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 42 publications

(33 citation statements)

References 30 publications

(38 reference statements)

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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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“…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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“…Tongue diagnosis parameters mainly come from the three color spaces of Lab, HIS and YCrCb [29][30][31][32] , and each parameter of tongue and pulse diagnosis has its corresponding medical signi cance [31,33] Statistical analysis SPSS 25.0 was used for statistical analysis. Count data was expressed as percentage N(%), measurement data obeyed normal distribution was expressed as "mean ± standard deviation", and those didn't obey is expressed as "median (upper quartile, lower quartile)".…”

Section: Collecting Clinical Tongue and Pulse Datamentioning

confidence: 99%

Study on the Syndrome Characteristics and Classification Model of Non- Small Cell Lung Cancer Based on Tongue and Pulse Data

Shi

Liu

et al. 2021

Preprint

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Background: Lung cancer is a common malignant tumor that affects people's health seriously. Traditional Chinese medicine (TCM) is one of the effective methods for the treatment of advanced lung cancer, accurate TCM syndrome differentiation is essential to treatment. When the symptoms are not obvious, the traditional symptom-based syndrome differentiation cannot be carried out. There is a close relationship between syndrom and index of western medicine, the combination of micro index and macro symptom can assist syndrome differentiation effectively.Methods: Tongue and pulse data of non-small cell lung cancer (NSCLC) patients with Qi deficiency syndrome (n=163), patients with Yin deficiency syndrome (n=174) and healthy controls (n=185) were collected by using intelligent Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument, respectively. The characteristics of tongue and pulse data were analyzed, the correlation analysis was also made on tongue and pulse data. And four machine learning methods, namely Random Forest, Logistic Regression, Support Vector Machine and Neural Network, were used to establish the classification models based on symptoms, tongue & pulse data, and symptoms & tongue & pulse data, respectively.Results: Significant difference indexes of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll and the tongue coating texture indexes including TC-Con, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indexes of pulse diagnosis were t4 and t5. The classification performance of each model based on different data sets was as follows: model of tongue & pulse data <model of symptom < model of symptom & tongue & pulse data. The Neural Network model had a better classification performance for the symptom & tongue & pulse data, with an area under the ROC curve and accuracy rate were 0.9401 and 0.8806.Conclusions:This study explored the characteristics of tongue and pulse data of NSCLC with Qi deficiency syndrome and Yin deficiency syndrome, and established syndromes classification model. It was feasible to use tongue and pulse data as one of the objective diagnostic indexes in Qi deficiency syndrome and Yin deficiency syndrome of NSCLC.Name of the registry: Chinese Clinical Trial RegistryTrial registration number: ChiCTR1900026008; ChiCTR-IOR-15006502 Date of registration: Jun. 04th, 2015URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119;http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

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

Cited by 42 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

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

Study on the Syndrome Characteristics and Classification Model of Non- Small Cell Lung Cancer Based on Tongue and Pulse Data

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