Bench testing of tracheostomy tube-related insults using an instrumented manikin.

PURPOSE: Choosing the right tracheal tube for the right patient is a daily preoccupation for intensivists and emergency physicians. Tracheal tubes can generate severe complications, which are chiefly due to the pressures applied by the tube to the trachea. We designed a bench study to assess the frequency of pressure levels likely to cause tracheal injury. METHODS: We tested the pressure applied on the trachea by 17 tube models of a given size range. To this end, we added a pressure sensor to the posterior tracheal wall of a standardized manikin. RESULTS: Only 2 of the 17 tubes generated pressures under the threshold likely to induce tracheal injury (30 mmHg/3.99 kPa). The force exerted on the posterior wall of the trachea varied widely across tube models. CONCLUSION: Most models of tracheal tubes resulted in forces applied to the trachea that are usually considered capable of causing tracheal tissue injury. LEVEL OF EVIDENCE: Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence: How common is the problem?: step 1; Is this diagnostic or monitoring test accurate? (Diagnosis) step 5; What will happen if we do not add a therapy? (Prognosis) n/a; Does this intervention help? (Treatment Benefits) step 5; What are the COMMON harms?(Treatment Harms) step 5; What are the RARE harms? (Treatment Harms) step 5; Is this (early detection) test worthwhile? (Screening) step 5.

U-Net Neural Network for Heartbeat Detection in Ballistocardiography.

Monitoring vital signs of neonates can be harmful and lead to developmental troubles. Ballistocardiography, a contactless heart rate monitoring method, has the potential to reduce this monitoring pain. However, signal processing is uneasy due to noise, inherent physiological variability and artifacts (e.g. respiratory amplitude modulation and body position shifts). We propose a new heartbeat detection method using neural networks to learn this variability. A U-Net model takes thirty-second-long records as inputs and acts like a nonlinear filter. For each record, it outputs the samples probabilities of belonging to IJK segments. A heartbeat detection algorithm finally detects heartbeats from those segments, based on a distance criterion. The U-Net has been trained on 30 healthy subjects and tested on 10 healthy subjects, from 8 to 74 years old. Heartbeats have been detected with 92% precision and 80% recall, with possible optimization in the future to achieve better performance.