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Zhao Y, Sun Q, Mei S, Gao L, Zhang X, Yang Z, Nan X, Zhang H, Xue C, Li J. Wearable multichannel-active pressurized pulse sensing platform. MICROSYSTEMS & NANOENGINEERING 2024; 10:77. [PMID: 38867942 PMCID: PMC11166975 DOI: 10.1038/s41378-024-00703-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/04/2024] [Accepted: 04/06/2024] [Indexed: 06/14/2024]
Abstract
With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human-computer interaction. Due to the high sensitivity (460.1 kPa-1), high linearity (R 2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual's blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.
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Affiliation(s)
- Yunlong Zhao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, 361102 Xiamen, China
- Discipline of Intelligent Instrument and Equipment, Xiamen University, 361102 Xiamen, China
| | - Qingxia Sun
- Department of Electronic Engineering, Ocean University of China, 266000 Qingdao, China
| | - Shixuan Mei
- School of Automation and Software Engineering, Shanxi University, 030006 Taiyuan, China
| | - Libo Gao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, 361102 Xiamen, China
- Discipline of Intelligent Instrument and Equipment, Xiamen University, 361102 Xiamen, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), 361005 Xiamen, China
| | - Xikuan Zhang
- Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education, North University of China, 030051 Taiyuan, China
| | - Zekun Yang
- Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education, North University of China, 030051 Taiyuan, China
| | - Xueli Nan
- School of Automation and Software Engineering, Shanxi University, 030006 Taiyuan, China
| | - Haiyan Zhang
- Science and Technology on Vacuum Technology and Physics Laboratory, Lanzhou Institute of Physics, Chinese Academy of Space Technology, 730000 Lanzhou, China
| | - Chenyang Xue
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, 361102 Xiamen, China
- Discipline of Intelligent Instrument and Equipment, Xiamen University, 361102 Xiamen, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), 361005 Xiamen, China
| | - Junyang Li
- Department of Electronic Engineering, Ocean University of China, 266000 Qingdao, China
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Fatangare M, Bhingarkar S. A comprehensive review on technological advancements for sensor-based Nadi Pariksha: An ancient Indian science for human health diagnosis. J Ayurveda Integr Med 2024; 15:100958. [PMID: 38815517 PMCID: PMC11166873 DOI: 10.1016/j.jaim.2024.100958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/11/2023] [Accepted: 04/24/2024] [Indexed: 06/01/2024] Open
Abstract
Nadi Pariksha is a significant, rather symbolic term for Ayurveda. Ancient Ayurvedic literature has prominently stated its importance in the judgment of Tridoshas (Vata, Pitta, and Kapha) which are the base of ailment diagnosis and prediction. The knowledge about Nadi Pariksha is uncovered in various ancient Ayurvedic literature like Ravansamhita, Bhavprakash, Nadivigyan by Kanad, Sharangdhar, and Yogratnakar. The various Nadi parameters are indicative of the diagnosis of diseases. These techniques were used as popular diagnostic tools in Indian culture from ancient days. Still, nowadays, these are not being used explicitly due to the lack of expertise, so it is necessary to establish their results once gained so that they can be used along with technical aspects in today's era. Ayurveda believes that all the elements of the Universe are present in any human body in minute, proportionate quantity, and the Nadi represents these elements, that is, Vata, Pitta, and Kapha (VPK). To facilitate the Nadi Pariksha using appropriate sensors may help the Ayurveda practitioners diagnose Prakriti and predict some diseases, making the Nadi Pariksha more reliable and faster. This review paper lists, 2 books and 67 research papers, mostly from countries like India, China, Japan, Korea, etc., from various reputed databases. The review primarily concentrates on six research themes: sensors and devices used for Nadi signal acquisition, signal pre-processing methods, feature extraction methods, feature selection approaches, classification practices, diseases diagnosed, and results attained. The paper also reviews the challenges in implementing the automated Nadi Pariksha with technological aid, which is a necessity of this period and is a very vibrant research arena. Yet significant work remains to be done, like bridging the gaps between technical and commercial development, and the procedure standardization is also required.
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Affiliation(s)
- Mrunal Fatangare
- School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India.
| | - Sukhada Bhingarkar
- School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India
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3
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Zhou X, Li C, Su H, Tang Z, Li P, Li Y, Hou J, Sun X. Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning. Technol Health Care 2024; 32:207-216. [PMID: 38759050 PMCID: PMC11191470 DOI: 10.3233/thc-248018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
BACKGROUND Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. OBJECTIVE This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. METHODS We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. RESULTS Experimental results show that the model's quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. CONCLUSIONS Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion.
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Affiliation(s)
- Xuezhong Zhou
- Community Health Center of Tianping Subdistrict, Xuhui District, Shanghai, China
| | - Chenxi Li
- Community Health Center of Tianping Subdistrict, Xuhui District, Shanghai, China
| | - Hai Su
- Community Health Center of Tianping Subdistrict, Xuhui District, Shanghai, China
| | - Zhixian Tang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Ping Li
- College of Medical Instrumentation, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yijie Li
- College of Information Technology, Shanghai Ocean University, Shanghai, China
| | - Jiawei Hou
- Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xun Sun
- Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Lim J, Li J, Feng X, Feng L, Xia Y, Xiao X, Wang Y, Xu Z. Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis. BMC Complement Med Ther 2023; 23:409. [PMID: 37957660 PMCID: PMC10644435 DOI: 10.1186/s12906-023-04249-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). METHODS A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. RESULTS There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. CONCLUSION Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Jiekee Lim
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Jieyun Li
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Xiao Feng
- The First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, P. R. China
| | - Lu Feng
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Yumo Xia
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Xinang Xiao
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Yiqin Wang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai, 201203, P. R. China
| | - Zhaoxia Xu
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China.
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai, 201203, P. R. China.
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5
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Zhu X, Wang F, Mao J, Huang Y, Zhou P, Luo J. A Protocol for Digitalized Collection of Traditional Chinese Medicine (TCM) Pulse Information Using Bionic Pulse Diagnosis Equipment. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:519-534. [PMID: 37881314 PMCID: PMC10593717 DOI: 10.1007/s43657-023-00104-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 10/27/2023]
Abstract
Pulse diagnosis equipment used in Traditional Chinese Medicine (TCM) has long been developed for collecting pulse information and in TCM research. However, it is still difficult to implement pulse taking automatically or efficiently in clinical practice. Here, we present a digital protocol for TCM pulse information collection based on bionic pulse diagnosis equipment, which ensures high efficiency, reliability and data integrity of pulse diagnosis information. A four-degree-of-freedom pulse taking platform together with a wrist bracket can satisfy the spatial positioning and angle requirements for individually adaptive pulse acquisition. Three-dimensional reconstruction of a wrist surface and an image localization model are combined to provide coordinates of the acquisition position and detection direction automatically. Three series elastic joints can not only simulate the TCM pulse taking method that "Three fingers in a straight line, the middle finger determining the 'Guan' location and finger pulp pressing on the radial artery," but also simultaneously carry out the force-controlled multi-gradient pressing process. In terms of pulse information integrity, this proposed protocol can generate rich pulse information, including basic individual information, pulse localization distribution, multi-gradient dynamic pulse force time series, and objective pulse parameters, which can help establish the fundamental data sets that are required as the pulse phenotype for subsequent comprehensive analysis of pulse diagnosis. The implementation of this scheme is beneficial to promote the standardization of the digitalized collection of pulse information, the effectiveness of detecting abnormal health status, and the promotion of the fundamental and clinical research of TCM, such as TCM pulse phenomics.
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Affiliation(s)
- Xing Zhu
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
- Jihua Laboratory, Foshan, Guangdong 528200 China
| | - Fanyu Wang
- Jihua Laboratory, Foshan, Guangdong 528200 China
| | - Jian Mao
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
| | - Yulin Huang
- Jihua Laboratory, Foshan, Guangdong 528200 China
| | - Peng Zhou
- Jihua Laboratory, Foshan, Guangdong 528200 China
- School of Precision Instrument and Optoelectronic Engineering, Tianjin University, Tianjin, 300072 China
| | - Jingjing Luo
- Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
- Jihua Laboratory, Foshan, Guangdong 528200 China
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6
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Fan L, Shi X, Wang Z, Zhang R, Zhang J. Disease identification method based on graph features between pulse cycles. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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7
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Wang X, Feng Z, Zhang G, Wang L, Chen L, Yang J, Wang Z. Flexible Sensors Array Based on Frosted Microstructured Ecoflex Film and TPU Nanofibers for Epidermal Pulse Wave Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:3717. [PMID: 37050777 PMCID: PMC10099249 DOI: 10.3390/s23073717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Recent advances in flexible pressure sensors have fueled increasing attention as promising technologies with which to realize human epidermal pulse wave monitoring for the early diagnosis and prevention of cardiovascular diseases. However, strict requirements of a single sensor on the arterial position make it difficult to meet the practical application scenarios. Herein, based on three single-electrode sensors with small area, a 3 × 1 flexible pressure sensor array was developed to enable measurement of epidermal pulse waves at different local positions of radial artery. The designed single sensor holds an area of 6 × 6 mm2, which mainly consists of frosted microstructured Ecoflex film and thermoplastic polyurethane (TPU) nanofibers. The Ecoflex film was formed by spinning Ecoflex solution onto a sandpaper surface. Micropatterned TPU nanofibers were prepared on a fluorinated ethylene propylene (FEP) film surface using the electrospinning method. The combination of frosted microstructure and nanofibers provides an increase in the contact separation of the tribopair, which is of great benefit for improving sensor performance. Due to this structure design, the single small-area sensor was characterized by pressure sensitivity of 0.14 V/kPa, a response time of 22 ms, a wide frequency band ranging from 1 to 23 Hz, and stability up to 7000 cycles. Given this output performance, the fabricated sensor can detect subtle physiological signals (e.g., respiration, ballistocardiogram, and heartbeat) and body movement. More importantly, the sensor can be utilized in capturing human epidermal pulse waves with rich details, and the consistency of each cycle in the same measurement is as high as 0.9987. The 3 × 1 flexible sensor array is employed to acquire pulse waves at different local positions of the radial artery. In addition, the time domain parameters including pulse wave transmission time (PTT) and pulse wave velocity (PWV) can be obtained successfully, which holds promising potential in pulse-based cardiovascular system status monitoring.
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Affiliation(s)
- Xue Wang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
- Key Laboratory of Optoelectronic Technology and Systems Ministry of Education, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Zhiping Feng
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
- Key Laboratory of Optoelectronic Technology and Systems Ministry of Education, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Gaoqiang Zhang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
- Key Laboratory of Optoelectronic Technology and Systems Ministry of Education, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Luna Wang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
- Key Laboratory of Optoelectronic Technology and Systems Ministry of Education, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Liang Chen
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
- Key Laboratory of Optoelectronic Technology and Systems Ministry of Education, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Jin Yang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
- Key Laboratory of Optoelectronic Technology and Systems Ministry of Education, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Zhonglin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
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8
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Liu T, Gou GY, Gao F, Yao P, Wu H, Guo Y, Yin M, Yang J, Wen T, Zhao M, Li T, Chen G, Sun J, Ma T, Cheng J, Qi Z, Chen J, Wang J, Han M, Fang Z, Gao Y, Liu C, Xue N. Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System. ACS NANO 2023; 17:5673-5685. [PMID: 36716225 PMCID: PMC10062340 DOI: 10.1021/acsnano.2c11897] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/23/2023] [Indexed: 05/25/2023]
Abstract
Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.
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Affiliation(s)
- Tiezhu Liu
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Guang-yang Gou
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Fupeng Gao
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Pan Yao
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Haoyu Wu
- State
Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing10029, China
| | - Yusen Guo
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Minghui Yin
- Department
of Materials and Manufacturing, Beijing
University of Technology, Beijing100124, China
| | - Jie Yang
- TCM
Data Center & Institute of Information on Traditional Chinese
Medicine, China Academy of Chinese Medical
Sciences (CAMS), Beijing100700, China
| | - Tiancai Wen
- TCM
Data Center & Institute of Information on Traditional Chinese
Medicine, China Academy of Chinese Medical
Sciences (CAMS), Beijing100700, China
| | - Ming Zhao
- Department
of Neurosurgery, the First Medical Center, Chinese PLA General Hospital, Beijing100853, China
| | - Tong Li
- School
of Modern Post (School of Automation), Beijing
University of Posts and Telecommunications, Beijing100876, China
| | - Gang Chen
- School
of Modern Post (School of Automation), Beijing
University of Posts and Telecommunications, Beijing100876, China
| | - Jianhai Sun
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Tianjun Ma
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Jianqun Cheng
- School
of Integrated Circuit, Quanzhou University
of Information Engineering, Quanzhou, Fujian362000, China
| | - Zhimei Qi
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Jiamin Chen
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Junbo Wang
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Mengdi Han
- Department
of Biomedical Engineering, College of Future Technology, Peking University, Beijing100091, China
| | - Zhen Fang
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
- Personalized
Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing100190, China
| | - Yangyang Gao
- State
Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing10029, China
| | - Chunxiu Liu
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
- Personalized
Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing100190, China
| | - Ning Xue
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
- Personalized
Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing100190, China
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9
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Tang Q, Xu S, Guo M, Wang G, Pan Z, Su B. Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression. Health Inf Sci Syst 2022; 10:7. [PMID: 35529250 PMCID: PMC9023627 DOI: 10.1007/s13755-022-00172-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/24/2022] [Indexed: 10/18/2022] Open
Abstract
Purpose Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal. Methods Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices. Results Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r = - 0.446, P < 0.001 to r = - 0.534, P < 0.001 in men; r = - 0.623, P < 0.001 to r = - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women). Conclusion The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.
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Affiliation(s)
- Qingfeng Tang
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 1318 Jixian North Road, Anqing, 246133 China
- School of Public Health, Hangzhou Normal University, 2318 Yuhangtang Road, Hangzhou, 311121 China
| | - Shoujiang Xu
- School of Public Health, Hangzhou Normal University, 2318 Yuhangtang Road, Hangzhou, 311121 China
- Jiangsu Food and Pharmaceutical Science College, Huai’an, 223023 China
| | - Mengjuan Guo
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 1318 Jixian North Road, Anqing, 246133 China
| | - Guangjun Wang
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 1318 Jixian North Road, Anqing, 246133 China
| | - Zhigeng Pan
- School of Public Health, Hangzhou Normal University, 2318 Yuhangtang Road, Hangzhou, 311121 China
- School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Benyue Su
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 1318 Jixian North Road, Anqing, 246133 China
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10
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Wang S, Zhang Z, Chen Z, Mei D, Wang Y. Development of Pressure Sensor Based Wearable Pulse Detection Device for Radial Pulse Monitoring. MICROMACHINES 2022; 13:mi13101699. [PMID: 36296052 PMCID: PMC9609944 DOI: 10.3390/mi13101699] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/27/2022] [Accepted: 10/06/2022] [Indexed: 05/31/2023]
Abstract
Wearable pulse detection devices can be used for daily human healthcare monitoring; however, the relatively poor flexibility and low sensitivity of the pulse detection devices are hindering the scrutiny of pulse information during pulse diagnosis of different pulse positions. This paper developed a novel and wearable pulse detection device based on three flexible pressure sensors using synthetic graphene and silver composites as the pressure sensing material. The structural design of the pulse detection device is firstly presented; the core component of pressure sensors is using the sawtooth protrusions to convert pressure induced by radial pulse vibrations into localized deformation of graphene composites. The fabricated pulse detection device is characterized by high pressure sensing performance, including relatively high sensitivity (8.65% kPa-1), broad sensing range (12 kPa), and good dynamic response with a response time of about 100 ms. Then, the pulse detection device is worn on a human wrist to detect the pulses from three pulse positions, namely, 'Cun', 'Guan', and 'Chi', and the results demonstrated the capability of using our device to detect pulse signals. The physical conditions of the subject, such as arterial stiffness index, can be further analyzed through the characteristics of the acquired pulse signals, demonstrating the potential application of using wearable pulse detection devices for human health monitoring.
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Affiliation(s)
- Shihang Wang
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhinan Zhang
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhijian Chen
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Deqing Mei
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yancheng Wang
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
- Donghai Laboratory, Zhoushan 316021, China
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11
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Tang Q, Tao C, Pan Z, Wang G, Liu K, Pan Z, Liu G, Su B, Liu N. A novel method for vascular age estimation via pressure pulse wave of radial artery. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Mehrabadi MA, Aqajari SAH, Zargari AHA, Dutt N, Rahmani AM. Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1906-1909. [PMID: 36086575 DOI: 10.1109/embc48229.2022.9871962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous monitoring of blood pressure (BP) can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethys-mograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends the GAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2× in BP estimation.
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13
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The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:7644524. [PMID: 35547656 PMCID: PMC9085309 DOI: 10.1155/2022/7644524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 03/04/2022] [Accepted: 04/08/2022] [Indexed: 12/02/2022]
Abstract
With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.
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14
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Guo C, Jiang Z, He H, Liao Y, Zhang D. Wrist pulse signal acquisition and analysis for disease diagnosis: A review. Comput Biol Med 2022; 143:105312. [PMID: 35203039 DOI: 10.1016/j.compbiomed.2022.105312] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 01/22/2022] [Accepted: 02/07/2022] [Indexed: 11/26/2022]
Abstract
Pulse diagnosis (PD) plays an indispensable role in healthcare in China, India, Korea, and other Orient countries. It requires considerable training and experience to master. The results of pulse diagnosis rely heavily on the practitioner's subjective analysis, which means that the results from different physicians may be inconsistent. To overcome these drawbacks, computational pulse diagnosis (CPD) is used with advanced sensing techniques and analytical methods. Focusing on the main processes of CPD, this paper provides a systematic review of the latest advances in pulse signal acquisition, signal preprocessing, feature extraction, and signal recognition. The most relevant principles and applications are presented along with current progress. Extensive comparisons and analyses are conducted to evaluate the merits of different methods employed in CPD. While much progress has been made, a lack of datasets and benchmarks has limited the development of CPD. To address this gap and facilitate further research, we present a benchmark to evaluate different methods. We conclude with observations of the status and prospects of CPD.
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Affiliation(s)
- Chaoxun Guo
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
| | - Zhixing Jiang
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
| | - Haoze He
- New York University, New York, 10012, New York, United States
| | - Yining Liao
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China
| | - David Zhang
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
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15
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Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2021.102855] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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16
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Landry C, Hedge ET, Hughson RL, Peterson SD, Arami A. Accurate Blood Pressure Estimation During Activities of Daily Living: A Wearable Cuffless Solution. IEEE J Biomed Health Inform 2021; 25:2510-2520. [PMID: 33497346 DOI: 10.1109/jbhi.2021.3054597] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The objective is to develop a cuffless method that accurately estimates blood pressure (BP) during activities of daily living. User-specific nonlinear autoregressive models with exogenous inputs (NARX) are implemented using artificial neural networks to estimate the BP waveforms from electrocardiography and photoplethysmography signals. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. The procedure was performed before and after a six-hour testing phase wherein five participants went about their normal daily living activities. Data were further collected at a four-month time point for two participants and again at six months for one of the two. The performance of three different NARX models was compared with three pulse arrival time (PAT) models. The NARX models demonstrate superior accuracy and correlation with "ground truth" systolic and diastolic BP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. This establishes a method for cuffless BP estimation during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection.
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17
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Athaya T, Choi S. An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach. SENSORS (BASEL, SWITZERLAND) 2021; 21:1867. [PMID: 33800106 PMCID: PMC7962188 DOI: 10.3390/s21051867] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/20/2023]
Abstract
Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson's correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.
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Affiliation(s)
| | - Sunwoong Choi
- School of Electrical Engineering, Kookimin University, Seoul 02707, Korea;
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18
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Matos LC, Machado JP, Monteiro FJ, Greten HJ. Can Traditional Chinese Medicine Diagnosis Be Parameterized and Standardized? A Narrative Review. Healthcare (Basel) 2021; 9:177. [PMID: 33562368 PMCID: PMC7914658 DOI: 10.3390/healthcare9020177] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/25/2021] [Accepted: 02/03/2021] [Indexed: 12/14/2022] Open
Abstract
The integration of Traditional Chinese Medicine (TCM) in Western health systems and research requires a rational communicable theory, scientific proof of efficacy and safety, and quality control measures. The existence of clear definitions and the diagnosis standardization are critical factors to establish the patient's vegetative functional status accurately and, therefore, systematically apply TCM therapeutics such as the stimulation of reflex skin areas known as acupoints. This science-based conceptualization entails using validated methods, or even developing new systems able to parameterize the diagnosis and assess TCM related effects by objective measurements. Traditionally, tongue and pulse diagnosis and the functional evaluation of action points by pressure sensitivity and physical examination may be regarded as essential diagnostic tools. Parameterizing these techniques is a future key point in the objectification of TCM diagnosis, such as by electronic digital image analysis, mechanical pulse diagnostic systems, or the systematic evaluation of acupoints' electrophysiology. This review aims to demonstrate and critically analyze some achievements and limitations in the clinical application of device-assisted TCM diagnosis systems to evaluate functional physiological patterns. Despite some limitations, tongue, pulse, and electrophysiological diagnosis devices have been reported as a useful tool while establishing a person's functional status.
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Affiliation(s)
- Luís Carlos Matos
- Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal;
- CBSIn—Centro de Biociências em Saúde Integrativa, Atlântico Business School, 4405-604 Vila Nova de Gaia, Portugal;
- CTEC—Centro Transdisciplinar de Estudos da Consciência da Universidade Fernando Pessoa, 4249-004 Porto, Portugal
| | - Jorge Pereira Machado
- CBSIn—Centro de Biociências em Saúde Integrativa, Atlântico Business School, 4405-604 Vila Nova de Gaia, Portugal;
- ICBAS—Institute of Biomedical Sciences Abel Salazar, University of Porto, 4050-313 Porto, Portugal;
| | - Fernando Jorge Monteiro
- Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal;
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
| | - Henry Johannes Greten
- ICBAS—Institute of Biomedical Sciences Abel Salazar, University of Porto, 4050-313 Porto, Portugal;
- German Society of Traditional Chinese Medicine, 69126 Heidelberg, Germany
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19
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Qi Z, Zhao ZY, Xu JT, Zhu LP, Zhang Y, Bao YM, Zhang ZF. Radial Pulse Wave Signals Combined with Ba-PWV for the Risk Prediction of Hypertension and the Monitoring of Its Accompanying Metabolic Risk Factors. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:3926851. [PMID: 32419802 PMCID: PMC7210560 DOI: 10.1155/2020/3926851] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 03/13/2020] [Accepted: 04/15/2020] [Indexed: 12/21/2022]
Abstract
Our aim was to study whether radial pulse wave signals can improve the risk prediction of incident hypertension and are associated with its concomitant metabolic risk factors beyond the traditional cardiovascular risk factor Ba-PWV. By enrolling 523 Chinese subjects in this study, linear and stepwise regression analysis was performed to assess the association of radial artery pulse wave signals and Ba-PWV with blood pressure and its related metabolic risk factors such as fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and uric acid (UA). The area under the receiver-operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated by risk assessment plot to compare the discriminative ability among models with and without radial artery pulse wave signals. After adjusting related confounding factors, radial artery pulse wave variable h 3/h 1 was selected as the sensitive influential factor for blood pressure. Moreover, a new model with h 3/h 1 had a higher AUC than the reference model without it (0.86 vs 0.84; P=0.030). And the NRI and IDI for the new model was 50.0% (P=0.017) and 3.16% (P=0.044), respectively. In addition to Ba-PWV, we found that the decrease of t 4, t 5, and h 5 might be associated with higher FPG, TC, LDL-C, and UA and lower HDL-C. This research might provide a valuable additional tool for remote wearable monitoring of radial artery pulse wave signals in hypertension risk evaluation and management.
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Affiliation(s)
- Zhen Qi
- Shanghai Geriatric Institute of Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 365 South Xiangyang Road, Shanghai 200031, China
| | - Zhi-Yue Zhao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Jia-Tuo Xu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Li-Ping Zhu
- Physical Examination Center, The First People's Hospital of Taicang Affiliated to Suzhou University, 58 South Changsheng Road, Taicang 215400, China
| | - Yu Zhang
- Department of Cerebral Surgery, The First People's Hospital of Taicang Affiliated to Suzhou University, 58 South Changsheng Road, Taicang 215400, China
| | - Yi-Min Bao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Zhi-Feng Zhang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
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20
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Landry C, Peterson SD, Arami A. Estimation of the Blood Pressure Waveform using Electrocardiography .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:7060-7063. [PMID: 31947463 DOI: 10.1109/embc.2019.8856399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This work presents a modelling approach to accurately predict the blood pressure (BP) waveform time series from a single input signal. A nonlinear autoregressive model with exogenous input (NARX) is implemented using artificial neural networks and trained on Electrocardiography (ECG) signals to predict the BP waveform. The efficacy of the model is demonstrated using the MIMIC II database. The proposed method can accurately estimate systolic and diastolic BP. The NARX model together with ECG measurement allows continuous monitoring of BP, enables the estimation of other physiological measurements, such as the cardiac output, and provides more insight on the patient health condition.
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21
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Wang J, Liu K, Sun Q, Ni X, Ai F, Wang S, Yan Z, Liu D. Diaphragm-based optical fiber sensor for pulse wave monitoring and cardiovascular diseases diagnosis. JOURNAL OF BIOPHOTONICS 2019; 12:e201900084. [PMID: 31219245 DOI: 10.1002/jbio.201900084] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 06/12/2019] [Accepted: 06/19/2019] [Indexed: 06/09/2023]
Abstract
Arterial pulse wave has been considered as a vital sign in assessment of cardiovascular diseases. Noninvasive pulse sensor with compact structure, immunity to electro-magnetic interference and high sensitivity is the research focus in recent years. While, optical fiber biosensor is a competitive option to meet these needs. Here, a diaphragm-based optical fiber pulse sensor was proposed to achieve high-precision radial pulse wave monitoring. A wearable device was developed, composed of a sports wristband and an aluminum diaphragm-based optical fiber sensor tip of only 1 cm in diameter, which was highly sensitive to the weak acoustic signal. In particular, coherent phase detection was adopted to improve detection signal-to-noise ratio, so as to recover the high-fidelity pulse waveforms. A clinical experiment was carried out to detect and morphological analyze the pulse waveforms of four subjects, the results of which preliminarily demonstrated the feasibility of pulse diagnosis method. The proposed pulse fiber sensor provides a comfortable way for pulse diagnosis, which is promising in early cardiovascular diseases indicating.
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Affiliation(s)
- Jingyi Wang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Kewei Liu
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Qizhen Sun
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xiaoling Ni
- Hospital of Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Fan Ai
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Senmao Wang
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Zhijun Yan
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Deming Liu
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, and National Engineering Laboratory for Next Generation Internet Access System, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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22
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An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:8617503. [PMID: 31662790 PMCID: PMC6791233 DOI: 10.1155/2019/8617503] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/30/2019] [Accepted: 07/30/2019] [Indexed: 11/18/2022]
Abstract
In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.
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23
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Fu Y, Zhao S, Wang L, Zhu R. A Wearable Sensor Using Structured Silver-Particle Reinforced PDMS for Radial Arterial Pulse Wave Monitoring. Adv Healthc Mater 2019; 8:e1900633. [PMID: 31293071 DOI: 10.1002/adhm.201900633] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 06/27/2019] [Indexed: 01/31/2023]
Abstract
Human pulse signals contain important and useful physiological information for the auxiliary diagnosis of cardiovascular disease. Here, a wearable pulse sensor based on piezo-thermic transduction is reported using a structured silver-particle reinforced polydimethylsiloxane (PDMS) membrane, for monitoring radial arterial pulse waves. The structured silver-particle reinforced PDMS membrane is optimally designed to meet the specific requirements on sensitivity, linearity, and effective preload measuring range for pulse detection by adjusting the air gap volume fraction and silver particle volume fraction of the structured material. The sensor is endowed with high sensitivity, good linearity in preload measuring range, allowing to detect the subtle pulse waveforms of subjects at different ages under different contact pressures, such as superficial (Fu), medium (Zhong) and deep (Chen). The developed pulse device provides a promising approach for homecare pulse monitoring.
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Affiliation(s)
- Yu Fu
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
| | - Shuai Zhao
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
| | - Liangqi Wang
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
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24
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Multiple linear regression model for vascular aging assessment based on radial artery pulse wave. Eur J Integr Med 2019. [DOI: 10.1016/j.eujim.2019.05.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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25
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Fu Y, Zhao S, Zhu R. A Wearable Multifunctional Pulse Monitor Using Thermosensation-Based Flexible Sensors. IEEE Trans Biomed Eng 2019; 66:1412-1421. [DOI: 10.1109/tbme.2018.2873754] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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26
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Yoo SY, Ahn JE, Cserey G, Lee HY, Seo JM. Reliability and Validity of Non-invasive Blood Pressure Measurement System Using Three-Axis Tactile Force Sensor. SENSORS 2019; 19:s19071744. [PMID: 30979050 PMCID: PMC6480067 DOI: 10.3390/s19071744] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 03/18/2019] [Accepted: 04/04/2019] [Indexed: 11/17/2022]
Abstract
Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the data were collected every 2 min for the short-term experiment, and every 10 min for the long-term experiment. In addition, the effects on the BP measurement of external physical factors such as the tension to the radial artery on applying the device and wrist circumference were evaluated. A high correlation between the measured BP with the proposed system and with the cuff-based non-invasive blood pressure, and reproducibility, were demonstrated. All data satisfied the Association for the Advancement of Medical Instrumentation criteria. The external physical factors did not affect the measurement results. In addition to previous research indicating the high reliability of the arterial pulse waveforms, the present results have demonstrated the reliability of numerical BP values, and this implies that the three-axis force sensor can be used as a patient monitoring device.
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Affiliation(s)
- Sun-Young Yoo
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea.
| | - Ji-Eun Ahn
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 03080, Korea.
| | - György Cserey
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary.
| | - Hae-Young Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul 03080, Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea.
| | - Jong-Mo Seo
- Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea.
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Qiao LJ, Qi Z, Tu LP, Zhang YH, Zhu LP, Xu JT, Zhang ZF. The Association of Radial Artery Pulse Wave Variables with the Pulse Wave Velocity and Echocardiographic Parameters in Hypertension. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2018; 2018:5291759. [PMID: 30622604 PMCID: PMC6304622 DOI: 10.1155/2018/5291759] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 10/24/2018] [Accepted: 11/14/2018] [Indexed: 01/10/2023]
Abstract
This study aims at exploring the cardiovascular pathophysiological mechanism of TCM (traditional Chinese medicine) pulse by detecting the correlation between radial artery pulse wave variables and pulse wave velocity/echocardiographic parameters. Two hundred Chinese subjects were enrolled in this study, which were grouped into health control group, hypertension group, and hypertensive heart disease group. Physical data obtained in this study contained TCM pulse images at "Guan" position of the left hand, pulse wave velocity, and echocardiographic parameters. Linear and stepwise regression analysis was performed to assess the association of radial artery pulse wave variables with pulse wave velocity and echocardiographic parameters in the total population and in each different group. After adjusting for related confounding factors, decrease of t1, t5 and increase of h1, h3/h1 were statistically associated with arterial stiffness in the total population (P<0.05). Moreover, the correlation study in each group showed that the decrease of both t3 and h5 was also related to arterial stiffness (P<0.05). In terms of echocardiographic parameters, the height of dicrotic wave indicated by h5 was the most relevant pulse wave variable. For the health control, h5 was negatively associated with interventricular septal thickness (VST) and left ventricular posterior wall thickness (PWT) (P<0.05), while for the hypertension population and those with target-organ damage to heart, increase of h5 might be associated with decrease of ejection fraction (EF) and increase of all the remaining echocardiographic parameters especially for left ventricular end-systolic diameter (LVDs) and Left ventricular end-diastolic diameter (LVDd) (P<0.05). In conclusion, we found radial artery pulse wave variables were in association with the arterial stiffness and echocardiographic changes in hypertension, which would provide an experimental basis for cardiovascular pathophysiological mechanism of radial artery pulse wave variables.
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Affiliation(s)
- Li-jie Qiao
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Zhen Qi
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Li-ping Tu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Yu-hang Zhang
- Ultrasonic Diagnosis Department, The First People's Hospital of Taicang Affiliated to Suzhou University, 58 South Changsheng Road, Taicang 215400, China
| | - Li-ping Zhu
- Physical Examination Center, The First People's Hospital of Taicang Affiliated to Suzhou University, 58 South Changsheng Road, Taicang 215400, China
| | - Jia-tuo Xu
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
| | - Zhi-feng Zhang
- Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China
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28
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Pulse Wave Cycle Features Analysis of Different Blood Pressure Grades in the Elderly. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2018; 2018:1976041. [PMID: 29951104 PMCID: PMC5987238 DOI: 10.1155/2018/1976041] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 03/24/2018] [Accepted: 04/05/2018] [Indexed: 11/29/2022]
Abstract
Background and Objective The same range of blood pressure values may reflect different vascular functions, especially in the elderly. Therefore, a single blood pressure value may not comprehensively reveal cardiovascular function. This study focused on identifying pulse wave features in the elderly that can be used to show functional differences when blood pressure values are in the same range. Methods First, pulse data were preprocessed and pulse cycles were segmented. Second, time domain, higher-order statistics, and energy features of wavelet packet decomposition coefficients were extracted. Finally, useful pulse wave features were evaluated using a feature selection and classifier design. Results A total of 6,075 pulse wave cycles were grouped into 3 types according to different blood pressure levels and each group was divided into 2 categories according to a history of hypertension. The classification accuracy of feature selection in the 3 groups was 97.91%, 95.24%, and 92.28%, respectively. Conclusion Selected features could be appropriately used to analyze cardiovascular function in the elderly and can serve as the basis for research on a cardiovascular risk assessment model based on Traditional Chinese Medicine pulse diagnosis.
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29
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A sensor-based wrist pulse signal processing and lung cancer recognition. J Biomed Inform 2018; 79:107-116. [DOI: 10.1016/j.jbi.2018.01.009] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 01/23/2018] [Accepted: 01/30/2018] [Indexed: 11/24/2022]
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30
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Földi S, Horváth T, Zieger F, Sótonyi P, Cserey G. A novel non-invasive blood pressure waveform measuring system compared to Millar applanation tonometry. J Clin Monit Comput 2017; 32:717-727. [DOI: 10.1007/s10877-017-0070-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 09/25/2017] [Indexed: 11/28/2022]
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31
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Association of lung cancer with skin diseases: A nationwide cohort study based on the “lung governing skin and hair” theory. Eur J Integr Med 2016. [DOI: 10.1016/j.eujim.2016.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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32
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Thelwall M, Kousha K. ResearchGate articles: Age, discipline, audience size, and impact. J Assoc Inf Sci Technol 2016. [DOI: 10.1002/asi.23675] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Mike Thelwall
- Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton; Wulfruna Street Wolverhampton WV1 1LY UK
| | - Kayvan Kousha
- Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton; Wulfruna Street Wolverhampton WV1 1LY UK
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