Edge AI for Real-Time ICU Alarm Fatigue Reduction: Federated Anomaly Detection on Wearable Streams

Abstract

ICU alarm fatigue is a serious problem in the healthcare sector since healthcare practitioners are overwhelmed by the number of alarms that are triggered, and most of them are false positives. It causes desensitization, a delay in response, and compromises patient safety. Federated learning and Edge AI present a viable solution to curbing alarm fatigue through optimization of alarm management systems and effective real-time patient monitoring. Edge AI enables data processing on wearable devices, ensuring that alerts occur promptly and with minimal delay. Federated learning allows machine learning models to be learned on decentralized and secure patient data without direct access to protected health information, maintaining privacy and customizing alarm limits. This paper discusses the possibilities of federal anomaly detection in wearable devices relating to ICU patients, in the context of real-time detection of health anomalies like abnormal heart rates and oxygen saturation. The objective is to evaluate how these technologies can streamline alarm systems by minimizing false alarms, while focusing on the important event. Important insights show the potential of Edge AI to enhance healthcare processes and deliver insights capable of driving interventions with minimal input. Federated anomaly detection is an innovative solution that can improve the work of an ICU, both in terms of operational efficiency and patient safety. This analysis seeks deeper research and the application of these technologies to clinical practice to reach their full potential.