A Cloud-based Learnable Agent-oriented Approach to Control and improve Pacemaker Operation

This paper aims to present a cloud-based learning agent-oriented approach for verification of the pacemaker behavior by monitoring and heart rate adjustment of an arrhythmic patient. In case of the pacemaker failure or inappropriate heart rate generation, the patient is put at risk. Using the proposed approach, one can directs the pacemaker rate to correct one when it is incorrect. Using a learnable software agent, the proposed approach is able to learn un-predefined situations and operates accordingly. The proposed approach is cloud based meaning that it sends a message through cloud in case of a critical situation. After determining the patient heart rate by pacemaker, the proposed method verifies this rate against the predefined physician suggestion and automatically corrects it based on a reinforcement learning mechanism if there is some conflict. The proposed method was implemented and installed on a tablet as a patient mobile device for monitoring the pacemaker implanted in the patient chest. The contrast between results of our approach and expected results existing in the dataset showed our approach improved the pacemaker accuracy until 13.24%. The use of the software agent with reinforcement learning is able to play a significant role in improving medical devices in case of critical situations.