Blood pressure prediction from speech recordings Abstract   The aim of this study is to extract new features to show the relationship between speech recordings and blood pressure (BP). For this purpose, a database consisting of  a  vowels with different BP values under the same room and environment conditions is presented to the literature. Convolutional Neural Networks Regression (CNNR), Support Vector Machines Regression (SVMsR) and Multi Linear Regression (MLR) are used in this study to predict BP with extracted features. From the experiments, the highest accuracy rates of BP prediction from  a  vowel have been obtained based on Systolic BP values with CNNR. In the study, 89.43 % for MLR, 92.15 % for SVMR and 93.65 % for CNNR are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNNR as RMSE  0.2355. In conclusion, it can be observed that the proposed feature vector (FVx) shows a relationship between BP and the human voices, and in this direction, it can be used as an FVx in a system that will be developed in order to follow the tension of individuals.

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.