#Adventitious breath sounds windows

We computed spectrograms with three windowing methods and six window lengths: Hamming, Blackman–Harris, and rectangular windows with window lengths of 16, 32, 64, 128, 256, and 512 ms with 75% overlap. To study how frame lengths influence spectrogram computation, a multiscale approach was followed for feature extraction. Health professionals have utilized wheezes for diagnosing various respiratory conditions in adults (e.g., chronic obstructive pulmonary disease) and in children (e.g., bronchiolitis). Wheezes occur when there is flow limitation and can be clinically defined by their duration, intensity, position in the respiratory cycle (inspiratory or expiratory), frequency (monophonic or polyphonic), number, gravity influence, and respiratory maneuvers. Their typical frequency range is between 1 Hz, with harmonics that occasionally exceed 1000 Hz. In contrast, wheezes are musical respiratory sounds usually longer than 100 ms. The characteristics of crackles, such as number, regional distribution, timing in the respiratory cycle, and especially the distinction between fine and coarse, can all be used in the diagnosis of various types of lung diseases, such as bronchiectasis or pneumonia. Although the frequency range of crackles is bounded by 60 Hz and 2 kHz, most of their energy is concentrated between 60 Hz and 1.2 kHz.

Fine crackles have short duration and high frequency, whereas coarse crackles have longer duration and lower frequency. They usually last less than 20 ms and can be classified as fine or coarse depending on their duration and frequency. These limitations could potentially be surmounted by automated respiratory sound analysis.Ĭrackles are explosive, short, discontinuous, and nonmusical ARS that are attributed to the sudden opening and closing of abnormally closed airways. Conventional auscultation is usually employed but has some drawbacks that limit its expansion in clinical practice and suitability in research due to: (i) the necessity of an expert to annotate the presence/absence and clinical meanings of normal/abnormal respiratory sounds (ii) the unfeasibility of providing continuous monitoring (iii) its inherent inter-listener variability (iv) human audition and memory limitations and (v) as demonstrated during the COVID-19 crisis, it might not be viable in highly contagious situations, as stethoscopes can be a source of infection and need to be constantly sanitized. Despite the technological advances in auscultation devices, which have enabled the storing, analysis, and visualization of respiratory sounds in computers, digital auscultation is not yet entirely computational. In clinical practice they are commonly assessed with pulmonary auscultation using a stethoscope. Respiratory sounds have been validated as an objective, simple, and noninvasive marker to check the respiratory system. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.

(4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms.

(3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles.

Adventitious breath sounds