Un novedoso algoritmo de filtro de Kalman extendido adaptativo impulsado por un modelo de mezcla gaussiana...

Seismocardiography (SCG) contains rich physiological information about the structure and function of the heart, providing a new dimension for early screening and dynamic monitoring of cardiovascular diseases. However, SCG is relatively weak, susceptible to severe external interference, even has strong individual differences in morphology, making it difficult for traditional algorithms to accurately detect AO, AC and other core features.

Herein, Combining the time-frequency joint distribution characteristics of SCG, we improve a novel adaptive extended Kalman Filter (AEKF) algorithm for accurate AO and AC detection. To achieve unified modeling for different individual SCG, Gaussian mixture module (GMM) is used to fit the morphological template in the time-frequency domain based on the optimal estimation strategy. Then, in order to balance the estimation accuracy and computational efficiency of the nonlinear system constructed based on SCG, the extended Kalman filter (EKF) is constructed by linearizing the state transition equation. Moreover, with the aim of accurately estimating the time-varying noise components in motion, the forgetting factor ak is introduced based on the residual ek.with the low-pass filtering strategy to adaptively update the measurement noise covariance matrix R, thereby achieving high-quality filtering to SCG with the AO and AC area.

In addition, the experiment is conducted on the open-source CEBS dataset, indicating that the proposed algorithm has better filtering effect and higher AO and AC' detection accurate on the static SCG. Furthermore, the portable hardware system is designed for collecting SCG during 6-minute walk test. Meanwhile, the Impedance Cardiography (ICG) equipment is employed to record heart rate (HR), Left Ventricular Ejection Time (LVET) and other hemodynamics parameters. Compared with these common algorithms, the proposed algorithm also has better detection performance on the SCG during exercise.

In the future, the proposed algorithm will be integrated with the portable SCG hardware system designed, which is expected to be applied in the convenient diagnosis of heart diseases, the dynamic measurement of cardiovascular parameters, the dynamic blood pressure measurement without the need for wearing cuffs and more medical scene.