{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-and-feature-engineering-for","title":"Machine Learning and Feature Engineering for Predicting Pulse Status during Chest Compressions","arxiv_id":"2008.01901","date":"2020-08-05","proceeding":null,"authors":["Diya Sashidhar","Heemun Kwok","Jason Coult","Jen Blackwood","Peter Kudenchuck","Shiv Bhandari","Thomas Rea","J. Nathan Kutz"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2008.01901v1","url_pdf":"https://arxiv.org/pdf/2008.01901v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-and-feature-engineering-for","repo_url":"https://github.com/dsashid/PulsePrediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"rhythm","task_name":"Rhythm"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}