March 3, 2026Open Access

Out-of-hospital cardiac arrest detection by a wearable: the first real-life case

Key Points

Automated detection has the potential to improve survival rates during cardiac arrest events.
The study highlights the first case using wrist-derived data to identify cardiac arrest accurately.
Cardiac arrest detection was achieved using photoplethysmography and accelerometry technology.
Further studies are essential to validate the algorithm and reduce false positives during daily use.

Abstract

This first real-life case shows feasibility of cardiac arrest detection using wrist-derived photoplethysmography and accelerometry. Further studies are warranted to validate the algorithm in additional cardiac arrest cases, and to minimize false positive alerts during daily-life use. Once implemented, automated cardiac arrest detection and alerting has the potential to shorten treatment delays and improve survival.

Out-of-hospital cardiac arrest detection by a wearable: the first real-life case

Key Points

Abstract

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