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学者姓名：杨涛
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Abstract ：
Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures. This study proposes a novel closed-loop epilepsy detection system and stimulation control chip. A time-domain detection algorithm based on amplitude, slope, line length, and signal energy characteristics is introduced. A new threshold calculation method is proposed; that is, the threshold is updated by means of the mean and standard deviation of four consecutive eigenvalues through parameter combination. Once a seizure is detected, the system begins to control the stimulation of a two-phase pulse current with an amplitude and frequency of 34 mu A and 200 Hz, respectively. The system is physically designed on the basis of the UMC 55 nm process and verified by a field programmable gate array verification board. This research is conducted through innovative algorithms to reduce power consumption and the area of the circuit. It can maintain a high accuracy of more than 90% and perform seizure detection every 64 ms. It is expected to provide a new treatment for patients with epilepsy.
Keyword ：
ASIC ASIC closed loop closed loop electrical stimulation electrical stimulation epilepsy detection epilepsy detection feature extraction feature extraction
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| GB/T 7714 | Wang, Liang-Hung , Zhang, Zhen-Nan , Xie, Chao-Xin et al. A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System [J]. | SENSORS , 2025 , 25 (1) . |
| MLA | Wang, Liang-Hung et al. "A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System" . | SENSORS 25 . 1 (2025) . |
| APA | Wang, Liang-Hung , Zhang, Zhen-Nan , Xie, Chao-Xin , Jiang, Hao , Yang, Tao , Ran, Qi-Peng et al. A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System . | SENSORS , 2025 , 25 (1) . |
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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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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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In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground-background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT-BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis.
Keyword ：
ECG data recovery ECG data recovery ECG signal extraction ECG signal extraction image distortion correction image distortion correction signal reconstruction signal reconstruction uneven light correction uneven light correction
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| GB/T 7714 | Wang, Liang-Hung , Xie, Chao-Xin , Yang, Tao et al. Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis [J]. | DIAGNOSTICS , 2024 , 14 (17) . |
| MLA | Wang, Liang-Hung et al. "Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis" . | DIAGNOSTICS 14 . 17 (2024) . |
| APA | Wang, Liang-Hung , Xie, Chao-Xin , Yang, Tao , Tan, Hong-Xin , Fan, Ming-Hui , Kuo, I-Chun et al. Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis . | DIAGNOSTICS , 2024 , 14 (17) . |
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Abstract ：
Objective: Lobula giant motion detectors (LGMDs) in locusts effectively predict collisions and trigger avoidance, with potential applications in autonomous driving and UAVs. Research on LGMD characteristics splits into two views: one focusing on the presynaptic visual pathway, the other on the postsynaptic LGMD neurons. Both perspectives have support, leading to two computational models, but they lack a biophysical description of the individual LGMD neuron behavior. This paper aims to mimic and explain LGMD behavior based on fractional spiking neurons (FSNs) and construct a biomimetic visual model for the LGMD compatible with these characteristics. Methods: We implement the visual model using an event camera to simulate photoreceptors and follow the ON/OFF visual pathway, incorporating lateral inhibition to mimic the LGMD 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 introduce FSN circuits by altering dendritic morphological parameters to simulate multi-scale spike frequency adaptation (SFA) observed in LGMDs. Additionally, we add one more circuit of dendritic trees into the FSNs to be compatible with the postsynaptic feed-forward inhibition (FFI) in LGMD neurons, providing a novel explanatory and predictive model. Results: We test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects.
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, Yabin , Ruan, Haojie , He, Shan 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) : 2978-2990 . |
| MLA | Deng, Yabin et al. "A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits" . | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 71 . 10 (2024) : 2978-2990 . |
| APA | Deng, Yabin , Ruan, Haojie , He, Shan , Yang, Tao , Guo, Donghui . A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits . | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING , 2024 , 71 (10) , 2978-2990 . |
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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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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%. © 2024 IEEE.
Keyword ：
Adversarial machine learning Adversarial machine learning Contrastive Learning Contrastive Learning Convolutional neural networks Convolutional neural networks Recurrent neural networks Recurrent neural networks
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| GB/T 7714 | Ji, Tian-Yun , Xie, Chao-Xin , Yang, Tao et al. Risk Stratification Model of Sudden Cardiac Death [C] . 2024 : 577-578 . |
| MLA | Ji, Tian-Yun et al. "Risk Stratification Model of Sudden Cardiac Death" . (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) : 577-578 . |
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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. © 2024 IEEE.
Keyword ：
Electrocardiograms Electrocardiograms
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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 [C] . 2024 : 575-576 . |
| MLA | Wang, Yu et al. "Multi-lead Branch Multi-class Arrhythmias Classification Based on Selective Kernel Block" . (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) : 575-576 . |
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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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Abstract ：
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%.
Cite：
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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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