Identifying Emphysema with Dynamic Modelling of ...

Identifying Emphysema with Dynamic Modelling of Flow on Spirometry Marko Topalovic, Vasileios Exadaktylos, Jean-Marie Aerts, Daniel Berckmans and Wim Janssens 

Abstract— The aim of this study is to develop an automatic model for detection of emphysema based on a forced expiratory flow measurement from spirometry. To reach the goal, a second order data-based model of the forced expiration from 423 individuals is developed. The dynamic components from model are used as input features for a k-nearest neighbor (KNN) classifier. The method has an accuracy of 81.6%, specificity of 82.4% and sensitivity of 80.4%. To conclude, the dynamics of the expiration in combination with machine learning can be considered as highly reliable for identifying the presence of emphysema.

model parameters and model order, a second order transfer function model (1) has been chosen as most appropriate [2]. 

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(1)

Dynamic components from the model (namely the two poles, the steady state gain (SSG) and the time constant (Tc)) were then correlated with the presence of emphysema. The capability of the model to predict presence of disease was explored using KNN classifier (K was set on 13) and furthermore evaluated using 10-fold cross-validation. Such development was performed in the Weka software.

I. INTRODUCTION Emphysema is a form of chronic and progressive disease of the lungs. It is characterized by the loss of lung tissue leading to breakdown of alveolar walls, which primarily cause shortness of breath and limitations in air exhalation [1]. Although emphysema is not curable, treatment may weaken the rate of progression, therefore prompt diagnosis is essential. The diagnosis is currently based on CT scan of the chest, which is expensive and not routinely available. On the other hand, most available and ordinary used test for monitoring respiratory system is spirometry. It measures amount and flow of air during breathing, however its most common parameters cannot confidently confirms emphysema presence. We therefore hypothesized that a detailed analysis of the dynamics of the airflow limitations may offer a more accurate indication of emphysema and together with machine learning serve as an additional diagnostic tool in primary care. II. METHODS To develop a data-based model and investigate the dynamics of the declining phase of the exhaled flow during a spirometry test, data from 423 individuals with at least 15 pack-years and with a minimal age of 50 years were used. Presence of emphysema has been explored by two independent radiologists who examined and visually scored all CT scans. The population consisted of 239 individuals with diagnosed emphysema and 184 without. For development of a data-based model we used MATLAB and the CAPTAIN toolbox. Aiming to explain the data in a parametrically efficient way, but still preserve simplicity in the sense of

III. RESULTS A clear significant difference was observed comparing subjects with and without emphysema for each of the dynamic components (Table I). TABLE I. Comparison of dynamic components over groups Component

No emphysema

Emphysema

p value

Pole 1

0.989 (0.986-0.991)

0.994 (0.991-0.996)

p