Key result
An automatic classification framework using short segments of ECG and impedance measurements correctly identified 90.0% of pulseless electrical activity and 91.5% of normal pulse rhythm segments.
Why the study?
Does an automatic classification framework using ECG and thoracic impedance accurately discriminate between pulseless electrical activity and normal pulse-generating rhythm during out-of-hospital cardiac arrest?
Observational
Does an automatic classification framework using ECG and thoracic impedance accurately discriminate between pulseless electrical activity and normal pulse-generating rhythm during out-of-hospital cardiac arrest?
Answers are grounded in this paper and cite their sources.
An automated classification framework using ECG and thoracic impedance can accurately distinguish between PEA and pulse-generating rhythms, which could reduce unnecessary pulse checks and minimize no-flow time during CPR.
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.
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Risdal et al. (2007) conducted an observational in Out-of-hospital cardiac arrest. Automatic classification framework using ECG and thoracic impedance measurements was evaluated on Discrimination between normal pulse-generating rhythm and pulseless electrical activity. An automatic classification framework using short segments of ECG and impedance measurements correctly identified 90.0% of pulseless electrical activity and 91.5% of normal pulse rhythm segments.
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