Waveform Tracker Alarm for Automatic Patient-Ventilator Asynchrony (PVA) and Mechanical State Recognition for Mechanical Ventilators Using Embedded Deep Learning
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
The Ateneo Innovation Center designs and develops a modular approach to medical alarm and alert systems for mechanical ventilators that enable clinicians to remotely monitor patient conditions and ventilator circuit status in near real-time, providing decision support that allows for a better diagnosis. It monitors and tracks the alarm events related to the ventilator waveform consisting of pressure, flow, and volume curves by using automatic peak detection of the curves and real-time recognition of time-series waveforms. The developed system combines the threshold alarms with embedded Artificial Intelligence to automatically detect complex alarms that need medical expertise such as issue detection on asynchrony, anomalies, and mechanical. It also differentiates the critical types of alarms, assisting clinicians via alarm prioritization, and remote patient monitoring via a near cloud system. Storing data in the near cloud system as a medical database enables building a rich dataset for upgrading the predictive model of alarm recognition.
Recommended Citation
Santiago, P.R.A. et al. (2024). Waveform Tracker Alarm for Automatic Patient-Ventilator Asynchrony (PVA) and Mechanical State Recognition for Mechanical Ventilators Using Embedded Deep Learning. In: Conte, G.L., Sename, O. (eds) Proceedings of the 11th International Conference on Mechatronics and Control Engineering . ICMCE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6523-6_10
