IoT Based Compressive Sensing for ECG Monitoring

The Internet of Things (IoT) has empowered several sets of applications related to remote monitoring for patients with chronic cardiovascular diseases, where, electrocardiogram (ECG) monitoring has been widely studied and applied. Furthermore, in order to optimize the energy consumption in these monitoring systems, compression techniques have been widely deployed. Compressive sensing (CS) has gained a lot of attention in ECG monitoring as a result of its ability to leverage the ECG signal structure in order to achieve a high efficient acquisition scheme. The paper investigates the incorporation of CS in IoT-based ECG monitoring platforms. The platform consists of a CS-based compression and recovery, in addition, the platform provides an abnormality detection for each heart beat using different pattern recognition algorithms. The obtained results reveal that transmitting only 15 % of the samples is enough to recover the signal efficiently. Moreover, using up to 20% of the total sample can achieve a high classification accuracy as using the original data with a maximum drop down of 3.3 % in the worst case scenario.