  Exercise testing has been available for more than a half-century and is a
remarkably versatile tool for diagnostic and prognostic information of patients
for a range of diseases, especially cardiovascular and pulmonary. With rapid
advancements in technology, wearables, and learning algorithm in the last
decade, its scope has evolved. Specifically, Cardiopulmonary exercise testing
(CPX) is one of the most commonly used laboratory tests for objective
evaluation of exercise capacity and performance levels in patients. CPX
provides a non-invasive, integrative assessment of the pulmonary,
cardiovascular, and skeletal muscle systems involving the measurement of gas
exchanges. However, its assessment is challenging, requiring the individual to
process multiple time series data points, leading to simplification to peak
values and slopes. But this simplification can discard the valuable trend
information present in these time series. In this work, we encode the time
series as images using the Gramian Angular Field and Markov Transition Field
and use it with a convolutional neural network and attention pooling approach
for the classification of heart failure and metabolic syndrome patients. Using
GradCAMs, we highlight the discriminative features identified by the model.
