This paper presents a novel approach using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS) for estimation of blood pressure (BP) from oscillometric waveforms. The proposed method consists of three stages. In the first stage, the oscillation amplitudes (OAs) of the oscillometric waveforms are represented as a function of the cuff pressure. In the second stage, the PCA is utilized to reduce the dimensionality of the input space by extracting the most effective features from the OAs. Finally, in the third stage, the ANFIS is employed to perform the BP estimation. The proposed method is tested on a dataset collected from 85 subjects and the results are compared with conventional maximum amplitude algorithm and published neural network-based methods. It is found that the proposed method achieves lower values of mean absolute error and standard deviation of error in estimation of BP compared with the other studied methods.

Additional Metadata
Keywords Adaptive neuro-fuzzy inference system (ANFIS), Blood pressure (BP) estimation, Neural network (NN), Oscillometric waveforms, Principal component analysis (PCA)
Persistent URL dx.doi.org/10.1109/MEMEA.2010.5480225
Conference 2010 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2010
Citation
Forouzanfar, M. (Mohamad), Dajani, H.R. (Hilmi R.), Groza, V.Z. (Voicu Z.), Bolic, M. (Miodrag), & Rajan, S. (2010). Adaptive neuro-fuzzy inference system for oscillometric blood pressure estimation. In 2010 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2010 - Proceedings (pp. 125–129). doi:10.1109/MEMEA.2010.5480225