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学者姓名：王量弘
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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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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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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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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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This study proposes a design of a multi-channel EEG acquisition device based on a Bluetooth microcontroller for continuous collection of EEG signals. Composed of an analog front-end circuit and a Bluetooth microcontroller, it can be connected to a universal EEG electrode strip through a hardware interface. It can record high-quality and continuous EEG signals from eight channels. The system realizes the collection, analog-to-digital conversion, and data transmission of EEG signals. The system can set the sampling rate to 250Hz to 1KHz through software programs. This device meets the requirements of portability and continuous collection of EEG signals. The test results indicate that this study can obtain multi-channel EEG signals with high SNR. © 2023 IEEE.
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| GB/T 7714 | Hu, H.-Y. , Wang, L.-H. , Kuo, I.-C. et al. A Multi-Channel EEG Acquisition Device Based on BT Microcontroller [未知]. |
| MLA | Hu, H.-Y. et al. "A Multi-Channel EEG Acquisition Device Based on BT Microcontroller" [未知]. |
| APA | Hu, H.-Y. , Wang, L.-H. , Kuo, I.-C. , Wang, M.-H. , Wang, S.-F. , Huang, P.-C. . A Multi-Channel EEG Acquisition Device Based on BT Microcontroller [未知]. |
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To address the conflict between the need to use 12-lead in detecting myocardial infarction (MI) and inadequate diagnostic data due to an insufficient number of leads, this study proposes a novel network called Lead Recovery Guide Residual Network (LRGRN), which mitigates the effect of the restricted number of leads. We constructed a lead recovery guide to restore the spatial information of all 12-lead, given only leads I, II, and V2. Limb leads were reconstructed through a linear model, while precordial leads were reconstructed using a convolutional bidirectional long short-term memory network to capture high-level abstract features and temporal characteristics of electrocardiogram (ECG) signals. The restored 12-lead ECG can overcome the limitations of the original 3-lead ECG and provide a comprehensive reflection of MI. In the overall architecture of LRGRN, patient data strictly follow the inter-patient principle. ResNet maintains a stable flow of frequency information based on the reconstructed 12-lead ECG data, while the multi-lead single-channel structure enables the model to better capture the overall ECG information. The average detection accuracy of the LRGRN model for MI is 96.33%. The correlation coefficient (CC) of the recovered limb leads was 100%, The CC for recovery of precordial leads for feedback was 95.62%. Compared with the models presented in other studies, the LRGRN model overcomes inter-individual variability and excels in MI detection with limited lead information. © 2023 IEEE.
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| GB/T 7714 | Wang, Lianghong , Zou, Yuyi , Yang, Tao et al. Lead Recovery Guide Residual Network for Myocardial Infarction Detection by Restoring 12-Lead Spatial Information [C] . 2023 : 730-733 . |
| MLA | Wang, Lianghong et al. "Lead Recovery Guide Residual Network for Myocardial Infarction Detection by Restoring 12-Lead Spatial Information" . (2023) : 730-733 . |
| APA | Wang, Lianghong , Zou, Yuyi , Yang, Tao , Xie, Chaoxin . Lead Recovery Guide Residual Network for Myocardial Infarction Detection by Restoring 12-Lead Spatial Information . (2023) : 730-733 . |
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本发明提出一种非线性支持向量机多特征量化及模型参数寻优重构心源性猝死风险因子的系统及方法，包括对心源性猝死心电信号数据集和正常窦性心律心电信号数据集进行数据预处理；对处理好的心电数据集进行心电波形检测；对心源性猝死风险因子进行提取；对提取的初始特征进行特征量化缩放处理；利用非线性支持向量机作为心源性猝死风险因子的验证模型，通过模型参数寻优，确定误差惩罚参数C和核参数γ；通过制定的心源性猝死风险因子和优化后的模型参数得到心源性猝死的预测模型；达到重构、验证心源性猝死风险因子的效果，对研究心源性猝死具有很好的指导意义。
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| GB/T 7714 | 王量弘 , 邹玉熠 , 余燕婷 et al. 重构心源性猝死风险因子的系统及方法 : CN202111251594.X[P]. | 2021-10-26 00:00:00 . |
| MLA | 王量弘 et al. "重构心源性猝死风险因子的系统及方法" : CN202111251594.X. | 2021-10-26 00:00:00 . |
| APA | 王量弘 , 邹玉熠 , 余燕婷 , 谢朝鑫 , 丁林娟 , 杨涛 . 重构心源性猝死风险因子的系统及方法 : CN202111251594.X. | 2021-10-26 00:00:00 . |
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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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