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学者姓名：王量弘
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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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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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This study presents a portable EEG signal acquisition board with 16 channels,the firmware development of the ESP32 module enables the transmission of the EEG signals acquired by the analog front-end ADS1299 to the host computer using the SPI communication interface. The ESP32 module has built-in Bluetooth and Wi-Fi communication peripherals, which enable fast, high-quality transmission of EEG signals. This study realized a 16-channel data acquisition system, and measured the shorting noise of the 16 channels which obtains an average noise of 1.782uV. The experimental results show that this study has a better recognition effect for identifying the four SSVEP signals. The average recognition accuracy of five subjects using the FFT and the CCA algorithms were 82% and 89.5% respectively.
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| GB/T 7714 | Chen, Hong-Ji , Lan, Yan-Yao , Wang, Liang-Hung et al. 16-Channel EEG Signal Acquisition Board for SSVEP [J]. | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 : 579-580 . |
| MLA | Chen, Hong-Ji et al. "16-Channel EEG Signal Acquisition Board for SSVEP" . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 (2024) : 579-580 . |
| APA | Chen, Hong-Ji , Lan, Yan-Yao , Wang, Liang-Hung , Kuo, I-Chun , Huang, Pao-Cheng . 16-Channel EEG Signal Acquisition Board for SSVEP . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 , 579-580 . |
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Sleep apnea syndrome episodes may induce high-risk complications such as pulmonary hypertension, cardiac arrhythmia, respiratory failure, and hypertension. It is of great significance to apply neural networks for efficient automatic diagnosis of sleep apnea syndrome. We propose a transfer learning-based classification model for sleep apnea syndrome using ECG signals and respiratory signals, which results in a 91.26% accuracy in recognizing three types of sleep apnea syndrome.
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| GB/T 7714 | Liu, Pei-Dong , Wang, Liang-Hung , Li, Xin et al. Multi-classification of Sleep Apnea Syndrome Based on Transfer Learning [J]. | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 : 581-582 . |
| MLA | Liu, Pei-Dong et al. "Multi-classification of Sleep Apnea Syndrome Based on Transfer Learning" . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 (2024) : 581-582 . |
| APA | Liu, Pei-Dong , Wang, Liang-Hung , Li, Xin , Huang, Pao-Cheng , Fan, Ming-Hui . Multi-classification of Sleep Apnea Syndrome Based on Transfer Learning . | 2024 11TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN, ICCE-TAIWAN 2024 , 2024 , 581-582 . |
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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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Sleep apnea syndrome episodes may induce high-risk complications such as pulmonary hypertension, cardiac arrhythmia, respiratory failure, and hypertension. It is of great significance to apply neural networks for efficient automatic diagnosis of sleep apnea syndrome. We propose a transfer learning-based classification model for sleep apnea syndrome using ECG signals and respiratory signals, which results in a 91.26% accuracy in recognizing three types of sleep apnea syndrome. © 2024 IEEE.
Keyword ：
Contrastive Learning Contrastive Learning Electrocardiography Electrocardiography Lung cancer Lung cancer Neural networks Neural networks Pulmonary diseases Pulmonary diseases Sleep research Sleep research Transfer learning Transfer learning
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| GB/T 7714 | Liu, Pei-Dong , Wang, Liang-Hung , Li, Xin et al. Multi-classification of Sleep Apnea Syndrome Based on Transfer Learning [C] . 2024 : 581-582 . |
| MLA | Liu, Pei-Dong et al. "Multi-classification of Sleep Apnea Syndrome Based on Transfer Learning" . (2024) : 581-582 . |
| APA | Liu, Pei-Dong , Wang, Liang-Hung , Li, Xin , Huang, Pao-Cheng , Fan, Ming-Hui . Multi-classification of Sleep Apnea Syndrome Based on Transfer Learning . (2024) : 581-582 . |
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Abstract ：
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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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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目的 研究基于脉搏波和心电信号的无创连续血压预测方法。方法 从MIMIC-Ⅲ数据库中选取300个病例，用于构建血压预测模型、模型验证；另收集2022年1月至6月入住福建省立医院重症监护病房的121例患者，用于测试模型；采集患者动脉血压、光电容积脉搏波和心电图信号。构建两个血压预测模型，一个是以人工提取出的8种特征参数构建的人工特征参数模型，另一个是以8种特征参数加1种卷积神经网络提取的特征进行融合构建的特征融合模型。对两个预测模型进行验证、测试，评价指标采用平均绝对误差（MAE）、标准差（SD）、均方根误差（RMSE），根据国际公认的美国医疗器械促进协会（AAMI）规定的标准进行评价，对比两个模型预测能力。结果 用MIMIC-Ⅲ数据对两个模型进行评价，特征融合模型的MAE、SD符合AAMI标准，RMSE比人工特征参数模型低。用实际收集的重症患者数据对两个模型进行评价，特征融合模型收缩压的SD、舒张压的MAE和SD达到AAMI标准，RMSE也比人工特征参数模型低。结论 特征融合模型的预测能力比人工特征参数模型好。
Keyword ：
光电容积脉搏波 光电容积脉搏波 可穿戴式血压设备 可穿戴式血压设备 心电图 心电图 无创连续血压预测 无创连续血压预测 融合特征 融合特征
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| GB/T 7714 | 张健春 , 王量弘 , 庄丽媛 et al. 基于脉搏波和心电信号的无创连续血压预测方法研究 [J]. | 中国医药导报 , 2024 , 21 (13) : 12-15 . |
| MLA | 张健春 et al. "基于脉搏波和心电信号的无创连续血压预测方法研究" . | 中国医药导报 21 . 13 (2024) : 12-15 . |
| APA | 张健春 , 王量弘 , 庄丽媛 , 张炜鑫 , 王新康 . 基于脉搏波和心电信号的无创连续血压预测方法研究 . | 中国医药导报 , 2024 , 21 (13) , 12-15 . |
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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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