Pulmonary Disease Classification on Electrocardiograms Using Machine Learning

Abstract

Pulmonary diseases, such as chronic obstructive pulmonary

disease (COPD) and asthma are among the leading causes of

death in the US. These lung diseases often are diagnosed by

pulmonologists using physical exam (e.g., lung auscultation)

and objective measurement of lung function with pulmonary

function testing (PFT). These extensive tests, however, can

be inaccessible to many patients due to limited resources and

availability. In this paper, we explore the use of the easily accessible

electrocardiograms (ECGs) to train machine learning

models to classify pulmonary diseases. To this end, we developed

and experimented with two approaches: deep neural

networkmodels trained (e.g., Convolutional Neural Networks

(CNNs) and Recurrent Neural Networks (RNNs)) on ECG

signals directly, and non-neural models (e.g., support vector

machines (SVMs) and logistic regression) trained on derived

features from ECGs. In the task of classifying whether a patient

has obstructive lung disease, our results show that deep

neural network models outperformed the non-neural models,

though the difference is within 3% on accuracy and F1-score

metrics.