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学者姓名：王量弘
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Abstract ：
In the dentistry field, dental caries is a common issue affecting all age groups. The presence of dental braces and dental restoration makes the detection of caries more challenging. Traditionally, dentists rely on visual examinations to diagnose caries under restoration and dental braces, which can be prone to errors and are time-consuming. This study proposes an innovative deep learning and image processing-based approach for automated caries detection under restoration and dental braces, aiming to reduce the clinical burden on dental practitioners. The contributions of this research are summarized as follows: (1) YOLOv8 was employed to detect individual teeth in bitewing radiographs, and a rotation-aware segmentation method was introduced to handle angular variations in BW. The method achieved a sensitivity of 99.40% and a recall of 98.5%. (2) Using the original unprocessed images, AlexNet achieved an accuracy of 95.83% for detecting caries under restoration and dental braces. By incorporating the image processing techniques developed in this study, the accuracy of Inception-v3 improved to a maximum of 99.17%, representing a 3.34% increase over the baseline. (3) In clinical evaluation scenarios, the proposed AlexNet-based model achieved a specificity of 99.94% for non-caries cases and a precision of 99.99% for detecting caries under restoration and dental braces. All datasets used in this study were obtained with IRB approval (certificate number: 02002030B0). A total of 505 bitewing radiographs were collected from Chang Gung Memorial Hospital in Taoyuan, Taiwan. Patients with a history of the human immunodeficiency virus (HIV) were excluded from the dataset. The proposed system effectively identifies caries under restoration and dental braces, strengthens the dentist-patient relationship, and reduces dentist time during clinical consultations.
Keyword ：
bitewing radiography bitewing radiography caries under dental restoration and dental braces caries under dental restoration and dental braces convolutional neural network convolutional neural network image enhancement image enhancement tooth segmentation tooth segmentation YOLOv8 YOLOv8
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| GB/T 7714 | Mao, Yi-Cheng , Lin, Yuan-Jin , Hu, Jen-Peng et al. Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning [J]. | BIOENGINEERING-BASEL , 2025 , 12 (5) . |
| MLA | Mao, Yi-Cheng et al. "Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning" . | BIOENGINEERING-BASEL 12 . 5 (2025) . |
| APA | Mao, Yi-Cheng , Lin, Yuan-Jin , Hu, Jen-Peng , Liu, Zi-Yu , Chen, Shih-Lun , Chen, Chiung-An et al. Automated Caries Detection Under Dental Restorations and Braces Using Deep Learning . | BIOENGINEERING-BASEL , 2025 , 12 (5) . |
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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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The electrocardiogram (ECG), which records variations in surface electrical potential over time, has been widely used in the diagnosis of cardiovascular diseases. In recent years, the artificial intelligence (AI) + ECG paradigm has attracted considerable interest, but the two intrinsic characteristics of the ECG, namely, inter-lead correlations and multi-label classification, are often overlooked. Given that this oversight may constrain the full potential of AI models to enhance diagnostic performance, this study focuses on investigating methods for fusing information from a 12-lead ECG. A series of comprehensive experiments was conducted to evaluate the performance of various lead configurations, that is, 1-, 3-, 6-, 9-, and 12-lead combinations, with different fusion strategies. Innovatively integrating medical theory, we propose a novel five-lead-grouping strategy and develop a neural network architecture named Lead-5-Group Net (L5G-Net). After ranking the 12 leads with the AUC, we found that the aVR, V5, and V6 leads are particularly informative for single-lead ECG diagnosis. Furthermore, in multi-lead ECG classification, adopting an orthogonal lead-selection strategy which is based on the hypothesis of spatial interdependence among ECG leads was shown to enhance performance by ensuring that the information provided by each lead is complementary. Finally, the proposed L5G-Net demonstrates outstanding performance, achieving a macro-AUC of 0.9357 on the PTB-XL multi-label dataset without the use of data augmentation, attention mechanisms, or other strategies. Furthermore, considerable performance gains were observed after the five-lead-grouping strategy was applied to DenseNet and ResNet. These results imply that the proposed strategy can be seamlessly integrated into various network architectures and considerably enhance performance.
Keyword ：
12-lead ECG 12-lead ECG arrhythmia detection arrhythmia detection lead grouping lead grouping multi-label classification multi-label classification
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| GB/T 7714 | Yang, Tao , Xie, Chao-Xin , Huang, Hui-Ming et al. Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design [J]. | ELECTRONICS , 2025 , 14 (16) . |
| MLA | Yang, Tao et al. "Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design" . | ELECTRONICS 14 . 16 (2025) . |
| APA | Yang, Tao , Xie, Chao-Xin , Huang, Hui-Ming , Wang, Yu , Fan, Ming-Hui , Kuo, I-Chun et al. Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design . | ELECTRONICS , 2025 , 14 (16) . |
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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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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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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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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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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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