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学者姓名：樊明辉
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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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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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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 ：
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 ：
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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Abstract ：
Prehypertension is difficult to diagnose early because of its hidden nature. Long-term monitoring of blood pressure (BP) can help in the early detection and timely treatment of this condition. This study proposes an innovative and efficient BP detection platform that combines portable electrocardiography (ECG) and photoplethysmogram (PPG) signals simultaneous acquisition equipment and BP detection algorithm to obtain real-time BP values conveniently and accurately for a long time. In this study, nine kinds of feature parameters and classification algorithm are used to build multiple linear regression (MLR) models. It not only adopts the multiparameter intelligent monitoring in intensive care units (MIMIC-II) database to train and validate the model but also uses self-developed equipment for acquisition and verification in long-term health monitoring. According to the experimental results, the mean absolute error (MAE) and standard deviation (SD) of systolic BP (SBP) have estimated values of 4.46 and 3.20 mmHg, respectively, and simultaneously, the MAE and SD of diastolic BP (DBP) are 4.20 and 3.28 mmHg, respectively. Moreover, both SBP and DBP experimental results conform to the Advancement of Medical Instrumentation (AAMI) BP standard. The proposed BP acquisition platform is proven to be capable of easily acquiring ECG and PPG signals with the proposed sensor device, and the MLR algorithm can also effectively and accurately monitor BP values for a long time.
Keyword ：
Biomedical monitoring Biomedical monitoring Electrocardiography Electrocardiography Electrocardiography (ECG) Electrocardiography (ECG) Feature extraction Feature extraction Monitoring Monitoring multiple linear regression (MLR) multiple linear regression (MLR) noninvasive continuous blood pressure (BP) measurement noninvasive continuous blood pressure (BP) measurement photoplethysmogram (PPG) photoplethysmogram (PPG) Physiology Physiology Predictive models Predictive models pulse wave arrival time (PAT) pulse wave arrival time (PAT) Sensors Sensors
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| GB/T 7714 | Wang, Liang-Hung , Sun, Kun-Kun , Xie, Chao-Xin et al. Cuffless Blood Pressure Estimation Using Dual Physiological Signal and Its Morphological Features [J]. | IEEE SENSORS JOURNAL , 2023 , 23 (11) : 11956-11967 . |
| MLA | Wang, Liang-Hung et al. "Cuffless Blood Pressure Estimation Using Dual Physiological Signal and Its Morphological Features" . | IEEE SENSORS JOURNAL 23 . 11 (2023) : 11956-11967 . |
| APA | Wang, Liang-Hung , Sun, Kun-Kun , Xie, Chao-Xin , Fan, Ming-Hui , Abu, Patricia Angela R. , Huang, Pao-Cheng . Cuffless Blood Pressure Estimation Using Dual Physiological Signal and Its Morphological Features . | IEEE SENSORS JOURNAL , 2023 , 23 (11) , 11956-11967 . |
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This study presents the design of a PPG and ECG synchronous acquisition system based on NRF52832. The synchronous acquisition of pulse wave signal and ECG signal is achieved by designing PPG circuit and ECG circuit, while data integration and Bluetooth transmission are realized by NRF52832. Finally, the collected data are input into the trained classification model for BP prediction, and the device achieves wearable, synchronous and real-time acquisition. The experimental results show that the signals acquired by the device can be used for noninvasive continuous BP prediction. © 2023 IEEE.
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| GB/T 7714 | Chen, H.-D. , Wang, L.-H. , Fan, M.-H. et al. Design of PPG and ECG synchronous acquisition system based on NRF52832 [未知]. |
| MLA | Chen, H.-D. et al. "Design of PPG and ECG synchronous acquisition system based on NRF52832" [未知]. |
| APA | Chen, H.-D. , Wang, L.-H. , Fan, M.-H. , Sun, K.-K. , Chen, C.-H. , Liu, C.-F. et al. Design of PPG and ECG synchronous acquisition system based on NRF52832 [未知]. |
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Abstract ：
随着生活越来越美好，人们对于人体健康情况非常重视。本文结合生物医疗领域的应用背景，设计了一款应用于人体温度监测的RFID电子标签芯片，提出一种适合于无源UHF RFID系统的基准电源电路，并且LDO的反馈网络采用的是二极管连接方式的PMOS有源电阻，相比传统的无源电阻，大大减小了面积以及功耗。张弛振荡器采用不同类型的电阻抵消温度偏差，并且通过一种电流镜阵列校准电路，采用自校准算法对时钟电路工艺角偏差进行校准，大大提高了时钟电路输出频率的精度。ASK解调电路采用反馈分压调节结构，能同时缩小调制信号的高低电平，增大了电子标签的灵活性。本文基于SMIC0.18μm CMOS Mixed Signal工艺，设计了一款应用于人体温度监测的全无源超高频标签。设计的RFID标签功耗小于10μW，时钟电路输出频率的精度为±1%。
Keyword ：
RFID RFID 人体温度 人体温度 时钟校准 时钟校准
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| GB/T 7714 | 樊明辉 , 许晴昊 , 潘拯 et al. UHF RFID标签芯片研究 [J]. | 微纳电子与智能制造 , 2023 , 5 (04) : 29-35 . |
| MLA | 樊明辉 et al. "UHF RFID标签芯片研究" . | 微纳电子与智能制造 5 . 04 (2023) : 29-35 . |
| APA | 樊明辉 , 许晴昊 , 潘拯 , 王量弘 , 赖华玲 , 江浩 . UHF RFID标签芯片研究 . | 微纳电子与智能制造 , 2023 , 5 (04) , 29-35 . |
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Abstract ：
The application of artificial intelligence to the diagnosis of ECG is of great significance. We combine machine learning algorithm with deep learning algorithm to give full play to the advantages of different algorithms by ensemble learning. Finally, we fuse the selected models so that the accuracy of identifying five kinds of arrhythmias can reach 94%. Particularly, the accuracy of class F beat which is difficult to identify has also been improved. © 2022 IEEE.
Keyword ：
Deep learning Deep learning Electrocardiograms Electrocardiograms Learning algorithms Learning algorithms
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| GB/T 7714 | Xie, Chao-Xin , Fan, Ming-Hui , Wang, Liang-Hung et al. An Integration Method for ECG Multi-Classification [C] . 2022 : 559-560 . |
| MLA | Xie, Chao-Xin et al. "An Integration Method for ECG Multi-Classification" . (2022) : 559-560 . |
| APA | Xie, Chao-Xin , Fan, Ming-Hui , Wang, Liang-Hung , Huang, Pao-Cheng . An Integration Method for ECG Multi-Classification . (2022) : 559-560 . |
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