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Objective: Current resuscitation protocols require pausing chest compressionsduring cardiopulmonary resuscitation (CPR) to check for a pulse. However,pausing CPR during a pulseless rhythm can worsen patient outcome. Our objectiveis to design an ECG-based algorithm that predicts pulse status duringuninterrupted CPR and evaluate its performance. Methods: We evaluated 383patients being treated for out-of-hospital cardiac arrest using defibrillatordata. We collected paired and immediately adjacent ECG segments having anorganized rhythm. Segments were collected during the 10s period of ongoing CPRprior to a pulse check, and 5s segments without CPR during the pulse check. ECGsegments with or without a pulse were identified by the audio annotation of aparamedic's pulse check findings and recorded blood pressures. We developed analgorithm to predict the clinical pulse status based on the wavelet transformof the bandpass-filtered ECG, applying principle component analysis. We thentrained a linear discriminant model using 3 principle component modes. Modelperformance was evaluated on test group segments with and without CPR usingreceiver 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 aspontaneous pulse. The areas under the receiver operating characteristic curve(AUCs) for predicting pulse status with and without CPR on test data were 0.84and 0.89, respectively. Conclusion: A novel ECG-based algorithm demonstratespotential to improve resuscitation by predicting presence of a spontaneouspulse without pausing CPR. Significance: Our algorithm predicts pulse statusduring uninterrupted CPR, allowing for CPR to proceed unimpeded by pauses tocheck for a pulse and potentially improving resuscitation performance.

Machine Learning and Feature Engineering for Predicting Pulse Status during Chest Compressions