Asthma severity identification from pulmonary acoustic signal for computerized decision support system

Abstract

Abstract

Objective: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study is to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis has been extended to observe behavior of wheeze sounds in different datasets.

Method: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F₂₅, F₅₀, F₇₅, F₉₀, F₉₉ and mean frequency (MF) has been calculated from normalized power spectrum. Subsequently, multivariate analysis has been performed for analysis.

Result: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level ? = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ????² = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples ? = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ????² = 0.386-0.568.

Conclusion: The results demonstrate that severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.

Keywords: Asthma, Breath Sounds, Wheeze Detection, Airway Obstruction, Severity Level

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