Considerations for an Optimal EAdi Threshold for Automated Detection of Ineffective Efforts

Overall, it seems that increasing the threshold of EAdi would decrease the false-negative rate, improving the sensitivity of any given automated detection software and keeping a good specificity. We believe that, according to our reassessed results, an EAdi .2 mV could be suitable for this purpose. In addition, as Jonkman and colleagues mentioned, the removal of cardiac electrical activity is technically challenging, particularly when the signal:noise ratio of the crural diaphragm electromyography signal is low. In this scenario, we hypothesized that the automatic detection of true ineffective efforts from EAdi will be improved by using a personalized adaptive threshold for each patient considering the signal:noise ratio of the diaphragm electromyography signal. Interestingly, nonlinear methods less sensitive to ECG interference based on sample entropy algorithms (4) could be used to reduce the delay on the neural onset when an ECG peak matches at the beginning of the breath.