ObjectiveFew studies have looked at the predictors of use of home sphygmomanometers among hypertensive patients in low-income countries such as Pakistan. Considering the importance of home blood pressure monitoring (HBPM), cross-sectional study was conducted to evaluate the prevalence and predictors of the usage of all kinds of HBPM devices.MethodThis study was conducted in Karachi during the time period of January-February 2017. Adult patients previously diagnosed with hypertension visiting tertiary care hospitals were selected for the study. Interviews from the individuals were conducted after verbal consent using a pre-coded questionnaire. The data was analyzed using Statistical Package for the Social Sciences v. 23.0 (SPSS, IBM Corporation, NY, USA). Chi-squared test was applied as the primary statistical test.ResultsMore than half of the participants used a home sphygmomanometer (n=250, 61.7%). The age, level of education, family history of hypertension, compliance to drugs and blood pressure (BP) monitoring, few times a month at clinics were significant determinants of HBPM (P values < 0.001). It was found that more individuals owned a digital sphygmomanometer (n=128, 51.3%) as compared to a manual type (n=122, 48.8%). Moreover, avoiding BP measurement in a noisy environment was the most common precaution taken (n=117, 46.8%).ConclusionThe study showed that around 40% of the hypertensive individuals did not own a sphygmomanometer and less than 25% performed HBPM regularly. General awareness by healthcare professionals can be a possible factor which can increase HBPM.
Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.
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