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L D Paarmann

Publications and source records attributed to L D Paarmann.

2 recordsLinked to original sources
PubMed1989

An orthogonal ARMA identifier with automatic order estimation for biological modeling.

In this paper an ARMA identification algorithm is developed for modeling biological time series data. The algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from a specified function space. The selection criterion is based on recursive testing of potential benefit to the model of candidate functions. The candidate functions, AR and MA terms, are tested in a pair-wise search direction until a least-squares criterion is satisfied, thereby estimating the order. Additive noise is considered and the basic algorithm extended to improve performance in noise. The algorithm is also extended to systems with inaccessible inputs (signal modeling). Modeling of biological data from speech is included, and indicates good performance. The algorithm is derived from earlier work on nonlinear systems identification.

Algorithms↗
PubMed1989

Applications of fast orthogonal search: time-series analysis and resolution of signals in noise.

In this paper a technique is examined for obtaining accurate and parsimonious sinusoidal series representations of biological time-series data, and for resolving sinusoidal signals in noise. The technique operates via a fast orthogonal search method discussed in the paper, and achieves economy of representation by finding the most significant sinusoidal frequencies first, in a least squares fit sense. Another reason for the parsimony in representation is that the identified sinusoidal series model is not restricted to frequencies which are commensurate or integral multiples of the fundamental frequency corresponding to the record length. Biological applications relate to spectral analysis of noisy time-series data such as EEG, ECG, EMG, EOG, and to speech analysis. Simulations are provided to demonstrate precise detection of component frequencies and weights in short data records, coping with missing or unequally spaced data, and recovery of signals heavily contaminated with noise. The technique is also shown to be capable of higher frequency resolution than is achievable by conventional Fourier series analysis.

Algorithms↗