80 research outputs found
Gaussian Mixture Model of Heart Rate Variability
Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters
A nine-input 1.25Â mW, 34Â ns CMOS analog median filter for image processing in real time
Local Dimensionality Reduction for Non-Parametric Regression
Locally-weighted regression is a computationally-efficient technique for
non-linear regression. However, for high-dimensional data, this technique becomes numerically
brittle and computationally too expensive if many local models need to be maintained
simultaneously. Thus, local linear dimensionality reduction combined with locally-weighted
regression seems to be a promising solution. In this context, we review linear dimensionalityreduction
methods, compare their performance on non-parametric locally-linear regression,
and discuss their ability to extend to incremental learning. The considered methods belong to
the following three groups: (1) reducing dimensionality only on the input data, (2) modeling
the joint input-output data distribution, and (3) optimizing the correlation between projection
directions and output data. Group 1 contains principal component regression (PCR);
group 2 contains principal component analysis (PCA) in joint input and output space, factor
analysis, and probabilistic PCA; and group 3 contains reduced rank regression (RRR) and
partial least squares (PLS) regression. Among the tested methods, only group 3 managed
to achieve robust performance even for a non-optimal number of components (factors or
projection directions). In contrast, group 1 and 2 failed for fewer components since these
methods rely on the correct estimate of the true intrinsic dimensionality. In group 3, PLS is
the only method for which a computationally-efficient incremental implementation exists
RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers
Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network
- …