A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

Shi, Yulin; Yao, Xinghua; Xu, Jiatuo; Hu, Xiaojuan; Tu, Liping; Lan, Fang; Cui, Ji; Cui, Longtao; Li, Jun; Bi, Zijuan; Li, Jiacai

doi:10.3389/fphys.2021.708742

Cited by 11 publications

(13 citation statements)

References 46 publications

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“…Inspired by the morphological difference between HF patients and healthy subjects ( Chang et al., 2020 ), the volatility and the irregularity of the BCG singals can be measured and analyzed in the power-domain. To eliminate individualized differences, in our experiments, the signal was normalized using Z-score before the power calculation ( Shi et al., 2022 ). As ( Li et al., 2020 ), the signals x ( n ) was first divided into L segments x ′ by the sliding window as

where

represents the number of segments.…”

Section: Methodsmentioning

confidence: 99%

“…Based on the above extracted features related to the BCG/respiratory/cardiopulmonary signals, we applied four supervised classifiers to evaluate the performance of the HF detection. The classifiers include the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM), the Random Forest (RF) and the eXtreme Gradient Boosting (XGBoost), where the features require to be normalized within [0,1] before performing the classification ( Shi et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

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Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Feng

Wu²,

Bao³

et al. 2023

Front. Physiol.

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Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers.Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤49%).Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

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where

represents the number of segments.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Feng

Wu²,

Bao³

et al. 2023

Front. Physiol.

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“…Thus, the color, thickness, frequency, and smell of these areas can be used to understand the severity and cause of disease. With the rapid development of TCM research, the four TCM diagnoses have advanced accordingly with modern science and technology (2), including artificial intelligence (AI). AI was proposed by McCarthy et al in the 50s of the 20th century (3).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the multistep approach (4), Genetic Algorithm_Extreme Gradient Boosting (GA_XGBT) model (5), random forest (6), and convolutional neural network (MIMT-CNN) (7) have been widely applied in diabetic tongue research, while AdaBoost (8), Support Vector Machine (SVM) (9), and logistic regression (10) have been applied in pulse research. The use of AI to understand the clinical data of diseases can help to objectively and efficiently improve the accuracy and precision of diagnosis (2).…”

Section: Introductionmentioning

confidence: 99%

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Tian¹,

Wang²,

Sun³

et al. 2023

Ann Transl Med

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Background: With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. Methods: Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. Results: China was the most productive country in this field, and Evidence-Based Complementary and Alternative Medicine published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and Artificial Intelligence in Medicine was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation.Conclusions: This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models.

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“…The collected tongue images are preprocessed with image correction, image denoising, tongue body and tongue coating segmentation, and then the color and morphological characteristics of the tongue body and tongue coating are analyzed and summarized. In modern tongue diagnosis research, digital image processing technology is widely used to extract features of color and texture, and various machine learning methods are used for analysis, all of which have achieved good results [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction Models for Different Stages of Non-small Cell Lung Cancer Based on Tongue and Tumor Marker

Shi

Wang

Yao

et al. 2022

Preprint

Self Cite

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Objective To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker. Methods Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, five classifiers, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker. Results There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. The number of index with statistically significant correlations between tongue feature and tumor marker in the advanced NSCLC group was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. The classifier of neural network based on the tongue feature & tumor marker & baseline data can well predict NSCLC at different stages, the accuracy rates of the five classifiers neural network, random forest, logistic regression, SVM, and naive bayes were 79.69%, 75.00%, 72.81%, 74.06%, 76.56%, and the ROCs were 0.8639, 0.8325, 0.8147, 0.8127, and 0.7969, respectively. Conclusions There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. To some extent, tongue feature, tumor marker, and baseline data could be combined to predict NSCLC at different stages. This study established a new methodological reference for the diagnosis of NSCLC at different stages, but more research with a larger sample size was still required in the future.

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A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

Cited by 11 publications

References 46 publications

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study

Machine Learning Prediction Models for Different Stages of Non-small Cell Lung Cancer Based on Tongue and Tumor Marker

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