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P A Ramamoorthy

Publications and source records attributed to P A Ramamoorthy.

7 recordsLinked to original sources
PubMed1991

Reproducibility of the vesicular breath sounds in normal subjects.

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 cancellation: 75-Hz high-pass filtering (75 HF), ECG-triggered blanking (BL) and adaptive noise cancelling (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 greater than 0.1). The data revealed a remarkable lack of variation within and between subjects, suggesting similar sites and mechanisms of production and transmission.

Adult↗
PubMed1990

Inspiratory and expiratory vesicular breath sounds.

Unfiltered breath sounds (NF) from the apical area of the heart, lung volume and ECG signals were recorded in 5 normal subjects. The signals were digitized and subjected to three methods of heart sound cancellation: 75-Hz high-pass filtering (75 HF), ECG-triggered blanking (BL) and adaptive filtering (AF). The sound signals were then subjected to the fast Fourier transform algorithm to obtain power spectra. Inspiratory and expiratory phase sounds of five breaths of each subject were analyzed separately. The inspiratory and expiratory sound power spectra were very similar and skewed slightly to the right, and therefore characterized by median frequencies. The differences between inspiratory and expiratory median frequencies were insignificant for NF: 42.90 +/- 2.03 (mean +/- SD) vs. 46.64 +/- 2.53 Hz (p greater than 0.1); for 75 HF: 106.43 +/- 10.27 vs. 118.22 +/- 6.30 Hz (p greater than 0.5); for BL: 44.46 +/- 3.33 vs. 66.73 +/- 2.93 Hz (p greater than 0.1), for AF: 49.72 +/- 5.68 vs. 79.20 +/- 13.07 Hz (p greater than 0.1). We conclude that the lack of significant differences suggests similar mechanisms and sites of production of inspiratory and expiratory vesicular breath sounds.

Adult↗
PubMed1990

Adaptive filtering in biological signal processing.

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.

Animals↗
PubMed1989

Autoregressive modeling of lung sounds: characterization of source and transmission.

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.

Analog-Digital Conversion↗
PubMed1989

Characteristics of normal lung sounds after adaptive filtering.

Lung sounds were recorded from five normal male subjects during tidal breathing. Simultaneous electrocardiograms were recorded and used as index signals to generate simulated heart sounds for digital subtraction from recorded lung sounds to obtain purer lung sounds. Five random breaths from each subject were analyzed. Sound signals were band-pass filtered 25 to 1,000 Hz (antialiasing), digitized at 3,000 Hz, and then subjected to (1) direct fast Fourier transform (FFT) without filtering (NF); (2) digital high-pass filtering at 75 Hz and subsequent FFT (75 HzF); (3) adaptive filtering and subsequent FFT (AF). The FFT algorithms of all lung sounds were characterized by mean, median, and mode frequencies. The mean, median, and mode of NF were lower than those of 75 HzF (64.98 +/- 4.04 versus 150.42 +/- 17.49, mean +/- SE, p less than 0.003; 44.57 +/- 2.06 versus 111.81.5.78, p less than 0.0003; 36.81 +/- 1.77 versus 86.16 +/- 3.13, p less than 0.0001) and those of AF (64.98 +/- 4.04 versus 96.87 +/- 11.58, p less than 0.01; 44.57 +/- 2.06 versus 68.23 +/- 10.44, p less than 0.05; 36.81 +/- 1.78 versus 52.24 +/- 8.97, p less than 0.06). The mean, median, and mode of AF were lower than those of 75 HzF (96.87 +/- 11.58 versus 150.42 +/- 17.49, p less than 0.02; 68.23 +/- 10.44 versus 111.81 +/- 5.77, p less than 0.007; 52.24 +/- 8.97 versus 86.16 +/- 3.73, p less than 0.01). The results indicated that by filtering out low frequency heart sounds, the frequency spectrum of lung sounds was moved upward.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗