was in the fracture level (2.21 MPa), whereas cellular and hypocellular plaques play a protective role by displaying less stress on their wall. The highest von Mises stresses were observed on less stiff media layer. The findings of this study suggest a lower risk of arterial vascular injury for calcified plaque, while higher risk of plaque ruptures for cellular and hypocellular plaques.
54 citations
TL;DR: This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field.
Abstract: Brain–computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest.
53 citations
TL;DR: Evaluating the intra- and inter-observer reliability and the precision of 3D scapula kinematics measurement using wireless sensors of an inertial and magnetic measurement system (IMMS) found low reliability and highest difference in range of motion were observed for anterior/posterior tilt.
Abstract: To direct interventions aimed at improving scapular position and motion in shoulder pathologies, a clinically feasible, objective, sensitive and reliable assessment of scapular dyskinesis is needed. The aim of this study is to evaluate the intra- and inter-observer reliability and the precision of 3D scapula kinematics measurement using wireless sensors of an inertial and magnetic measurement system (IMMS). Scapular kinematics during humerus anteflexion and abduction of 20 subjects without shoulder pathologies were measured twice by two observers at two different days, using IMMS. Similar movement patterns and corresponding high intraclass correlation coefficients were found within (intra) and between (inter) observers, especially for scapular retraction/protraction (0.65–0.85) and medio/lateral rotation (0.56–0.91). Lowest reliability and highest difference in range of motion were observed for anterior/posterior tilt. Medio/lateral rotation and anterior/posterior tilt showed a high precision, with standard error of measurement being mostly below 5°. The inter-observer measurements of retraction/protraction showed lowest precision, reflected in systematic differences. This is caused by an offset in anatomical calibration of the sensors. IMMS enables easy and objective measurement of 3D scapula kinematics. Further research in a patient population should focus on clinical feasibility and validity for measurement of scapular dyskinesis. This would include the application of a scapula locator to enhance anatomical calibration.
51 citations
TL;DR: The results suggest that neither the type nor the degree of disability is a relevant issue to suitably operate a P300-based BCI and could be useful to assist disabled people at home improving their personal autonomy.
Abstract: The present study aims at developing and assessing an assistive tool for operating electronic devices at home by means of a P300-based brain-computer interface (BCI). Fifteen severely impaired subjects participated in the study. The developed tool allows users to interact with their usual environment fulfilling their main needs. It allows for navigation through ten menus and to manage up to 113 control commands from eight electronic devices. Ten out of the fifteen subjects were able to operate the proposed tool with accuracy above 77 %. Eight out of them reached accuracies higher than 95 %. Moreover, bitrates up to 20.1 bit/min were achieved. The novelty of this study lies in the use of an environment control application in a real scenario: real devices managed by potential BCI end-users. Although impaired users might not be able to set up this system without aid of others, this study takes a significant step to evaluate the degree to which such populations could eventually operate a stand-alone system. Our results suggest that neither the type nor the degree of disability is a relevant issue to suitably operate a P300-based BCI. Hence, it could be useful to assist disabled people at home improving their personal autonomy.
TL;DR: This study used trace transforms to model a human visual system which would replicate the way a human observer views an image, and used support vector machine (SVM) with quadratic, polynomial, radial basis function kernels and probabilistic neural network (PNN).
Abstract: Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients in developed countries. Early detection of occurrence of DR can greatly help in effective treatment. Unfortunately, symptoms of DR do not show up till an advanced stage. To counter this, regular screening for DR is essential in diabetic patients. Due to lack of enough skilled medical professionals, this task can become tedious as the number of images to be screened becomes high with regular screening of diabetic patients. An automated DR screening system can help in early diagnosis without the need for a large number of medical professionals. To improve detection, several pattern recognition techniques are being developed. In our study, we used trace transforms to model a human visual system which would replicate the way a human observer views an image. To classify features extracted using this technique, we used support vector machine (SVM) with quadratic, polynomial, radial basis function kernels and probabilistic neural network (PNN). Genetic algorithm (GA) was used to fine tune classification parameters. We obtained an accuracy of 99.41 and 99.12 % with PNN–GA and SVM quadratic kernels, respectively.
TL;DR: The results show that a correction based on the coil-target distance alone does not correctly adjust the induced electric field for regions other than M1, and a Correction based solely on the TMS-induced electric field (primary field) does not suffice.
Abstract: Many human cortical regions are targeted with transcranial magnetic stimulation (TMS). The stimulus intensity used for a certain region is generally based on the motor threshold stimulation intensity determined over the motor cortex (M1). However, it is well known that differences exist in coil-target distance and target site anatomy between cortical regions. These differences may well make the stimulation intensity derived from M1 sub-optimal for other regions. Our goal was to determine in what way the induced electric fields differ between cortical target regions. We used finite element method modeling to calculate the induced electric field for multiple target sites in a realistic head model. The effects on the electric field due to coil-target distance and target site anatomy have been quantified. The results show that a correction based on the distance alone does not correctly adjust the induced electric field for regions other than M1. In addition, a correction based solely on the TMS-induced electric field (primary field) does not suffice. A precise adjustment should include coil-target distance, the secondary field caused by charge accumulation at conductivity discontinuities and the direction of the field relative to the local cerebrospinal fluid-grey matter boundary.
TL;DR: Two novel methods are presented that decrease the discrepancy between experimental data and simulations of musculoskeletal models and might lead to more realistic shoulder simulations, especially for applications that involve scapular imbalance.
Abstract: Musculoskeletal models are intended to be used to assist in prevention and treatments of musculoskeletal disorders. To capture important aspects of shoulder dysfunction, realistic simulation of clavicular and scapular movements is crucial. The range of motion of these bones is dependent on thoracic, clavicular and scapular anatomy and therefore different for each individual. Typically, patient or subject measurements will therefore not fit on a model that uses a cadaveric morphology. Up till now, this problem was solved by adjusting measured bone rotations such that they fit on the model, but this leads to adjustments of on average 3.98° and, in some cases, even more than 8°. Two novel methods are presented that decrease this discrepancy between experimental data and simulations. For one method, the model is scaled to fit the subject, leading to a 34 % better fit compared to the existing method. In the other method, the set of possible joint rotations is increased by allowing some variation on motion constraints, resulting in a 42 % better fit. This change in kinematics also affected the kinetics: muscle forces of some important scapular stabilizing muscles, as predicted by the Delft Shoulder and Elbow Model, were altered by maximally 17 %. The effect on the glenohumeral joint contact force was however marginal (1.3 %). The methods presented in this paper might lead to more realistic shoulder simulations and can therefore be considered a step towards (clinical) application, especially for applications that involve scapular imbalance.
TL;DR: A novel Gini importance-based binary random forest feature selection method is introduced that effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.
Abstract: In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.
TL;DR: The results suggest that the primary mechanism of Parachute® is a reduction in ED myofiber stress, which may reverse eccentric post-infarct LV hypertrophy.
Abstract: The Parachute(®) (Cardiokinetix, Inc., Menlo Park, California) is a catheter-based device intended to reverse left ventricular (LV) remodeling after antero-apical myocardial infarction. When deployed, the device partitions the LV into upper and lower chambers. To simulate its mechanical effects, we created a finite element LV model based on computed tomography (CT) images from a patient before and 6 months after Parachute(®) implantation. Acute mechanical effects were determined by in silico device implantation (VIRTUAL-Parachute). Chronic effects of the device were determined by adjusting the diastolic and systolic material parameters to better match the 6-month post-implantation CT data and LV pressure data at end-diastole (ED) (POST-OP). Regional myofiber stress and pump function were calculated in each case. The principal finding is that VIRTUAL-Parachute was associated with a 61.2 % reduction in the lower chamber myofiber stress at ED. The POST-OP model was associated with a decrease in LV diastolic stiffness and a larger reduction in myofiber stress at the upper (27.1%) and lower chamber (78.4%) at ED. Myofiber stress at end-systole and stroke volume was little changed in the POST-OP case. These results suggest that the primary mechanism of Parachute(®) is a reduction in ED myofiber stress, which may reverse eccentric post-infarct LV hypertrophy.
TL;DR: The development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions in trauma patients and demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.
Abstract: Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.
TL;DR: A hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO) and the proposed CFS–SMO method outperforms all other classifiers giving a sensitivity of 100 %.
Abstract: It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46 % is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naive Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS–SMO method outperforms all other classifiers giving a sensitivity of 100 %. This makes it suitable to be taken as a second opinion in classifying breast tissue density.
TL;DR: Examination of gluteal muscle group activation patterns and their relationship with shoulder and elbow kinematics and kinetics during the overhead throwing motion of softball position players revealed a significant negative correlation between non-throwing gluteus maximus during the phase of maximum external rotation to maximum internal rotation (MIR).
Abstract: As the biomechanical literature concerning softball pitching is evolving, there are no data to support the mechanics of softball position players. Pitching literature supports the whole kinetic chain approach including the lower extremity in proper throwing mechanics. The purpose of this project was to examine the gluteal muscle group activation patterns and their relationship with shoulder and elbow kinematics and kinetics during the overhead throwing motion of softball position players. Eighteen Division I National Collegiate Athletic Association softball players (19.2 ± 1.0 years; 68.9 ± 8.7 kg; 168.6 ± 6.6 cm) who were listed on the active playing roster volunteered. Electromyographic, kinematic, and kinetic data were collected while players caught a simulated hit or pitched ball and perform their position throw. Pearson correlation revealed a significant negative correlation between non-throwing gluteus maximus during the phase of maximum external rotation to maximum internal rotation (MIR) and elbow moments at ball release (r = −0.52). While at ball release, trunk flexion and rotation both had a positive relationship with shoulder moments at MIR (r = 0.69, r = 0.82, respectively) suggesting that the kinematic actions of the pelvis and trunk are strongly related to the actions of the shoulder during throwing.
TL;DR: In this paper, the authors quantified and compared the electrical current distributions as well as the spatial recruitment profiles resulting from extra-and intra-dural electrode arrangements, using a 3D finite element model of the human thoracic spinal canal.
Abstract: Spinal cord stimulation currently relies on extradural electrode arrays that are separated from the spinal cord surface by a highly conducting layer of cerebrospinal fluid. It has recently been suggested that intradural placement of the electrodes in direct contact with the pial surface could greatly enhance the specificity and efficiency of stimulation. The present computational study aims at quantifying and comparing the electrical current distributions as well as the spatial recruitment profiles resulting from extra- and intra-dural electrode arrangements. The electrical potential distribution is calculated using a 3D finite element model of the human thoracic spinal canal. The likely recruitment areas are then obtained using the potential as input to an equivalent circuit model of the pre-threshold axonal response. The results show that the current threshold to recruitment of axons in the dorsal column is more than an order of magnitude smaller for intradural than extradural stimulation. Intradural placement of the electrodes also leads to much higher contrast between the stimulation thresholds for the dorsal root entry zone and the dorsal column, allowing better focusing of the stimulus.
TL;DR: A computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest, while detecting skin vibrations at the posterior chest, and predicted wave patterns inside the chest.
Abstract: Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest, while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions.
TL;DR: In this article, the authors proposed a new method for the generation of the Purkinje network using clinical measures of the activation times on the endocardium related to a normal electrical propagation.
Abstract: The propagation of the electrical signal in the Purkinje network is the starting point for the activation of the ventricular muscular cells leading to the contraction of the ventricle. In the computational models, describing the electrical activity of the ventricle is therefore important to account for the Purkinje fibers. Until now, the inclusion of such fibers has been obtained either by using surrogates such as space-dependent conduction properties or by generating a network based on an a priori anatomical knowledge. The aim of this work was to propose a new method for the generation of the Purkinje network using clinical measures of the activation times on the endocardium related to a normal electrical propagation, allowing to generate a patient-specific network. The measures were acquired by means of the EnSite NavX system. This system allows to measure for each point of the ventricular endocardium the time at which the activation front, that spreads through the ventricle, has reached the subjacent muscle. We compared the accuracy of the proposed method with the one of other strategies proposed so far in the literature for three subjects with a normal electrical propagation. The results showed that with our method we were able to reduce the absolute errors, intended as the difference between the measured and the computed data, by a factor in the range 9-25 %, with respect to the best of the other strategies. This highlighted the reliability of the proposed method and the importance of including a patient-specific Purkinje network in computational models.
TL;DR: An automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies is proposed and an AMD Risk Index is formulated using selected features to classify the normal and dry AMD classes using one number.
Abstract: Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, [Formula: see text]-nearest neighbor ([Formula: see text]-NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70%, sensitivity of 91.11%, and specificity of 96.30% using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.
TL;DR: Whether or not Newtonian rheology assumption in image-based patient-specific computational fluid dynamics (CFD) cerebrovascular models harboring cerebral aneurysms may affect the hemodynamics characteristics is investigated to find no evidence that Newtonian model overestimates WSS.
Abstract: The aim of this work was to determine whether or not Newtonian rheology assumption in image-based patient-specific computational fluid dynamics (CFD) cerebrovascular models harboring cerebral aneurysms may affect the hemodynamics characteristics, which have been previously associated with aneurysm progression and rupture. Ten patients with cerebral aneurysms with lobulations were considered. CFD models were reconstructed from 3DRA and 4DCTA images by means of region growing, deformable models, and an advancing front technique. Patient-specific FEM blood flow simulations were performed under Newtonian and Casson rheological models. Wall shear stress (WSS) maps were created and distributions were compared at the end diastole. Regions of lower WSS (lobulation) and higher WSS (neck) were identified. WSS changes in time were analyzed. Maximum, minimum and time-averaged values were calculated and statistically compared. WSS characterization remained unchanged. At high WSS regions, Casson rheology systematically produced higher WSS minimum, maximum and time-averaged values. However, those differences were not statistically significant. At low WSS regions, when averaging over all cases, the Casson model produced higher stresses, although in some cases the Newtonian model did. However, those differences were not significant either. There is no evidence that Newtonian model overestimates WSS. Differences are not statistically significant.
TL;DR: In this paper, the global response of the cardiovascular system during paroxysmal atrial fibrillation through a lumped-parameter approach was analyzed by paying particular attention to the stochastic modeling of the irregular heartbeats and the reduced contractility of the heart.
Abstract: Atrial fibrillation (AF) is the most common arrhythmia affecting millions of people in the Western countries and, due to the widespread impact on the population and its medical relevance, is largely investigated in both clinical and bioengineering sciences. However, some important feedback mechanisms are still not clearly established. The present study aims at understanding the global response of the cardiovascular system during paroxysmal AF through a lumped-parameter approach, which is here performed paying particular attention to the stochastic modeling of the irregular heartbeats and the reduced contractility of the heart. AF can be here analyzed by means of a wide number of hemodynamic parameters and avoiding the presence of other pathologies, which usually accompany AF. Reduced cardiac output with correlated drop of ejection fraction and decreased amount of energy converted to work by the heart during blood pumping, as well as higher left atrial volumes and pressures are some of the most representative results aligned with the existing clinical literature and here emerging during acute AF. The present modeling, providing new insights on cardiovascular variables which are difficult to measure and rarely reported in literature, turns out to be an efficient and powerful tool for a deeper comprehension and prediction of the arrythmia impact on the whole cardiovascular system.
TL;DR: Simulated hemodynamic parameters such as velocities, wall shear stress (WSS) and derived descriptors were able to predict disturbed flow conditions which play an important role in the development of local atherosclerotic plaques.
Abstract: The ability of using non-expensive ultrasound (US) image data together with computer fluid simulation to access various severities of carotid stenosis was inquired in this study. Subject-specific hemodynamic conditions were simulated using a developed finite element solver. Individual structured meshing of the common carotid artery (CCA) bifurcation was built from segmented longitudinal and cross-sectional US images; imposed boundary velocities were based on Doppler US measurements. Simulated hemodynamic parameters such as velocities, wall shear stress (WSS) and derived descriptors were able to predict disturbed flow conditions which play an important role in the development of local atherosclerotic plaques. Hemodynamic features from six individual CCA bifurcations were analyzed. High values of time-averaged WSS (TAWSS) were found at stenosis site. Low values of TAWSS were found at the bulb and at the carotid internal and external branches depending on the particular features of each patient. High oscillating shear index and relative residence time values assigned highly disturbed flows at the same artery surface regions that correlate only moderately with low TAWSS results. Based on clinic US examinations, results provide estimates of flow changes and forces at the carotid artery wall toward the link between hemodynamic behavior and stenosis pathophysiology.
TL;DR: Competitive repeatability for the ST and ISEO protocols and between-protocol agreement for two scapula rotations are suggested and different thresholds for repeatability and LoA may be adapted to suit different clinical hypotheses.
Abstract: Multi-center clinical trials incorporating shoulder kinematics are currently uncommon. The absence of repeatability and limits of agreement (LoA) studies between different centers employing different motion analysis protocols has led to a lack dataset compatibility. Therefore, the aim of this work was to determine the repeatability and LoA between two shoulder kinematic protocols. The first one uses a scapula tracker (ST), the International Society of Biomechanics anatomical frames and an optoelectronic measurement system, and the second uses a spine tracker, the INAIL Shoulder and Elbow Outpatient protocol (ISEO) and an inertial and magnetic measurement system. First within-protocol repeatability for each approach was assessed on a group of 23 healthy subjects and compared with the literature. Then, the between-protocol agreement was evaluated. The within-protocol repeatability was similar for the ST (
$$\overline{\text{RMSE}}$$
= 2.35°, $$\sigma_{\text{RMSE}}$$
= 0.97°, SEM = 2.5°) and ISEO (
$$\overline{\text{RMSE}}$$
= 2.24°, $$\sigma_{\text{RMSE}}$$
= 0.97°, SEM = 2.3°) protocols and comparable with data from published literature. The between-protocol agreement analysis showed comparable scapula medio-lateral rotation measurements for up to 120° of flexion-extension and up to 100° of scapula plane ab-adduction. Scapula protraction–retraction measurements were in agreement for a smaller range of humeral elevation. The results of this study suggest comparable repeatability for the ST and ISEO protocols and between-protocol agreement for two scapula rotations. Different thresholds for repeatability and LoA may be adapted to suit different clinical hypotheses.
TL;DR: Simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators.
Abstract: In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG–force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80 % MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators.
TL;DR: High-frequency acoustic signals might disclose additional clues to the mechanism of apneic snoring and should be included in future acoustic studies.
Abstract: The objective of this study was to systematically assess the effects of pharyngeal anatomical details on breathing resistance and acoustic characteristics by means of computational modeling. A physiologically realistic nose-throat airway was reconstructed from medical images. Individual airway anatomy such as the uvula, pharynx, and larynx was then isolated for examination by gradually simplifying this image-based model geometry. Large eddy simulations with the FW-H acoustics model were used to simulate airflows and acoustic sound generation with constant flow inhalations in rigid-walled airway geometries. Results showed that pharyngeal anatomical details exerted a significant impact on breathing resistance and energy distribution of acoustic sound. The uvula constriction induced considerably increased levels of pressure drop and acoustic power in the pharynx, which could start and worsen snoring symptoms. Each source anatomy was observed to generate a unique spectrum with signature peak frequencies and energy distribution. Moreover, severe pharyngeal airway narrowing led to an upward shift of sound energy in the high-frequency range. Results indicated that computational aeroacoustic modeling appeared to be a practical tool to study breathing-related disorders. Specifically, high-frequency acoustic signals might disclose additional clues to the mechanism of apneic snoring and should be included in future acoustic studies.
TL;DR: A solution by which animals with implantable devices can move freely without attachments is proposed by which power is transmitted using coils attached to the animal’s cage and is received by a receiver coil implanted in the animal.
Abstract: Reliable wireless power delivery for implantable devices in animals is highly desired for safe and effective experimental use. Batteries require frequent replacement; wired connections are inconvenient and unsafe, and short-distance inductive coupling requires the attachment of an exterior transmitter to the animal's body. In this article, we propose a solution by which animals with implantable devices can move freely without attachments. Power is transmitted using coils attached to the animal's cage and is received by a receiver coil implanted in the animal. For a three-dimensionally uniform delivery of power, we designed a columnar dual-transmitter coil configuration. A resonator-based inductive link was adopted for efficient long-range power delivery, and we used a novel biocompatible liquid crystal polymer substrate as the implantable receiver device. Using this wireless power delivery system, we obtain an average power transfer efficiency of 15.2% (minimum efficiency of 10% and a standard deviation of 2.6) within a cage of 15×20×15 cm3.