A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation

Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial Blood Pressure (ABP) signals as dependent variables, and then using machine learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR), and Heart Rate (HR) features from Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster. The method was implemented using the MIMICII dataset with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multi-trend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster, and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36)

The impact of ISO/TS 16949 on automotive industries and created organizational capabilities from its implementation

ISO/TS 16949 is an ISO Technical Specification. ISO/TS 16949 achieves the objectives which are continually to improve the production of automobile parts and related services, and to strengthen the international competition for the automotive industry and its suppliers. By applying this quality system standard, automotive manufacturers could offer superior products and good services to customers. The more the companies know about the benefits if quality management systems such as ISO/TS 16949, better they can seek interest and determine indices of these systems. So, this standard has been implemented in companies considering a number of benefits. In this paper, we carry out an empirical study in order to verify the importance these benefits and ranking them based on the value of importance. Finally, the study tends to provide a reference guide considering benefits assessment and created organizational capabilities from this standard for the automotive industry in pursuing ISO/TS 16949 and procuring maximum benefit from the results

Importance analysis considering time‐varying parameters and different perturbation occurrence times

International audienc

Bi-objective single machine scheduling problem with stochastic processing times

In this study, a static single machine scheduling problem is investigated, where processing times are stochastic, due dates are deterministic and inserted idle time is allowed. Two objective functions are simultaneously taken into account, minimization of mean completion time and minimization of earliness and tardiness costs. A robust model is presented to tackle the problem, based on goal programming and a stochastic programming model named E-model. The proposed model not only obtains optimal operating systems, but also considers the variance of the objective functions and the correlation between them. Moreover, chance-constrained programming model is used to take into account the randomness in the constraints of the model. The model is presented with general distribution of processing times and the normal case is explored in experiments. Two sets of computational experiments are presented to test the efficiency of the proposed model. In the first set, the performance obtained by the bi-objective formulation is measured, where in the second set the performance obtained by incorporating robustness is measured. Results confirm the effectiveness of the proposed model, in both directions.</p

System Risk Importance Analysis Using Bayesian Networks

International audienc