RESEARCH PAPER
Digital analysis of respiratory sounds in healthy patients and patients with chronic obstructive pulmonary disease
1
Katedra Pielęgniarstwa, Wydział Nauk o Zdrowiu, Gdański Uniwersytet Medyczny, Gdańsk
2
Katedra Pneumonologii i Alergologii, Wydział Nauk o Zdrowiu, Gdański Uniwersytet Medyczny, Gdańsk
3
Instytut Hydroakustyki, Akademia Marynarki Wojennej, Gdynia
4
Szpital Morski, Gdynia
Corresponding author
Michał Grzegorczyk
Katedra Pielęgniarstwa, Wydział Nauk o Zdrowiu, Gdański Uniwersytet Medyczny, ul.Dębinki 7 bud. 15, 80-211 Gdańsk
Katedra Pielęgniarstwa, Wydział Nauk o Zdrowiu, Gdański Uniwersytet Medyczny, ul.Dębinki 7 bud. 15, 80-211 Gdańsk
Med Og Nauk Zdr. 2013;19(2):89-94
KEYWORDS
ABSTRACT
Introduction:
Auscultation and recognition of respiratory sounds are of great diagnostic importance. However, due to subjective evaluation and the lack of an objective pattern of auscultated sounds for COPD, recognizing and differentiating the respiratory sounds causes some problems in the process of teaching students, or in clinical practice for newly qualified doctors and nurses.
Aim:
The aim of the research was to carry out a comparative analysis between ‘the visual patterns’ of lung sounds of healthy patients and patients with CODP.
Materials and methods:
In this research, there were 32 patients with CODP and 60 healthy people. Respiratory murmurs were registered with the help of an electronic Littmann 3200 stethoscope; the sounds were recorded and analyzed using computer programmer MATLAB. Based on Fast Fourier Transform, correlations of amplitude to time, amplitude to frequency and frequency to time were carried out. The average results create ‘patterns’ of amplitude-frequency waveform and a spectrogram for healthy individuals and patients with COPD.
Results:
Individual respiratory sounds for healthy individuals and those ill with COPD do not always show a statistically significant difference, whereas the average values in both groups show a significant difference. Results from the healthy group show that the amplitude waveform hardly changes over the whole measurement period, while the spectrogram for patients with COPD has variable frequencies during this period.
Conclusions:
Electronic registration and computer analysis of respiratory sounds makes it possible to describe precisely the characteristic features and visualization of the sound (spectrogram). This can contribute to the creation of a pattern for certain illness groups, as well as improve the methods of teaching the auscultation of respiratory sounds, and the minimalisation of mistaken diagnoses.
Auscultation and recognition of respiratory sounds are of great diagnostic importance. However, due to subjective evaluation and the lack of an objective pattern of auscultated sounds for COPD, recognizing and differentiating the respiratory sounds causes some problems in the process of teaching students, or in clinical practice for newly qualified doctors and nurses.
Aim:
The aim of the research was to carry out a comparative analysis between ‘the visual patterns’ of lung sounds of healthy patients and patients with CODP.
Materials and methods:
In this research, there were 32 patients with CODP and 60 healthy people. Respiratory murmurs were registered with the help of an electronic Littmann 3200 stethoscope; the sounds were recorded and analyzed using computer programmer MATLAB. Based on Fast Fourier Transform, correlations of amplitude to time, amplitude to frequency and frequency to time were carried out. The average results create ‘patterns’ of amplitude-frequency waveform and a spectrogram for healthy individuals and patients with COPD.
Results:
Individual respiratory sounds for healthy individuals and those ill with COPD do not always show a statistically significant difference, whereas the average values in both groups show a significant difference. Results from the healthy group show that the amplitude waveform hardly changes over the whole measurement period, while the spectrogram for patients with COPD has variable frequencies during this period.
Conclusions:
Electronic registration and computer analysis of respiratory sounds makes it possible to describe precisely the characteristic features and visualization of the sound (spectrogram). This can contribute to the creation of a pattern for certain illness groups, as well as improve the methods of teaching the auscultation of respiratory sounds, and the minimalisation of mistaken diagnoses.
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| eISSN: | 2084-4905 |
| ISSN: | 2083-4543 |
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