Taikang Ning

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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)
Hippocampal EEGs at subfields CA1 and the dentate gyrus (DG) are modeled as stationary, multi-channel autoregressive (MAR) process. This work discusses the development of a new MAR modeling algorithm that can efficiently compute MAR coefficient matrices through progressive multichannel orthogonal projection. The resultant MAR coefficients are least square(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)
— This paper extends our previous studies and presents a fast, automatic cardiac auscultation scoring system that effectively identifies the first and second heart sounds (S 1 and S 2) and extracts clinical features of heart murmurs to assist clinical diagnosis. Using the indices derived from AR modeling, the underlying scoring system is capable of(More)