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2024 Vol. 43, No. 3 Published: 2024-06-20

Reviews Regular Papers CONTENTS

CONTENTS

	0	CONTENTS


		2024 Vol. 43 (3): 0-0 [Abstract] ( 123 ) HTML (1 KB) PDF (244 KB) ( 176 )

Regular Papers

	257	A New Ensemble Clustering Dynamic Functional Connectivity Analysis Method for ExploringBrain Connectivity Variation in Schizophrenia
		Fang Songke, Du Yuhui
		DOI: 10.3969/j.issn.0258-8021.2024.03.001
		In recent years, dynamic functional connectivity based on functional magnetic resonance imaging data has shown great potentials in studying psychiatric disorders. Traditional clustering-based dynamic functional connectivity analysis methods, such as K-means, are sensitive to the influence of class numbers, initial values, and noise, which may result in unreliable functional connectivity states (FCSs). This paper proposed a new dynamic functional connectivity analysis method based on ensemble clustering. First, K-means was performed with multiple different class numbers (k values) to generate diverse clusters. Then, the similarity between different clusters was mined based on Jaccard coefficient and random walk to construct a weighted graph reflecting the relationship between clusters. Finally, reliable meta-clusters were obtained by community detection on the weighted graph, and each functional connectivity window was grouped into different meta-clusters by voting, and its centroid was calculated as the functional connectivity states. Based on fMRI data from 105 healthy controls (HC) and 70 schizophrenia (SZ) patients, we comprehensively compared the performance of the proposed method with the commonly used K-means based method for dynamic functional network analysis. Compared with K-means method, the average inter class similarity of the proposed method decreased from 83.2% to 81.1% on FCS 2, and decreased from 76.8% to 73.5% on FCS 3. The Davies Bouldin index decreased from 6.74 to 6.44, and the Silhouette coefficacy index increased from 0.018 to 0.031. In terms of group differences, results obtained from our proposed method showed that the differences between the HC and SZ groups were mainly concentrated in FCS 2. In contrast, group differences resulted from K-means method were dispersed across FCS 2, FCS 3, and FCS 4, primarily due to subpar clustering metrics and high average inter-cluster similarity. In summary, the proposed method in this study was able to automatically determine the number of FCSs and exhibit better clustering quality and more reliable FCSs. Additionally, the method revealed more meaningful inter-group differences than k-means, indicating the potentials of exploring biomarkers for mental disorders.
		2024 Vol. 43 (3): 257-266 [Abstract] ( 370 ) HTML (1 KB) PDF (6713 KB) ( 659 )

	267	Research on Multi Network Carotid Artery Image Classification and Detection Based on TransferLearning and CNN
		Sui Xiaoyu, Han Jing, Cao Yankun, Mi Jia, Song Yanyun, Wang Jianlei, Wang Chun, Liu Zhi
		DOI: 10.3969/j.issn.0258-8021.2024.03.002
		Carotid ultrasound is a main and convenient method for plaque diagnosis. Therefore, it is very important to obtain accurate information about plaque from ultrasound images for further clinical diagnosis. Due to the noise interference of ultrasonic machine and the difference of manual technical operation, the displayed section image is not clear and standard, which is easy to lead to false detection or missed detection of the plaque. In this work, a deep learning algorithm based on migration learning and CNN was proposed to realize the research of more accurate identification of carotid plaque. Firstly, 2591 longitudinal ultrasound images with clear and fuzzy carotid artery were selected to classify and control the lumen quality through ResNet network; After the lumen classification, 1114 longitudinal images with clear soft and hard plaque information were selected. The carotid lumen and plaque were classified and detected by RetinaNet network based on migration learning, and the comparative experiment was carried out by using Faster R-CNN and SSD network. For the lumen classification network, the classification accuracy of the test set was 93%. For the lumen and plaque classification detection network, 113 test set images were used to obtain the average accuracy of lumen detection, which reached 1 when the intersection union ratio (IOU) value was 0.5, 0.988 when the IOU value was 0.75, 0.838 when the IOU value was 0.5: 0.95, and the average recall rate reached 0.869, which were higher than those of Faster R-CNN and SSD networks; The average accuracy of hard plaque and soft plaque detection was 0.899 and 0.855 when IOU = 0.5, and the average recall was 0.609 and 0.578 respectively. Before the classification and recognition of carotid plaque, the quality classification control of carotid lumen image can effectively avoid the false detection of plaque caused by unclear image, improve the correctness of plaque detection, and is of great significance for the follow-up three-dimensional reconstruction of carotid artery.
		2024 Vol. 43 (3): 267-277 [Abstract] ( 341 ) HTML (1 KB) PDF (8476 KB) ( 359 )

	278	Implementation of Wide-Beam Ultrasound Imaging Based on a Convex Transducer
		Shi Xinwang, Feng Lian, Zhou Xiaowei
		DOI: 10.3969/j.issn.0258-8021.2024.03.003
		Ultrasound wide-beam imaging is a high frame rate ultrasound imaging method that can better balance several important imaging properties such as image spatial resolution, contrast ratio, and frame rate, which is implemented by putting the transmitting focus point far away from the actual imaging depth when compared to the traditional line-by-line scanning, plane and diverging wave imaging methods. Current studies on ultrasound wide-beam imaging are only based on linear array ultrasound transducers that have a limited imaging field of view and are not suitable for imaging of deep tissues and organs. In this paper, we investigated the wide-beam imaging algorithm based on a convex array ultrasound transducer and evaluated performance of the wide-beam imaging method with convex linear array. With a convex array transducer with 128 arrays, wide-beam imaging was implemented in two environments, a simulation platform and a real experiment scenario. The original channel data were first collected in both scenarios and then a delay and sum method was used to beamform and reconstruct the wide-beam ultrasound images. Quantitative analysis was performed in terms of the image contrast and spatial resolution and compared with the traditional focused line-by-line scanning, the diverging wave imaging methods. Compared with compound diverging wave imaging, the wide-beam imaging had higher image contrast (simulated case: 19.6 dB, increased by 67.1%; experimental case: 19.1 dB, increased by 33.1%), but its performance on resolution was different in the simulated and experimental cases (spatial resolution in the simulated case: 1.11 mm, increased by 18.1%; in the experimental case: 1.48 mm, decreased by 15.4%). Overall, the wide-beam imaging had better performance than compound diverging wave imaging. In comparison with the focused line-by-line scanning, wide beam imaging exhibited more uniform imaging resolution and a higher frame rate. This study implemented the wide-beam ultrasound imaging based on a convex imaging transducer for the first time and validated that the imaging method had more balanced imaging performance than the diverging wave imaging methods and traditional focused method, which offered a better imaging strategy for some relevant clinical applications, especially for the abdominal ultrasound.
		2024 Vol. 43 (3): 278-285 [Abstract] ( 220 ) HTML (1 KB) PDF (5091 KB) ( 196 )

	286	Analysis Method of Brain Region Entropy of Human Balance Function Based on Differentiatedfrom Visual Proprioception
		Su Qiaozuan, Luo Zhizeng, Wang Zheyuan
		DOI: 10.3969/j.issn.0258-8021.2024.03.004
		Balance is the foundation of all human movements, and existing methods of assessing human balance are mostly based on external performance. In this paper, we took the endogenous perspective of balance central neuromodulation as an entry point to study the sensorimotor cortical integration in the process of static balance regulation, analyze the activation state of the cerebral cortex, and establish an entropic network of static balance EEG transmission. The experimental paradigm was designed under the conditions of differentiation of visual and proprioceptive inputs, and the phase synchronization criterion of balance EEG was defined. The phase synchronization relationship of the transmission between the brain regions of balance information was defined. Based on the EEG data of 20 subjects, the central nervous system regulation period of balance events was determined based on the phase synchronization relationship, various endogenous characteristics of balanced EEG were extracted, and the average classification accuracy was improved by 14.66% compared with the traditional network feature classification results by using a combination of network clustering coefficient (C), shortest path (E) and maximum Lyapunov index (MLE) [C, E, MLE]. The new feature of maximum Lyapunov index (M) added in the analysis of transfer entropy network fully expressed the internal law evolution process of human balance adjustment and improved the classification ability of human balance.
		2024 Vol. 43 (3): 286-294 [Abstract] ( 144 ) HTML (1 KB) PDF (4682 KB) ( 231 )

	295	A Hybrid Neural Network Electrocardiogram Signal Classification Algorithm Based on LocationAttention Mechanisms
		Gong Yuxiao, Gao Shuping
		DOI: 10.3969/j.issn.0258-8021.2024.03.005
		Electrocardiogram (ECG) signal classification is significant research issue in the healthcare field. Signal data from ECG are classification imbalanced, and different classification of arrhythmias depend on long-term variation features, local variation features and their relative location of electrocardiogram. Most existing methods are not able to solve the classification imbalance problem well and consider the importance of specific waveforms. In this study, a hybrid neural network algorithm based on the location attention mechanisms was proposed for classifying ECG signals, referred to as DCLB algorithm. Firstly, the small-size classification samples were augmented adopting the deep convolutional generative adversarial networks (DCGAN) to solve the classification imbalance problem. Secondly, the local variation features and long-term variation features of ECG signals were extracted utilizing two-dimensional convolutional neural networks (2DCNN) and bi-directional long short-term memory network (BLSTM). Next, the location attention mechanisms (LAM) were nested behind each 2DCNN for enhancing the effects of key location features. Finally, the classification results were output using the fully connected neural networks. Experimental results on 30 584 samples of the MIT-BIH arrhythmia database showed that the proposed algorithm achieved the average accuracy of 98.79%, sensitivity of 94.21%, specificity of 98.98%, and positive predictive value of 93.70%. respectively. The results indicated that DCLB was able to extract effectively ECG signal features and suitable for the diagnosis of arrhythmia in the monitoring system.
		2024 Vol. 43 (3): 295-305 [Abstract] ( 198 ) HTML (1 KB) PDF (3163 KB) ( 248 )

	306	Muscle Strength Estimation by Fusion of Surface EMG and Ultrasonic RF Signals
		Han Huan, Lv Qian, Yin Guanjun, Zhang Liangmei, Zhang Beilei, Guo Jianzhong
		DOI: 10.3969/j.issn.0258-8021.2024.03.006
		Muscle strength is an important parameter reflecting the state of muscles, which can characterize human body's motor function, muscle health and fatigue level. Non-invasive muscle strength assessment technology has significance and wide application value in many fields, such as sports guidance, muscle disease diagnosis, and rehabilitation status evaluation. In this paper, we proposed a muscle strength assessment method based on feature fusion analysis of surface electromyography (sEMG) and ultrasound radiofrequency (RF) signals by deep learning algorithms, which utilized convolutional neural network (CNN) and pooling operations to extract the effective features (CNNFeat) of the signals and serve as inputs to a support vector machine (SVM) classifier for processing and classification. The method explored the ability of CNNFeat to recognize signal features using CNN-SVM network to verify the complementary nature of sEMG and ultrasound RF signal fusion. The sEMG and ultrasound RF signals of the biceps brachii muscle of 10 healthy subjects were collected under different loads, and the processing results in the multi-user scenario mode showed that CNNFeat was able to improve the classification performance with strong robustness compared to the features of conventional EMG and ultrasound RF signals. The accuracy was 84.23% for EMG signals and 89.34% for ultrasound signals, while the accuracy of the fused signals was as high as 96%, and the fused signals have less oscillations and faster loss convergence.
		2024 Vol. 43 (3): 306-314 [Abstract] ( 193 ) HTML (1 KB) PDF (4789 KB) ( 255 )

	315	Knowledge Graph Powered Human Proteome Knowledge Annotation and Knowledge ExplorationStudy
		Yuan Yize, Wang Zhigang, Wang Zhe, Shi Furen, Yang Sheng, Yang Xiaolin
		DOI: 10.3969/j.issn.0258-8021.2024.03.007
		Proteome knowledge annotation facilitates the derivation of scientific hypotheses from existing knowledge. However, traditional annotation approaches are often not comprehensive and lack systemic integration, being limited to knowledge retrieval and aggregation. In this paper, a novel method involving knowledge graphs is proposed to integrate biomedical knowledge from 13 biomedical ontologies and databases. The knowledge graph, Biomedical Knowledge Graph (BMKG), was constructed with the graph database Neo4j. Metapaths were designed to create knowledge annotation schemes which incorporated prior knowledge with graph algorithms such as centrality measures. By leveraging similarity calculations, link prediction algorithms, and node2vec graph embedding, knowledge exploration analysis was facilitated. BMKG encompasses 2 508 348 nodes of 9 types and 25 362 594 relationships of 20 types. The BMKG knowledge annotation scheme facilitates diverse perspectives and multi-level annotation, which is demonstrated by its application to renal cell carcinoma tissue proteome data in annotating various biological aspects comprehensively, such as pathways, drugs, and phenotypes. Additionally, BMKG supports knowledge exploration studies, such as drug-disease association prediction, and the clustering of disease knowledge exhibits strong concordance with the Mondo ontology structure. Moreover, an online platform (http://bmkg.bmicc.org) has been established, with three analysis modules: knowledge retrieval, knowledge annotation, and knowledge analysis. Collectively, this study demonstrates the potential of knowledge graph approaches to enhance human proteome knowledge annotation and knowledge exploration.
		2024 Vol. 43 (3): 315-326 [Abstract] ( 182 ) HTML (1 KB) PDF (4314 KB) ( 271 )

	327	Exploration of a Novel Noninvasive Deep Brain Precision Stimulation Method Based on MultiphysicsField Complexes
		Zhu Kai, Zhou Xiaoqing, Ma Ren, Liu Xu, Yin Tao, Liu Zhipeng
		DOI: 10.3969/j.issn.0258-8021.2024.03.008
		Existing noninvasive electromagnetic stimulation technologies, represented by transcranial magnetic stimulation (TMS), the most widely used clinical technology, are still unable to break through the technical bottlenecks of focus and stimulation depth and achieve direct and precise stimulation of deep functional nuclei. Based on the theory of magneto-acoustic coupling, combined with the alternating magnetic field in TMS, we proposed a new non-invasive deep brain stimulation method (magnetic induction-transcranial magneto-acoustic stimulation, MI-TMAS) based on multi-physical field composite, aiming to achieve deep brain stimulation, direct and precise focus stimulation. This paper simulated the stimulation physical fields in MI-TMAS based on the magneto-acoustic coupling theory and magnetic induction theory; and built the MI-TMAS system and multi-physics test platform and conducted actual measurements. Combining simulation and actual measurement results, the focus and electric field strength of the MI-TMAS composite stimulation electric field were explored. The results showed that this method generated three composite stimulation physical fields including focused acoustic field, magneto-acoustic coupling electric field, and magnetic induction electric field in the deep brain target area. By this way, a composite stimulation electric field with width of6.2 mm (-3dB) at a stimulation depth of 50 mm was formed, which is in the deep brain. The focusing of the electric field was significantly better than that of TMS, and the stimulation electrical field intensity was greater than that of TMAS under a steady magnetic field. In conclusion, this method provided a theoretical and technical basis for the MI-TMAS method to be applied to in vivo neuromodulation.
		2024 Vol. 43 (3): 327-337 [Abstract] ( 176 ) HTML (1 KB) PDF (3892 KB) ( 289 )

	338	Rapid Cranial Contour Measurement Algorithm Based on Phased-Array Synthetic Aperture
		Li Hanze, Liu Ruixu, Zhou Xiaoqing, Yin Tao, Liu Zhipeng, Ma Ren
		DOI: 10.3969/j.issn.0258-8021.2024.03.009
		Transcranial focused ultrasound technology as an emerging neuromodulation technology has been widely used in neuromodulation and treatment of the deep brain. The heteromorphism of the skull and the great variability of the acoustic parameters are the main reasons for the shift of the actual focal point and the scattering of the focal domain after the focused ultrasound penetrates the skull. In this study, based on the synthetic aperture technique, a phased array transmitted ultrasound signals and received the echo signals reflected from the inner and outer contours of the skull, and the coordinates of the skull contour points were simultaneously calculated to realize the rapid measurement of the skull contour. A real head simulation model and a skull simulation body were established to simulate and experimentally verify the algorithm. The simulation results showed that the maximum detection error of the outer contour center area of the skull model was 0.15 mm, and the edge area was 0.4 mm; the maximum detection error of the inner contour center area was 0.3 mm, and the edge area was 0.5 mm; the maximum detection error of the inner contour center area is 0.6 mm, and the maximum detection error of the inner contour center area was 0.6 mm, and the maximum detection error of the inner contour center area was 0.6 mm, and the maximum detection error of the inner contour center area was 0.6 mm, and the maximum detection error of the inner contour center area is 0.6 mm. The maximum detection error was 0.6 mm in the center of the inner contour and 0.9 mm in the edge area, and the rapid measurement algorithm designed in this paper was able to complete the accurate measurement of the inner and outer contour of the skull within 2 minutes, and controlled the maximum measurement error within 1 mm. Compared with magnetic resonance scanning (MRI) and electronic computed tomography (CT), it reduced the treatment cost and treatment time, and provided a new method and idea for the next step of adjusting the phased-array array element emission delay to realize real-time precise focusing in the skull.
		2024 Vol. 43 (3): 338-347 [Abstract] ( 144 ) HTML (1 KB) PDF (5420 KB) ( 211 )

	348	Study on the Selection and Fatigue Performance of Porous Bone Scaffolds Fabricated by ElectronBeam Melting
		Xie Haiqiong, Gan Daoqi, Liu Fei, Xie Haitao, Yang Baiyin, Zhou Tianyu
		DOI: 10.3969/j.issn.0258-8021.2024.03.010
		Additive manufactured porous structure offers excellent mechanical biomimicry and osseointegration properties, supporting long-term stability of orthopedic implants in human body. This study employed triply periodic minimal surfaces (TPMS) method and Electron Beam Melting (EBM) technology to design and fabricate porous bone mimicking scaffolds. By investigating the pore characteristics, mechanical performance and fatigue life, a novel selection method suitable for bone implantable devices was proposed to meet the requirements of porosity connectivity, mechanical stability, and high-cycle fatigue life. Micro-CT and SEM characterization revealed the porous scaffolds with unit cell sizes ≥1.5 mm had biomimetic pore sizes (748 μm) and good pore connectivity. The mechanical stability and reliability of TPMS-Gyroid scaffolds were superior to TPMS-Diamond. The established Gibson-Ashby equation in this work provided mechanical performance prediction for titanium alloy porous scaffolds. The fatigue life of the scaffolds exceeded 10⁶ cycles at a stress level of 0.2, satisfying the long-term safety requirements for implant materials, with an elastic modulus similar to that of human cancellous bone (0.1~1.1 GPa). Furthermore, fatigue behavior studies also indicated that fatigue ratchet and fatigue damage were main causes of porous scaffold failure. In the design of metal porous scaffold’s structure, the number of cracks could be reduced by increasing the size of the scaffold unit cell, which was helpful to improve the fatigue life of the scaffolds.
		2024 Vol. 43 (3): 348-357 [Abstract] ( 149 ) HTML (1 KB) PDF (11192 KB) ( 181 )

Reviews

	358	A Review of Open-Source Datasets of Physiological Signals for Sleep Research
		Lu Jingyi, Yan Chang, Yu Guangyi, Li Jianqing, Liu Chengyu
		DOI: 10.3969/j.issn.0258-8021.2024.03.011
		The collection and labeling of clinical polysomnography data are time-consuming and costly, and the differences between different populations, collection devices, and expert labeling create challenges for sleep-related research. The open-source datasets provide rich data resources and a unified comparison platform for global researchers to conduct sleep studies. This paper reviewed the characteristics and applications of 18 open-source datasets commonly used in the field of sleep. The datasets include electroencephalogram (EEG), electrocardiogram (ECG),electro-oculogram (EOG), electromyography (EMG), etc., covering multiple clinical fields such as sleep disorders, cardiovascular diseases, obesity, etc., promoting in-depth research in the field of sleep medicine. This paper also summarized the limitations of existing sleep open-source datasets in terms of data quality, data standards, data security, sample representation and external validity, and put forward specific suggestions and prospects.
		2024 Vol. 43 (3): 358-368 [Abstract] ( 216 ) HTML (1 KB) PDF (850 KB) ( 597 )

	369	Research Progress of Nanomaterial Targeted Drug Delivery in Osteoarthritis Treatment
		Zhang Yingyu, Zhao Linlin, Li Liangxiao, Liu Yingying, Liu Yajun
		DOI: 10.3969/j.issn.0258-8021.2024.03.012
		Osteoarthritis is a chronic, highly prevalent degenerative disease. Delivering drugs to the joints presents challenges, as free drugs injected into the joints are prone to degradation and rapid clearance through the lymphatic and blood systems. Nano-targeted drug delivery systems, designed with specific assembly and appearance modifications, offer significant advantages. They can increase the drug concentration at the arthritic site, improve efficacy, reduce toxicity, therefore, have been extensively studied for uses in osteoarthritis treatment. This review introduced current nano-drug delivery strategies that featured active targeting in treating osteoarthritis, summarized research work on nano-drug delivery systems enhanced by biotargeting ligands and biomimetic biofilms, highlighting their advantages and limitations. In addition, we discussed the potential future developments of nano-drug delivery systems, aiming to provide references and constructive suggestions for improving the past active targeting and modification strategies of nanomaterials.
		2024 Vol. 43 (3): 369-376 [Abstract] ( 256 ) HTML (1 KB) PDF (2058 KB) ( 412 )

	377	The Technologies and Perspectives of Anticoagulant Coating
		Lei Shaojin, Zheng Yi, Shi Jie, Dong Yunsheng, Wang Shufang
		DOI: 10.3969/j.issn.0258-8021.2024.03.013
		With acceleration of population aging and urbanization, the number of patients with cardiovascular diseases has gradually increased. Development of cardiovascular implantable devices have attracted increasing attentions in recent years. The high risk of thrombosis is a major factor limiting the application of cardiovascular implanted devices, that is why it is so important to construct anticoagulant coatings on the surface of cardiovascular implanted devices. In this review, we summarized the research progress of anticoagulant coating construction methods in both domestic and foreign researches, analyzed and discussed the conventional and latest anticoagulation strategies from the aspects of physical, chemical and biological methods. Existing problems and prospects of anticoagulation coating construction were also discussed.
		2024 Vol. 43 (3): 377-384 [Abstract] ( 204 ) HTML (1 KB) PDF (1054 KB) ( 319 )

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