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At least 163 records · Page 9Linked to original sources
PubMed1997

Spectrum analysis of respiratory sound: application to smokers and non-smokers.

Previous studies have indicated that respiratory sound signals may contain information useful in the detection of lung diseases. In this study, measurement and recordings of respiratory sound signal segments were obtained in normal subjects (non-smokers) and smokers in both inspiration and expiration phases. By using the autoregressive (AR) method, it is possible to produce power spectra of respiratory sound signals in inspiration and expiration phases for smokers and non-smokers of each group. The selection of the AR model order of the respiratory sound signals is achieved using Akaike criterion. The AR model order of 9 is required for completely described respiration sound signal segments in inspiration and expiration phases for both groups. The power spectra in the smoker group show larger distinct peaks at lower frequencies as well as more harmonics in both inspiration and expiration phases compared to the power spectra of the non-smoker group. Another diagnostic indicator was derived from the relative position of poles of the AR model of respiratory sound signals. In all smokers it was found that the first, third and fourth poles were closer to a unit circle than those in non-smokers (P < 0.01). It seems that the use of these indicators may be useful as early diagnostic tool for lung diseases.

Adult↗
PubMed1993

[Line width addition method and its application to biologic materials emission spectrum analysis].

Using alternating current are grating spectrograph, and self-made recording microphotometer for line contour and accurate measurement of line width, we measured the concentration of Cu and Ca in serum-like synthetic standard solution with line width addition method. The experimental results had better linearity, reproducibility and higher precision compared with those by density method, suggesting that line width method is better than density method in the analysis of biologic material concentration.

Animals↗
PubMed2002

A framework for noise-power spectrum analysis of multidimensional images.

A methodological framework for experimental analysis of the noise-power spectrum (NPS) of multidimensional images is presented that employs well-known properties of the n-dimensional (nD) Fourier transform. The approach is generalized to n dimensions, reducing to familiar cases for n = 1 (e.g., time series) and n = 2 (e.g., projection radiography) and demonstrated experimentally for two cases in which n = 3 (viz., using an active matrix flat-panel imager for x-ray fluoroscopy and cone-beam CT to form three-dimensional (3D) images in spatiotemporal and volumetric domains, respectively). The relationship between fully nD NPS analysis and various techniques for analyzing a "central slice" of the NPS is formulated in a manner that is directly applicable to measured nD data, highlights the effects of correlation, and renders issues of NPS normalization transparent. The spatiotemporal NPS of fluoroscopic images is analyzed under varying conditions of temporal correlation (image lag) to investigate the degree to which the NPS is reduced by such correlation. For first-frame image lag of approximately 5-8%, the NPS is reduced by approximately 20% compared to the lag-free case. A simple model is presented that results in an approximate rule of thumb for computing the effect of image lag on NPS under conditions of spatiotemporal separability. The volumetric NPS of cone-beam CT images is analyzed under varying conditions of spatial correlation, controlled by adjustment of the reconstruction filter. The volumetric NPS is found to be highly asymmetric, exhibiting a ramp characteristic in transverse planes (typical of filtered back-rojection) and a band-limited characteristic in the longitudinal direction (resulting from low-pass characteristics of the imager). Such asymmetry could have implications regarding the detectability of structures visualized in transverse versus sagittal or coronal planes. In all cases, appreciation of the full dimensionality of the image data is essential to obtaining meaningful NPS results. The framework may be applied to NPS analysis of image data of arbitrary dimensionality provided the system satisfies conditions of NPS existence.

Algorithms↗