Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis

Zhang, Qi; Zhou, Jianhang; Zhang, Bob

doi:10.1109/jbhi.2020.3045274

Cited by 31 publications

(11 citation statements)

References 43 publications

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“…For example, professor Cui Ji has developed the pulse map identification standards of flat, smooth, and chord pulse by measuring the index range of pulse map characteristic parameters [ 41 ]. Zhang et al proposed a graph-based multichannel feature fusion (GBMFF) method using multichannel features of wrist pulse information effectively [ 42 ]. On the other hand, tongue diagnosis also is the core component of TCM.…”

Section: The Influence Of Ai In Tcm Fieldsmentioning

confidence: 99%

The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine

Huang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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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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Section: The Influence Of Ai In Tcm Fieldsmentioning

confidence: 99%

The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine

Huang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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“…However, in [20] , the performance of each radiographic dataset was evaluated separately. Besides the automated diagnosis of COVID-19, there are some other studies [32] – [34] related to other medical diagnostic domains based on the fusion of different CNN models. These methods mainly utilized the concept of deep information fusion [35] to improve the overall CAD performance.…”

Section: Related Workmentioning

confidence: 99%

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data

Owais

Lee

Mahmood

et al. 2021

IEEE J. Biomed. Health Inform.

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In the present epidemic of the coronavirus disease 2019 (COVID-19), radiological imaging modalities, such as X-ray and computed tomography (CT), have been identified as effective diagnostic tools. However, the subjective assessment of radiographic examination is a timeconsuming task and demands expert radiologists. Recent advancements in artificial intelligence have enhanced the diagnostic power of computer-aided diagnosis (CAD) tools and assisted medical specialists in making efficient diagnostic decisions. In this work, we propose an optimal multilevel deep-aggregated boosted network to recognize COVID-19 infection from heterogeneous radiographic data, including X-ray and CT images. Our method leverages multilevel deep-aggregated features and multistage training via a mutually beneficial approach to maximize the overall CAD performance. To improve the interpretation of CAD predictions, these multilevel deep features are visualized as additional outputs that can assist radiologists in validating the CAD results. A total of six publicly available datasets were fused to build a single large-scale heterogeneous radiographic collection that was used to analyze the performance of the proposed technique and other baseline methods. To preserve generality of our method, we selected different patient data for training, validation, and testing, and consequently, the data of same patient were not included in training, validation, and testing subsets. In addition, fivefold cross-validation was performed in all the experiments for a fair evaluation. Our method exhibits promising performance values of 95.38%, 95.57%, 92.53%, 98.14%, 93.16%, and 98.55% in terms of average accuracy, F-measure, specificity, sensitivity, precision, and area under the curve, respectively and outperforms various stateof-the-art methods.

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“…Combining non-threshold recurrence plot (RP) and deep VGG-16 nerwork, Yan et al proposed a pulse recognition strategy, which outperforms other approaches [ 22 ]. Treating the acquired correlations between various features as nodes on a graph, Zhang [ 23 ] et al utilized the graph convolutional network (GCN) to classify pulse signals. A four-layer multi-task fusion convolutional neural network (CNN) for type 2 diabetes detection was constructed by encoding pulse signals into 2D images using several time-series imgaing methods, including the gramian angular field (GAF), Markov transition field (MTF) and recurrence plots (RPs) [ 24 ].…”

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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Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis

Cited by 31 publications

References 43 publications

The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine

The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data

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

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