A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring
This study pres ents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts.
Source: Journal of Clinical Monitoring and Computing - Category: Information Technology Source Type: research
More News: Information Technology | Learning | Respiratory Medicine | Study | Training | Universities & Medical Training