Automatic Identification of Return of Spontaneous Circulation During Cardiopulmonary Resuscitation

Abstract

The main problem during pulse check in out-of-hospital cardiac arrest is the discrimination between normal pulse-generating rhythm (PR) and pulseless electrical activity (PEA). It has been suggested that circulatory information can be acquired by measuring the thoracic impedance via the defibrillator pads. To investigate this, we performed an experimental study where we retrospectively analyzed 127 PEA segments and 91 PR segments out of 219 and 113 segments. A PEA versus PR classification framework was developed, that uses short segments (< 10 s) of ECG and impedance measurements to discriminate between the two rhythms. Using realistic data analyzed over a duration of 3 s, our system correctly identifies 90.0% of the segments with rhythm being pulseless electrical activity, and 91.5% of the normal pulse rhythm segments. Automatic identification of pulse could avoid unnecessary pulse checks and thereby reduce no-flow time and potentially increase the chance of survival.

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

“…18,19,21 Combining ECG and ICC features improved the accuracy of the RLS based detector by over 7 points (see supplementary materials). The RLS filter we propose is a more efficient estimator of the ICC component than our previous LMS method, 22 and permits a reliable PEA/PR discrimination using shorter ECG/TI segments of duration below 5 s with minimal computational burden, around 50ms (see supplementary materials).…”

“…In 2007 Losert et al 18 used the invasive measurement of the systolic blood pressure as gold standard in an in-hospital setting. Risdal et al 19 used as gold standard the manual rhythm annotations made by expert reviewers in an OHCA scenario. These annotations were made by visual inspection of the ECG and TI signals looking for fluctuations in the TI correlated with the heartbeats.…”

“…These annotations were made by visual inspection of the ECG and TI signals looking for fluctuations in the TI correlated with the heartbeats. In Ruiz et al 22 we based our study on the dataset used by Risdal et al 19 in which the gold standard PEA/PR annotations were made based on the ECG and TI signals and were therefore not independent of the methods used for PEA/PR discrimination. Furthermore, the criteria for segment selection were very restrictive since the data comprised only PR segments at the end of the episodes in patients admitted alive to hospital, and PEA segments on patients with no PR annotations along the episode.…”

“…18–22 The TI signal shows a very small fluctuation (usually less than 100m) with every effective heartbeat. Studies by Losert et al 18 and Risdal et al 19 concluded that a combination of features extracted from ECG and TI allows a proper discrimination between pulse-generating rhythms (PR) and pulseless electrical activity (PEA). Cromie et al used a single feature computed from the spectrum of the processed TI as a clinical marker of circulatory collapse in animals 20 and humans.…”

Towards the Automated Analysis and Database Development of Defibrillator Data from Cardiac Arrest

“…This will allow the automatic reading using impedance in machines from other manufacturers who do not have the “puck” and in instances where the puck is not used even though it is at the scene. The impedance can also be used to automatically determine the presence of ventilations [15] and circulation [16] which has been demonstrated by Risdal et al A future RORE can be extended to include information about ventilations, circulation, and possibly drug administration.…”