Biomedical subjects

S Haltsonen

Publications and source records attributed to S Haltsonen.

4 recordsLinked to original sources

PubMed1998

A new versatile PC-based lung sound analyzer with automatic crackle analysis (HeLSA); repeatability of spectral parameters and sound amplitude in healthy subjects.

A versatile PC-based lung sound analyzer has been developed for short-term recording and analysis of respiratory sounds in research and clinical applications. The system consists of two sound sensors, a flow sensor, a filtering signal amplifier and a PC with a data acquisition card and software for measurement and analysis of the sounds. The analyses include phonopneumography, time expanded waveform analysis, spectral analysis with time averaged Fast Fourier Transform, frequency analysis in time domain (sonogram), and automatic detection and waveform analysis of crackles. Short-term repeatability of spectral parameters of tracheal and lung sounds was studied in 10 healthy subjects. The coefficients of variation (CoV) of the averaged quartile frequencies (F25, F50 and F75) of lung sounds during flow-controlled tidal breathing were 3.7, 4.0 and 8.9% in expiration and 2.7, 3.5 and 4.5% in inspiration, respectively. CoVs of the averaged F25, F50 and F75 of expiratory tracheal sounds were 6.9, 3.0 and 2.4%, and those of inspiratory tracheal sounds 6.3, 2.6 and 3.3%, respectively. Examples of lung sound analysis of samples containing adventitious sounds such as crackles and wheezes are presented. The results indicate that the median frequency has the best repeatability of quartile frequencies of breath sounds and they suggest that the variations of those parameters are low enough for diagnostic purposes. The results also suggest that the analyzer can be a useful new tool for pulmonary research in the fields of physiological and clinical short-term studies of respiratory sounds.

Adult↗

PubMed1998

A new method for automatic wheeze detection.

A new automatic wheeze detection method which is based on image processing techniques applied to the sonagram was developed here. In the calculation of the sonagram, autoregressive and FFT spectrum estimation methods were compared. The method was validated in four wheezing asthmatic patients by a pulmonary physician. Nine out of ten wheezes longer than 250 ms were detected. Very short wheezes were not detected. The false positive amount of wheezing in control subjects was only about 1%. The method extracts also information about the frequency, duration, flow and volume associated with the wheezes.

Asthma↗

PubMed1996

Classification of lung sounds in patients with asthma, emphysema, fibrosing alveolitis and healthy lungs by using self-organizing maps.

The performance of the self-organizing map (SOM), an artificial neural network, was evaluated in the classification of lung sounds. Patients with asthma (n = 8), emphysema (n = 8) and fibrosing alveolitis (n = 8), and patients with healthy lungs (n = 8) were selected for the study. Fast Fourier transform (FFT) spectra from midinspiratory breath sounds recorded at the right lower lobe area were used to construct feature vectors in the learning and classification process of SOM. The sound segments did not contain wheezing sounds. The lung sounds of 25/32 (78%) patients were classified correctly, with an overall kappa (kappa) value of 0.71. The agreement between the clinical and proposed diagnoses based on classification of lung sounds was good among patients with emphysema (kappa = 0.92) and those with healthy lungs (kappa = 0.83), but only moderate among patients with asthma (kappa = 0.52) and fibrosing alveolitis (kappa = 0.54). This is due to the limitations in distinguishing breath sounds of asthmatics without wheezing sounds from those with crackles in fibrosing alveolitis by the spectral pattern alone. The results indicate that SOM based on FFT spectra is potentially useful in the classification of lung sounds, e.g. in health screening or in differential diagnosis of pulmonary disorders. To enhance the performance of SOM, other features of lung sounds should be combined with FFT spectra.

Adult↗

PubMed1991

Validated method for automatic detection of lung sound crackles.

Crackling lung sounds are associated with many pulmonary diseases. Their occurrence reflects the quality and the severity of the disease. An automatic method for crackle detection is developed, based on analysing the spectral stationarity of the lung sound. The method is validated by studying the crackles of 20 adult patients; 10 with fibrosing alveolitis (FA) and 10 with bronchiectasis (BE). The number of crackles detected by the automatic method in inspiratory cycles is compared to the number of crackles counted from time-expanded waveforms by two expert observers. The total number of inspiratory cycles studied is 117 and that of crackles 1064. The method has a sensitivity of 89 per cent and a positive predictivity of 88 per cent for patients with FA, and 80 per cent and 83 per cent respectively, for patients with BE. The linear correlation coefficients between the numbers of crackles counted by the automatic method and by the observers is 0.86 (p less than 0.001) for the patients with FA and 0.93 (p less than 0.001) for the patients with BE. The values refer to whole inspiratory cycles. The new automatic method seems reliable enough for clinical and scientific purposes. It enables a rapid and objective analysis of large materials with crackling lung sounds.

Aged↗