A Model for the Genesis of Arterial Pressure Mayer Waves from Heart Rate and Sympathetic Activity

Both theoretic models and cross-spectral analyses suggest that an oscillating sympathetic nervous outflow generates the low frequency arterial pressure fluctuations termed Mayer waves. Fluctuations in heart rate also have been suggested to relate closely to Mayer waves, but empiric models have not assessed the joint causative influences of hemt rate and sympathetic activity. Therefore, we constructed a model based simply upon the hemodynamic equation deriving from Ohm's Law. With this model, we determined time relations and relative contributions of heart rate and sympathetic activity to the genesis of arterial pressure Mayer waves. We assessed data from eight healthy young volunteers in the basal state and in a high sympathetic state known to produce concurrent increases in sympathetic nervous outflow and Mayer wave amplitude. We fit the Mayer waves (0.05-0.20 Hz) in mean arterial pressure by the weighted sum ofleading oscillations in heart rate and sympathetic nerve activity. This model of our data showed heart rate oscillations leading by 2-3.75 seconds were responsible for almost half of the variance in arterial pressure (basal R^2=0.435±0.140, high sympathetic R^2=0.438±0.180). Surprisingly, sympathetic activity (lead 0-5 seconds) contributed only modestly to the explained variance in Mayer waves during either sympathetic state (basal: ∆R^2=0.046±0.026; heightened: ∆R^2=0.085±0.036). Thus, it appears that heart rate oscillations contribute to Mayer waves in a simple linear fashion, whereas sympathetic fluctuations contribute little to Mayer waves in this way. Although these results do not exclude an important vascular sympathetic role, they do suggest that additional Ji1ctors, such as sympathetic transduction into vascular resistance, modulate its influence.Binda and Fred Shuman Foundation; National Institute on Aging (AG14376)

A Model for the Genesis of Arterial Pressure Mayer Waves from Heart Rate and Sympathetic Activity

Both theoretic models and cross-spectral analyses suggest that an oscillating sympathetic nervous outflow generates the low frequency arterial pressure fluctuations termed Mayer waves. Fluctuations in heart rate also have been suggested to relate closely to Mayer waves, but empiric models have not assessed the joint causative influences of hemt rate and sympathetic activity. Therefore, we constructed a model based simply upon the hemodynamic equation deriving from Ohm's Law. With this model, we determined time relations and relative contributions of heart rate and sympathetic activity to the genesis of arterial pressure Mayer waves. We assessed data from eight healthy young volunteers in the basal state and in a high sympathetic state known to produce concurrent increases in sympathetic nervous outflow and Mayer wave amplitude. We fit the Mayer waves (0.05-0.20 Hz) in mean arterial pressure by the weighted sum ofleading oscillations in heart rate and sympathetic nerve activity. This model of our data showed heart rate oscillations leading by 2-3.75 seconds were responsible for almost half of the variance in arterial pressure (basal R^2=0.435±0.140, high sympathetic R^2=0.438±0.180). Surprisingly, sympathetic activity (lead 0-5 seconds) contributed only modestly to the explained variance in Mayer waves during either sympathetic state (basal: ∆R^2=0.046±0.026; heightened: ∆R^2=0.085±0.036). Thus, it appears that heart rate oscillations contribute to Mayer waves in a simple linear fashion, whereas sympathetic fluctuations contribute little to Mayer waves in this way. Although these results do not exclude an important vascular sympathetic role, they do suggest that additional Ji1ctors, such as sympathetic transduction into vascular resistance, modulate its influence.Binda and Fred Shuman Foundation; National Institute on Aging (AG14376)

Bayesian Model Search for Nonstationary Periodic Time Series

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behaviour. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo (MCMC) algorithm that simultaneously updates the change-points and the periodicities relevant to any segment between them. We show that the proposed methodology successfully identifies time changing oscillatory behaviour in two applications which are relevant to e-Health and sleep research, namely the occurrence of ultradian oscillations in human skin temperature during the time of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces.Comment: Received 23 Oct 2018, Accepted 12 May 201

Instantaneous frequency estimation of multicomponent non- stationary signals using Fourier Bessel series and Time-Varying Auto Regressive Model

In this paper, we propose a novel technique for Instantaneous frequency (IF) estimation of multi component non stationary signals using Fourier Bessel Series and Time–Varying Auto Regressive (FB-TVAR) model. In the proposed technique, the Fourier-Bessel (FB) expansion decomposes the multicomponent non stationary signal into a number of monocomponent signals and TVAR model is used to model each monocomponent signal. In TVAR modeling approach the time varying parameters are expanded as a linear combination of basis functions. In this paper, the TVAR parameters are expanded by a discrete cosine basis functions. The maximum likelihood estimation algorithm for model order selection in TVAR models is also discussed. The Instantaneous frequency (IF) is extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. The estimation of the TVAR parameters of a multicomponent signal requires the inversion of a large covariance matrix, while the projected technique (FB-TVAR) requires the inversion of a number of comparatively small covariance matrices with better numerical stability properties. Simulation results are presented for three component discrete Amplitude and Frequency modulated(AM-FM)signa

Evaluation of output frequency responses of nonlinear systems under multiple inputs

In this paper, a new method for evaluating output frequency responses of nonlinear systems under multiple inputs, defined as a sum of sinusoids of different frequencies, is developed. The method circumvents difficulties associated with the existing “frequency-mix vector” based approaches and can easily be applied to investigate nonlinear behaviors of practical systems, including electronic circuits, at the system simulation and design stages. Application of the method to the analysis of nonlinear interference and distortion effects in communication receivers is studied, and specific procedures are proposed which can be directly used in practice for this analysi

Hidden conditional random fields for classification of imaginary motor tasks from EEG data

Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electroencephalography (EEG) signals recorded over the motor cortex. In this paper, we propose a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem; they do not suffer from some of the limitations of generative models; and they include latent variables that can be used to model different brain states in the signal. Our approach involves auto-regressive modeling of the EEG signals, followed by the computation of the power spectrum. Frequency band selection is performed on the resulting time-frequency representation through feature selection methods. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV and the results show that our approach overperforms all methods proposed in the competition. In addition, we present a comparison with an HMM-based method, and observe that the proposed method produces better classification accuracy

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on auto-regressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy