Automated Cardiac Arrest Detection and Emergency Service Alerting Using Device-Independent Smartwatch Technology: Proof-of-Principle.

Abstract

Introduction: Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Automated detection could improve survival by reducing delays in first responder activation. This study provides proof-of-principle for a device-independent technology that can (A) distinguish presence versus absence of spontaneous circulation, and (B) reliably alert emergency medical services (EMS).

Methods: Circulatory arrest data were collected from three groups: (1) volunteers undergoing temporarily restricted blood flow to the arm using a cuff, (2) patients undergoing cardioplegic cardiac arrest for heart surgery, and (3) domestic swine, slaughtered in food industry. Data were collected using Samsung Watch5 and Watch5 Pro. An algorithm was developed to analyze photoplethysmography signals and detect circulatory arrest. Emergency response was tested via the Dutch community first responder network HartslagNu, using their test environment to activate test responders and EMS.

Results: Nineteen participants were analyzed. Across all three groups, 28 of 31 circulatory arrests were correctly identified, sensitivity 90.3% (95% CI: 74.2% - 98.0%), and hour-level specificity was 94.1% (95% CI: 71.3% - 99.9%). Triggering a circulatory arrest consistently resulted in an audiovisual smartwatch alarm and an instantaneous alert to the virtual EMS at the HartslagNu test server.

Conclusion: This study demonstrates the feasibility of detecting circulatory arrest using commercially available smartwatch sensors, achieving high sensitivity and specificity. Additionally, we integrated an automated alerting system with emergency networks to notify first responders. While this technology shows promise to improve survival, higher specificity is needed to prevent overburdening EMS. Future research should focus on real-world validation using actual cardiac arrest data.