The pulse waveform quantification method basing on contour and derivative

Jin, Jing; Zhang, Haiying; Geng, Xingguang; Zhang, Yitao; Ye, Tianchun

doi:10.1016/j.cmpb.2022.106784

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…Relevant experiments are performed by wearing the iontronic capacitive sensor on the wrist to guarantee the detected pulse signal is originated from the radial artery of the body. According to the literatures, [66][67][68] theoretically, there are generally one major peak and other relatively smaller peaks in the usual waveform of the pulse signal. The characteristics of the pulse waveform can be used as an indicator of arterial stiffness and can reflect the health condition of the human body to a certain extent.…”

Section: Resultsmentioning

confidence: 99%

Versatile Ion‐Gel Fibrous Membrane for Energy‐Harvesting Iontronic Skin

Liu

Zhao

Xiong

et al. 2023

Adv Funct Materials

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Developing versatile and high sensitivity sensors is beneficial for promoting flexible electronic devices and human‐machine interactive systems. Researchers are working on the exploration of various active sensing materials toward broad detection, multifunction, and low‐power consumption. Here, a versatile ion‐gel fibrous membrane is presented by electrospinning technology and utilized to construct capacitive sensors and triboelectric nanogenerator (TENG). The iontronic capacitive sensor exhibits inherently favorable sensitivity and repeatability, which retains long‐term stability after 5000 cycles. The capacitive sensor can also detect a clear pulse waveform at the human wrist and enable the mapping of pressure distribution by a capacitive sensory matrix. For the iontronic TENG, the maximum peak power is 54.56 µW and can be used to power commercial electronics. In addition, the prepared iontronic TENG array can achieve interactive, rapidly responsive, and accurate dynamic monitoring, which broadens the exploration to direct and effective sensory devices. The versatile ion‐gel fibrous membrane is promising to provide an outstanding approach for physiological detection, biomechanical energy harvesting, human‐machine interaction, and self‐powered monitoring systems.

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Section: Resultsmentioning

confidence: 99%

Versatile Ion‐Gel Fibrous Membrane for Energy‐Harvesting Iontronic Skin

Liu

Zhao

Xiong

et al. 2023

Adv Funct Materials

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“…And the traditional pulse diagnosis has a large subjectivity, which seriously affects the accuracy of pulse diagnosis judgment. In recent years, there has been a gradual increase in the number of studies on the objectification of pulse diagnosis [10][11][12][13][14], and it has been applied to the study of various diseases, where the pulse characteristics of different TCM evidence types vary for the same disease, reflecting different disease states.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-based traditional Chinese medicine assistive diagnostic system: analysis of pulse parameters in patients with systemic lupus erythematosus and the factors influencing their Chinese medical evidence patterns

Pan¹,

Gong²,

Wei³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Objective: To analyze the characteristics of pulse pattern parameters of systemic lupus erythematosus (SLE) patients and the factors influencing their TCM patterns, and to provide an objective basis for their TCM pulse diagnosis and diagnosis. Methods: The SmartTCM-A1 TCM intelligent detection system was used to collect pulse diagnosis image information from 267 SLE patients (140 in the Yin deficiency internal heat evidence group, 96 in the heat toxin incandescence evidence group, and 31 in the rheumatic heat paralysis evidence group) and 130 healthy individuals, extract the image parameters of pulse diagnosis, and analyze the characteristics of pulse diagnosis parameters of SLE patients and their influencing factors of pulse diagnosis parameters of different TCM evidence types by logistic regression model. Results: ① Compared with the healthy group, the pulse parameters h1 value, h4 value, and t1/t value were significantly higher in the SLE group (P<0.05) and the pulse parameter t value was significantly lower in the SLE group (P<0.05); logistic regression analysis showed that the independent influencing factors in SLE patients included: h4 value (OR=1.073; 95% CI=1.003-1.148 ;P<0.05), t-value (OR=0.003;95% CI=0.000-0.763;P<0.05) and t1/t-value (OR=0.000;95% CI=0.000-1.199;P<0.05). ② Compared with the group with incandescent heat toxin evidence, the h4 value, h5 value, t4 value, t5 value, t5/t4 value were significantly lower in the group with Yin deficiency internal heat evidence (P<0.05), the h4/h1 value and t1/t4 value were significantly higher in the group with Yin deficiency internal heat evidence (P<0.05), the h4 value, h5 value, t5 value, h4/h1 value were significantly lower in the group with rheumatic heat paralysis evidence (P<0.05), and the t4 value, h5/h1 value, t5/t1 value were significantly lower in the group with rheumatic heat paralysis evidence (P<0.05). , h5/h1 values, and t1/t4 values were significantly higher in the rheumatism-heat paralysis group (P<0.05); logistic regression analysis. The results showed that the independent influencing factors of TCM evidence in SLE patients included: h3 values in the yin deficiency internal heat evidence group (OR=2.295; 95% CI=1.843-2.858; P<0.05) and h3 values in the rheumatic heat paralysis evidence group (OR=2.309; 95% CI=1.87-2.85; P<0.05). Conclusion:. Conclusion Pulse diagnosis parameters h4 value, t value and t1/t value are one of the influencing factors for the diagnosis of SLE patients, and pulse diagnosis parameter h3 value is one of the influencing factors for the diagnosis of SLE Chinese medical evidence.

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“…To address these issues, there has been an increase in interest in developing sensors to acquire wrist pulse signals and exploring pattern recognition/machine learning techniques to analyze health conditions [ 6 , 7 , 8 , 9 , 10 , 11 ], also known as computational pulse diagnosis [ 12 ]. Generally speaking, excluding preprocessing, computational pulse diagnosis mainly consists of two parts: feature extraction and pattern classification.…”

Section: Introductionmentioning

confidence: 99%

Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN

Chang

et al. 2023

Sensors

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For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.

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The pulse waveform quantification method basing on contour and derivative

Cited by 7 publications

References 30 publications

Versatile Ion‐Gel Fibrous Membrane for Energy‐Harvesting Iontronic Skin

Versatile Ion‐Gel Fibrous Membrane for Energy‐Harvesting Iontronic Skin

Artificial intelligence-based traditional Chinese medicine assistive diagnostic system: analysis of pulse parameters in patients with systemic lupus erythematosus and the factors influencing their Chinese medical evidence patterns

Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN

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