# Contactless Cardiac Arrest Detection Using Smart Devices

## TL;DR

This paper presents a contactless system using smart devices to detect cardiac arrest by classifying agonal breathing with high accuracy in real-time, potentially enabling faster emergency response in home environments.

## Contribution

The study introduces a novel contactless method employing smart devices and machine learning to detect agonal breathing, a key indicator of cardiac arrest, with high accuracy in real-world settings.

## Key findings

- Achieved an AUC of 0.998 in classifying agonal breathing.
- Sensitivity of 97.03% and specificity of 98.20% in detection.
- False positive rate below 0.10% over extensive sleep data.

## Abstract

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide. Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is the cornerstone of therapy for victims of cardiac arrest. Yet a significant fraction of cardiac arrest victims have no chance of survival because they experience an unwitnessed event, often in the privacy of their own homes. An under-appreciated diagnostic element of cardiac arrest is the presence of agonal breathing, an audible biomarker and brainstem reflex that arises in the setting of severe hypoxia. Here, we demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a bedroom environment. Using real-world labeled 9-1-1 audio of cardiac arrests, we train the SVM to accurately classify agonal breathing instances. We obtain an area under the curve (AUC) of 0.998 and an operating point with an overall sensitivity 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% over 82 hours (117,895 audio segments) of polysomnographic sleep lab data that includes snoring, hypopnea, central and obstructive sleep apnea events. We demonstrate the effectiveness of our contactless system in identifying real-world cardiac arrest-associated agonal breathing instances and successfully evaluate our classifier using commodity smart devices (Amazon Echo and Apple iPhone).