The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.

Oscillometric Blood Pressure Estimation: Past, Present, and Future

A nonlinear filter is proposed for estimating a complex sinusoidal signal and its parameters (frequency, amplitude, and phase) from measurements corrupted by white noise. This filter is derived by applying an extended complex Kalman filter (ECKF) to a nonlinear stochastic system whose state variables are a function of its frequency and a sample of an original signal, and then, proof of the stability is given in the case of a single complex sinusoid. Simulations demonstrate that the proposed nonlinear filter is effective as a method for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR). In addition, the effect of the initial condition in the filter on frequency estimation is also discussed.

A nonlinear filter for estimating a sinusoidal signal and its parameters in white noise: on the case of a single sinusoid

3D ultrasound imaging has enabled minimally invasive, beating heart intracardiac procedures. However, rapid heart motion poses a serious challenge to the surgeon that is compounded by significant time delays and noise in 3D ultrasound. This paper investigates the concept of using a one-degree-of-freedom motion compensation system to synchronize with tissue motions that may be approximated by 1D motion models. We characterize the motion of the mitral valve annulus and show that it is well approximated by a 1D model. The subsequent development of a motion compensation instrument (MCI) is described, as well as an extended Kalman filter (EKF) that compensates for system delays. The benefits and robustness of motion compensation are tested in user trials under a series of non-ideal tracking conditions. Results indicate that the MCI provides an approximately 50% increase in dexterity and 50% decrease in force when compared with a solid tool, but is sensitive to time delays. We demonstrate that the use of the EKF for delay compensation restores performance, even in situations of high heart rate variability. The resulting system is tested in an in vitro 3D ultrasound-guided servoing task, yielding accurate tracking (1.15 mm root mean square) in the presence of noisy, time-delayed 3D ultrasound measurements.

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Robotic Motion Compensation for Beating Heart Intracardiac Surgery

The error dynamics of the extended Kalman filter (EKF), employed as an observer for a general nonlinear, stochastic discrete time system, are analyzed. Sufficient conditions for the boundedness of the errors of the EKF are determined. An expression for the bound on the errors is given in terms of the size of the nonlinearities of the system and the error covariance matrices used in the design of the EKF. The results are applied to the design of a stable EKF frequency tracker for a signal with time-varying frequency.

Conditions for stability of the extended Kalman filter and their application to the frequency tracking problem

An important question in sensory neuroscience is what coding strategies and mechanisms are used by the brain to detect and discriminate among behaviorally relevant stimuli. There is evidence that sensory systems migrate from a distributed and redundant encoding strategy at the periphery to a more heterogeneous encoding in cortical structures. It has been hypothesized that heterogeneity is an efficient encoding strategy that minimizes the redundancy of the neural code and maximizes information throughput. Evidence of this mechanism has been documented in cortical structures. In this study, we examined whether heterogeneous encoding of complex sounds contributes to efficient encoding in the auditory midbrain by characterizing neural responses to behaviorally relevant vocalizations in the mouse inferior colliculus (IC). We independently manipulated the frequency, amplitude, duration, and harmonic structure of the vocalizations to create a suite of modified vocalizations. Based on measures of both spike rate and timing, we characterized the heterogeneity of neural responses to the natural vocalizations and their perturbed variants. Using information theoretic measures, we found that heterogeneous response properties of IC neurons contribute to efficient encoding of behaviorally relevant vocalizations.

https://www.jneurosci.org/content/jneuro/30/3/802.full.pdf

P. J. Parker

Papers

Frequency tracking of nonsinusoidal periodic signals in noise