"Recently, there is a great focus on preventive medicine and consequently, lots of telemedicine applications have been developed for real-time health monitoring. The monitoring requires a set of biosensors and tablets, smartphones or other mobile devices. The biosensors continuously scan and transmit data and the mobile devices have the capacity to process the data and at the same time to save the battery as much as possible. In this paper, we assess the performance of an algorithm for ECG derived heart rate and respiratory rate. Three different scenarios are inspected encompassing local, remote Linux server, and remote MatLab server processing of the locally collected ECG signals by using the biosensors attached to a person. We set a hypothesis that processing locally instead of remotely is worse in terms of time and power consumption. Thus, developing a web service for processing will be best in both time and battery consumption. A total of 1297 signals have been processed. The results show that the mobile device is appropriate for processing ECG files up to approximately 5MB size. The processing of larger files spends too many resources when compared to the remote processing solution. Additionally, we have proved that splitting large files in chunks up to 4 MB per chunk resulted in faster processing for the local scenario."
ECG, Algorithm, Signal processing,Mobile Platform