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Some basic properties of autoregressive (AR) modeling and bispectral analysis are reviewed, and examples of their application in electroencephalography (EEG) research are provided. A second-order AR model was used to score cortical EEGs in order. In tests performed on five adult rats to distinguish between different vigilance states such a quiet-waking(More)
The paper discusses the use of nonlinear bispectral analysis in examining the hippocampal EEG collected at subfields of CA1 and the dentate gyrus during the vigilance state of REM sleep. The cross-bispectrum and its unique capabilities of detecting and quantifying quadratic nonlinear interactions occurring between these two hippocampal subfields are(More)
This paper presents the bispectral analysis of the ontogeny of the hippocampal EEG recorded from the dentate gyrus and CA1, the primary sites that generate theta (theta) rhythm. The hippocampal EEG was collected during the vigilance state of rapid eye movement (REM) sleep of freely moving rats at 15, 30 and 90 days of age. In previous studies we(More)
The paper presents the results of a signal processing approach to detect and isolate systolic murmurs. The identification of the first and second heart sounds and separating systole and diastole from a complete cardiac cycle were successfully carried out through wavelet analysis using an orthogonal Daubechies (db6) wavelet as the mother wavelet. At the(More)
– In this paper, the focus is on systolic heart murmurs of clinical significance. Quantitative features characterizing the murmurs are derived by dividing the systole into many short non-overlapping segments and using second order autoregressive (AR) models. Features thus derived can provide a quantitative delineation of the murmur with respect to the(More)
The Grassberger-Procaccia method is revisited in this paper with a modified approach to compute the correlation integral through a Euclidean distance measure normalized by the embedding dimension. The performance of the suggested modification is assessed using three different types of signals, including Lorenz attractor, mechanical vibrations of helicopter(More)
A quantitative approach integrating AR modeling and wavelet transform is presented in this paper to analyze the digitized phonocardiogram. The recognition of the first and the second heart sounds (S(1) and S(2)) were facilitated with wavelet transform without referring to the QRS waveform. We found that the Daubechies wavelet is most effective in(More)
The correlation dimension was used in this paper as a quantifier to describe the chaotic behavior of sleep EEG recorded from the hippocampus of adult rats during vigilance states of quiet-waking, slow-wave sleep, and REM sleep. A modified Grassberger-Procaccia method was implemented to compute the correlation integral using a Euclidean distance normalized(More)