Technology and Health Care - Volume Pre-press, issue Pre-press
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Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured.
The following types of contributions and areas are considered:
1. Original articles:
Technology development in medicine: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine.
Significance of medical technology and informatics for healthcare: The appropriateness, efficacy and usefulness deriving from the application of engineering methods, devices and informatics in medicine and with respect to public health are discussed.
2. Technical notes:
Short communications on novel technical developments with relevance for clinical medicine.
3. Reviews and tutorials (upon invitation only):
Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented.
4. Minisymposia (upon invitation only):
Under the leadership of a Special Editor, controversial issues relating to healthcare are highlighted and discussed by various authors.
Abstract: OBJECTIVES: Autism Spectrum Disorder (ASD) is a complex range of neurodegenerative conditions that impact individuals’ social behaviour and communication skills. However, ASD data often contains far more controls than cases. This poses a serious challenge when creating classification models due to deriving models that favour controls during the classification of individuals. This problem is known as class imbalance, and it may reduce the performance in classification models derived by machine learning (ML) techniques due to individuals may remain undetected. METHODS: ML appears to help in the distressing disorder by improving outcome quality besides speeding up the…access to early diagnosis and consequential treatment. A screening dataset that consists of over 1100 instances was used to perform extensive quantitative analysis using different data resampling techniques and according to specific evaluation metrics. We measure the effect of class imbalance on autism screening performance using different data resampling techniques with a ML classifier and with respect to sensitivity, specificity, and F1-measure. We would like to know which resampling methods work well in balancing autism screening data. RESULTS: The results reveal that data resampling, and especially oversampling, improve results derived by the considered ML classifier. More importantly, there was superiority in terms of sensitivity and specificity for models derived by Naive Bayes classifier when oversampling methods have been used for data pre-processing on the autism data considered. CONCLUSION: The results reported encourages further improvement of the design and implementation of ASD screening systems using intelligent technology.
Keywords: Artificial intelligence, autism screening, classification, class imbalance, data resampling, machine learning
Abstract: BACKGROUND: Traditional least mean square algorithm (LMS) tends to converge faster and thus the larger the steady-state error of the algorithm. OBJECTIVE: In order to solve this issue, an improved adaptive normalized least mean square (NLMS) ECG signal denoising algorithm is proposed through utilizing the NLMS and the least mean square algorithm with added momentum term (MLMS). METHODS: The algorithm firstly performs LMS adaptive filtering on the original ECG signal. Then, the algorithm uses the relative error of the prior error signal and the posterior error signal before and after filtering to adaptively…determine the iteration step factor. Finally, the expected error is set to determine whether the denoising meets the expected requirements. This method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology. RESULTS: Experimental results have shown that the proposed algorithm can achieve good denoising for the target signal, and the average signal to noise ratio (SNR) of the proposed method is 17.6016, the RMSE is only 0.0334, and the average smoothness index R is only 0.0325. CONCLUSION: The proposed algorithm effectively removes the original ECG signal noise, and improves the smoothness of the signal the denoising efficiency.
Keywords: ECG signal, adaptive filter, normalized minimum mean square, signal to noise ratio
Abstract: BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima. OBJECTIVE: The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM). METHOD: The features…of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs. RESULTS: Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively. CONCLUSION: The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well.
Keywords: Motor imagery electroencephalogram, fuzzy c-means, genetic algorithm, fuzzy support vector machine, joint optimization
Abstract: OBJECTIVE: This study aims to accurately measure the range of motion of the sternoclavicular (SC) joint using 3D reconstruction and image registration. The motion of the SC joint is analyzed by means of axial angle representation to identify the kinematical characteristics of this joint. METHODS: A total of 13 healthy volunteers were enrolled in the study. The limit postures of four SC joint movements were scanned by computerized tomography. The images were integrated with reconstruction and registration techniques. The range of motion of the SC joint was measured using 3D modelling. The axial angle was used…to indicate the range of motion of the SC joint. The difference between the dominant side and non-dominant side was compared and the differences in axial angle of the SC joint in different postures were compared. RESULTS: The active axial angle of the SC joint on the dominant side was approximately 1 ∘ higher than that of the non-dominant side when the upper limb moved from a rest position to a posteroinferior position. In the sagittal motion of the upper limbs, the axial angle of the SC joint was greatest when moving from a horizontal position to a posterosuperior position, with an average of 23.55 ∘ . Of the flexion and extension movements of the upper limbs from a rest position to a horizontal position, 13.66% (the smallest proportion) were completed by the SC joint. CONCLUSION: The combination of 3D reconstruction and image registration is a direct and accurate method of measuring the motion of the SC joint. Axial angle representation is an intuitive method of expressing rotation in a 3D space that allows for more convenient comparison; it is also more in line with the characteristics of human anatomy and kinesiology and therefore more accurately reflects the characteristics of joint motion. It is therefore useful for guiding clinical practice. In a physical examination, the extension of the upper limb from the horizontal position to the posterosuperior position and from the rest position to the posteroinferior position can best reflect the rotation function of the SC joint in the combined motion of shoulder joints.
Abstract: BACKGROUND: Many medical image processing problems can be translated into solving the optimization models. In reality, there are lots of nonconvex optimization problems in medical image processing. OBJECTIVE: In this paper, we focus on a special class of robust nonconvex optimization, namely, robust optimization where given the parameters, the objective function can be expressed as the difference of convex functions. METHODS: We present the necessary condition for optimality under general assumptions. To solve this problem, a sequential robust convex optimization algorithm is proposed. RESULTS: We show that the new algorithm…is globally convergent to a stationary point of the original problem under the general assumption about the uncertain set. The application of medical image enhancement is conducted and the numerical result shows its efficiency.
Keywords: Medical image processing, robust nonconvex optimization, sequential robust convex optimization algorithm, medical image enhancement
Abstract: BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo…method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.
Abstract: BACKGROUND: The pulse transit time is an important factor that can be used to estimate the blood pressure indirectly. In many studies, pressures in the artery near and far from the heart are measured or the electrocardiogram and photoplethysmography are used to calculate the pulse transit time. In other words, the so-called contact measurements have been mainly used in these studies. OBJECTIVE: In this paper, a new method based on radar technology to measure the pulse transit time in a non-contact manner is proposed. METHODS: Radar pulses were simultaneously emitted to the chest…and the wrist, and the reflected pulses were accumulated. Heartbeats were extracted by performing principal component analysis on each time series belonging to the accumulated pulses. Then, the matched heartbeat pairs were found among the heartbeats obtained from the chest and wrist and the time delay between them, i.e. the pulse transit time, was obtained. RESULTS: By comparing the pulse transit times obtained by the proposed method with those obtained by conventional methods, it is confirmed that the proposed method using the radar can be used to obtain the pulse transit time in a non-contact manner.
Keywords: Ultra-wide band, impulse radar, pulse transit time, principal component analysis, heartbeat
Abstract: BACKGROUND: Gait impairment is an essential symptom of Parkinson’s disease (PD). OBJECTIVE: This paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis. METHODS: Four hundred and fifty-six videos were recorded from 19 PD patients using an RGB camera during clinical gait assessment. Gait performance in each video was rated by a neurologist using the unified Parkinson’s disease rating scale for gait examination (UPDRS-gait). The proposed algorithm detects and tracks the silhouette of the test subject in the video to generate a height signal.…Gait features were extracted from the height signal. Feature analysis was performed using the Kruskal-Wallis rank test. A support vector machine was trained using the features to classify the severity levels according to UPDRS-gait in 10-fold cross-validation. RESULTS: Features significantly (p < 0.05) differentiated between median-ranks of UPDRS-gait levels. The SVM classified the levels with a promising area under the ROC of 80.88%. CONCLUSION: Findings support the feasibility of this model for Parkinson’s gait assessment in the home environment.
Abstract: BACKGROUND: Grape seed proanthocyanidin extract (GSPE) has a certain resistance to contrast light, which makes the boundary of the imaging image unclear. OBJECTIVE: Because of this, an image processing algorithm is needed to process the contrast image to study the role of GSPE in the process of anti-ultraviolet. METHODS: In this paper, the fuzzy edges of contrast images were processed by deep learning algorithm, and the changes of VEGF and PEDF expression in HaCaT cells before and after UVA irradiation and after GSPE intervention were studied. RESULTS: The experiment results…show that after processing, the edge and boundary of the image become clear and separable, which can be used to compare and analyze the test process. The image comparison results show that GSPE can down regulate the expression of VEGF gene and protein, and up regulate the expression of PEDF gene and protein. The synergistic effect of GSPE and GSPE can inhibit angiogenesis. It is confirmed that GSPE has the effect of anti-ultraviolet ray induced early angiogenesis.
Keywords: HaCaT cells, SURF, deep learning, binary image
Abstract: OBJECTIVE: This study aims to compare and analyze the difference of impact force attenuation according to size and impact location on a Taekwondo body protector. METHODS: Body protectors sized 1 to 5, were impact tested by equipment based on the specifications in the European standard manual (EN 13277-1, 3). The impactor release heights were set to match impact energies of 3 and 15 J. The impactor was made from a 2.5 kg cylindrically cut piece of aluminum. Each body protector was impacted 10 times at the two impact energies and two locations. The differences in performance…for each body protector size were compared using a two-way analysis of variance with a significance level of p < 005. The effect sizes were investigated using a partial eta squared value (η 2 ). RESULTS: The significant mean differences between the body protector size and impact area (p < 005) and the average impact time of impact strengths 3 and 15 J were 0.0017 and 0.0012 s, respectively In addition, when an impact strength of 15 J was applied, the maximum resulting impact force exceeded 2000 N for both locations on all sizes. Furthermore, at an impact strength of 3 J size 3 significantly reduced the impact force more than the other sizes; however, size 1 showed the greatest shock absorption at an impact of 15 J. CONCLUSION: The results of this study show that the shock absorption of body protectors does not increase according to size; i.e., a larger body protector does not reduce the impact load more effectively. To improve safety performance, we recommend a maximum impact force of 2000 N or less for all body protectors.