An Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees

Zhang, Fangfang; Wei, Zhiyao; Ren, Jiadong; Cheng, Yongqiang; Zheng, Zhangqi

doi:10.1109/access.2017.2787980

Cited by 59 publications

(26 citation statements)

References 22 publications

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“…The conventional cuff-based BP measurement devices have repeated measurement operation which is discontinuous in nature, with an operation interval greater than at least one minute [ 4 ]. Hence, cuff-less BP measurement utilizing related biomedical signals becomes sought-after while accurate and effective BP estimation plays a vital role in clinical practice [ 5 ]. In recent years, some methods of BP detection and evaluation have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Harfiya

Purwandari

et al. 2020

Sensors

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Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

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Section: Introductionmentioning

confidence: 99%

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Harfiya

Purwandari

et al. 2020

Sensors

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“…CART is an explanatory technique that has been widely used to reveal data structure, identify important characteristics, and develop decision trees [42]. The CART model is composed of three main steps: (1) CART initialization: generating a decision tree based on training data set; (2) CART pruning and optimization: the regression tree is pruned according to constraints, such as the maximum depth of the tree, the minimum sample size of the leaf node and the node's minimum impurity as the model has best generalization through the combination of different parameters that generated different CART models -the maximum depth of the tree (max depth), the minimum sample number of leaf nodes (min_samples_leaf), the minimum impurity of the nodes (min_impurity_split) (3) CART prediction: put the test set into the trained model and creating prediction [43].…”

Section: Discussionmentioning

confidence: 99%

Perceived stress of clinical practice among associate degree nursing students in Taiwan

Wu¹,

Rong²,

Huang³

2020

Preprint

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Background: Clinical placements play an important role in helping nursing students to achieve clinical competence, but these placements can be highly challenging and stressful. This study aimed to examine factors associated with perceived stress of clinical practice and to investigate the effect of health status on perceived stress.Methods: This was a cross-sectional quantitative study conducted among 724 associate nursing degree students in Southern Taiwan.Results: Health status scores varied from 28–139, with an average of 68.40 (SD = 25.75). Perceived stress scores ranged from 0–95 points with an average score of 36.65 (SD ± 15.95). Factors linked to perceived stress were general health status, practice subject, practice grade, gender, and sleeping hours. The CART analysis showed health status as the most important predictor to perceived stress.Conclusions: General health status can be used as a measure to predict vulnerability to stress and to identify students who are most vulnerable.

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“…But in overcast day or rainy day, the deviation between the predicted curves of three single prediction methods and the actual curve is more obvious. In order to evaluate the validity of each forecast method, the mean absolute percent error (MAPE) and the Theil inequality coefficient (TIC) [20] are selected as evaluation indices. The smaller the TIC value, the better the prediction performance of the model is.…”

Section: Case Studymentioning

confidence: 99%

Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

Wang

et al. 2018

International Journal of Photoenergy

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The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

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An Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees

Cited by 59 publications

References 22 publications

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Perceived stress of clinical practice among associate degree nursing students in Taiwan

Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

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