IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Automatic Reconstruction of Deep Brain Stimulation Lead Trajectories From CT Images Using Tracking and Morphological Analysis","authors":"Wanxuan Sang;Zhiwen Xiao;Tiangang Long;Changqing Jiang;Luming Li","doi":"10.1109/TNSRE.2024.3493862","DOIUrl":"10.1109/TNSRE.2024.3493862","url":null,"abstract":"Deep brain stimulation (DBS) is an effective treatment for neurological disorders, and accurately reconstructing the DBS lead trajectories is crucial for MRI compatibility assessment and surgical planning. This paper presents a novel fully automated framework for reconstructing DBS lead trajectories from postoperative CT images. The leads were first segmented by thresholding, but would be fused together somewhere. Mean curvature analysis of multi-layer CT number isosurfaces was introduced to effectively address lead fusion, due to the different topological characteristics of the isosurfaces in and out of the fusion regions. The position of electrode contacts was determined through morphological analysis to get the starting point and the initial direction for trajectory tracking. The next trajectory point was derived by calculating the weighted average coordinates of the candidate points, using the distance from the current estimated trajectory and the CT number as weights. This method has demonstrated high accuracy and efficiency, successfully and automatically reconstructing complex bilateral trajectories for 13 patient cases in less than 10 minutes with errors less than 1 mm. This work overcomes the limitations of existing semi-automatic techniques that require extensive manual intervention. It paves the way for optimizing DBS lead trajectory to reduce tissue heating and image artifacts, which will contribute to neuroimaging studies and improve clinical outcomes. Code for our proposed algorithm is publicly available on Github.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4014-4021"},"PeriodicalIF":4.8,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale coupling between LFP and EMG in mice by low- intensity pulsed ultrasound stimulation with different number of tone-burst.","authors":"Ziqiang Jin, Xiaoling Chen, Zechuan Du, Yi Yuan, Xiaoli Li, Ping Xie","doi":"10.1109/TNSRE.2024.3492158","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3492158","url":null,"abstract":"<p><p>Low-intensity pulsed ultrasound stimulation (LIPUS) as a non-invasive, high-spatial resolution and high penetration depth brain modulation technology has been used for modulating neuromuscular function. However, the modulation of neural electrical signal changes in the neuromuscular system by LIPUS remains to be explored. In this study, we stimulated the mouse brain motor cortex by LIPUS with different number of tone burst (NTB) and recorded the local field potential (LFP) signals of the target region and electromyography (EMG) of tail muscle. Multi-Scale Transfer Entropy (MSTE) analysis method was used to explore the multi-scale synchronization characteristics and functional cortico-muscular coupling (FCMC) strength changes of mice LFP-EMG before and after LIPUS under different NTBs. The results show that the MSTE of LFP-EMG before and after LIPUS stimulation was higher than that of EMG-LFP. After adding multi-scale, MSTE has a significant relationship with time scales. When NTB = 200, the scale of extremum is the largest. There was a fitting intersection between LFP-EMG and EMG-LFP scale 7-21 before and after stimulation. After scale averaging, the LFP-EMG after stimulation was lower than that before stimulation, and the EMG-LFP after stimulation was higher than that before stimulation.Conclusion: There is a significant correlation between NTB and time scale before and after LIPUS,as well as upward and downward. Consequently,This study used FCMC methods to study different NTBs and multi-scale relationships, provides new variables from LIPUS parameters and analysis, and provides new reference for clinical applications of LIPUS.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing Multi-Stroke Characters From Brain Signals Toward Generalizable Handwriting Brain–Computer Interfaces","authors":"Xiaomeng Yang;Xinzhu Xiong;Xufei Li;Qi Lian;Junming Zhu;Jianmin Zhang;Yu Qi;Yueming Wang","doi":"10.1109/TNSRE.2024.3492191","DOIUrl":"10.1109/TNSRE.2024.3492191","url":null,"abstract":"Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large character sets. The Chinese characters, including over 3500 commonly used characters with 10.3 strokes per character on average, represent a highly complex writing system. This paper proposes a Chinese handwriting BCI system, which reconstructs multi-stroke handwriting trajectories from brain signals. Through the recording of cortical neural signals from the motor cortex, we reveal distinct neural representations for stroke-writing and pen-lift phases. Leveraging this finding, we propose a stroke-aware approach to decode stroke-writing trajectories and pen-lift movements individually, which can reconstruct recognizable characters (accuracy of 86% with 400 characters). Our approach demonstrates high stability over 5 months, shedding light on generalized and adaptable handwriting BCIs.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4230-4239"},"PeriodicalIF":4.8,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10745614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Dynamics of Periodic and Aperiodic Brain Activity Under Peripheral Nerve Stimulation With Acupuncture","authors":"Haitao Yu;Fan Li;Jialin Liu;Chen Liu;Guiping Li;Jiang Wang","doi":"10.1109/TNSRE.2024.3492014","DOIUrl":"10.1109/TNSRE.2024.3492014","url":null,"abstract":"Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3993-4003"},"PeriodicalIF":4.8,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10744423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation","authors":"Giovanni Rolandino;Chiara Zangrandi;Taian Vieira;Giacinto Luigi Cerone;Brian Andrews;James J. FitzGerald","doi":"10.1109/TNSRE.2024.3490796","DOIUrl":"10.1109/TNSRE.2024.3490796","url":null,"abstract":"This paper aims to introduce HDE-Array (High-Density Electrode Array), a novel dry electrode array for acquiring High-Density surface electromyography (HD-sEMG) for hand position estimation through RPC-Net (Recursive Prosthetic Control Network), a neural network defined in a previous study. We aim to demonstrate the hypothesis that the position estimates returned by RPC-Net using HD-sEMG signals acquired with HDE-Array are as accurate as those obtained from signals acquired with gel electrodes. We compared the results, in terms of precision of hand position estimation by RPC-Net, using signals acquired by traditional gel electrodes and by HDE-Array. As additional validation, we performed a variance analysis to confirm that the presence of only two rows of electrodes does not result in an excessive loss of information, and we characterized the electrode-skin impedance to assess the effects of the voltage divider effect and power line interference. Performance tests indicated that RPC-Net, used with HDE-Array, achieved comparable or superior results to those observed when used with the gel electrode setup. The dry electrodes demonstrated effective performance even with a simplified setup, highlighting potential cost and usability benefits. These results suggest improvements in the accessibility and user-friendliness of upper-limb rehabilitation devices and underscore the potential of HDE-Array and RPC-Net to revolutionize control for medical and non-medical applications.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4004-4013"},"PeriodicalIF":4.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging","authors":"Yunfeng Zhu;Yunxiao Wu;Zhiya Wang;Ligang Zhou;Chen Chen;Zhifei Xu;Wei Chen","doi":"10.1109/TNSRE.2024.3490757","DOIUrl":"10.1109/TNSRE.2024.3490757","url":null,"abstract":"The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional raw polysomnograms or two-dimensional spectrograms, which omit critical details due to single-view processing. This shortcoming is particularly apparent in pediatric sleep staging, where the lack of a specialized network fails to meet the needs of precision medicine. Therefore, we introduce AFSleepNet, a novel attention-based multi-view feature fusion network tailored for pediatric sleep analysis. The model utilizes multimodal data (EEG, EOG, EMG), combining one-dimensional convolutional neural networks to extract time-invariant features and bidirectional-long-short-term memory to learn the transition rules among sleep stages, as well as employing short-time Fourier transform to generate two-dimensional spectral maps. This network employs a fusion method with self-attention mechanism and innovative pre-training strategy. This strategy can maintain the feature extraction capabilities of AFSleepNet from different views, enhancing the robustness of the multi-view model while effectively preventing model overfitting, thereby achieving efficient and accurate automatic sleep stage analysis. A “leave-one-subject-out” cross-validation on CHAT and clinical datasets demonstrated the excellent performance of AFSleepNet, with mean accuracies of 87.5% and 88.1%, respectively. Superiority over existing methods improves the accuracy and reliability of pediatric sleep staging.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4022-4032"},"PeriodicalIF":4.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Powered Wheelchair Driving Characteristics of Real Driving and Two Types of Simulated Driving","authors":"Myeonghwan Bang;Min A Kim;Myung Joon Lim;Hyoung Seop Kim","doi":"10.1109/TNSRE.2024.3481277","DOIUrl":"10.1109/TNSRE.2024.3481277","url":null,"abstract":"We aimed to gather evidence on the feasibility of using simulator-based driving assessments for prescribing powered mobility devices (PMDs). Therefore, we compared the driving characteristics of real driving and two types of simulated driving. Thirty participants with difficulty walking more than 100 meters independently were enrolled. We developed a full-cabin and desktop simulator and created driving scenarios that closely resembled a real driving route in a park. They participated in three separate driving sessions, each using a powered wheelchair, full-cabin simulator, and desktop simulator. The driving characteristics, such as driving distance, mean speed, and standard deviation (SD) of speed, were obtained and analyzed to assess differences and correlations. Statistically significant differences were found in the driving distance and the SD of speed, respectively. However, for the mean speed, there was no statistically significant difference among the three types of driving. The intraclass correlation coefficient (ICC) for the driving distance was 0.154, which was not statistically significant. However, for mean speed, the ICC was 0.752, indicating a strong correlation. The ICC for the SD of speed was 0.562, indicating a moderate correlation. We demonstrated that the two types of simulators have characteristics that are similar to real-world driving characteristics. The mean speed showed the highest similarity, and the SD of the speed showed a moderate degree of similarity. These results highlight the significant potential of employing simulator-based driving to evaluate the use of PMDs.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3987-3992"},"PeriodicalIF":4.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142576053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks","authors":"Chamalka Kenneth Perera;Alpha. A. Gopalai;Darwin Gouwanda;Siti. A. Ahmad;Pei-Lee Teh","doi":"10.1109/TNSRE.2024.3488052","DOIUrl":"10.1109/TNSRE.2024.3488052","url":null,"abstract":"Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient during rehabilitation or when aiding essential daily tasks such as sit-to-walk (STW). STW itself can be generalized into strategies based on individual needs and movement patterns. In this study, three long short-term memory (LSTM) neural networks were empirically trained for hip and knee torque prediction considering these STW strategies and subject anthropometry. The hip and knee are the drivers of STW, while the network architectures were selected for recognizing temporal and spatial relationships. Performance of the LSTMs were compared and evaluated against the STW strategies to accurately generate strategy-specific and user-oriented torque. As such, train and test STW data were obtained from 65 subjects across three age groups: young, middle-aged, and older adults (19-73 years). Model inputs were hip and knee angles with horizontal center of mass velocity, while windowing allowed the LSTMs to dynamically adapt to real-time changes in STW transitions. The encoder-decoder LSTM showcased optimal performance with robust recognition of temporal features. It produced significantly (\u0000<inline-formula> <tex-math>${P}lt 0.05$ </tex-math></inline-formula>\u0000) low hip and knee root mean square error (\u0000<inline-formula> <tex-math>$0.24~pm ~0.07$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$0.15~pm ~0.02$ </tex-math></inline-formula>\u0000 Nm/kg), strong Spearman’s correlation (\u0000<inline-formula> <tex-math>$93.43~pm ~2.86$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$84.83~pm ~2.96$ </tex-math></inline-formula>\u0000%) and good intraclass correlation coefficients (greater than 0.75), demonstrating model reliability. Hence, this network predicts strategy and user oriented reference torques for personalized controllers in assistive devices, with more natural application of assistance.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3977-3986"},"PeriodicalIF":4.8,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10739358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Multi-Feature Fusion Network With Spatial Partitioning Strategy and Cross-Attention for Armband-Based Gesture Recognition","authors":"Fo Hu;Mengyuan Qian;Kailun He;Wen-An Zhang;Xusheng Yang","doi":"10.1109/TNSRE.2024.3487216","DOIUrl":"10.1109/TNSRE.2024.3487216","url":null,"abstract":"Effectively integrating the time-space-frequency information of multi-modal signals from armband sensor, including surface electromyogram (sEMG) and accelerometer data, is critical for accurate gesture recognition. Existing approaches often neglect the abundant spatial relationships inherent in multi-channel sEMG signals obtained via armband sensors and face challenges in harnessing the correlations across multiple feature domains. To address this issue, we propose a novel multi-feature fusion network with spatial partitioning strategy and cross-attention (MFN-SPSCA) to improve the accuracy and robustness of gesture recognition. Specifically, a spatiotemporal graph convolution module with a spatial partitioning strategy is designed to capture potential spatial feature of multi-channel sEMG signals. Additionally, we design a cross-attention fusion module to learn and prioritize the importance and correlation of multi-feature domain. Extensive experiment demonstrate that the MFN-SPSCA method outperforms other state-of-the-art methods on self-collected dataset and the Ninapro DB5 dataset. Our work addresses the challenge of recognizing gestures from the multi-modal data collected by armband sensor, emphasizing the importance of integrating time-space-frequency information. Codes are available at \u0000<uri>https://github.com/ZJUTofBrainIntelligence/MFN-SPSCA</uri>\u0000.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3878-3890"},"PeriodicalIF":4.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trial-by-Trial Variability of TMS-EEG in Healthy Controls and Patients With Depressive Disorder","authors":"Zikang Niu;Lina Jia;Yang Li;Lijuan Yang;Yi Liu;Siyuan Lian;Dan Wang;Wen Wang;Liu Yang;Weigang Pan;Xiaoli Li","doi":"10.1109/TNSRE.2024.3486759","DOIUrl":"10.1109/TNSRE.2024.3486759","url":null,"abstract":"Depressive disorder has been known to be associated with high variability in resting-state electroencephalography (EEG) signals. However, this phenomenon is often ignored in stimulus-related brain activities. This study proposed a new method to explore the EEG variability evoked by transcranial magnetic stimulation (TMS, TMS-EEG) in depressive disorder (DE) patients. The TMS-EEG data were collected from 34 DE patients and 36 healthy controls (HC). The maximum eigenvalue of the real binary correlation matrix, calculated between different trials using cross-correlation and surrogate methods, was extracted to assess trial-by-trial variability (TTV) of TMS-EEG. The new method was found to more sensitive and reliable than the standard deviation method. DE patients exhibited significantly smaller TTV in Gamma band and greater TTV in Delta band than HC. Furthermore, the HAMD-17 scores were negatively correlated with TTV values in Gamma band. This study represented the first investigation into the TTV in TMS-EEG data and revealed abnormal values in DE patients. Those findings enhance our understanding of TMS-EEG technology and provide valuable insights for studying the characteristics of DE.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"3869-3877"},"PeriodicalIF":4.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736641","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}