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Review of Feature Extraction from Exhaled Aerosol Fingerprints to Diagnose Lung Structural Remolding

Volume 11 - Issue 3

Jinxiang Xi1* and Xiuhua April Si2

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    • 1Department of Biomedical Engineering, USA
    • 2Department of Aerospace, Industrial and Mechanical Engineering, USA
    • *Corresponding author: Jinxiang Xi, Department of Biomedical Engineering, USA

Received: November 17, 2018;   Published: November 26, 2018

DOI: 10.26717/BJSTR.2018.11.002097

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Previous studies indicated that the patterns of the exhaled aerosol distributions, even though seemingly chaotic, are indeed unique with respect to the airway geometry. These patterns will differ whenever there is a change to the airway geometry. It is hypothesized that each lung structure has a unique Aerosol Fingerprint (AFP). As such, any difference from the normal AFP indicates an anomaly in the lung structure. However, these exhaled aerosol profiles exhibit highly complex patterns and should be quantified as feature vectors before they can be used for classification. This paper reviews the feature extraction algorithms to characterize the aerosol fingerprints from different airway geometries. These include local deposition fraction, fractal dimension, multifractal dimension, spatial-temporal dynamics and unsupervised feature extraction (deep learning).

Keywords : Lung Diagnosis; Obstructive Lung Diseases; Breath Test; Exhaled Aerosol Fingerprint

Introduction| Exhaled Aerosol Profiles (Fingerprint)| Conclusion| References|