Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals

Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities. Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring, however, relatively little attention is paid to accuracy and reliability. In previous study, a wearable respiration biofeedback system was designed. In this work, three kinds of signals were mixed to extract respiratory rate, i.e., respiration inductive plethysmography (RIP), 3D-acceleration and ECG. In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate. It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37% of a reference gold standard. Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17% of a reference gold standard.