Severity Classification of Chronic Obstructive Pulmonary Disease and Asthma with Heart Rate and SpO2 Sensors

Asthma and Chronic Obstructive Pulmonary Disease are chronic and long-term lung diseases. Disease monitoring with minimal sensors with high efficacy can make the disease control simple and practical for patients. We propose a model for the severity assessment of the diseases through wearables and compatible with mobile health applications, using only heart rate and SpO
₂
(from pulse oximeter sensor). Patient data were obtained from the MIMIC-III Waveform Database Matched Subset. The dataset consists of 158 subjects. Both heart rate and SpO
₂
signal of patients are analyzed via the proposed algorithm to classify the severity of the diseases. Strategically, a rule-based threshold approach in real time evaluation is considered for the categorization scheme. Furthermore, a method is proposed to assess severity as an Event of Interest (EOI) from the computed metrics in retrospective. This type of autonomous system for real-time evaluation of patient's condition has the potential to improve individual health through continual monitoring and self- management, as well as improve the health status of the overall Smart and Connected Community (SCC).