  Objective: Current resuscitation protocols require pausing chest compressions
during cardiopulmonary resuscitation (CPR) to check for a pulse. However,
pausing CPR during a pulseless rhythm can worsen patient outcome. Our objective
is to design an ECG-based algorithm that predicts pulse status during
uninterrupted CPR and evaluate its performance. Methods: We evaluated 383
patients being treated for out-of-hospital cardiac arrest using defibrillator
data. We collected paired and immediately adjacent ECG segments having an
organized rhythm. Segments were collected during the 10s period of ongoing CPR
prior to a pulse check, and 5s segments without CPR during the pulse check. ECG
segments with or without a pulse were identified by the audio annotation of a
paramedic's pulse check findings and recorded blood pressures. We developed an
algorithm to predict the clinical pulse status based on the wavelet transform
of the bandpass-filtered ECG, applying principle component analysis. We then
trained a linear discriminant model using 3 principle component modes. Model
performance was evaluated on test group segments with and without CPR using
receiver operating curves and according to the initial arrest rhythm. Results:
There were 230 patients (540 pulse checks) in the training set and 153 patients
(372 pulse checks) in the test set. Overall 38% (351/912) of checks had a
spontaneous pulse. The areas under the receiver operating characteristic curve
(AUCs) for predicting pulse status with and without CPR on test data were 0.84
and 0.89, respectively. Conclusion: A novel ECG-based algorithm demonstrates
potential to improve resuscitation by predicting presence of a spontaneous
pulse without pausing CPR. Significance: Our algorithm predicts pulse status
during uninterrupted CPR, allowing for CPR to proceed unimpeded by pauses to
check for a pulse and potentially improving resuscitation performance.
