Papers › Machine Learning and Feature Engineering for Predicting Pulse Status during Chest Compressions
Machine Learning and Feature Engineering for Predicting Pulse Status during Chest Compressions
Diya Sashidhar, Heemun Kwok, Jason Coult, Jen Blackwood, Peter Kudenchuck, Shiv Bhandari, Thomas Rea, J. Nathan Kutz
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.
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