Nonparametric Hammerstein model based model predictive control for heart rate regulation.

This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints

Identification and control for heart rate regulation during treadmill exercise

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful ε-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An H ∞ controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance. © 2007 IEEE

Nonlinear modeling of cardiovascular response to exercise

This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Support vector method for robust ARMA system identification

This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals andSVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.Publicad

Optimizing heart rate regulation for safe exercise

Safe exercise protocols are critical for effective rehabilitation programs. This paper aims to develop a novel control strategy for an automated treadmill system to reduce the danger of injury during cardiac rehabilitation. We have developed a control-oriented nonparametric Hammerstein model for the control of heart rate during exercises by using support vector regression and correlation analysis. Based on this nonparametric model, a model predictive controller has been built. In order to guarantee the safety of treadmill exercise during rehabilitation, this new automated treadmill system is capable of optimizing system performance over predefined ranges of speed and acceleration. The effectiveness of the proposed approach was demonstrated with six subjects by having their heart rate track successfully a predetermined heart rate. © 2009 Biomedical Engineering Society

Systemic lupus erythematosus in African-American women: immune cognitive modules, autoimmune disease, and pathogenic social hierarchy

Examining elevated rates of systemic lupus erythematosus in African-American women from the perspective of the emerging theory of immune cognition suggests the disease constitutes an internalized physiological image of external patterns of psychosocial stress, a 'pathogenic social hierarchy' involving the synergism of racism and gender discrimination. The disorder represents the punctuated resetting of 'normal' immune self-image to a self-attacking 'excited' state, a process formally analogous to models of punctuated equilibrium in evolutionary theory. We speculate that this punctuated onset takes place in the context of an immunological 'cognitive module' similar to what has been proposed by evolutionary psychologists for the human mind. We discuss the broader implications of a high rate of this disorder within a marginalized population, finding it to be a leading indicator for phenomena likely to entrain powerful subgroups into a larger pattern of embedding patholog