1
|
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.
Collapse
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
| |
Collapse
|
2
|
Zhang Q, Wen J, Zhou J, Zhang B. Missing-view completion for fatty liver disease detection. Comput Biol Med 2022; 150:106097. [PMID: 36244304 DOI: 10.1016/j.compbiomed.2022.106097] [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] [Received: 04/21/2022] [Revised: 08/22/2022] [Accepted: 09/10/2022] [Indexed: 11/15/2022]
Abstract
Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10% to 30% in many countries. In general, detecting fatty liver requires professional neuroimaging modalities or methods such as computed tomography, ultrasound, and medical experts' practical experiences. Considering this point, finding intelligent electronic noninvasive diagnostic approaches are desired at present. Currently, most existing works in the area of computerized noninvasive disease detection often apply one view (modality) or perform multi-view (several modalities) analysis, e.g., face, tongue, and/or sublingual for disease detection. The multi-view data of patients provides more complementary information for diagnosis. However, due to the conditions of data acquisition, interference by human factors, etc., many multi-view data are defective with some missing-view information, making these multi-view data difficult to evaluate. This factor largely affects the performance of classifying disease and the development of fully computerized noninvasive methods. Thus, the purpose of this study is to address the missing view issue among noninvasive disease detection. In this work, a multi-view dataset containing facial, sublingual vein, and tongue images are initially processed to produce corresponding feature for incomplete multi-view disease diagnostic evaluation. Hereby, we propose a novel method, i.e., multi-view completion, to process the incomplete multi-view data in order to complete the missing-view information for classifying fatty liver disease from healthy candidates. In particular, this method can explore the intra-view and inter-view information to produce the missing-view data effectively. Extensive experiments on a collected dataset with 220 fatty liver patients and 220 healthy samples show that our proposed approach achieves better diagnostic results with missing-view completion compared to the original incomplete multi-view data under various classifiers. Related results prove that our method can effectively process the missing-view issue and improve the noninvasive disease detection performance.
Collapse
Affiliation(s)
- Qi Zhang
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Macau, China
| | - Jie Wen
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Jianhang Zhou
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Macau, China
| | - Bob Zhang
- PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Macau, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, China.
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Nie J, Zhang L, Liu J, Wang Y. Pulse taking by a piezoelectric film sensor via mode energy ratio analysis helps identify pregnancy status. IEEE J Biomed Health Inform 2021; 26:2116-2123. [PMID: 34748506 DOI: 10.1109/jbhi.2021.3125707] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In order to solve the problem of non-invasive diagnosis and monitoring of women during pregnancy, a piezoelectric film pulse sensing system combined with the mode energy ratio (MER) analysis is utilized to detect human pulses to reveal pregnant conditions. Inspired by traditional Chinese medicine (TCM), pulse diagnosis has a history of more than 2,500 years. The life energy of the human body helps the diagnosis of the disease through the circulation of blood vessels connected to the organs. A PVDF piezoelectric film sensor is used to emulate the pulse taking process in TCM to record the pulse signals. And the algorithm of MER is proposed based on empirical mode decomposition (EMD). Through the MER analysis of 83 female volunteers with different pregnancy statuses, the identification and warning of pregnancy status and physical health indicators are realized.
Collapse
|
5
|
Xu Y, Wen G, Yang P, Fan B, Hu Y, Luo M, Wang C. Task-Coupling Elastic Learning for Physical Sign-based Medical Image Classification. IEEE J Biomed Health Inform 2021; 26:626-637. [PMID: 34428166 DOI: 10.1109/jbhi.2021.3106837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Physical signs of patients indicate crucial evidence for diagnosing both location and nature of the disease, where there is a sequential relationship between the two tasks. Thus their joint learning can utilize intrinsic association by transferring related knowledge across relevant tasks. Choosing the right time to transfer is a critical problem for joint learning. However, how to dynamically adjust when tasks interact to capture the right time for transferring related knowledge is still an open issue. To this end, we propose a Task-Coupling Elastic Learning (TCEL) framework to model the task relatedness for classifying disease-location and disease-nature based on physical sign images. The main idea is to dynamically transfer relevant knowledge by progressively shifting task-coupling from loose to tight during the multi-stage training. In the early stage of training, we relax the constraints of modeling relations to focus more in learning the generic task-common features. In the later stage, the semantic guidance will be strengthened to learn the task-specific features. Specifically, a dynamic sequential module (DSM) is proposed to explicitly model the sequential relationship and enable multi-stage training. Moreover, to address the side effect of DSM, a new loss regularization is proposed. The extensive experiments on these two clinical datasets show the superiority of the proposed method over the baselines, and demonstrate the effectiveness of the proposed task-coupling elastic mechanism.
Collapse
|
6
|
Lin F, Zhang J, Wang Z, Zhang X, Yao R, Li Y. Research on feature mining algorithm and disease diagnosis of pulse signal based on piezoelectric sensor. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
|
7
|
He K, Peng Y, Liu S, Li J. Regularized Negative Label Relaxation Least Squares Regression for Face Recognition. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10219-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
8
|
Jiang Z, Guo C, Zang J, Lu G, Zhang D. Features fusion of multichannel wrist pulse signal based on KL-MGDCCA and decision level combination. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101751] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
9
|
Wang H, Wang L, Sun N, Yao Y, Hao L, Xu L, Greenwald SE. Quantitative Comparison of the Performance of Piezoresistive, Piezoelectric, Acceleration, and Optical Pulse Wave Sensors. Front Physiol 2020; 10:1563. [PMID: 32009976 PMCID: PMC6971205 DOI: 10.3389/fphys.2019.01563] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 12/12/2019] [Indexed: 11/16/2022] Open
Abstract
The accurate measurement of the arterial pulse wave is beneficial to clinical health assessment and is important for the effective diagnosis of many types of cardiovascular disease. A variety of sensors have been developed for the non-invasive detection of these waves, but the type of sensor has an impact on the measurement results. Therefore, it is necessary to compare and analyze the signals obtained under a range of conditions using various pulse sensors to aid in making an informed choice of the appropriate type. From the available types we have selected four: a piezoresistive strain gauge sensor (PESG) and a piezoelectric Millar tonometer (the former with the ability to measure contact force), a circular film acceleration sensor, and an optical reflection sensor. Pulse wave signals were recorded from the left radial, carotid, femoral, and digital arteries of 60 subjects using these four sensors. Their performance was evaluated by analyzing their susceptibilities to external factors (contact force, measuring site, and ambient light intensity) and by comparing their stability and reproducibility. Under medium contact force, the peak-to-peak amplitude of the signals was higher than that at high and low force levels and the variability of signal waveform was small. The optical sensor was susceptible to ambient light. Analysis of the intra-class correlation coefficients (ICCs) of the pulse wave parameters showed that the tonometer and accelerometer had good stability (ICC > 0.80), and the PESG and optical sensor had moderate stability (0.46 < ICC < 0.86). Intra-observer analysis showed that the tonometer and accelerometer had good reproducibility (ICC > 0.75) and the PESG and optical sensor had moderate reproducibility (0.42 < ICC < 0.91). Inter-observer analysis demonstrated that the accelerometer had good reproducibility (ICC > 0.85) and the three other sensors had moderate reproducibility (0.52 < ICC < 0.96). We conclude that the type of sensor and measurement site affect pulse wave characteristics and the careful selection of appropriate sensor and measurement site are required according to the research and clinical need. Moreover, the influence of external factors such as contact pressure and ambient light should be fully taken into account.
Collapse
Affiliation(s)
- Hongju Wang
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
| | - Lu Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Nannan Sun
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
| | - Yang Yao
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
| | - Liling Hao
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
| | - Lisheng Xu
- College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Stephen E. Greenwald
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| |
Collapse
|
10
|
Huang CC, Lin YJ. Is it the time to use wrist devices for health diagnosis in clinical practice? J Chin Med Assoc 2019; 82:675-676. [PMID: 31335629 DOI: 10.1097/jcma.0000000000000154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Chin-Chou Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
- Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Yenn-Jiang Lin
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
| |
Collapse
|
11
|
Jiang Z, Zhang D, Lu G. Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 174:25-31. [PMID: 29709314 DOI: 10.1016/j.cmpb.2018.04.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 01/22/2018] [Accepted: 04/17/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. METHODS In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. RESULTS Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. CONCLUSIONS The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states.
Collapse
Affiliation(s)
- Zhixing Jiang
- Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - David Zhang
- Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Biometrics Research Center, Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong.
| | - Guangming Lu
- Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
| |
Collapse
|
12
|
Experimental investigation on the suitability of flexible pressure sensor for wrist pulse measurement. HEALTH AND TECHNOLOGY 2018. [DOI: 10.1007/s12553-018-0264-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
13
|
Garg N, Ryait HS, Kumar A, Bisht A. An effective method to identify various factors for denoising wrist pulse signal using wavelet denoising algorithm. Biomed Mater Eng 2017; 29:53-65. [PMID: 29254073 DOI: 10.3233/bme-171712] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND WPS is a non-invasive method to investigate human health. During signal acquisition, noises are also recorded along with WPS. OBJECTIVE Clean WPS with high peak signal to noise ratio is a prerequisite before use in disease diagnosis. Wavelet Transform is a commonly used method in the filtration process. Apart from its extensive use, the appropriate factors for wavelet denoising algorithm is not yet clear in WPS application. The presented work gives an effective approach to select various factors for wavelet denoise algorithm. With the appropriate selection of wavelet and factors, it is possible to reduce noise in WPS. METHODS In this work, all the factors of wavelet denoising are varied successively. Various evaluation parameters such as MSE, PSNR, PRD and Fit Coefficient are used to find out the performance of the wavelet denoised algorithm at every one step. RESULTS The results obtained from computerized WPS illustrates that the presented approach can successfully select the mother wavelet and other factors for wavelet denoise algorithm. The selection of db9 as mother wavelet with sure threshold function and single rescaling function using UWT has been a better option for our database. CONCLUSION The empirical results proves that the methodology discussed here could be effective in denoising WPS of any morphological pattern.
Collapse
Affiliation(s)
- Nidhi Garg
- I.K. Gujral Punjab Technical University (PTU), Jalandhar, Punjab-144001, India.,Department of Electronics and Communication, University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh-160023, India
| | - Hardeep S Ryait
- Department of Electronics and Communication, BBSBEC, Fatehgarh Sahib, Punjab-140407, India
| | - Amod Kumar
- Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh-160030, India
| | - Amandeep Bisht
- Department of Electronics and Communication, University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh-160023, India
| |
Collapse
|
14
|
Development of a Tonometric Sensor with a Decoupled Circular Array for Precisely Measuring Radial Artery Pulse. SENSORS 2016; 16:s16060768. [PMID: 27240363 PMCID: PMC4934194 DOI: 10.3390/s16060768] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 05/20/2016] [Accepted: 05/23/2016] [Indexed: 11/16/2022]
Abstract
The radial artery pulse is one of the major diagnostic indices used clinically in both Eastern and Western medicine. One of the prominent methods for measuring the radial artery pulse is the piezoresistive sensor array. Independence among channels and an appropriate sensor arrangement are important for effectively assessing the spatial-temporal information of the pulse. This study developed a circular-type seven-channel piezoresistive sensor array using face-down bonding (FDB) as one of the sensor combination methods. The three-layered housing structure that included independent pressure sensor units using the FDB method not only enabled elimination of the crosstalk among channels, but also allowed various array patterns to be created for effective pulse measurement. The sensors were arranged in a circular-type arrangement such that they could estimate the direction of the radial artery and precisely measure the pulse wave. The performance of the fabricated sensor array was validated by evaluating the sensor sensitivity per channel, and the possibility of estimating the blood vessel direction was demonstrated through a radial artery pulse simulator. We expect the proposed sensor to allow accurate extraction of the pulse indices for pulse diagnosis.
Collapse
|