Special issue

1 Intraoperative Hypothermia Prediction Model Based on Feature Selection and XGBoost Optimization

CAO Liyuan , FAN Qinqin , HUANG Jingying

2022, 37(1):134-146. DOI: 10.16337/j.1004-9037.2022.01.011

[Abstract](1034) [HTML](1519) [PDF 1.89 M](2039)

Abstract:
In view of the high incidence of intraoperative hypothermia and complex influencing factors in patients undergoing anesthesia， a prediction model of intraoperative hypothermia based on feature selection and XGBoost optimization is proposed to better assist doctors in the clinical diagnosis of patients. Firstly， the random forest （RF） is used to deal with the high-dimensional data sets， and features are selected by the RF out-of-bag estimation. Then， XGBoost hyperparameters are optimized using the genetic algorithm based on elite retention strategy， i.e.， EGA. Finally， the prediction is trained according to the optimal parameters and thus can be used to predict intraoperative hypothermia. This model combines the advantages of three algorithms to improve model generalization ability and prediction accuracy. The experimental result shows that the proposed model performs better other seven machine learning classification prediction models such as logistic regression， support vector machine， and so on in prediction accuracy， precision， recall and AUC， and overcomes the three representative hyperparameter tuning methods.

2 Medical Image Synthesis Based on Optimized Cycle-Generative Adversarial Networks

CAO Guogang , LIU Shunkun , MAO Hongdong , ZHANG Shu , CHEN Ying , DAI Cuixia

2022, 37(1):155-163. DOI: 10.16337/j.1004-9037.2022.01.013

[Abstract](1066) [HTML](1654) [PDF 1.56 M](2304)

Abstract:
The radiation treatment plan system needs to calculate the dose distribution accurately based on CT images， but sometimes clinical MR images can only be obtained. Image synthesis effectively creates new modality images from another modality， which enhances image information. This paper presents a new method of synthesizing high precision and definition of CT images from MR images. To synthesize clearly pseudo CT images， an improved cycle-consistent generative adversarial network （CycleGAN） with densely connected convolutional network （DenseNet） is proposed. Avoiding the disappearance of input information and the vanishing of gradient information， the improved network can synthesize more credible CT images. Compared with the original method， the proposed method is reduced by 5.9% on mean absolute error， increased by 1.1% on structural similarity and increased by 4.4% on peak signal to ratio， which is trained and tested on the dataset of 18 patients. And compared with the deep convolutional neural network and the atlas-based method， the improved CycleGAN is reduced by 0.065% and 0.55% on relative error， respectively. The proposed method can synthesize more vivid CT images owing to the advantages of deep learning model， which better meets the requirements of dose calculation in radiation treatment planning system.

3 MEL-YOLO：Multi-task Human Eye Attribute Recognition and Key Point Location Network

WU Dongliang , SHEN Wenzhong , LIU Linsong

2022, 37(1):82-93. DOI: 10.16337/j.1004-9037.2022.01.007

[Abstract](1028) [HTML](2637) [PDF 2.41 M](2383)

Abstract:
The existing eye location algorithms have some disadvantages of single task and performance degrade in complex environment such as illumination， glasses and occlusion， so a multi- efficient， light-YOLO and lightweight neural network， MEL-YOLO， is designed for obtaining eye multi-attributes and landmarks. Based on the YOLOV3 network， combining with the enhanced DS-sandglass block， a denormalized coding and encoding method is used in the regression branch of key points to promote the network positioning depth， and the complete intersection-over-union （CIoU） and the mean square error （MSE） are introduced into the loss function， so promoting the overall performance of the network. On the near-infrared dataset， the MEL-YOLO network achieves the position accuracy of 100%， and achieves the attribute recognition rate and the landmark accuracy rate of 98.7% and 96.5%， while reaches 92% and 91% on the UBIRS dataset. The experimental results demonstrate that the MEL-YOLO network can accurately obtain eye multi-attributes and key point information. Also， it is proved that MEL-YOLO is small and robust， and has the firm generalization ability， thus applying to low-performance edge computing devices.

4 Dual-Attention Network for Acute Pancreatitis Diagnosis with CT Images

Zhang Jinyi , Wan Peng , Sun Liang , Zhang Daoqiang

2022, 37(1):147-154. DOI: 10.16337/j.1004-9037.2022.01.012

[Abstract](887) [HTML](1702) [PDF 2.27 M](2430)

Abstract:
Acute pancreatitis （AP） is one of the most common digestive disease， while the analysis based on medical images of AP still depends on simple manual features with low efficiency and accuracy， which is not commensurate with AP’s harmfulness. Due to the anatomical variation of pancreas and complications of AP， AP has complex imaging manifestations and large appearance pattern variation of lesions that exist among patients and lesion kinds. It is challenging for diagnosis of acute pancreatitis based on CT images. To address these issues， we propose a dual-attention network for acute pancreatitis diagnosis. Specifically， the dual-attention network utilizes the global feature to generate local attention feature for each local feature on different stages， and final classification is facilitated by the fusion of multi-scale attention features focusing on lesions of different scales. Meanwhile， channel-domain attention is used to produce attention features based on the dependencies between each channel to improve the model’s feature representation ability. We evaluate the proposed method on the collected real acute pancreatitis dataset. Results show that the proposed network achieve superior performance in acute pancreatitis diagnosis compared with several competing methods， with the sensitivity improved by 3.4%. And the improvement of area under the curve （AUC） the proposed network brings to ResNet is 2.7% higher than other attention model such as SENet.

5 Somatosensory Interaction Technology Based on Limiting Weighted Skeleton Node Filtering

CHEN Jinyi , LUO Shengqin , LI Hongjun

2022, 37(3):715-724. DOI: 10.16337/j.1004-9037.2022.03.020

[Abstract](652) [HTML](938) [PDF 1.56 M](1815)

Abstract:
To improve the operation mode of robots and improve the recognition accuracy of somatosensory interaction， a somatosensory interaction technique based on the limiting weighted skeleton node filtering is proposed. Firstly， the Kinect sensor is used to acquire the depth scene information， the obtained depth information is processed by the skeleton tracking technology to match the joints of the human body， and the 3D coordinates of the joints of the human body are established. Then the rotation angles of each joint are calculated in the form of space vector mapping， and the proposed limiting weighted filtering algorithm is used to reduce the influence of bone noise by limiting weighted filtering the acquired and calculated joint rotation angles. Finally， the rotation angle is converted into a control command， which is sent to the mechanical arm controller through the Bluetooth serial port， and the steering of the mechanical arm is controlled. Experimental results show that the method can realize the somatosensory interaction effect， and the recognition rate of the robot arm with the human arm movement is 96.3%， and the limiting weighted filtering algorithm can effectively reduce the influence of skeleton noise.

6 Multi-structure Segmentation of Intracranial Vessels with Aneurysms Based on Adaptive Sampling and Dense Mechanism

ZHANG Xuyang , YAO Yunchu , SHI Yue , TONG Xin , LIANG Xinyu , TONG Xinyu , LIU Aihua , CHEN Duanduan

2022, 37(4):766-775. DOI: 10.16337/j.1004-9037.2022.04.006

[Abstract](1006) [HTML](951) [PDF 3.71 M](2387)

Abstract:
Intracranial aneurysm is a common cerebral vascular disease with a relatively high lethiferous and disable rate. An image-based intelligent and accurate diagnosis method of the disease is urgently needed by the clinic in recent years， for which the accurate segmentation of the vessels and aneurysms is very essential. In this work， we present a novel segmentation framework for the multi-structure intracranial vessels with aneurysms. An adaptive image sampling method is designed using the prior gray-level vascular features， and a Dense mechanism-based network is proposed for the vessel segmentation. Time-of-flight magnetic resonance angiography images of 135 patients （age： 54.7±12.7， 75 males） with intracranial aneurysms are collected for training and testing the framework. Compared with the sampling in the original space and image compression （mean DSC： 0.829 and 0.780）， the adaptive sampling can obviously improve the accuracy of the vessel segmentation （mean DSC： 0.858）. The Dense mechanism-based network can achieve better segmentation result while using less calculation space than the traditional models of 3D UNet， SegNet and DeepLabV3+ （mean DSC： 0.854，0.824 and 0.800）. It also shows good robustness for the segmentation of aneurysms with various locations and sizes.

7 Prediction on Pulse-Taking for H-type Hypertension Under Hybrid Deep Learning Mechanism

Yang Jingdong , Chen Lei , Cai Shuchen , Xie Tianxiao , Yan Haixia

2022, 37(4):883-893. DOI: 10.16337/j.1004-9037.2022.04.016

[Abstract](1090) [HTML](817) [PDF 2.08 M](2255)

Abstract:
The diagnosis of H-type hypertension requires the determination of the patient’s plasma homocysteine content， which is inefficient and has a wound. Chinese pulse diagnosis helps doctors diagnose H-type hypertension by analyzing patient’s pulse activity and combining inquiry information. Therefore， we put forward a pulse-taking diagnosis classifiction model based on hybrid deep learning model， which can extract the local features via convolutional neural network（CNN） block， and long-term dependency features via Bi-directional long short-term memory（BiLSTM） block. The data come from 325 suspected cases of pulse diagnosis collected by Longhua Hospital affiliated to Shanghai University of Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We compare the proposed model with other machine learning models on the pulse diagnosis data respectively. The sensitivity， specificity， accuracy， F₁-score， receiver operating characteristic（ROC）area under curve （AUC） values of the proposed model are 79.71%， 69.56%， 77.17%， 83.96%， 0.850 0， respectively， higher than the performance of other machine learning models. The results show that our model has good performance and has good reference value for the clinical diagnosis of traditional Chinese medicine.

8 Brain Disease Prediction Based on Noise Confusion to Enhance Robustness of Features

HAO Xiaoke , TAN Qihao , LI Jiawang , GUO Yingchun , YU Ming

2022, 37(4):776-786. DOI: 10.16337/j.1004-9037.2022.04.007

[Abstract](1096) [HTML](539) [PDF 2.91 M](2188)

Abstract:
With the continuous development of medical imaging data， longitudinal data analysis is gradually becoming an important research direction to understand and trace the process of the Alzheimer’s disease （AD）. At present， many longitudinal data analysis methods have been proposed， among which multi-task learning is widely used， which can integrate imaging data of multiple time points to improve the generalization ability of the model. Most existing methods can identify shared features at different time points， but these features will contain a certain amount of noise. At the same time， potential associations of disease progression at different time points remain under explored. In this paper， we propose a parameter decomposition and relation-induced multi-task learning （PDRIMTL） method to identify features from longitudinal data. The method can not only identify shared features after noise removal and improve the robustness of shared features， but also can model the intrinsic associations between different time points. The results show that the model can effectively improve the accuracy of AD identification on structural magnetic resonance imaging （sMRI） data at different time points.

9 Extraction Method of Gait Parameters Based on Kinect System

ZHANG Xiaoyu , CHEN Kai , YANG Ying

2022, 37(4):872-882. DOI: 10.16337/j.1004-9037.2022.04.015

[Abstract](1159) [HTML](489) [PDF 2.00 M](2231)

Abstract:
Based on the definition of gait parameters， this paper proposes and studies a method of collecting and extracting gait parameters using the Microsoft’s Azure Kinect maker-free motion capture system（hereinafter referred to as Kinect system）. At the same time， adaptive filtering， exponential filtering， Kalman filtering and no filtering conditions are used in data processing to improve the smoothness of gait data. In order to evaluate the accuracy of Kinect system and the effectiveness of filtering， the results of extracted gait parameter are statistically compared with those of the Qualisys marker-based motion capture system （Company of Sweden， hereinafter referred to as Q marker-based） in the synchronous experiment， and the different filtering methods are evaluated accordingly. The results show that， in general， the Kinect system has a high consistency with the Q marker-based， and the results under the three filtering conditions all fall within the 95% consistency limit. In terms of their gait parameters， the results of the gait speed are quite different under all filtering conditions， which cannot be applied. For other parameters， adaptive filtering and Kalman filtering show good consistency. Kinect system can accurately calculate the gait parameters of healthy people by applying the proposed method and smoothing it with Kalman filtering， and it can replace the marker-based device in some cases.

10 Human Activity Recognition Based on Heuristic Integrated Feature Selection

Dai Jianwei , Li Ruixiang , Chen Jinyao , Le Yanfen , Shi Weibin

2022, 37(4):860-871. DOI: 10.16337/j.1004-9037.2022.04.014

[Abstract](872) [HTML](420) [PDF 1.75 M](2146)

Abstract:
To address the problem that artificially extracted redundant feature sets and irrelevant feature sets lead to the degradation of human activity recognition classification performance of wearable sensor， this paper proposes a human activity recognition method based on heuristic integrated feature selection. The method first selects the feature set containing power spectral density （PSD） for recognizing confusing activities. Then， on this basis， the method screens out the lowly correlated feature subsets with the help of Pearson correlation coefficient （PCC） method， then uses an improved sine cosine algorithm （SCA） for features and obtains the optimal feature subset by screening the feature twice. The experimental results show that the feature subset dimension after using this method in the data set collected in the laboratory is 34， and the recognition accuracy rate reaches 98.21%. In the public SCUT-NAA data set for comparison experiments， the feature subset dimension is 39， lower than the feature dimension of previous research methods， and the recognition accuracy rate reaches 96.51%.

11 Diagnosis of Intracranial Hemorrhage in Brain CT Images Based on Cost-Sensitive Faster R-CNN

ZHU Xiaowei , WAN Peng , ZHANG Daoqiang , CHENG Le , WANG Yi

2022, 37(4):757-765. DOI: 10.16337/j.1004-9037.2022.04.005

[Abstract](1006) [HTML](677) [PDF 1.59 M](2120)

Abstract:
An intracranial hemorrhage （ICH） is a kind of severe emergency that occurs suddenly in patients’ brain with strong symptoms and high mortality. So it is of great significance to diagnose ICH automatically and quickly based on brain CT images. However， effective clinical application requires not only the accuracy， speed and interpretation ability of models， but also especially the emphasis given to the missed detection of bleeding. Therefore， cost-sensitive Faster R-CNN is proposed in this paper to diagnose ICH， through an automatic adjustment mechanism for the proportion of training samples and a hyperparameter introduced to loss function to measure the importance of positive samples. It can pay more attention to the missed detection situations to improve the detection effect， and diagnose ICH by located target region. A network structure with optimal performance and appropriate parameter is selected for good effect of detection and diagnosis through experiments. And then， results are measured by several indexes. It is shown that the cost-sensitive Faster R-CNN model can detect bleeding well by focusing on missed checks， so as to improve the diagnosis effect under the unbalanced cost.

12 Clustering Related Factors of Intrinsic Frequency Dynamic Functional Connection in MRI Signal of Mild cognitive Impairment

LI Dong , WU Haifeng , BAO Han , MA Jia , ZENG Yu

2022, 37(4):798-813. DOI: 10.16337/j.1004-9037.2022.04.009

[Abstract](1086) [HTML](449) [PDF 4.56 M](2356)

Abstract:
Functional connectivity （FC） can represent the ability of brain regions to work together. At present， a combination of dynamic functional connectivity （DFC） and cluster analysis is widely used to study the significant difference analysis and classification of diseases. However， in the existing study， there is no clear standard for the determination of the number of clusters and the selection of clustering results， and the traditional DFC cannot represent the FC information of different frequencies. Therefore， this paper studies the clustering related factors of intrinsic frequency DFC in MRI signal of mild cognitive impairment （MCI）. First， the noise-assisted multivariate empirical mode decomposition of the time course （TC） data is performed and the DFC is calculated. Then， the cluster is analyzed through the evaluation-assisted clustering method， and the least square method is used to fit the clustering results. Finally， classifier is used for classification. The contribution of this paper is to suggest a more reasonable clustering method and a more number of clusters to obtain functional connections at different intrinsic frequencies. In the experiment， we used the Alzheimer’s disease neuroimaging （ANDI） database to test the proposed method. The experimental results show that the accuracy of supervised clustering used in this paper is higher than that of unsupervised clustering； the classification accuracy of DFC with natural frequency is higher than that of traditional DFC； the least square fitting can improve classification accuracy.

13 Acquisition Technology of Multimodality Neurophysiological Signals Based on Asynchronous Chip

Zhu Lixian , Tian Fuze , Dong Qunxi , Zhao Qinglin , He Anping , Zheng Weihao , Hu Bin

2022, 37(4):848-859. DOI: 10.16337/j.1004-9037.2022.04.013

[Abstract](1462) [HTML](1173) [PDF 1.58 M](2783)

Abstract:
Most of psychophysiological computing （PPC） studies are under the experimental environments of synchronization theory hypothesis， however neurophysiological representations have asynchronous properties， which cannot be precisely and effectively described in real time using synchronized recording technology. It is being the first issue of PPC to resolve how to recode these asynchronous multi-modality neurophysiological activities with low-power， low-redundancy， real-time and accurate. For this issue， this study focuses on the goals of microscopic neurophysiological activities and macroscopic psychological variables， resolves the design challenges of asynchronous multimodality physiological information recording scheme and corresponding passive physiological signals sensing technology， and designs and develops the first asynchronous physiological process unit （PPU）. The PPU has the characteristics of low power consumption， high time series precision， high computing performance and strong anti-interference ability. Finally，we look forward to the future of PPU applied in the research area of brain science and brain-like computing.

14 Research on Neurodynamic Coupling Based on Synchronization Analysis Between EEG and IMU Signals

XIE Ping , YU Jian , ZHANG Tengyu , CHENG Shengcui , LYU Yan , CHEN Xiaoling

2022, 37(4):736-746. DOI: 10.16337/j.1004-9037.2022.04.003

[Abstract](905) [HTML](973) [PDF 3.59 M](2243)

Abstract:
Motor control is a process of multifaceted coordination and information interaction among neural， motor and sensory functions. The relationships between motion and physiological information in the motor control system is helpful to understand the mechanism of human motion control. Therefore， to explore the causal relationship and the evolutionary law between electroencephalogram （EEG） and acceleration （ACC） signals during upper limb movement and rest， we apply the coherence method in this study. Firstly， the EEG and ACC signals of 7 subjects are preprocessed to remove the interference components in the signals. Secondly， the coherence values between EEG and ACC signals during the resting， motion-action and motion-maintaining states are calculated respectively， and the significant area is then calculated by the threshold index of significant coherence. The results show that the significant areas in the motion-action state are larger than that of in the motion-maintaining state， and in the motion maintenance state is larger than that of in the resting state. Furthermore， the significant areas between EEG signals of C3 and C4 channels and ACC signals are more significant in the contralateral motor cortex during left and right upper limb movements. These results indicate that there are significant differences between EEG and ACC signals during the resting， motion-action and motion-maintaining states of upper limb movements， which can be helpful to deeply understand the neuromotor control mechanism， and also provide a new quantitative index and the theoretical basis for the assessment of motor function and the early diagnosis of motor dysfunction diseases.

15 EEG Emotion Recognition Based on Convolutional Joint Adaptation Network

CHEN Jingxia , HU Xiuwen , TANG Zhezhe , LIU Yang , HU Kailei

2022, 37(4):814-824. DOI: 10.16337/j.1004-9037.2022.04.010

[Abstract](1012) [HTML](1034) [PDF 1.16 M](1903)

Abstract:
A new electroencephalogram （EEG） emotion recognition method based on deep convolutional neural network-joint adaptation network （CNN-JAN） is presented. It incorporates the idea of joint adaptation in transfer learning into deep convolutional networks. Firstly， the model uses a rectangular convolution kernel to extract the deep emotion-related spatial features between EEG data channels. Then， the extracted spatial features are input into the adaptation layer with multi-kernel joint maximum mean discrepancy （MK-JMMD） for transfer learning， aiming to reduce the distribution differences between the source and target domains. The experiments are carried out on differential entropy features and differential causality features of EEG data from the SEED dataset to verify the effectiveness and advantages of the proposed method. As a result， the within-subject emotion classification accuracy on differential entropy features reaches 84.01%， and the cross-subject emotion classification accuracy is also improved compared with other current popular transfer learning methods.

16 Early Diagnosis of Alzheimer’s Disease Based on Feature Enhanced Pyramid Network

SHI Lei , PENG Shaokang , ZHANG Yameng , ZHAO Guohua , GAO Yufei

2022, 37(4):727-735. DOI: 10.16337/j.1004-9037.2022.04.002

[Abstract](1082) [HTML](583) [PDF 1.69 M](5746)

Abstract:
Alzheimer’s disease （AD） is an irreversible neurodegenerative disease， whose early medical intervention is of great significance to control and improve the condition. In recent years， deep learning methods have been widely used by researchers to analyze magnetic resonance imaging （MRI） of AD for early diagnosis. However， the changes of brain structure are less different from those of normal people in the early stage， and the existing single-scale analysis methods are difficult to capture these subtle differences. Aiming at the above problem， this paper proposes a feature enhanced pyramid network （FEPN） for early diagnosis of AD. The high-level features are supplemented by the contextual information extracted from the designed shallow feature re-extraction， and the fusion weights are calculated to guide the fusion of high-level and low-level feature maps， which enhance the interaction of contextual information and the matching degree of multi-scale feature fusion. The Alzheimer datasets published by Kaggle are employed to conduct comparison experiments to verify the performance of the proposed approach. The comparison experiment employs the Alzheimer dataset published by Kaggle to verify the performance. Compared with related methods， FEPN achieves the SOTA classification accuracy of MRI of four AD brain states （non-demented， very mild demented， mild demented， moderate demented）.

17 Cerebral Hematoma Segmentation and Bleeding Volume Measurement Based on Self-attention Mechanism

LI Yao , YU Nannan , HU Chunai , KE Mingchi , YU Jinkou

2022, 37(4):839-847. DOI: 10.16337/j.1004-9037.2022.04.012

[Abstract](887) [HTML](834) [PDF 3.52 M](2393)

Abstract:
Hemorrhage volume is an important indicator for the grading of intracerebral hemorrhage disease， the determination of treatment options， and the judgment of prognosis. However， because of the complexity of the brain structure and the variety of morphology and location of the hematoma， accurate and reliable segmentation of the hematoma and measurement of the amount of hemorrhage are extremely difficult. This paper presents an algorithm for cerebral hematoma segmentation and blood volume measurement based on a self-attention mechanism deep learning network. First， to overcome the complexity of brain structure and make up for the shortcomings that convolution module can only perform linear operations and extract local features， a self-attention module is introduced at the end of the encoder of the segmentation network， and through higher order operations， the feature association properties of the whole domain of the image are extracted and the hematoma is extracted from a global perspective. Second， a channel and spatial attention module is introduced to obtain weights on the individual channels and feature regions through training learning， by which useful information is highlighted and useless information is suppressed. Finally， the hemorrhage volume is calculated based on the hematoma segmentation results of multislice CT imaging slices in patients with intracerebral hemorrhage. The experimental results on the real CT imaging data set of intracerebral hemorrhage show that the proposed algorithm achieves better results on cerebral hematoma segmentation and hemorrhage volume measurement in multiple cases， and even is still relatively effective in the case of irregular shape or close to skull.

18 EEG Signal Classification of Epilepsy Based on Deep Learning

XU Qing , GE Cheng , CAI Biao , LU Yi , CHANG Shan

2022, 37(4):787-797. DOI: 10.16337/j.1004-9037.2022.04.008

[Abstract](1740) [HTML](1359) [PDF 2.74 M](2987)

Abstract:
Effectively analyzing， processing and accurately classifying epileptic electroencephalographic （EEG） signals can further improve the problem of epilepsy detection. Therefore， various deep learning approaches have been gradually applied to this problem， such as using the BiLSTM model to process the 1D time series data of epileptic EEG. To further improve the accuracy of epileptic EEG classification， the 1D time series data of epileptic EEG is converted into 2D images and the EfficientNetV2 model is used to achieve binary classification for epilepsy detection in this paper. At the same time， the gradient-weighted class activation mapping （Grad-CAM） is introduced for visual analysis of 2D images classification. By performing classification experiments on a pre-processed version of the epilepsy EEG signal dataset from the University of Bern， Germany， the EfficientNetV2 model achieves the accuracy of 98.69%， which is better than the BiLSTM model. The result indicates that the EfficientNetV2 model can effectively achieve epileptic EEG classification by 2D EEG images with higher classification accuracy.

19 Attention Training Based on Double Convolutional Neural Network Fusion

XU Xin , ZHANG Jiaxin , ZHANG Ruhao

2022, 37(4):825-838. DOI: 10.16337/j.1004-9037.2022.04.011

[Abstract](944) [HTML](901) [PDF 2.02 M](2175)

Abstract:
Students’ learning situation is closely related to their classroom attention state. In order to explore whether attention training can improve classroom attention， the electroencephalogram （EEG） signals of non-attention and attention states of ten students before and after α music training are collected and compared. It is worth noting that EEG signal is dynamic in nature and has the characteristics of low signal-to-noise ratio and high redundancy. In order to avoid the problem of poor recognition of EEG signals directly through neural network， 11 features of signal sample entropy （SampEn）， energy and energy ratio of each band are extracted， and these features are fused into multi-feature images as the input of neural network model. In addition， the weighted fusion of AlexNet and VGG11 network models is used to form a double convolution neural network （CNN）， which can further improve the performance of image classification. The results show that the performance of the fusion model with double CNN can achieve a better performance compared with the model with single CNN. In particular， the recognition accuracy of the proposed model can reach 97.53%. It can be found that after α music training， the EEG features of the subjects are significantly different from those before， and the classification accuracy of the network model can be 4% higher than that before training. This observations show that the considered α music training can improve the attention level of healthy students.

20 Neural Network for Parpameter Estimation of Intravoxel Incoherent Motion Based on Sparse Coding

ZHENG Tianshu , YAN Guohui , YE Chuyang , WU Dan

2022, 37(4):747-756. DOI: 10.16337/j.1004-9037.2022.04.004

[Abstract](883) [HTML](657) [PDF 1.34 M](1870)

Abstract:
Diffusion magnetic resonance imaging （dMRI） is an important medical imaging tool for the non-invasive detection of microstructures in biological tissues. Among others， intravoxel incoherent motion （IVIM） is a widely used dMRI model to separate diffusion and microvascular perfusion. Conventional methods to resolve IVIM parameters rely on fitting a biexponential model from multi-b-value dMRI data （typically ≥10 b-values）， which requires a relatively long acquisition time. Such an acquisition is challenging for IVIM imaging of the body， such as placental IVIM， which is strongly influenced by both fetal and maternal motions. Deep learning models can accelerate the dMRI acquisition using a subset of the q-space data. However， common deep learning based on convolutional neural networks is not relevant to biophysical models and， therefore， the outputs of the network are difficult to interpret. Here， this work combines sparse coding with deep learning to develop a sparse coding based deep neural network for the IVIM parameter estimation that takes advantage of the feature representation of deep networks while incorporating a potential bi-exponential model to estimate the microcirculation parameters of the placenta. Compared with other algorithms， the proposed algorithm demonstrates advantages in accuracy and generalizability.

21 Homogenization Study of Brain Network of Suicidal Patients with Major Depressive Disorder from Multiple Imaging Sites

LIANG Jun , SONG Yanxin , WANG Yueyun , WAN Chunxiao

2022, 37(5):1115-1125. DOI: 10.16337/j.1004-9037.2022.05.016

[Abstract](868) [HTML](807) [PDF 2.65 M](2022)

Abstract:
Currently， there is heterogeneity in the functional brain images of suicidal patients with major depressive disorder （MDD） from multiple imaging sites， resulting in computational difficulties and affecting the reliability. According to the data from homogenizing multisite resting-state functional magnetic resonance imaging（rfMRI） from patients with MDD， the influence of suicidal tendencies on the MDD brain functional network is studied. Firstly， rfMRI of 99 MDD patients （including 67 non-suicidal MDD（nMDD）， 32 suicidal MDD（sMDD）） along with 72 healthy controls（HC） subjects from 3 sites are enrolled. After preprocessing of rfMRI， the functional connectivity of the Pearson correlation is calculated on the whole brain， and multisite functional connectivity is homogenized by ComBat technology. Then， the brain network topology is established and the graph theory analysis is performed by taking the existence of small-world attributes as the criterion for sparsity， the functional connectivity as edges and the brain areas as the nodes. Comparisons of the significance between groups are made on node degrees and node efficiency indicators in the graph theory. Experimental results show that the heterogeneity of functional connectivity in sites is effectively eliminated by the homogenization algorithm.Compared with the nMDD and HC groups，the sMDD group has siginificant between-group difference （p_FDF<0.05）in inferior cerebellar lobule and vermis cone. There exist abnormal functional activities in the inferior cerebellar lobules and vermis cones due to the suicidal tendencies. Based on the multisite homogenization of MDD network-level functional connectivity， this study effectively extracts the network characteristic indicators of suicidal patients and provides the functional imaging markers for the suicide risk assessment.

22 Two-Person Collaborative Brain-Controlled Robotic Arm System for Writing Chinese Character Using P300 and SSVEP Features

HAN Jin , DONG Bowen , LIU Miao , XU Minpeng , MING Dong

2022, 37(6):1401-1411. DOI: 10.16337/j.1004-9037.2022.06.020

[Abstract](1074) [HTML](584) [PDF 3.27 M](1897)

Abstract:
Brain-controlled technology based on brain-computer interface （BCI） has developed rapidly and made great progress. However， the existing research mostly adopts the single-person brain-controlled manner， which has the problems of poor execution efficiency and low degree of controllability， making it difficult to meet the needs of complex manipulation tasks. To address this problem， this study adopts a time-frequency-phase hybrid encoding method， and designs a collaborative strategy. A two-person collaborative brain-controlled robotic arm system with 108 instructions has been developed， enabling two people to write Chinese characters simultaneously one stroke by one stroke. The average online accuracy of the eight subjects is 87.92%， and the corresponding average online information-transfer rate （ITR） is 66.00 b/min. This system extends the BCI information interaction manner， and preliminarily verifies the feasibility and effectiveness of collaborative BCI manipulation of robotic arm. It provides technical support for collaborative BCI.

23 Character Analysis and Unfolding Study of Protein Molecular Machine Structures

ZHANG Lili , JIANG Yifeng , XIE Liangxu , KONG Ren , CHANG Shan

2022, 37(5):1126-1133. DOI: 10.16337/j.1004-9037.2022.05.017

[Abstract](742) [HTML](866) [PDF 2.31 M](1964)

Abstract:
Molecular machine is a kind of machine composed of molecular scale materials and can perform a certain processing function. Three-dimensional structure determines the related properties and functions of proteins. How the amino acid （residue） sequence of a protein folds into a specific three-dimensional structure， that is， understanding the folding mechanism and characteristics of protein structure is of great significance for the study of molecular machines. Therefore， it is necessary to use a fast and simple simulation method to study the folding mechanism information of protein structure. In this paper， based on the natural state topology of protein， we use Gaussian network model to study protein GB1 and analyze the structural characteristics of protein GB1 and its unfolding process. The results are in good agreement with experimental data and molecular dynamics simulation data， showing that the elastic network model is suitable for the study of protein structure.

24 Realistic Medical Image Augmentation by Using Multi-loss Hybrid Adversarial Function and Heuristic Projection Algorithm

WANG Jian , CHENG Chufan , CHEN Fang

2023, 38(5):1104-1111. DOI: 10.16337/j.1004-9037.2023.05.009

[Abstract](570) [HTML](598) [PDF 2.15 M](797)

Abstract:
Early detection of COVID-19 allows medical intervention to improve the survival rate of patients. The use of deep neural networks （DNN） to detect COVID-19 can improve the sensitivity and speed of interpretation of chest CT for COVID-19 screening. However， applying DNN for the medical field is known to be influenced by the limited samples and imperceptible noise perturbations. In this paper， we propose a multi-loss hybrid adversarial function （MLAdv） to search the effective adversarial attack samples containing potential spoofing networks. These adversarial attack samples are then added to the training data to improve the robustness and the generalization of the network for unanticipated noise perturbations. Especially， MLAdv not only implements the multiple-loss function including style， origin， and detail losses to craft medical adversarial samples into realistic-looking styles， but also uses the heuristic projection algorithm to produce the noise with strong aggregation and interference. These samples are proven to have stronger anti-noise ability and attack transferability. By evaluating on COVID-19 dataset， it is shown that the augmented networks by using adversarial attacks from the MLAdv algorithm can improve the diagnosis accuracy by 4.75%. Therefore， the augmented network based on MLAdv adversarial attacks can improve the ability of models and is resistant to noise perturbations.

25 Domain Generalization via Domain-Specific Decoding for Medical Image Segmentation

Ye Huaize , Zhou Ziqi , Qi Lei , Shi Yinghuan

2023, 38(2):324-335. DOI: 10.16337/j.1004-9037.2023.02.009

[Abstract](1300) [HTML](593) [PDF 3.11 M](1756)

Abstract:
Multi-source domain generalization （DG） aims to train a model uses semantic information of different domains and can be generalized to unknown domains. In the medical image， the gap between different domains is relatively large， and the model will suffer from performance drop in the unknown domain. In order to solve this problem， this paper proposes a network structure which encodes images for features and decodes domain specific features. The model uses a generic encoder， which learns all source domains for the domain-invariant features， and several domain-specific decoders to reconstruct the original images to promote the ability of extracting image features. Meanwhile， these decoders also help to generate transferred image to engage in adversarial learning with images of source domains in order to improve the encoder’s ability of learning invariant features. In addition， we also introduce a special Cutmix strategy which change foreground information of different domain images to augment the data set in the model to enhance the generalization ability of the model and further improve the performance of our network structure. In two medical image segmentation tasks， a large number of experimental data show that the proposed model has excellent performance compared with the existing advanced models. In addition， a series of ablation experiments are carried out to prove the effectiveness of the model.

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