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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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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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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: Ovarian cancer is a difficult and lethal illness that requires early detection and precise classification for effective therapy. Microarray technology has permitted the simultaneous assessment of hundreds of genes' expression levels, yielding important insights into the molecular pathways driving ovarian cancer. To reduce computational complexity and improve accuracy, choosing the most likely differential genes to explain the impacts of ovarian cancer is necessary. Medical datasets, including those related to ovarian cancer, are often limited in size due to privacy concerns, data collection challenges, and the rarity of certain conditions. Data augmentation allows researchers to expand the dataset, providing a larger and more diverse set of examples for model training. Recent advances in machine learning and bioinformatics have shown promise in improving ovarian cancer classification based on gene information. Methods: In this paper, we present an ensemble algorithm based on gene selection, data augmentation, and boosting approaches for ovarian cancer classification. In the proposed approach, the initial genetic data were first subjected to feature selection. Results: The target genes were screened and combined with data augmentation and ensemble boosting algorithms. From the results, the chosen ten genes could accurately classify ovarian cancer at 98.21%. Conclusions: We further show that the proposed algorithm based on clustering approaches is effective for real-world ovarian cancer data, with 100% accuracy and strong performance in distinguishing between distinct ovarian cancer subtypes. The proposed algorithm may help doctors identify ovarian cancer patients early and develop individualized treatment plans.
Keyword ：
boosting algorithm boosting algorithm classification classification data augmentation data augmentation gene selection gene selection microarray data microarray data ovarian cancer ovarian cancer
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| GB/T 7714 | Lee, Zne-Jung , Cai, Jing-Xun , Wang, Liang-Hung et al. Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification [J]. | DIAGNOSTICS , 2024 , 14 (24) . |
| MLA | Lee, Zne-Jung et al. "Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification" . | DIAGNOSTICS 14 . 24 (2024) . |
| APA | Lee, Zne-Jung , Cai, Jing-Xun , Wang, Liang-Hung , Yang, Ming-Ren . Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification . | DIAGNOSTICS , 2024 , 14 (24) . |
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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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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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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 ：
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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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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