Abstract

Cardiopulmonary resuscitation (CPR) is a critical intervention aimed at restoring vital blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure, representing a crucial aspect of emergency medical care. Numerous biomedical signals are associated with CPR execution and monitoring, from initial out-of-hospital treatment to the hospital's intensive care unit (ICU). Machine learning (ML) can play a crucial role in automating the CPR process by utilizing signals to identify complex patterns and in decision-making. In this context, we explored the existing role of ML in CPR and analyzed current ML approaches for various CPR-related tasks in this thesis. Our review highlights research gaps and sets new directions for empirical studies, uncovering the unexplored potential of ML applications in CPR. Through the analysis, we identified that CPR signals often suffer from noise, complicating accurate clinical interpretation and decision-making. Conventional denoising methods using filters exhibit limitations in addressing the complex noise characteristics inherent in CPR signals. Although ML is known for handling complex data characteristics, a dedicated ML-based unsupervised approach for denoising CPR signals is still missing. To this end, we proposed a novel ML framework tailored for denoising biomedical signals during CPR. Utilizing a multi-modality approach, our framework leverages a dedicated ML algorithm for individual signals while concurrently denoising multiple signals through unsupervised ML approaches considering real-life scenarios. Our framework demonstrates significant noise removal and signal fidelity enhancements. Furthermore, our methodology preserves signal correlations, essential for downstream tasks. Finally, the proposed framework aims to improve CPR monitoring and decision-making, offering adaptability and extensibility to denoise a range of biomedical signals beyond CPR scenarios.