Towards resiliency in embedded medical monitoring devices

Safety-critical medical monitoring systems have always suffered from false alarms and misdetection issues, sensitivity to external perturbations and internal faults, which could be catastrophic for patients. We address the main challenges faced towards the resiliency of medical monitoring devices by introducing a novel reconfigurable hardware architecture that enables: (i) accurate detection of medical conditions by means of a fusion and decision support mechanism based on concurrent analysis of multiple physiological signals and computing a unified health index, (ii) dynamic system adaptation to patient-specific diagnostic needs, and (iii) availability of system despite the occurrence of accidental errors and unexpected failures. This paper presents an overview on the monitoring algorithms implemented in the architecture for analysis of multi-parameter patient data from a cardiac Intensive Care Unit (ICU). An evaluation framework is proposed for assessing the resiliency of the detection and fusion mechanisms to data artifacts and their effectiveness in masking false alarms.