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学者姓名：王量弘
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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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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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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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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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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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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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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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It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 x 4 block are achieved. An image is divided into 4 x 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 mu m CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 mu m(2) and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.
Keyword ：
bit map bit map block truncation coding block truncation coding color sampling color sampling Golomb-Rice coding Golomb-Rice coding image compression image compression image sensor image sensor IoT IoT machine learning machine learning YEF color space YEF color space
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| GB/T 7714 | Chen, Shih-Lun , Chou, He-Sheng , Ke, Shih-Yao et al. VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT [J]. | SENSORS , 2023 , 23 (3) . |
| MLA | Chen, Shih-Lun et al. "VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT" . | SENSORS 23 . 3 (2023) . |
| APA | Chen, Shih-Lun , Chou, He-Sheng , Ke, Shih-Yao , Chen, Chiung-An , Chen, Tsung-Yi , Chan, Mei-Ling et al. VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT . | SENSORS , 2023 , 23 (3) . |
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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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