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Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide.Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is thecornerstone of therapy for victims of cardiac arrest. Yet a significantfraction of cardiac arrest victims have no chance of survival because theyexperience an unwitnessed event, often in the privacy of their own homes. Anunder-appreciated diagnostic element of cardiac arrest is the presence ofagonal breathing, an audible biomarker and brainstem reflex that arises in thesetting of severe hypoxia. Here, we demonstrate that a support vector machine(SVM) can classify agonal breathing instances in real-time within a bedroomenvironment. Using real-world labeled 9-1-1 audio of cardiac arrests, we trainthe SVM to accurately classify agonal breathing instances. We obtain an areaunder the curve (AUC) of 0.998 and an operating point with an overallsensitivity and specificity of 97.03% (95% CI: 96.62 -- 97.41%) and 98.20% (95%CI: 97.87 -- 98.49%). We achieve a false positive rate between 0% -- 0.10% over82 hours (117,895 audio segments) of polysomnographic sleep lab data thatincludes snoring, hypopnea, central and obstructive sleep apnea events. Wedemonstrate the effectiveness of our contactless system in identifyingreal-world cardiac arrest-associated agonal breathing instances andsuccessfully evaluate our classifier using commodity smart devices (Amazon Echoand Apple iPhone).

Contactless Cardiac Arrest Detection Using Smart Devices