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2022 Vol. 41, No. 3
Published: 2022-06-20
Reviews
Communications
Regular Papers
CONTENTS
Regular Papers
257
Study on the Parkinson's Disease EEG Tracing and Characteristics of Brain Functional Network
Wang Zhaoya, Yao Yao, Yin Ning, Feng Keke, Xu Guizhi, Yin Shaoya
DOI: 10.3969/j.issn.0258-8021.2022.03.001
Parkinson's disease is a common neurodegenerative disease in the elderly, but there is still no recognized specific biochemical diagnostic index. To explore the EEG differences between Parkinson's disease patients and healthy subjects, the resting state EEG of 26 Parkinson's disease patients and 26 healthy subjects were collected. Subjects' cognitive function was assessed subjectively by MMSE and MoCA scales, and their motor function was assessed by UPDRS and H&Y scales. Firstly, the EEG of the two groups were traced by sLORETA, and then by adopting the method of lag phase synchronization of brain function network difference analysis, topological property of brain functional network of the two groups were compared and analyzed. The results showed that the occipital lobe and parietal lobe were the main brain regions of PD group compared with healthy control group, and the α2 band precuneus (BA7) current density was significantly decreased. The functional connectivity between the frontal lobe and occipital lobe in β1 band was significantly decreased, the mean clustering coefficient (
C
), global efficiency (
E
g
) and node degree (
D
) of patients' brain functional network weresignificantly lower than those of healthy control group (patients:
C=0.54±0.14, Eg=0.71±0.09, D
=26.88±9.88; healthy subjects:
C=0.75±0.13, E
g
=0.83±0.09, D
=42.21±11.19), and the mean characteristic path length (
L
) of patients' brain functional network wassignificantly higher than that of healthy control group (patient:
L
=1.61±0.2; healthy subjects:
L
=1.34±0.19), with statistical significance (
P
<0.01). In this study, the α2 band precuneus (BA7) current density was significantly decreased and the decrease of functional the decrease of functional connection efficiency in β1 band have guiding significance for the diagnosis of PD.
2022 Vol. 41 (3): 257-265 [
Abstract
] (
318
)
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266
A Study for ERP Classification of Food Preference Based on CSP and SVM
Li Chunyu, He Feng, Qi Hongzhi, Guo Xiaoyi, Chen Long, Ming Dong
DOI: 10.3969/j.issn.0258-8021.2022.03.002
In this study an ERP experiment was conducted to investigate the difference of ERP evoked by individual food preferences. A new classification method was proposed. The oddball paradigm was adopted and 18 subjects participated in this experiment. After ranking 5 kinds of food, ERP was induced by different food stimulus, and the highest and lowest score were collected. The ERPs were analyzed to determine whether there were significant differences in the signals related to the different food. Next, common spatial pattern and support vector machines were used for feature extraction and single-trial ERP classification respectively. The leave-one-out method was used for cross validation. Results showed that P3 amplitudes for food with high or low score were different significantly and P3 amplitudes were larger in the former compared to the latter. The average amplitude increased by about 15%. A positive correlation between P3 amplitudes and food scores was seen. The averaged accuracy of classification could reach 93.16% when 4 single-trial ERP were used. These results suggested that brain reactivities responding to food preferences were quite different and the proposed method achieved expected results. In conclusion, ERP can be used as a new tool for food preference analysis and provides a new solution to food evaluation and assistant treatment about anorexia.
2022 Vol. 41 (3): 266-272 [
Abstract
] (
246
)
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273
A Study on Sleep Staging Algorithm for Patients with Sleep Apnea Syndrome
Lu Keke, Qi Xia, Zhang Jianbao, Wang Gang, Yan Xiangguo
DOI: 10.3969/j.issn.0258-8021.2022.03.003
Aiming at the lack of effective automatic sleep staging method for wearable sleep monitoring, an automatic sleep staging algorithm suitable for patients with sleep apnea syndrome was proposed in this paper. Through the R-R interval sequence of an ECG signal, the signals of heart rate variability (HRV), respiratory amplitude variability (RAV) and respiratory rate variability (RRV) were obtained. On this basis, 55 features of time, frequency, and nonlinear domains were extracted. Four sleep staging models with different classification granularity were constructed by using gated circulation unit network: W-S two classification, W-REM-NREM three classification, W-REM-LS-SWS four classification, W-REM-N1-N2-N3 five classification. A categorical weighting method for the loss function was used to effectively reduce the impact of data imbalance on the sleep staging results. The accuracy, Cohen's kappa coefficient and sleep structure indexes were used to evaluate the performance of the sleep staging algorithm. The results showed that the values of accuracy and kappa of the four classifiers were 85.06%, 75.44%, 63.80%, 62.13%, and 0.54, 0.49, 0.41, 0.41, respectively, and the sleep structure analyzing showed there was no statistically significant difference from clinical results. The method proposed displayed the ability of meeting the needs of sleep quality evaluation and is expected to be used for wearable sleep monitoring applications.
2022 Vol. 41 (3): 273-281 [
Abstract
] (
245
)
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282
The Large-Scale Brain Network Dysfunction of Juvenile Myoclonus Epilepsy Patients
Ke Ming, Cui Jiping, Liu Guangyao
DOI: 10.3969/j.issn.0258-8021.2022.03.004
Resting-state functional magnetic resonance imaging (fMRI) data were used to investigate the changes of large-scale brain networks in juvenile myoclonus epilepsy (JME) patients. The brain rs-fMRI data of 17 JME patients and 15 normal volunteers were collected. The partial correlation coefficient was used to construct resting state brain networks in both groups. Thresholds were independently calculated for the JME group and the normal control group. A binary brain network was built. The betweenness values of each brain region of the two groups were calculated. Two-sample T test was used to compare the differences in the betweenness values of brain network between the two groups (Bonferroni correction,
P
<0.01). The brain regions with significant changes of betweenness values were identified. The brain network was constructed by the partial correlation coefficient, and the brain network showed small-world property. The betweenness values of brain regions of JME patients group was significantly different from that of normal control group. Compared with the normal control group, in the JME patient group, there were 2 brain regions with significantly lower betweenness values and 17 brain regions with significantly higher betweenness values. Among them, 8 brain regions belong to the default mode network (DMN), and 5 brain regions belong to the salience network (SN). The betweenness values of the right paracentral lobule and the right posterior cingulate gyrus were significantly reduced in JME group. The regions of significantly increased betweenness values were mainly distributed in the right dorsolateral superior frontal gyrus, the left middle occipital gyrus, the right precuneus, the right lingual gyrus. The brain regions where the betweenness value of JME patients had significantly changed mainly belong to the default mode network and the salience network. It can be inferred that the connections between internal brain regions had changed in default mode network and salience network separately. These changes showed that JME patients' brain functions had been influenced and changed, resulting in impaired cognitive and executive functions.
2022 Vol. 41 (3): 282-289 [
Abstract
] (
183
)
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290
Research on Brain Glioma Segmentation Algorithm
Zhang Shiqang, Shi Lei, Cheng Xiaodong
DOI: 10.3969/j.issn.0258-8021.2022.03.005
Due to the complexity of medical imaging and the high heterogeneity of the surface of gliomas, image segmentation of human brain gliomas is one of the most challenging tasks in medical image analysis. This paper aimed to improve the UNet++ medical image segmentation network, the improved network can fuse coarse-grained semantics and fine-grained semantics at full scale. Experiments were performed on 335 images obtained from the public BraTS brain tumor segmentation data set, using 2D and 3D comparative segmentation experiments to comprehensively evaluate the segmentation performance of the improved network and compare the segmentation results with the results of UNet, UNet++, and UNet3+ medical image segmentation networks. Among the four indicators of Dice similarity coefficient (DSC), 95% Hausdorff surface distance (HSD95),sensitivity, and positive predictive value (PPV), 2D contrast segmentation achieved the mean values of the indicators of 83.70%, 1.7, 88.40%, 84.96% respectively; the mean values of the 3D contrast segmentation reached 90.79%, 0.242, 91.23%, 91.06% respectively. Compared with the segmentation result indicators of the other three networks, in the 2D comparison experiment, DSC increased by 1.82% on average, HSD95 decreased by 0.35 on average, sensitivity increased by 2.13% on average, and PPV increased by 0.80% on average; in the 3D comparison experiment, DSC increased by 2.78% on average, HSD95 decreased by 0.076 on average, Sensitivity increased by 3.81% on average, and PPV increased by 0.68% on average. It was shown that the proposed algorithm made the segmentation result of glioma and the gold standard overlap more in the region, and completed the segmentation of glioma better. It is expected to help neurosurgeons to more precisely separate brain tumors and tissues around the brain and achieve rapid computer diagnosis and treatment.
2022 Vol. 41 (3): 290-300 [
Abstract
] (
260
)
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301
Arrhythmia Classification Based on Time-Series Cardiac Model Sample Equalization
Xu Yonghong, Wang Jinping, Ma Jiayue
DOI: 10.3969/j.issn.0258-8021.2022.03.006
As an important clinical application of computer, automatic classification of arrhythmia can effectivelyassist in the diagnosis of cardiovascular diseases. However, the sample imbalance in experiments seriously affects the classification accuracy. At present, the mainstream method to solve the problem of sample imbalance is counter neural network, but it has some problems such as unstable training and mode collapse, and only relies on data for learning, which is lack of certain physiological significance. Therefore, this paper proposed a sample equalization method based on the time-series cardiac model to generate ECG data. The experiment was carried out on the 12 lead dataset provided by China physiological signal challenge (CPSC) in 2018. The deep residual network was used as the classification network to train each lead, and the lead fusion was realized by XGBoost algorithm. After sample equalization, F1 scores of all types were improved, especially in left bundle branch block (LBBB), ST segment depression (STD) and ST segment elevation (STE), which was increased from 0.706, 0.684 and 0.524 to 0.832, 0.809 and 0.618, respectively. In order to verify the universality of this method, PTB dataset was tested independently, and the classification accuracy reached 97.42%. The experimental results showed that the generation of simulation data based on the sequential heart model effectively improved the imbalance of experimental samples.
2022 Vol. 41 (3): 301-309 [
Abstract
] (
236
)
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310
Biomechanical Modeling and Experimental Study on the Common Sites of Pressure Ulcers inthe Process of Assisted Turning over from Supine Position
Lun Qinglong, Su Peng, Lu Da, Li Shuo, Li Jian
DOI: 10.3969/j.issn.0258-8021.2022.03.007
A pressure ulcer is a condition in which the long-term pressure on local tissues leads to tissue ulceration and necrosis. Relief of local pressure through supine lateral rotation is one of the effective measures to prevent the development of pressure ulcers in long-term bedridden patients, so it is significant to study the biomechanics of the typical site of pressure ulcers during assisted lateral rotation. Based on anatomical theory, a finite element model of the bones and soft tissues of the pressure ulcer-prone areas of the trunk was established using CT image data to study the maximum stresses on the pressure ulcer-prone areas of the trunk at different lateral turning angles, and a human supine lateral turning experiment was conducted on 10 volunteers using the TEKSCAN pressure distribution test system to verify the correctness of the simulation analysis. Results showed that the stresses on the same part of the body were different during the rollover process, with extreme points of stress peaks at 90° position, producing a stress concentration phenomenon. The peak stresses at each part of the shoulder and hip were relatively evenly balanced within the angular threshold range of 30° to 45°, both at 0.035~0.070 MPa, indicating that the body was in contact with multiple parts of the auxiliary surface within this threshold and that the stresses are more dispersed, with a form of contact between the two that optimized pressure on multiple pressure ulcer prone areas. This study demonstrated the changes in pressure on pressure-prone areas of the trunk during assisted supine lateral rotation, and the obtained results would be useful for the design and use of supine lateral rotation aids and as a reference for clinical care.
2022 Vol. 41 (3): 310-319 [
Abstract
] (
202
)
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320
Research on Automatic Optimization of Weighting Factors in Radiotherapy Based on Definite Integral
Guo Caiping, Zhang Junsheng, Zhang Xiaojuan
DOI: 10.3969/j.issn.0258-8021.2022.03.008
To solve the problem of time-consuming and low efficiency that exist in the process of manual inverse planning, a strategy adjusting weighting factor for sub-objective function of physical criteria was constructed based on the definite integral theory, and an automatic iterative adjustment method of weighting factors was proposed in this paper. In the automatic method, the weighting factors are automatically and iteratively adjusted first based on proposed penalty strategies. Then, plan evaluation was performed to determine whether the obtained plan was acceptable. If not, a higher penalty was assigned to the unsatisfied objective by multiplying it by a compensation coefficient. The optimization processes were performed alternately until an acceptable plan was obtained or the maximum number of iteration was reached. The effectiveness of the method was verified on 10 prostate cancer cases and compared with the manual planning from the perspective of dosimetry and biology. Experimental results showed that, in terms of DVH curves and dose statistics, the mean dose, V
65
, V
70
, normal tissue complications probability and generalized equivalent uniform dose of bladder were reduced by 0.53Gy, 4.6%, 3.33%, 0.37% and 0.22Gy, respectively, under the premise that the dose coverage characteristics of target area were similar. And the maximum dose of rectum, V
50
and V
65
decreased by 0.54Gy, 0.66% and 1.64%, respectively. There was no significant difference in other indexes (
P
>0.05) except V
75
of bladder (
P
<0.05); it took 1~3 minutes to produce an acceptable plan applying our proposed automatic optimization method of weighting factors, while for the manual trial-and-error method, it needed professional physicians to take 1~3 hours to obtained an acceptable plan. In conclusion, the automatic optimization method of radiotherapy weighting factors based on definite integral improved the efficiency of radiotherapy and generated satisfactory radiotherapy plans.
2022 Vol. 41 (3): 320-327 [
Abstract
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150
)
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Reviews
328
A Review on Hybrid FES-Robotic Control Strategies of Lower-Limb Exoskeleton Robots for Gait Rehabilitation
Meng Lin, Hou Jie, Dong Hongtao, Liu Yuan, Xu Rui, Ming Dong
DOI: 10.3969/j.issn.0258-8021.2022.03.009
Motion rehabilitation plays a significant role in encouraging neuroplasticity and improving motor functions of patients with spinal cord injuries or strokes. In recent years, the incorporation of functional electrical stimulation (FES) and exoskeleton robots has gradually become a research hotspot in the field as the method takes advantages of both rehabilitation technologies while complementing each other. This article introduced a comprehensive review of hybrid FES-robotic control strategies by analyzing technical difficulties from the aspects ofunidirectional FES-orthosis control strategies and cooperative FES-exoskeleton control strategies. We discussed the key problems and potential solutions to design an optimized cooperative control strategy based on a closed-loop human-computer interaction to realize dynamic control allocation and therefore maximize active rehabilitation performance of patients. Finally, we prospected future development of the hybrid rehabilitation technology.
2022 Vol. 41 (3): 328-338 [
Abstract
] (
228
)
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339
Research Progress and Application of the Third Generation of Artificial Heart Pump
Liu Xin, Qu Hongyi, Wang Cong , Liu Jianhua, Wang Qiuliang
DOI: 10.3969/j.issn.0258-8021.2022.03.010
The death rate of heart failure is extremely high and the number ofpatients continues to rise. Artificial heart pump is the last hope and the most effective way to prolong the survival time of many patients with heart failure. The development and application of the third generation of artificial heart pump can push the treatment of heart failure to a new level. First of all, this paper summarized the research status and application of the third generation of artificial heart pump, and the development of the third generation of artificial heart pump in China was introduced as well. Secondly, the third generation of artificial heart pump related suspension technology, bearingless motor, pump control algorithm, impeller optimization design, blood compatibility and other key technologies were described and summarized in detail. Finally, the research trends of miniaturization lightweight, bionic stroke, intelligent control technology, blood compatibility, reliability and fault tolerance technology are proposed and discussed.
2022 Vol. 41 (3): 339-350 [
Abstract
] (
431
)
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351
Research Progresses on Attentional Modulation via Neurofeedback in Healthy Individuals
Yang Wenjie, Nan Wenya, Gong Anmin, Fu Yunfa
DOI: 10.3969/j.issn.0258-8021.2022.03.011
Neurofeedback converts brain activity into sound, image, game and other forms and feedback to individuals via brain-computer interaction, to achieve self-regulation of brain function. As an important aspect of cognitive function, attention not only affects the development and growth of children, but also stands as an irreplaceable psychological quality of adults in daily life. A growing number of studies have found out that neurofeedback can modulate attention in healthy individuals, however, there are lack of reviews to sort out and summarize the emerging research. This article reviewed the research progress of brain rhythm-based neurofeedback effects on attention in healthy individuals. The impact of experimental factors on the neurofeedback training efficacy were analyzed, including the paradigm of attention measurement, the division of learners and non-learners and the type of experimental design. We also emphasized the importance of follow-up experiments. Finally, some other neurofeedback techniques that have potentials of regulating attention in healthy individuals were introduced. This review aimed to provide a reference for the research and practice of attention enhancement using neurofeedback.
2022 Vol. 41 (3): 351-359 [
Abstract
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179
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360
Knowledge Mapping Analysis of Motor Related Cortical Potentials
Dai Wenhao, Chen Jie, Xie Ping, Li Guoqiang, Wang Dapeng
DOI: 10.3969/j.issn.0258-8021.2022.03.012
Motor-related cortical potential is an event-related potential, which can reflect the pre-exercise planning, preparation, and early exercise execution process. It has attracted much attention in recent years. Based on the 498 literatures with research theme "motor-related cortical potential" collected in the core collection of the web of science from 2000 to now, the city space V visualization technology was used to draw maps and analyze related problems. The analysis showed that the research hotspots mainly focued on motor-related potential, motor cortex, EEG, motor, and so on. The research and development of motor-related cortical potential could be divided into three stages according to time. The first stage mainly focused on the research of frontal lobe, evoked potential, and swallowing evoked motor-related cortical potential. The second stage was mainly focused on the brain-computer interface, motor imagination, motor detection potential, and Parkinson's syndrome. In the third stage, the research trend was gradually developing towards rehabilitation research, mainly focusing on EEG, attention and grip analysis. The analysis of the research hotspots and development process of motor-related cortical potential provided a reference for theoretical and applied research in the fields of physical training and sports rehabilitation.
2022 Vol. 41 (3): 360-369 [
Abstract
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285
)
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Communications
370
An Automated Apnea Detection Method Based on Wavelet Decomposition of EEG Signals
Wang Yao, Yang Tianshun, Ji Siyu, Wang Xiaohong, Wang Huiquan, Wang Jinhai, Zhao Xiaoyun
DOI: 10.3969/j.issn.0258-8021.2022.03.013
2022 Vol. 41 (3): 370-374 [
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184
)
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455
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375
Design of Miniaturized Two-Photon Microscopy Probe Based on Electrothermal MEMS Mirror
Yu Xiaomin, Zhang Linjia, Luo Wensong, Xie Huikai
DOI: 10.3969/j.issn.0258-8021.2022.03.014
2022 Vol. 41 (3): 375-379 [
Abstract
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139
)
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272
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380
Preparation and Performance Analysis of Novel Carbon- Based Scalp EEG Dry Electrodes
Zhang Lijian, Ming Dong, Jia Zhengwei
DOI: 10.3969/j.issn.0258-8021.2022.03.015
2022 Vol. 41 (3): 380-384 [
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157
)
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