A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

Luo, Zhi-yu; Cui, Ji; Hu, Xiaojuan; Tu, Liping; Liu, Hai-Dan; Jiao, Wen; Zeng, Ling-Zhi; Jing, Congcong; Qiao, Lijie; Ma, Xuxiang; Wang, Yu; Wang, Jue; Pai, Ching-Hsuan; Qi, Zhengjian; Zhang, Zhifeng; Xu, Jiatuo

doi:10.1155/2018/2964816

Cited by 46 publications

(40 citation statements)

References 30 publications

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“…The wrist pulse waveform is an important physiological signal generated by the periodic contraction and dilation of arteries and contains abundant information regarding an individual’s physical condition. Specifically, many characteristics of the pulse waveform can be assessed to diagnose health conditions, such as hypertension [ 1 ], arterial stiffness [ 2 ], vascular aging [ 3 ], and atrial fibrillation [ 4 ], as well as other cardiovascular health information, such as cardiac output [ 5 ]. Pulse signal analysis is already widely applied for arterial stiffness assessment in assorted commercial instruments, such as the Complior and SphygmoCor [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays

et al. 2021

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In this study, we developed a radial artery pulse acquisition system based on finger-worn dense pressure sensor arrays to enable three-dimensional pulse signals acquisition. The finger-worn dense pressure-sensor arrays were fabricated by packaging 18 ultra-small MEMS pressure sensors (0.4 mm × 0.4 mm × 0.2 mm each) with a pitch of 0.65 mm on flexible printed circuit boards. Pulse signals are measured and recorded simultaneously when traditional Chinese medicine practitioners wear the arrays on the fingers while palpating the radial pulse. Given that the pitches are much smaller than the diameter of the human radial artery, three-dimensional pulse envelope images can be measured with the system, as can the width and the dynamic width of the pulse signals. Furthermore, the array has an effective span of 11.6 mm—3–5 times the diameter of the radial artery—which enables easy and accurate positioning of the sensor array on the radial artery. This study also outlines proposed methods for measuring the pulse width and dynamic pulse width. The dynamic pulse widths of three volunteers were measured, and the dynamic pulse width measurements were consistent with those obtained by color Doppler ultrasound. The pulse wave velocity can also be measured with the system by measuring the pulse transit time between the pulse signals at the brachial and radial arteries using the finger-worn sensor arrays.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays

et al. 2021

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“…Nowadays, breakthroughs have been achieved in fatigue quantification and standardization. Artificial Neural Network [ 13 ], Support Vector Machine [ 14 ], K Nearest Neighbor [ 15 ], and other machine learning methods have helped to achieve the digitalization of TCM tongue and pulse diagnoses and establish corresponding disease diagnostic models [ 16 , 17 ]. The diagnostic relationship between tongue and pulse and healthy state can be better established through accurate detection, identification, and multi-dimensional quantitative analysis of tongue and pulse data to save medical resources and improve diagnosis efficiency and treatment efficacy [ 18 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

Shi

Cui

et al. 2021

BMC Med Inform Decis Mak

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Background Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. Methods In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. Results Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. Conclusions The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.

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“…Furthermore, the radial artery pulse wave variables were applied to predict the development of hypertension by varying machine learning methods (ANN, AdaBoost, Gradient Boosting, and Random Forest, etc. ), and they achieved an accuracy of about 80% [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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Our aim was to study whether radial pulse wave signals can improve the risk prediction of incident hypertension and are associated with its concomitant metabolic risk factors beyond the traditional cardiovascular risk factor Ba-PWV. By enrolling 523 Chinese subjects in this study, linear and stepwise regression analysis was performed to assess the association of radial artery pulse wave signals and Ba-PWV with blood pressure and its related metabolic risk factors such as fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and uric acid (UA). The area under the receiver-operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated by risk assessment plot to compare the discriminative ability among models with and without radial artery pulse wave signals. After adjusting related confounding factors, radial artery pulse wave variable h3/h1 was selected as the sensitive influential factor for blood pressure. Moreover, a new model with h3/h1 had a higher AUC than the reference model without it (0.86 vs 0.84; P=0.030). And the NRI and IDI for the new model was 50.0% (P=0.017) and 3.16% (P=0.044), respectively. In addition to Ba-PWV, we found that the decrease of t4, t5, and h5 might be associated with higher FPG, TC, LDL-C, and UA and lower HDL-C. This research might provide a valuable additional tool for remote wearable monitoring of radial artery pulse wave signals in hypertension risk evaluation and management.

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A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

Cited by 46 publications

References 30 publications

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

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

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