  Breathing is one of the most important body functions because it provides it
with oxygen, which is vital for energy production. In addition, the removal of
carbon dioxide actively regulates the acid-base level, which is essential for
the physiological function of the body. Due to its close connection with many
other body functions, respiration can also be used as an indicator for a wide
spectrum of medical conditions, which at first glance have little to do with
breathing. Neurological, cardiological, inflammatory, metabolic, and even
psychological conditions symptomatically show up in breathing patterns. Hence,
being able to classify them automatically and unobtrusively, can allow
cost-effective monitoring systems to continuously assess the health of a
patient. In this work, multiple respiratory signal-extraction algorithms for
thermal and RGB cameras are presented and compared. A novel algorithm for the
extraction of multiple respiratory features is presented and evaluated. Using a
one vs. one multiclass support vector machine, these features were used to
classify a wide range of respiratory patterns with an accuracy of up to 95.79%.
