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A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Technique


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Abstract


Continuousblood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted for beat-to-beat BP estimation. A genetic algorithm-based feature selection method was then used to select BP indicators for each subject. Multivariate linear regression and support vector regression were employed to develop the BP model. The accuracy and robustnessof the proposed approach were validated for static, dynamic, and follow-upperformance. Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy instatic BP estimation, with a correlation coefficient and mean error of 0.852 and ??0.001 ± 3.102 mmHg for systolic BP, and 0.790 and ??0.004 ± 2.199 mmHg for diastolic BP. Similar performance was observed for dynamic BP estimation. The robustness results indicated that the estimation accuracy was lower by a certain degree 1 day after model construction but was relatively stable from 1 day to 6 months after construction. The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentiallynovel insights for cuffless BP estimation.

KeyWords
Continuous blood pressure (BP), feature selection, multivariate linear regression (MLR), support vector regression (SVR)



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