bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on upper limb rehabilitation system based on surface EMG
Authors: Wang Lan , Li Hailong , Wang Zhengyu , Meng Fandong 
Published: August 2015

Abstract During the rehabilitation process, it is essential to accurately judge a patient’s recovery in a timely manner. A reasonable and matched training program is significant in the development of rehabilitation system. This paper presents a new upper limb rehabilitation training system, which consists of an upper limb rehabilitation training device, a current detection circuit, a motor speed test circuit, a surface EMG (sEMG) sensor, and a dSPACE HIL simulation platform. The real-time output torque of the servo motor is calculated by using the motor’s real-time current and speed, in order to monitor the patient’s training situation. The signal of sEMG is collected in real time and is processed with root mean square (RMS) to characterize the degree of muscle activation. Based on this rehabilitation system, maximum voluntary contraction (MVC) experiments, passive training experiments under different speeds, and active training experiments under different damping are studied. The results show that this new system performs real-time and accurate monitoring of a patient’s training situation. It can also assess a patient’s recovery through muscle activation. To a certain extent, this system provides a platform for research and development of rehabilitation medical engineering.
Sensory electrical stimulation for suppression of postural tremor in patients with essential tremor
Authors: Heo Jae-Hoon , Kim Ji-Won , Kwon Yuri , Lee Sang-Ki , Eom Gwang-Moon , Kwon Do-Young , Lee Chan-Nyeong , Park Kun-Woo , Manto Mario 
Published: August 2015

Abstract Essential tremor is an involuntary trembling of body limbs in people without tremor-related disease. In previous study, suppression of tremor by sensory electrical stimulation was confirmed on the index finger. This study investigates the effect of sensory stimulation on multiple segments and joints of the upper limb. It denotes the observation regarding the effect’s continuity after halting the stimulation. 18 patients with essential tremor (8 men and 10 women) participated in this study. The task, “arms stretched forward”, was performed and sensory electrical stimulation was applied on four muscles of the upper limb (Flexor Carpi Radialis, Extensor Carpi Radialis, Biceps Brachii, and Triceps Brachii) for 15 seconds. Three 3-D gyro sensors were used to measure the angular velocities of segments (finger, hand, and forearm) and joints (metacarpophalangeal and wrist joints) for three phases of pre-stimulation (Pre), during-stimulation (On), and 5 minute post-stimulation (P5). Three characteristic variables of root-mean-squared angular velocity, peak power, and peak power frequency were derived from the vector sum of the sensor signals. At On phase, RMS velocity was reduced from Pre in all segments and joints while peak power was reduced from Pre in all segments and joints except for forearm segment. Sensory stimulation showed no effect on peak power frequency. All variables at P5 were similar to those at On at all segments and joints. The decrease of peak power of the index finger was noted by 90% during stimulation from that of On phase, which was maintained even after 5 min. The results indicate that sensory stimulation may be an effective clinical method to treat the essential tremor.
Cognitive rehabilitation system for children with autism spectrum disorder using serious games: A pilot study
Authors: Aresti-Bartolome Nuria , Garcia-Zapirain Begonya 
Published: August 2015

Abstract This paper studies and assesses how rehabilitation activities and supervised computer games incorporated into a system aimed at people diagnosed with Autism Spectrum Disorder (ASD) can be used to work on the areas affected by ASD at any time and in any place. This research specifically assesses the areas that affect communication and interaction between people with ASD and professionals. In order to do this, a group of 20 children diagnosed with ASD of between 3 and 8 years old (clinical group) was used, together with a group of 20 children of between 3 and 8 years old with a neurotypical development, which served as a control group. During the tests, response time and visual interaction with the session leader were evaluated. Despite the fact that the clinical group spent more time (M = 21.08 sec) than the control group (M = 4.52) to interact leader, eye contact predominated in the interaction. As a result of the pilot study, the system obtained could help in cognitive rehabilitation.
Change of neuronal activations induced by the passive perception of driving speed difference
Authors: Kim Hyun-Jun , Yi Jeong-Han , Kim Hyung-Sik , Chung Soon-Cheol , Baek Ji-Hye , Lee Jung-Chul , Park Sung-Jun , Jeong Ul-Ho , Gim Seon-Young , Kim Sung-Phil , Lim Dae-Woon , Choi Mi-Hyun 
Published: August 2015

Abstract The change of neuronal activation due to the passive perception of various driving speeds in comparison to a reference driving speed was assessed using functional Magnetic Resonance Imaging. Videos recorded in real driving conditions on the road at driving speeds of 50, 70, 90, and 110 km/h were shown as visual stimuli. An experiment consisted of three blocks, each having a control phase (50km/h) and a stimulation phase (70, 90, or 110 km/h). In the passive perception of various driving speed differences, the areas related to visual cognition and spatial attention such as temporal, occipital, parietal, frontal areas, and cerebellum were activated. As the driving speed difference increased, the number of activated voxels also increased in the areas related to visual cognition. However, the visual cognition related areas showed a different pattern from the spatial attention related area with an increase of the driving speed difference. This implies that each brain area has a different level of involvement in the passive perception of the driving speed difference, although both visual cognitions related areas and spatial attention related area are related to it.
Electromyogram-controlled assistive exercise for the motor recovery of shoulder in chronic hemiplegia: A pilot study
Authors: Park Byung Kyu , Kim Ji-Won , Kwon Yuri , Heo Jae-Hoon , Yoon Joo-Seok , Eom Gwang-Moon 
Published: August 2015

Abstract Correct-active-repetitive exercise is important for the motor recovery in hemiplegics. The present study hypothesizes that the electromyogram (EMG)-controlled assistance of motion would be an effective implementation of the concept for the rehabilitation of the hemiplegic shoulder, even in chronic patients. This study aims to investigate the feasibility of the suggested method. The motor intention is derived from the EMG of the shoulder muscles and the shoulder movement (flexion and abduction) is assisted by an electro-mechanical system only when the motor intention (EMG amplitude) exceeded the threshold. Twelve patients in the chronic stage of stroke participated in this pilot study. The EMG-controlled assistive exercise lasts for two weeks, 20 min per day and 5 days a week. The active range of motion in both abduction and flexion increases significantly after the intervention (p < 0.01). The maximum torque increases in both directions, and the increase is significant in the abduction (p < 0.01). The Fugl–Myer motor assessment score is improved greatly in the shoulder-related items (p < 0.01), but neither in the shoulder-unrelated items of the upper extremity (p = 0.13) nor in the lower extremity items (p = 0.19). This pilot study demonstrates that EMG-controlled assistive exercise can improve shoulder motor functions related to selected muscles and the suggested method is promising for the motor recovery of the shoulder in chronic hemiplegia.
An analysis of pulse wave signals during visual display terminal operations
Authors: Lu Yang , Wang Qi , Zhang Song , Yang Lin , Liang Liyan , Cui Jingxuan 
Published: August 2015

Abstract In this paper, participants operated visual display terminals (VDT); the authors analyzed their pulse wave signals to develop objective indices for diagnosing and assessing VDT-induced. The experiment used 30 healthy undergraduates test subjects; each subject operated static PC for 4 hours. The pulse waves of local blood volumes in the test subjects’ finger-tips were measured every 30 minutes for 2 minutes. After the experiment, the pulse wave parameters were analyzed. All test subjects exhibited signs of visual fatigue according to the fatigue scale results. There were significant differences of heart rate (HR), which clearly declined before and after the experiment (P<0.01). The indexes RMSSD, SDNN, P3, P4, P5, P6, and TP had significant differences (P<0.01) and all rose. The positions of the main and dicrotic waves advanced (P<0.01). The experiment parameters prompted significant changes; pulse wave forms changed during VDT work. Based on these results, pulse wave analysis holds promise as an effective, noninvasive technique for measuring VDT fatigue.
Lung carcinoma recognition by blood dielectric spectroscopy
Authors: Chen Xiaoyan , Lv Xinqiang , Wang Huaxiang 
Published: August 2015

Abstract Lung carcinoma has become one of the malignancies whose morbidity and mortality are growing significantly. Blood is a functional body fluid, it delivers oxygen and nutrients to the other parts of the body, and it is a pathway of tumor metastasis. This paper studies the possibility of diagnosing lung carcinoma by blood dielectric spectroscopy. Comparison experiments were carried out to trace the dielectric spectroscopy of blood with and without lung carcinoma changing at different tumor stages from 100 Hz to 100 MHz. The research results show that the discrepancy of complex permittivity between rat blood with and without lung carcinoma becomes significant with tumor growing, which is worthy for clinical diagnosis.
Noncontact measurement of heart rate using facial video illuminated under natural light and signal weighted analysis
Authors: Yan Yonggang , Ma Xiang , Yao Lifeng , Ouyang Jianfei 
Published: August 2015

Abstract Non-contact and remote measurements of vital physical signals are important for reliable and comfortable physiological self-assessment. We presented a novel optical imaging-based method to measure the vital physical signals. Using a digital camera and ambient light, the cardiovascular pulse waves were extracted better from human color facial videos correctly. And the vital physiological parameters like heart rate were measured using a proposed signal-weighted analysis method. The measured HRs consistent with those measured simultaneously with reference technologies ( r=0.94, p<0.001 for HR). The results show that the imaging–based method is suitable for measuring the physiological parameters, and provide a reliable and comfortable measurement mode. The study lays a physical foundation for measuring multi-physiological parameters of human noninvasively.
Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system
Authors: Wang Dongcui , Mo Fongming , Zhang Yangde , Yang Chao , Liu Jun , Chen Zhencheng , Zhao Jinfeng 
Published: August 2015

Abstract In a previous study (unpublished), Emotiv headset was validated for capturing event-related potentials (ERPs) from normal subjects. In the present follow-up study, the signal quality of Emotiv headset was tested by the accuracy rate of discriminating Major Depressive Disorder (MDD) patients from the normal subjects. ERPs of 22 MDD patients and 15 normal subjects were induced by an auditory oddball task and the amplitude of N1, N2 and P3 of ERP components were specifically analyzed. The features of ERPs were statistically investigated. It is found that Emotiv headset is capable of discriminating the abnormal N1, N2 and P3 components in MDD patients. Relief-F algorithm was applied to all features for feature selection. The selected features were then input to a linear discriminant analysis (LDA) classifier with leave-one-out cross-validation to characterize the ERP features of MDD. 127 possible combinations out of the selected 7 ERP features were classified using LDA. The best classification accuracy was achieved to be 89.66%. These results suggest that MDD patients are identifiable from normal subjects by ERPs measured by Emotiv headset.
AFW extraction based on MCA
Authors: Zhu Junjiang , He Lingsong , Du Jianhao 
Published: August 2015

Abstract This paper improves the learning dictionary construction method for morphological component analysis (MCA) to separate the atrial and ventricular signals. The incoherence is added into the objective function to reduce the sparsity ratio between the atrial and ventricular dictionaries. By using the dictionaries, atrial and ventricular activities are separated from the location of the coefficients. We test the methods on both the synthetic and real atrial data. While extracting AFW from synthetic data, we use the Poisson relation as the measure. The result shows that we can obtain greater relation value using the method this paper presents than using the methods of ABS and PCA. We also conduct spectral analysis on AFW extracted from real atrial data.
Towards estimating fiducial localization error of point-based registration in image-guided neurosurgery
Authors: Zhi Deng 
Published: August 2015

Abstract Fiducial Localization Error (FLE) is one of the major reasons of inaccuracy in point-based spatial registration of Image-Guided Neurosurgery System (IGNS), and minimizing FLE is the fundamental way to improve spatial registration accuracy. A reliable estimation of FLE is needed, as it cannot be measured directly in real application of IGNS. In this paper, we propose a method to estimate the FLE in a point-based registration of IGNS. Test fiducial point sets were generated in one coordinate system around the given fiducial point set by utilizing simple random sampling. Further, these points were registered to the fiducial point set in the other coordinate system. The average position of the test fiducial point sets with small FRE are calculated and its displacement from the given fiducial point set is the parameter used to estimate the FLE of each fiducial point. The correlation between the displacement and the FLE of each fiducial point is greater than 0.75 when nine or more fiducial points were utilized. This correlation gradually increases up to 0.9 with the increase of the number of fiducial points.
Assessment of various traditional Chinese medicine formulas on skin micro-circulatory perfusion
Authors: He YiFan , Meng Hong , Dong YinMao 
Published: August 2015

Abstract Laser Doppler imagery is a contemporary form biomedical science specializing in non-evasive micro-circulation investigation. This paper outlines the process and results of applying twelve different test samples to mouse skin to collect data variation of micro-circulation, then judge the effects of different formulas in skin health improvement. More specifically, it is said [15] that micro-circulation is associated with anti-aging (otherwise known as micro-circulation acceleration) by the way of improvement of nutrition supply and hazard substance elimination, has some effects of skin anti-aging. In this paper, compared with Group 0 (the blank group), Group 1, and Group 2 formulas have statistically significant differences in skin micro-circulation perfusion, which implies that those two groups are provided with better anti-aging formulas. So, Group 1’s formula and Group 2’s formula have the most significant positive effect on the skin health.
A novel registration method for image-guided neurosurgery system based on stereo vision
Authors: An Yong , Wang Manning , Song Zhijian 
Published: August 2015

Abstract This study presents a novel spatial registration method of Image-guided neurosurgery system (IGNS) based on stereo-vision. Images of the patient’s head are captured by a video camera, which is calibrated and tracked by an optical tracking system. Then, a set of sparse facial data points are reconstructed from them by stereo vision in the patient space. Surface matching method is utilized to register the reconstructed sparse points and the facial surface reconstructed from preoperative images of the patient. Simulation experiments verified the feasibility of the proposed method. The proposed method it is a new low-cost and easy-to-use spatial registration method for IGNS, with good prospects for clinical application.
Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging
Authors: Wang Rui , Li Rui , Lei Yanyan , Zhu Quing 
Published: August 2015

Abstract Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.
CUDA accelerated uniform re-sampling for non-Cartesian MR reconstruction
Authors: Feng Chaolu , Zhao Dazhe 
Published: August 2015

Abstract A grid-driven gridding (GDG) method is proposed to uniformly re-sample non-Cartesian raw data acquired in PROPELLER, in which a trajectory window for each Cartesian grid is first computed. The intensity of the reconstructed image at this grid is the weighted average of raw data in this window. Taking consider of the single instruction multiple data (SIMD) property of the proposed GDG, a CUDA accelerated method is then proposed to improve the performance of the proposed GDG. Two groups of raw data sampled by PROPELLER in two resolutions are reconstructed by the proposed method. To balance computation resources of the GPU and obtain the best performance improvement, four thread-block strategies are adopted. Experimental results demonstrate that although the proposed GDG is more time consuming than traditional DDG, the CUDA accelerated GDG is almost 10 times faster than traditional DDG.
Study of somesthesis according to change in pulse diode laser parameters
Authors: Kim Ji-Sun , Oh Han-Byeol , Kim A-Hee , Kim Jun-Sik , Lee Eun-Suk , Goh Bong-Jun , Lee Tae-Hee , Chung Soon-Cheol , Jun Jae-Hoon 
Published: August 2015

Abstract Laser can precisely deliver quantitative energy to a desired region in a non-contact way. Since it can stimulate regions and minutely control parameters such as the intensity, duration and frequency of stimulus, laser is often used for the areas such as low power laser treatment and clinical physiology. This study proposes simulation using pulse diode laser with reliable output and identifies laser parameters that can present a variety of somesthesis. It is found that typically, as frequency and energy increase, the ratio of feeling senses increases, and dominant sense moves from the sense of heat through tactile sense to pain. This study will be baseline data for studies of the sense of heat, tactile sense and pain, contribute to studying neurophysiology sector and be applied to basic clinical research.
EEG feature selection method based on decision tree
Authors: Duan Lijuan , Ge Hui , Ma Wei , Miao Jun 
Published: August 2015

Abstract This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.
A block-wise approximate parallel implementation for ART algorithm on CUDA-enabled GPU
Authors: Fan Zhongyin , Xie Yaoqin 
Published: August 2015

Abstract Computed tomography (CT) has been widely used to acquire volumetric anatomical information in the diagnosis and treatment of illnesses in many clinics. However, the ART algorithm for reconstruction from under-sampled and noisy projection is still time-consuming. It is the goal of our work to improve a block-wise approximate parallel implementation for the ART algorithm on CUDA-enabled GPU to make the ART algorithm applicable to the clinical environment. The resulting method has several compelling features: (1) the rays are allotted into blocks, making the rays in the same block parallel; (2) GPU implementation caters to the actual industrial and medical application demand. We test the algorithm on a digital shepp-logan phantom, and the results indicate that our method is more efficient than the existing CPU implementation. The high computation efficiency achieved in our algorithm makes it possible for clinicians to obtain real-time 3D images.
An epileptic focus location method based on ECoG
Authors: Tang Xiaoying , Zhang Xiying , Cai Dongqin , Du Juncheng , Liu Weifeng 
Published: August 2015

Abstract Epilepsy is a neurological disorder characterized by the sudden abnormal discharging of brain neurons that can lead to encephalographic (EEG) abnormalities. In this study, data was obtained from epileptic patients with intracranial depth electrodes and analyzed using wavelet entropy algorithms in order to locate the epileptic foci. Significant increases in the wavelet entropy of the epileptic signals were identified during multiple episodes of clinical seizures. The results indicated that the algorithm was capable of identifying entropy changes in the epileptic sources. Furthermore, the correlations among the electrocorticogram (ECoG) signals of different channels determined using the amplitude-amplitude coupling method verified that the epileptic foci exhibited significantly higher coupling strengths. Thus, cross frequency coupling (CFC) could be an inspiration to energy and signal transitive mode of seizure and, thereby, improve diagnostic processes.