Instantaneous frequency based index to characterize respiratory crackles

Area of Science:

• Pulmonary Medicine
• Biomedical Signal Processing
• Acoustics

Background:

• Lung crackles are vital auditory indicators used by healthcare professionals to diagnose respiratory conditions.
• The current standard for classifying crackles (fine vs. coarse) is the two-cycle duration (2CD) index, recommended by major respiratory societies.
• The 2CD index is significantly limited by noise and filtering in recording systems, hindering consistent data analysis across studies.

Purpose of the Study:

• To introduce a novel quantitative index for respiratory crackle analysis.
• To overcome the limitations of the traditional 2CD index by utilizing instantaneous frequency.
• To enhance the reliability and consistency of crackle classification in respiratory diagnostics.

Main Methods:

• A new index was developed, leveraging the discrete-time pseudo Wigner-Ville distribution to estimate the instantaneous frequency of crackles.
• The proposed index and the 2CD index were compared using both simulated and real lung crackle datasets.
• Simulated crackles were subjected to noise and filtering to assess index robustness; statistical analyses (Kruskal-Wallis, Dunn's tests, GMM) were applied to 382 actual crackles.

Main Results:

• The proposed instantaneous frequency-based index demonstrated significantly greater resilience to waveform distortions caused by noise and filtering compared to the 2CD index.
• Statistical analysis of actual crackle data enabled clear differentiation into two distinct classes using the new index.
• The 2CD index failed to achieve similar class separation with the same dataset.

Conclusions:

• The novel index offers improved robustness against common signal processing artifacts in lung sound recordings.
• This new method facilitates more reliable classification of respiratory crackles, potentially distinguishing between different underlying respiratory diseases.
• The proposed index holds promise for enhancing diagnostic capabilities during clinical examinations of respiratory conditions.