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学者姓名：杨涛
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Abstract ：
Background: In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes due to considerations, such as portability and battery life. This situation leads to a contradiction between the demand for standard 12-lead ECG information and the limited number of leads collected by portable devices. Methods: This study introduces a composite ECG vector reconstruction network architecture based on convolutional neural network (CNN) combined with recurrent neural network by using leads I, II, and V2. This network is designed to reconstruct three-lead ECG signals into 12-lead ECG signals. A 1D CNN abstracts and extracts features from the spatial domain of the ECG signals, and a bidirectional long short-term memory network analyzes the temporal trends in the signals. Then, the ECG signals are inputted into the model in a multilead, singlechannel manner. Results: Under inter-patient conditions, the mean reconstructed Root mean squared error (RMSE) for precordial leads V1, V3, V4, V5, and V6 were 28.7, 17.3, 24.2, 36.5, and 25.5 mu V, respectively. The mean overall RMSE and reconstructed Correlation coefficient (CC) were 26.44 mu V and 0.9562, respectively. Conclusion: This paper presents a solution and innovative approach for recovering 12-lead ECG information when only three-lead information is available. After supplementing with comprehensive leads, we can analyze the cardiac health status more comprehensively across 12 dimensions.
Keyword ：
Bidirectional long short-term memory network Bidirectional long short-term memory network Convolutional neural network Convolutional neural network Heartbeat segmentation Heartbeat segmentation Lead reconstruction Lead reconstruction
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| GB/T 7714 | Wang, Liang-Hung , Zou, Yu -Yi , Xie, Chao-Xin et al. Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three-lead signals [J]. | JOURNAL OF ELECTROCARDIOLOGY , 2024 , 84 : 27-31 . |
| MLA | Wang, Liang-Hung et al. "Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three-lead signals" . | JOURNAL OF ELECTROCARDIOLOGY 84 (2024) : 27-31 . |
| APA | Wang, Liang-Hung , Zou, Yu -Yi , Xie, Chao-Xin , Yang, Tao , Abu, Patricia Angela R. . Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three-lead signals . | JOURNAL OF ELECTROCARDIOLOGY , 2024 , 84 , 27-31 . |
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Objective: As biological wide-field visual neurons in locusts, lobula giant motion detectors (LGMDs) can effectively predict collisions and trigger avoidance before the collision occurs. This capability has extensive potential applications in autonomous driving, unmanned aerial vehicles, and more. Currently, describing the LGMD characteristics is divided into two viewpoints, one emphasizing the presynaptic visual pathway and the other emphasizing the postsynaptic LGMDs neuron. Indeed, both have their research support leading to the emergence of two computational models, but both lack a biophysical description of the behavior in the individual LGMD neuron. This paper aims to mimic and explain LGMD's behavior based on fractional spiking neurons and construct a biomimetic visual model for the LGMD compatible with these two characteristics. Methods: We implement the visual model in the form of spikes by choosing an event camera rather than a conventional CMOS camera to simulate the photoreceptors and follow the topology of the ON/OFF visual pathway, enabling it to incorporate the lateral inhibition to mimic the LGMD's system from the bottom up. Second, most computational models of motion perception use only the dendrites within the LGMD neurons as the ideal pathway for linear summation, ignoring dendritic effects inducing neuronal properties. Thus, we introduced fractional spiking neuron (FSN) circuits into the model by altering dendritic morphological parameters to simulate multi-scale spike frequency adaptation (SFA) observed in LGMDs. In addition, we have attempted to add one more circuit of dendritic trees into fractional spiking neurons to be compatible with the postsynaptic FFI in LGMDs and provide a novel explanatory approach and a predictive model for studying LGMD neurons. Results: Finally, we test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects. IEEE
Keyword ：
Biological system modeling Biological system modeling Biology Biology Collision detection Collision detection Computational modeling Computational modeling Dendrites (neurons) Dendrites (neurons) Dendritic nonlinear Dendritic nonlinear Event camera Event camera Integrated circuit modeling Integrated circuit modeling LGMD LGMD Looming selection Looming selection Multi-scale spike frequency Multi-scale spike frequency Neurons Neurons Spiking neuronal dynamic Spiking neuronal dynamic Visualization Visualization
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| GB/T 7714 | Deng, Y. , Ruan, H. , He, S. et al. A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits [J]. | IEEE Transactions on Biomedical Engineering , 2024 , 71 (10) : 1-12 . |
| MLA | Deng, Y. et al. "A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits" . | IEEE Transactions on Biomedical Engineering 71 . 10 (2024) : 1-12 . |
| APA | Deng, Y. , Ruan, H. , He, S. , Yang, T. , Guo, D. . A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits . | IEEE Transactions on Biomedical Engineering , 2024 , 71 (10) , 1-12 . |
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目的 采用人工智能技术提出一种模型，以对房颤进行早期预防和诊断。方法 提出一种基于卷积神经网络(convolutional neural network, CNN)与通道和空间注意力机制(convolutional block attention module, CBAM)的模型用于对房颤的诊断与预测。结果 根据长期心房颤动数据库、MIT-BIH心房颤动数据库和MIT-BIH正常窦性心律数据库的数据，提出的模型在全盲的情况下总体准确率达94.2%。结论 提出的模型满足了医学心电图解释的需要，为房颤的预测研究提供了新思路。
Keyword ：
卷积神经网络 卷积神经网络 心电信号 心电信号 房颤 房颤 通道和空间注意力机制 通道和空间注意力机制
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| GB/T 7714 | 王量弘 , 蔡冰洁 , 刘硕 et al. 基于卷积神经网络与通道和空间注意力机制的房颤预测模型研究 [J]. | 福建医药杂志 , 2024 , 46 (01) : 1-4 . |
| MLA | 王量弘 et al. "基于卷积神经网络与通道和空间注意力机制的房颤预测模型研究" . | 福建医药杂志 46 . 01 (2024) : 1-4 . |
| APA | 王量弘 , 蔡冰洁 , 刘硕 , 杨涛 , 王新康 , 高洁 . 基于卷积神经网络与通道和空间注意力机制的房颤预测模型研究 . | 福建医药杂志 , 2024 , 46 (01) , 1-4 . |
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The application of artificial intelligence in electrocardiogram (ECG) diagnosis holds substantial significance. Most ECG classification methods concatenate 12-lead ECG into a 2-D matrix for model input. This study proposed a multi-branch and multi-class model for arrhythmias classification. The model utilizes selective kernel block to independently extract features from each lead, which are fed into Bi-LSTM for fusion. Additionally, batch-free normalization module is employed to reduce estimation shift. Finally, the proposed model achieved an accuracy of 0.871 and a macro F1 score of 0.841 in identifying nine types of arrhythmias.
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| GB/T 7714 | Wang, Yu , Yang, Tao , Xie, Chao-Xin et al. Multi-lead Branch Multi-class Arrhythmias Classification Based on Selective Kernel Block [J]. | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 : 575-576 . |
| MLA | Wang, Yu et al. "Multi-lead Branch Multi-class Arrhythmias Classification Based on Selective Kernel Block" . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 (2024) : 575-576 . |
| APA | Wang, Yu , Yang, Tao , Xie, Chao-Xin , Fan, Ming-Hui , Kuo, I-Chun , Wang, Xin-Kang et al. Multi-lead Branch Multi-class Arrhythmias Classification Based on Selective Kernel Block . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 , 575-576 . |
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Current sudden cardiac death (SCD) studies mostly use traditional machine learning algorithms and suffer from low accuracy. Deep learning has a promising application in the field of SCD research. The study extract R-R interval and R amplitude from ECG signals as inputs, combined convolutional neural network and gated recurrent unit, and take full advantage of hybrid neural network structure to realize the risk stratification of high-risk patients who may have SCD within 90 minutes, with the highest accuracy of 95.33%.
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| GB/T 7714 | Ji, Tian-Yun , Xie, Chao-Xin , Yang, Tao et al. Risk Stratification Model of Sudden Cardiac Death [J]. | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 : 577-578 . |
| MLA | Ji, Tian-Yun et al. "Risk Stratification Model of Sudden Cardiac Death" . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 (2024) : 577-578 . |
| APA | Ji, Tian-Yun , Xie, Chao-Xin , Yang, Tao , Kuo, I-Chun , Chen, Shih-Lun , Wang, Liang-Hung . Risk Stratification Model of Sudden Cardiac Death . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 , 577-578 . |
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Keyword ：
Black matrix Black matrix Crosstalk Crosstalk Displays Displays Light emitting diodes Light emitting diodes Micro LED Micro LED Quantum dots Quantum dots Reflection Reflection Scattering Scattering Sociology Sociology Statistics Statistics
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| GB/T 7714 | Zhang, Xiang , Chen, Anlan , Yang, Tao et al. Tripling Light Conversion Efficiency of mu LED Displays by Light Recycling Black Matrix (vol 14, 7014207, 2022) [J]. | IEEE PHOTONICS JOURNAL , 2023 , 15 (3) . |
| MLA | Zhang, Xiang et al. "Tripling Light Conversion Efficiency of mu LED Displays by Light Recycling Black Matrix (vol 14, 7014207, 2022)" . | IEEE PHOTONICS JOURNAL 15 . 3 (2023) . |
| APA | Zhang, Xiang , Chen, Anlan , Yang, Tao , Cai, Junhu , Ye, Yuanyuan , Chen, Enguo et al. Tripling Light Conversion Efficiency of mu LED Displays by Light Recycling Black Matrix (vol 14, 7014207, 2022) . | IEEE PHOTONICS JOURNAL , 2023 , 15 (3) . |
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To address the conflict between the need to use 12-lead in detecting myocardial infarction (MI) and inadequate diagnostic data due to an insufficient number of leads, this study proposes a novel network called Lead Recovery Guide Residual Network (LRGRN), which mitigates the effect of the restricted number of leads. We constructed a lead recovery guide to restore the spatial information of all 12-lead, given only leads I, II, and V2. Limb leads were reconstructed through a linear model, while precordial leads were reconstructed using a convolutional bidirectional long short-term memory network to capture high-level abstract features and temporal characteristics of electrocardiogram (ECG) signals. The restored 12-lead ECG can overcome the limitations of the original 3-lead ECG and provide a comprehensive reflection of MI. In the overall architecture of LRGRN, patient data strictly follow the inter-patient principle. ResNet maintains a stable flow of frequency information based on the reconstructed 12-lead ECG data, while the multi-lead single-channel structure enables the model to better capture the overall ECG information. The average detection accuracy of the LRGRN model for MI is 96.33%. The correlation coefficient (CC) of the recovered limb leads was 100%, The CC for recovery of precordial leads for feedback was 95.62%. Compared with the models presented in other studies, the LRGRN model overcomes inter-individual variability and excels in MI detection with limited lead information. © 2023 IEEE.
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| GB/T 7714 | Wang, Lianghong , Zou, Yuyi , Yang, Tao et al. Lead Recovery Guide Residual Network for Myocardial Infarction Detection by Restoring 12-Lead Spatial Information [C] . 2023 : 730-733 . |
| MLA | Wang, Lianghong et al. "Lead Recovery Guide Residual Network for Myocardial Infarction Detection by Restoring 12-Lead Spatial Information" . (2023) : 730-733 . |
| APA | Wang, Lianghong , Zou, Yuyi , Yang, Tao , Xie, Chaoxin . Lead Recovery Guide Residual Network for Myocardial Infarction Detection by Restoring 12-Lead Spatial Information . (2023) : 730-733 . |
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本发明提出一种非线性支持向量机多特征量化及模型参数寻优重构心源性猝死风险因子的系统及方法，包括对心源性猝死心电信号数据集和正常窦性心律心电信号数据集进行数据预处理；对处理好的心电数据集进行心电波形检测；对心源性猝死风险因子进行提取；对提取的初始特征进行特征量化缩放处理；利用非线性支持向量机作为心源性猝死风险因子的验证模型，通过模型参数寻优，确定误差惩罚参数C和核参数γ；通过制定的心源性猝死风险因子和优化后的模型参数得到心源性猝死的预测模型；达到重构、验证心源性猝死风险因子的效果，对研究心源性猝死具有很好的指导意义。
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| GB/T 7714 | 王量弘 , 邹玉熠 , 余燕婷 et al. 重构心源性猝死风险因子的系统及方法 : CN202111251594.X[P]. | 2021-10-26 00:00:00 . |
| MLA | 王量弘 et al. "重构心源性猝死风险因子的系统及方法" : CN202111251594.X. | 2021-10-26 00:00:00 . |
| APA | 王量弘 , 邹玉熠 , 余燕婷 , 谢朝鑫 , 丁林娟 , 杨涛 . 重构心源性猝死风险因子的系统及方法 : CN202111251594.X. | 2021-10-26 00:00:00 . |
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本文设计了一种适用于无源UHFRFID的温度传感器。该温度传感器应用于人体温度监测领域，通过环形振荡器输出频率与温度线性相关的特性对温度进行感知。电路中采用二进制计数器对振荡器进行计数，最终通过公式计算出温度。该计数方法相比于传统的ADC量化形式，所消耗的功耗比较小，并且易于集成到RFID系统中。在校准方面，采用一种两点校准的方式，将两点数据储存在存储器中进行重复调用，能够降低整体功耗。通过仿真得出该温度传感器在0～60°C范围内的温度测量精度高达±0.15°C。
Keyword ：
RFID RFID 温度传感器 温度传感器 环形振荡器 环形振荡器
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| GB/T 7714 | 王量弘 , 吴玉山 , 杨涛 et al. 基于无源UHF RFID的温度传感器设计 [J]. | 微纳电子与智能制造 , 2023 , 5 (04) : 36-41 . |
| MLA | 王量弘 et al. "基于无源UHF RFID的温度传感器设计" . | 微纳电子与智能制造 5 . 04 (2023) : 36-41 . |
| APA | 王量弘 , 吴玉山 , 杨涛 , 江浩 , 赖华玲 . 基于无源UHF RFID的温度传感器设计 . | 微纳电子与智能制造 , 2023 , 5 (04) , 36-41 . |
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针对于深度图数据缺乏大量的3D标签、泛化能力差的问题,结合现有的弱监督网络结构,提出一种基于RGB-D图像的弱监督模型实现3 D人体姿态估计的方法,整体呈现两级级联结构.首先通过使用预处理后的RGB-D数据作为2 D姿态估计模块的输入,提取出人体关节热图;然后将热图进行积分回归生成对应的关节点坐标;最后将生成的关节点作为改进型深度回归模块的输入完成姿态估计.通过在公开数据集Human 3.6M和ITOP上进行验证,实验结果表明:本文提出的弱监督网络模型在参数量上减少了20.9％,训练时间上降低了37.9％.提出的模型能同时适用于深度图和彩色图,且具有较强的鲁棒性.
Keyword ：
3D人体姿态估计 3D人体姿态估计 弱监督 弱监督 沙漏结构 沙漏结构 深度图像 深度图像 积分回归 积分回归
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| GB/T 7714 | 申琼鑫 , 杨涛 , 徐胜 . 基于RGB-D图像弱监督学习的3D人体姿态估计 [J]. | 传感器与微系统 , 2022 , 41 (1) : 69-71,84 . |
| MLA | 申琼鑫 et al. "基于RGB-D图像弱监督学习的3D人体姿态估计" . | 传感器与微系统 41 . 1 (2022) : 69-71,84 . |
| APA | 申琼鑫 , 杨涛 , 徐胜 . 基于RGB-D图像弱监督学习的3D人体姿态估计 . | 传感器与微系统 , 2022 , 41 (1) , 69-71,84 . |
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