Taikang Ning

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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)
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)
– Sampling jitters can cause phase shifts in the Fourier coefficients of an underlying time series and lead to, among other distortions, attenuation in spectral estimation. If the probability density function of sampling jitters is known, then the attenuation can be modeled as an amplitude modulation in the frequency domain. This paper examines the possible(More)