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Utility of a Nonlinear Joint Dynamical Framework to Model a Pair of Coupled Cardiovascular Signals

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2 Author(s)
Sayadi, O. ; Cardiovascular Res. Center, Massachusetts Gen. Hosp., Charlestown, MA, USA ; Shamsollahi, M.B.

We have recently proposed a correlated model to provide a Gaussian mixture representation of the cardiovascular signals, with promising results in identifying rhythm disturbances. The approach provides a transformation of the data into a set of integrable Gaussians distributed over time. Looking into the model from a new joint modeling perspective, it is capable of assembling a filtered estimation, and can be used to derive temporal information of the waveforms. In this paper, we present a step-by-step derivation of the joint model putting correlation assumptions together to conclude a minimal joint description for a pair of ECG-ABP signals. We then probe novel applications of this model, including Kalman filter based denoising and fiducial point detection. In particular, we use the joint model for denoising and employ the denoised signals for pulse transit time (PTT) estimation. We analyzed more than 70 h of data from 76 patients from the MIMIC database to illustrate the accuracy of the algorithm. We have found that this method can be effectively used for robust joint ECG-ABP noise suppression, with mean signal-to-noise ratio (SNR) improvement up to 23.2 (12.0) dB and weighted diagnostic distortion measures as low as 2.1 (3.3)% for artificial (real) noises, respectively. In addition, we have estimated the error distributions for QT interval, systolic and diastolic blood pressure before and after filtering to demonstrate the maximal preservation of morphological features (ΔQT: mean ± std = 2.2 ± 6.1 ms; ΔSBP: mean ± std = 2.3 ± 1.9 mmHg; ΔDBP: mean ± std = 1.9 ± 1.4 mmHg). Finally, we have been able to present a systematic approach for robust PTT estimation (r = 0.98, p <; 0.001, mean ± std of error = -0.26 ± 2.93 ms). These findings may have important implications for reliable monitoring and estimation of clinically important features in clinical settings. In - onclusion, the proposed framework opens the door to the possibility of deploying a hybrid system that integrates these algorithmic approaches for index estimation and filtering scenarios with high output SNRs and low distortion.

Published in:

Biomedical and Health Informatics, IEEE Journal of  (Volume:17 ,  Issue: 4 )

Date of Publication:

July 2013

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