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.