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- W3001736596 abstract "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 (CNN-R), Support Vector Machines- Regression (SVMs-R) 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 CNN-R. In the study, 89.43 % for MLR, 92.15 % for SVM-R and 93.65 % for CNN-R are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNN-R 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." @default.
- W3001736596 created "2020-01-30" @default.
- W3001736596 creator A5054401075 @default.
- W3001736596 date "2020-04-01" @default.
- W3001736596 modified "2023-10-16" @default.
- W3001736596 title "Blood pressure prediction from speech recordings" @default.
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- W3001736596 doi "https://doi.org/10.1016/j.bspc.2019.101842" @default.
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