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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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Sudden cardiac death (SCD) occurs when an individual experiences ventricular fibrillation (VF) and does not receive intervention within several minutes. Predicting SCD or VF can provide medical professionals with additional time to perform rescues, thereby reducing mortality. This study proposes a novel high-efficiency grid search-based support vector machine algorithm (GSSVM) for SCD risk prediction. It significantly reduces the time required to construct models. Nineteen VF-related visualization features (i.e., mean, standard deviation, approximate entropy of RR interval, QRS duration, corrected QT interval, Tp-Te interval, Tp-Te/QT ratio, and T-wave amplitude, as well as heart rate variability) were innovatively extracted from electrocardiogram (ECG) signals. Next, a distribution analysis of the features was conducted to convincingly highlight the differences between those derived from SCD samples and healthy controls. Furthermore, the GS-SVM algorithm was used to construct five SCD risk prediction models in accordance with the interval before the occurrence of VF. The highest accuracy of 95.78 % was obtained for predicting VF when 30 min before its occurrence. In addition, this study extended the prediction time to 70 min and achieved an accuracy of 90.08 %. Finally, to demonstrate the generalizability and clinical applicability of the proposed algorithm, two external datasets were used, the Creighton University Ventricular Tachyarrhythmia Database and the clinical Fujian Provincial Hospital Database. The overall accuracies achieved on them are 83.12 % and 93.75 %, respectively. The proposed algorithm effectively predicts the SCD at an earlier stage. Additionally, it can be integrated into ECG monitoring systems to provide real-time alerts for individuals.
Keyword ：
ECG ECG Sudden cardiac death prediction Sudden cardiac death prediction SVM SVM Visualizing feature Visualizing feature
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| GB/T 7714 | Xie, Chao-Xin , Wang, Liang-Hung , Yu, Yan-Ting et al. Clinical sudden cardiac death risk prediction: A grid search support vector machine multimodel base on ventricular fibrillation visualization features [J]. | COMPUTERS & ELECTRICAL ENGINEERING , 2025 , 123 . |
| MLA | Xie, Chao-Xin et al. "Clinical sudden cardiac death risk prediction: A grid search support vector machine multimodel base on ventricular fibrillation visualization features" . | COMPUTERS & ELECTRICAL ENGINEERING 123 (2025) . |
| APA | Xie, Chao-Xin , Wang, Liang-Hung , Yu, Yan-Ting , Ding, Lin-Juan , Yang, Tao , Kuo, I. -Chun et al. Clinical sudden cardiac death risk prediction: A grid search support vector machine multimodel base on ventricular fibrillation visualization features . | COMPUTERS & ELECTRICAL ENGINEERING , 2025 , 123 . |
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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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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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Abstract ：
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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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%. © 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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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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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: 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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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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