Digital-twin-based Online Parameter Personalization for Implantable Cardiac Defibrillators.

ABSTRACT

Implantable cardioverter defibrillators (ICDs) are developed to provide timely therapies when adverse patient conditions are detected. Device therapies need to be adjusted for individual patients and evolving patient conditions, which can be achieved by adjusting device parameter settings. However, there are no validated clinical guidelines for parameter personalization, especially for patients with complex and rare conditions. In this paper, we propose a reinforcement learning framework for online parameter personalization of ICDs. Heart states can be inferred from ECG signals from ECG patches, which can be used to create a digital twin of the patient. Reinforcement learning then use the digital twin as environment to explore parameter settings with less misdiagnosis. Experiments were performed on three virtual patients with specific and evolving heart conditions, and the result shows that our proposed approach can identify ICD parameter settings that can achieve better performance compared to default parameter settings. Clinical relevance-Patients with ICD and ECG patch can receive periodic ICD parameter adjustments that are appropriate for their current heart conditions.