Biomedical subjects

M Oud

Publications and source records attributed to M Oud.

5 recordsLinked to original sources

PubMed2004

Spirometry and forced oscillometry assisted optimal frequency band determination for the computerized analysis of tracheal lung sounds in asthma.

We analysed respiration sounds of individual asthmatic patients, in the scope of the development of a method for computerized recognition of the degree of airway obstruction. Respiration sounds were recorded during laboratory sessions of histamine-provoked airway obstruction. We applied an interpolation technique using supervised artificial neural networks to investigate the optimal frequency band required for studying tracheal asthmatic lung sounds. The optimal band was found to be 100-2300 Hz. The forced expiratory volume in 1 s (FEV1) and the respiratory resistance parameter Rrs(4) were used to describe the degree of airway obstruction that is associated with the lung sounds. By comparing the results obtained with the two parameters, we found that for parametrization of the associated degree of airway obstruction respiratory resistance measurements are preferable over forced expiratory volume measurements.

Adult↗

PubMed2003

Lung function interpolation by means of neural-network-supported analysis of respiration sounds.

Respiration sounds of individual asthmatic patients were analysed in the scope of the development of a method for computerised recognition of the degree of airways obstruction. Respiration sounds were recorded during laboratory sessions of allergen provoked airways obstruction, during several stages of advancing obstruction. The technique of artificial neural networks was applied for relating sound spectra and simultaneously measured lung function values (spirometry parameter FEV(1)). The ability of feedforward neural networks was tested to interpolate obstruction levels of FEV(1)-classes of which no members were included in the set used for training a network. In this way, a situation was simulated of an existing network recognising a new asthmatic attack under the same physiological conditions. It appeared to be possible to interpolate FEV(1) values, and it is concluded that a deterministic relationship exists between sound spectra and lung function parameter FEV(1). Variance optimisation appeared to be important in optimising the neural network configuration.

Asthma↗

PubMed2000

Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra.

This paper concerns the analysis of adventitious sounds produced by individual asthmatic patients, and relates the sounds to the degree of airways obstruction at the moment of sound recording. In this study, airways obstruction is represented by a parameter commonly used in clinical tests, the forced expiratory volume in one second. A nonrestrictive approach using spectral information in detail is followed, resulting in a fairly high resolution of respiration sounds with respect to airways obstruction. The beneficial effect of a power raising transformation is presented, together with an illumination of the background of this effect.

Acoustics↗

PubMed1999

Classification of asthmatic breath sounds: preliminary results of the classifying capacity of human examiners versus artificial neural networks.

For continuous monitoring of the respiratory condition of patients, e.g., at the intensive care unit, computer assistance is required. Existing mechanical devices, such as the peak expiratory flow meter, provide only with incidental measurements. Moreover, such methods require cooperation of the patient, which at, e.g., the ICU is usually not possible. The evaluation of complicated phenomena such as asthmatic respiratory sounds may be accomplished by use of artificial neural networks. To investigate the merit of artificial neural networks, the capacities of neural networks and human examiners to classify breath sounds were compared in this study. Breath sounds were in vivo recorded from 50 school-age children with asthma and from 10 controls. Sound intervals with a duration of 20 seconds were randomly sampled from asthmatics during exacerbation, asthmatics in remission, and controls. The samples were digitized and related to peak expiratory flow. From each interval, two full breath cycles were selected. Of each selected breath cycle, a Fourier power spectrum was calculated. The so-obtained set of spectral vectors was classified by means of artificial neural networks. Humans evaluated graphic displays of the spectra. Human examiners could not clearly discriminate between the three groups by inspecting the spectrograms. Classification by self-classifying neural networks confirmed the existence of at least three classes; however, discrimination of 11 classes seemed more appropriate. Good results were obtained with supervised networks: as much as 95% of the training vectors could be classified correctly, and 43% of the test vectors. The three patient groups, as discriminated in advance, do not correspond with three sharply separated sets of spectrograms. More than three classes seem to be present. Humans cannot take up the spectral complexity and showed negative classification results. Artificial neural networks, however, are able to handle classification tasks and show positive results.

Acoustics↗

PubMed1999

Characteristics and diagnostic significance of spontaneous wheezing in children with asthma: results of continuous in vivo sound recording.

The characteristics and diagnostics of wheezing during induced airway obstruction are well documented. The present study addressed (a) the characteristics of spontaneous wheezing with respect to a possible distinction between wheezes during in vivo versus induced airway obstruction, and (b) the relationship between in vivo wheezing and fluctuations in peak expiratory flow (PEF). Tracheal sounds were continuously recorded from 50 children and adolescents with asthma and 10 without asthma in the home environment. Wheezes underwent a qualitative analysis, including their concomitant sound frequencies. Presence of wheezing was scored by two examiners independently and was related to PEF. Spontaneous wheeze varied from solitary rhonchi to prolonged rhythms of loud stridor, and resembled the "induced" wheezes recorded previously. Power spectra showed that the spectral contents (frequency distribution) were comparable, although the in vivo patterns were more prolonged in duration. The diagnostic sensitivity and specificity of wheezing for a reduction in PEF of >20% were 88% and 92%, respectively. It was concluded that in vivo wheeze resembled induced wheeze and was a diagnostically reliable symptom with respect to asthma exacerbations.

Adolescent↗