Development of a Remote Monitoring System for Respiratory Analysis

In order to prevent the lack of appropriate respiratory ventilation which causes brain damage and critical problems, it is required to continuously monitor the breathing signal of a patient. There are different conventional methods for capturing respiration signal, such as polysomnography and spirometer. In spite of their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based respiration monitoring platform which allows the patient to continue treatment and diagnosis from different places such as home. These remote services are designed for patients who suffer from breathing problems or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud database. Based on the high correlation between spirometer and accelerometer signals, the Detrended Fluctuation Analysis (DFA) has been applied on respiration signals. The obtained results show that DFA can be used as an efficient feature while classifying the healthy people from patients suffering from breath abnormalities.