Key result
A Deep Neural Network model using ECG and PPG features accurately estimated continuous blood pressure with a Pearson's R of 0.90 for systolic and 0.82 for diastolic BP, outperforming MLR and SVR.
Why the study?
Hypertension is a predictor of cardiovascular disease, and unobtrusive continuous blood pressure estimation using pulse transit time is a useful technique.
Does a Deep Neural Network (DNN) model improve the accuracy of continuous blood pressure estimation using ECG and PPG sensors compared to traditional regression models?
Does a Deep Neural Network (DNN) model improve the accuracy of continuous blood pressure estimation using ECG and PPG sensors compared to traditional regression models?
Effect estimate: Pearson's R 0.90 (SBP) and 0.82 (DBP)
A Deep Neural Network model using ECG and PPG data provides highly accurate continuous blood pressure estimation, outperforming traditional regression techniques.
May facilitate wearable BP monitoring; leaves open prospective validation before clinical use.
Cardiovascular disease (CVD) is a common disease nowadays, and hypertension is one of the predictors of it. For unobtrusive BP measurement, continuous blood pressure (BP) estimation using Pulse Transit Time (PTT) is a useful technique. The accuracy is a fact as it is feasible for a large number of phases in our life. So a Deep Neural Network (DNN) based blood pressure estimation, using data mining is proposed in this paper. Instruments were made by ourselves for collecting real data using an electrocardiogram (ECG) and photoplethysmogram (PPG) sensor as the real machine was so much expensive. Fourteen features were extracted, and best features were selected using a genetic algorithm. After this, DNN, Multivariate linear regression (MLR) and Support vector regression (SVR) models were constructed using these features. The accuracy and robustness were validated according to our equipment as the Pearson's R between referenced and estimated BP for the static experiment (SBP) were 0.90 and for dynamic (DBP) is 0.82 for DNN model. DNN model is compared with MLR and SVR. DNN gave comparatively good results than MLR and SVR and also better than the PTT-based model.
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Das et al. (2019) studied Hypertension. Deep Neural Network (DNN) model vs. Multivariate linear regression (MLR), Support vector regression (SVR), and PTT-based model was evaluated on Pearson's R between referenced and estimated BP (Pearson's R 0.90 (SBP) and 0.82 (DBP)). A Deep Neural Network model using ECG and PPG features accurately estimated continuous blood pressure with a Pearson's R of 0.90 for systolic and 0.82 for diastolic BP, outperforming MLR and SVR.
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