Radial Pulse Wave Signals Combined with Ba‐PWV for the Risk Prediction of Hypertension and the Monitoring of Its Accompanying Metabolic Risk Factors

Qi, Zhengjian; Zhao, Zhiyue; Xu, Jiatuo; Zhu, Liping; Zhang, Yu; Bao, Yong; Zhang, Zhifeng

doi:10.1155/2020/3926851

Cited by 8 publications

(5 citation statements)

References 41 publications

(39 reference statements)

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“…A single central aortic pulse from another set of experiments (figure 6(c)) exhibits three characteristic waves called systolic wave (P 1 ), tidal wave (P 2 ), and dicrotic wave (P 3 ), in agreement with the literature [36]. The shape and time-domain variables of pulse measured in this work matched with data obtained from a standard DDMX-100 type pulse measurement device [37]; thus, the proposed sensor was accurate as of the standard device (figure S7 and table S1).…”

Section: Wearable Applicationssupporting

confidence: 85%

Flexible strain sensor with high sensitivity, fast response, and good sensing range for wearable applications

et al. 2021

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Flexible strain sensors are emerging rapidly and overcoming the drawbacks of traditional strain sensors. However, many flexible sensors failed to balance the sensitivity, response time, and the desired sensing range. This work proposes a novel and cost-effective strain sensor which simultaneously achieved high sensitivity, fast response, and a good sensing range. It illustrates a prototype strain sensor realized with a nanocomposite constituting reduced graphene oxide and palladium as the primary sensing elements. These sensors were fabricated with manual screen-printing technology. The sensor exhibited an outstanding performance for the different strains ranging from 0.1% to 45%. As a result, a substantially high gauge factor around 1523 at a strain of as high as 45% and a rapid response time of 47 ms was obtained. This work demonstrated potential applications like real-time monitoring of pulse and respiration, and other physical movement detection, which become crucial parameters to be measured continuously during the COVID-19 pandemic.

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Section: Wearable Applicationssupporting

confidence: 85%

Flexible strain sensor with high sensitivity, fast response, and good sensing range for wearable applications

et al. 2021

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“…Machine learning (ML), the core AI technology, provides intelligent solutions to medical classification problems. Wrist pulse detection technology, including signal identification and processing, has gradually been applied for classification in cardiovascular diseases [11][12][13][14][15]. The development of wearable healthcare devices has facilitated the application of AI to the monitoring of multiple diseases such as CVDs, corneal disease, and cancer [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis

Chen

Guo

et al. 2023

Ir J Med Sci

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Background The timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients’ mortality. Objective To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD. Methods A total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models’ performance metrics were compared. Results The pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively. Conclusions The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

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“…The pressure pulse wave can be measured at the radial artery, because the radial artery is shallow and its diameter is relatively thick, which is easy to touch and is not easily disturbed by subcutaneous tissue, fat, and other factors. Modern research has indicated that features of the pressure pulse wave in the radial artery can reveal arterial stiffness and indicate cardiovascular function [10,11]. It is recognized that atherosclerosis affects blood vessels throughout the body; coronary atherosclerosis has the same pathological mechanism as large-and medium-sized elastic arteries.…”

Section: Introductionmentioning

confidence: 99%

“…These amplitudes and phases have specific pathophysiological implication reflecting the cardiovascular status. Qi et al [10] incorporated radial pulse wave time-domain feature H3/H1 reflecting peripheral vascular resistance and arterial stiffness into a hypertension risk prediction model and compared it with the model using Ba-PWV. The results revealed that the model using time-domain H3/H1 of pulse signal as a predicting factor had higher accuracy in predicting hypertension risk.…”

Section: Introductionmentioning

confidence: 99%

Identifying Coronary Artery Lesions by Feature Analysis of Radial Pulse Wave: A Case-Control Study

Zhang

Liu

et al. 2021

BioMed Research International

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Background. Cardiovascular diseases have been always the most common cause of morbidity and mortality worldwide. Health monitoring of high-risk and suspected patients is essential. Currently, invasive coronary angiography is still the most direct and accurate method of determining the severity of coronary artery lesions, but it may not be the optimal clinical choice for suspected patients who had clinical symptoms of coronary heart disease (CHD) such as chest pain but no coronary artery lesion. Modern medical research indicates that radial pulse waves contain substantial pathophysiologic information about the cardiovascular and circulation systems; therefore, analysis of these waves could be a noninvasive technique for assessing cardiovascular disease. Objective. The objective of this study was to analyze the radial pulse wave to construct models for assessing the extent of coronary artery lesions based on pulse features and investigate the latent value of noninvasive detection technology based on pulse wave in the evaluation of cardiovascular disease, so as to promote the development of wearable devices and mobile medicine. Method. This study included 529 patients suspected of CHD who had undergone coronary angiography. Patients were sorted into a control group with no lesions, a 1 or 2 lesion group, and a multiple (3 or more) lesion group as determined by coronary angiography. The linear time-domain features and the nonlinear multiscale entropy features of their radial pulse wave signals were compared, and these features were used to construct models for identifying the range of coronary artery lesions using the k -nearest neighbor (KNN), decision tree (DT), and random forest (RF) machine learning algorithms. The average precision of these algorithms was then compared. Results. (1) Compared with the control group, the group with 1 or 2 lesions had increases in their radial pulse wave time-domain features H2/H1, H3/H1, and W2 ( P < 0.05 ), whereas the group with multiple lesions had decreases in MSE1, MSE2, MSE3, MSE4, and MSE5 ( P < 0.05 ). (2) Compared with the 1 or 2 lesion group, the multiple lesion group had increases in T1/T ( P < 0.05 ) and decreases in T and W1 ( P < 0.05 ). (3) The RF model for identifying numbers of coronary artery lesions had a higher average precision than the models built with KNN or DT. Furthermore, average precision of the model was highest (80.98%) if both time-domain features and multiscale entropy features of radial pulse signals were used to construct the model. Conclusion. Pulse wave signal can identify the range of coronary artery lesions with acceptable accuracy; this result is promising valuable for assessing the severity of coronary artery lesions. The technique could be used to development of mobile medical treatments or remote home monitoring systems for patients suspected or those at high risk of coronary atherosclerotic heart disease.

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Radial Pulse Wave Signals Combined with Ba‐PWV for the Risk Prediction of Hypertension and the Monitoring of Its Accompanying Metabolic Risk Factors

Cited by 8 publications

References 41 publications

Flexible strain sensor with high sensitivity, fast response, and good sensing range for wearable applications

Flexible strain sensor with high sensitivity, fast response, and good sensing range for wearable applications

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis

Identifying Coronary Artery Lesions by Feature Analysis of Radial Pulse Wave: A Case-Control Study

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