AIM: To design and evaluate a simple algorithm able to discriminate pulsatile rhythms from pulseless electrical activity during automated external defibrillator (AED) analysis intervals, using the ECG and the transthoracic impedance (TI) acquired from defibrillation pads. METHODS: ECG and TI signals from out-of-hospital AED recordings were retrospectively analysed. Experts annotated the cardiac rhythm during AED analysis intervals and at the end of each episode. We developed an algorithm to classify 3-s segments of non-shockable and non-asystole rhythms as either pulsatile rhythm or pulseless electrical activity. The algorithm consisted on a decision tree based on two features: the mean power of the TI segment and the mean cross-power between ECG and TI segments. RESULTS: From the 302 annotated episodes, 167 contained segments eligible for the study. The circulation detector algorithm presented a sensitivity (ability of detecting pulsatile rhythms) of 98.3% (95% CI: 95.1-100) and a specificity (ability to detect pulseless electrical activity) of 98.4% (95% CI: 97.1-99.8) in the validation subset. Absence of pulsatile rhythm was confirmed during the first AED analysis interval in 98.9% of the episodes, and presence of a pulse was confirmed in the first 3 s of all intervals with annotated return of spontaneous circulation. CONCLUSION: Accurate automated detection of circulation based on TI and ECG is possible during AED analysis intervals. This functionality could potentially contribute to enhance patient's care by laypersons using AEDs.
AIM: To design and evaluate a simple algorithm able to discriminate pulsatile rhythms from pulseless electrical activity during automated external defibrillator (AED) analysis intervals, using the ECG and the transthoracic impedance (TI) acquired from defibrillation pads. METHODS: ECG and TI signals from out-of-hospital AED recordings were retrospectively analysed. Experts annotated the cardiac rhythm during AED analysis intervals and at the end of each episode. We developed an algorithm to classify 3-s segments of non-shockable and non-asystole rhythms as either pulsatile rhythm or pulseless electrical activity. The algorithm consisted on a decision tree based on two features: the mean power of the TI segment and the mean cross-power between ECG and TI segments. RESULTS: From the 302 annotated episodes, 167 contained segments eligible for the study. The circulation detector algorithm presented a sensitivity (ability of detecting pulsatile rhythms) of 98.3% (95% CI: 95.1-100) and a specificity (ability to detect pulseless electrical activity) of 98.4% (95% CI: 97.1-99.8) in the validation subset. Absence of pulsatile rhythm was confirmed during the first AED analysis interval in 98.9% of the episodes, and presence of a pulse was confirmed in the first 3 s of all intervals with annotated return of spontaneous circulation. CONCLUSION: Accurate automated detection of circulation based on TI and ECG is possible during AED analysis intervals. This functionality could potentially contribute to enhance patient's care by laypersons using AEDs.
Authors: Jose Julio Gutiérrez; Mikel Leturiondo; Sofía Ruiz de Gauna; Jesus María Ruiz; Izaskun Azcarate; Digna María González-Otero; Juan Francisco Urtusagasti; James Knox Russell; Mohamud Ramzan Daya Journal: PLoS One Date: 2021-05-18 Impact factor: 3.240
Authors: Jose Julio Gutiérrez; Jesus María Ruiz; Sofía Ruiz de Gauna; Digna María González-Otero; Mikel Leturiondo; James Knox Russell; Carlos Corcuera; Juan Francisco Urtusagasti; Mohamud Ramzan Daya Journal: PLoS One Date: 2020-02-05 Impact factor: 3.240
Authors: Sofía Ruiz de Gauna; Jesus María Ruiz; Jose Julio Gutiérrez; Digna María González-Otero; Daniel Alonso; Carlos Corcuera; Juan Francisco Urtusagasti Journal: PLoS One Date: 2020-09-30 Impact factor: 3.240