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Biomedical subjects

L Pesu

Publications and source records attributed to L Pesu.

3 recordsLinked to original sources

Technical description of the IBIS data library. Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery.

The IBIS Data Library (DL) is an annotated data library that contains practically all the monitored data and other clinical information from critically ill patients during surgery and in intensive care. The data have been collected at three sites: the intensive care unit of the Kuopio University Hospital, Finland; Royal Brompton Hospital, London, UK; and St. Bartholomew's Hospital, London, UK. The purpose of the DL is to form the basis for development of biosignal interpretation methods in the Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery project in the European Union (EU) BIOMED2 programme (BMH4-97-2570). The DL contains continuous electroencephalography signals, multimodal evoked potential recordings and diagnostic electrocardiography recorded during intensive care and surgery. In addition, signal types similar to those recorded during an earlier project, the EU-BIOMED1 project IMPROVE, are stored in the DL. In addition, trend data from patient monitors, laboratory data, annotations, nursing actions, and medications recorded and stored by a Patient Data Management System (PDMS) during routine care are included. The data obtained routinely are complemented by special annotations made by a physician who observes the patient during the data collection session. Annotations include, for example, assessment of the awareness of the patient and specific events during surgery not recorded routinely by the PDMS. Inclusion of information about the care plan and the aims of the care make the contents of the DL complete. The present paper describes the technical set-up used for recording of the DL and the contents of the DL. The paper also includes an appendix defining a new data format, the extended evoked potentials format, used for storage of sweep data in the DL.

Brain↗

Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization.

In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.

Algorithms↗

Significant differences in flow standardised breath sound spectra in patients with chronic obstructive pulmonary disease, stable asthma, and healthy lungs.

BACKGROUND: Spectral characteristics of breath sounds in asthma and chronic obstructive pulmonary disease (COPD) have not previously been compared, although the structural differences in these disorders might be reflected in breath sounds. METHODS: Flow standardised inspiratory breath sounds in patients with COPD (n = 17) and stable asthma (n = 10) with significant airways obstruction and in control patients without any respiratory disorders (n = 11) were compared in terms of estimates of the power spectrum. Breath sounds were recorded simultaneously at the chest and at the trachea. RESULTS: The median frequency (F50) of the mean (SD) breath sound spectra recorded at the chest was higher in asthmatics (239 (19) Hz) than in both the control patients (206 (14) Hz) and the patients with COPD (201 (21) Hz). The total spectral power of breath sounds recorded at the chest in terms of root mean square (RMS) was higher in asthmatics than in patients with COPD. In patients with COPD the spectral parameters were not statistically different from those of control patients. The F50 recorded at the trachea in the asthmatics was significantly related to forced expiratory volume in one second (FEV1) (r = -0.77), but this was not seen in the other groups. CONCLUSIONS: The observed differences in frequency content of breath sounds in patients with asthma and COPD may reflect altered sound generation or transmission due to structural changes of the bronchi and the surrounding lung tissue in these diseases. Spectral analysis of breath sounds may provide a new non-invasive method for differential diagnosis of obstructive pulmonary diseases.

Adult↗