Nonfiltered (NF) lung sounds from the apical area of the heart along with lung volumes and ECG signals were recorded from 5 normal subjects. The signals were digitized and subjected to three methods of heart sound cancelation: 75-Hz high-pass filtering (75 HF), ECG-triggered blanking (BL) and adaptive noise canceling (AF) [IEEE Trans. Biomed. Engng 33:1141–1148,1986]. The sound signals were then subjected to the fast Fourier transform algorithm to obtain power spectra. Five breaths from each subject were analyzed, and their spectra were similar and slightly skewed to the right. The average values of mean, median and mode frequencies of the whole breath of 5 subjects, respectively, were for NF: 64.62 ± 3.74, 44.57 ± 2.06 and 36.75 ± 1.79 Hz; for 75 HF: 150.42 ± 17.49, 114.02 ± 6.43 and 86.16 ± 3.13 Hz; for BL: 81.76 ± 6.02, 52.36 ± 2.79, 41.10 ± 3.15 Hz; for AF: 96.87 ± 11.58, 68.23 ± 10.44 and 52.25 ± 8.97 Hz. These values showed no differences between subjects. The F values obtained by the two-way analysis of variance of all breaths of all subjects (mean, median, mode) were: NF: 0.161, 0.341, 0.089; 75 HF: 0.455, 0.042, 0.085; BL: 0.108, 0.082, 0.057; AF: 0.130, 0.204, 0.113 (all p > 0.1). The data revealed a remarkable lack of variation within and between subjects, suggesting similar sites and mechanisms of production and transmission
Pulse oximetry is a widely used technique in biomedical optics, but currently available pulse oximeters rely on empirical calibration approaches, which perform poorly at low saturations. We present an exact solution for pulse oximetry and show how this can be used as the basis for the development of a semiempirical calibration approach that may be useful, especially at low saturations and variable probe geometries. This new approach was experimentally tested against traditional empirical calibration techniques on transmission pulse oximetry for monitoring of fetal sheep using a minimally invasive spiral probe. The results open the way for the development of more accurate pulse oximetry.
In this communication, we discuss the application of autoregressive modeling to lung sounds analysis. The lung sounds source in the airway is modeled as a white noise source, consisting of one or a combination of the following sources: random white noise sequence, periodic train of impulses, and impulsive bursts of energy. The acoustic transmission through the lung parenchyma and chest wall is modeled as an all-pole filter. Using this method, the source and transmission characteristics of lung sounds are estimated separately, based on the lung sounds at the chest wall. To illustrate the potential validity of the model, lung sound segments in known disease conditions were selected from teaching tapes and the source and transmission characteristics were estimated by applying the model. The estimated characteristics were found to be consistent with current knowledge of the generation and transmission of lung sounds in the known conditions.
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