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Blood pressure measurement and continuous control are essential for heart and blood pressure patients. Therefore, continuous blood pressure measurement from these patients is required. In this paper, a novel hybrid prediction method combining Gaussian process regression (GPR) and feature extraction stage has been proposed and then applied to the estimation of blood pressure from cuff Oscillometric waveforms (or signals). First of all, 27 features including time, chaotic, and frequency domain have been extracted from these waveforms to detect the systolic blood pressure (SBP) and diastolic blood pressure (DBP) automatically. As the second stage in the proposed method, three different GPR methods comprising Exponential GPR, Matern 5/2 GPR, and Rational Quadratic GPR, have been used to estimate the SBP and DBP values based the extracted 27 features. As the performance measures, we have used seven different metrics including MAE (mean absolute error), MSE (mean square error), RMSE (root mean square error), R2, IA (index of agreement), and MAPE (mean absolute percentage error) for evaluation of the proposed methods concerning estimation performance of SBP and DBP values from cuff Oscillometric waveforms. The obtained MAPE values for Exponential GPR, Matern 5/2 GPR, and Rational Quadratic GPR in the estimation of SBP values from cuff Oscillometric signals are 0.1136, 0.2286, and 0.1745, respectively. The obtained MAPE values for Exponential GPR, Matern 5/2 GPR and Rational Quadratic GPR in the estimation of DBP values from cuff Oscillometric signals are 0.2878, 0.4220, and 0.4150, respectively. The experimental results have demonstrated that the best-proposed hybrid model is the combination of the Exponential GPR and the feature extraction stage for the estimation of both SBP and DBP values. The proposed method could be safely used in the medical blood pressure measurement systems in the hospital and clinics. [ABSTRACT FROM AUTHOR]

Blood pressure measurement and prediction is an important condition for heart patients and people with heart problems and should be kept under constant control. In this study, based on the oscillometric waveform obtained from individuals using a cuff, the oscillometric waveforms are divided into three periods. These periods are; the first period from the starting point to the systolic blood pressure (SBP), the second period is between systolic blood pressure (SBP) and diastolic blood pressure (DBP), and the third period is between diastolic blood pressure (DBP) and end of the waveform. In the dataset used, the attributes obtained from the oscillometric wave envelope were subtracted for each pulse. On the dataset, the attributes of the beat corresponding to the systolic pressure point are labeled 1, and the attributes of the beat corresponding to the diastolic pressure point are labeled with 2. Other beats are labeled with 0. In the study, the dataset was first re-labeled. Systolic beats were labeled with 1, beats before systolic point, 2 with systolic, diastolic point including diastolic point, and 3 with a diastolic point. After re-labeling, 350 measurements, 300 measurements were divided into training and 50 measurements were divided into test data subset. Classifiers were trained with 300 subsets and the classifier model was produced. With the generated model, the classification of the pulse sequences in the test data subsets was performed. In the found label series, the first 1 to 2 label was marked as the systolic pressure point and the last 2 to 3 labels as the diastolic pressure point and the corresponding cuff pressures were estimated as systolic and diastolic pressure values. By classifying these periods, the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values have been estimated using three classifier algorithms including k-nearest neighbor (kNN), weighted k-nearest neighbor (WkNN), and Bagged Trees algorithms. To evaluate the performance of the prediction algorithms, four different performance metrics comprising MAE (mean absolute error), MSE (mean square error), RMSE (root mean square error), and R2 have been used. For the estimation of SBP values using the kNN algorithm, weighted kNN, and Bagged Trees, the obtained MAEs are 3.590, 3.520, and 4.499, respectively. As for the estimation of DBP values using kNN algorithm, weighted kNN and, Bagged Trees, the obtained MAEs are 11.077, 11.032, and 13.069, respectively. The obtained results demonstrated that the proposed method could be used in the blood pressure estimation as the new approach. [ABSTRACT FROM AUTHOR]

AIM: To compare the effect of different treatment regimens (oral hypoglycemic agents [OHGs], insulin therapy, and combination of both) on glycemic control and other cardiometabolic risk factors in type 2 diabetes mellitus (T2DM) patients in Saudi. SUBJECTS AND METHODS: Patients with T2DM, but no serious diabetic complications, were randomly recruited from the diabetes clinics at two large hospitals in Jeddah, Saudi Arabia, during June 2013 to July 2014. Only those without change in treatment modality for the last 18 months were included. Blood pressure and anthropometric measurements were measured. Treatment plan was recorded from the patients’ files. Fasting blood sample was obtained to measure glucose, HbA1c, and lipid profile. RESULTS: A total of 197 patients were recruited; 41.1% were men and 58.9% were women. The mean (±SD) age was 58.5 ± 10.5 years. Most patients (60.7%) were on OHGs, 11.5% on insulin therapy, and 27.7% were using a combination of insulin and OHGs. The mean HbA1c was lower in patients using OHGs only, compared with means in those using insulin, or combined therapy in patients with disease duration of #10 years (P = 0.001) and also in those with a longer duration of the disease (P , 0.001). A lower mean diastolic and systolic blood pressure was found among patients on insulin alone (P , 0.01). No significant differences were found in lipid profiles among the groups. CONCLUSION: Insulin therapy, without adequate diabetes education, fails to control hyperglycemia adequately in Saudi T2DM patients. There is a challenge to find out reasons for poor control and the ways as to how to improve glycemic control in T2DM. [ABSTRACT FROM AUTHOR]