First automated detection of a cardiac arrest using a commercially available smartwatch: a case report
Wisse M.F. van den Beuken, Pieter Roel Tuinman, Beat Nideröst , Sebastiaan A. Goossen, Hans van Schuppen , Stephan A. Loer, Lothar A. Schwarte , Patrick Schober
Abstract
Background
Automated cardiac arrest detection aims to shorten the time between arrest onset and emergency medical services activation, thereby reducing the number of unwitnessed out-of-hospital cardiac arrests (OHCA) and shortening time to treatment in witnessed OHCA. Current arrest detection algorithms are largely developed using simulated or artificially induced cardiac arrest data. To our knowledge, this case report provides the first detailed description of the automated detection of spontaneous, non-procedural, end-of-life cardiac arrest using consumer-grade smartwatch-derived sensor data.
Case report
An 82-year-old patient presented to the emergency department with a severe intracerebral hemorrhage with poor prognosis. Following shared decision-making with the family, palliative management was initiated. The patient was continuously monitored with electrocardiography (ECG), invasive arterial blood pressure, and clinical photoplethysmography (PPG). In addition, a commercial smartwatch was placed on the wrist to collect sensor data during the palliative phase and up to 20 min after confirmed cardiac arrest. The smartwatch PPG data were retrospectively analyzed using a previously described diagnostic algorithm. This preliminary algorithm detects circulatory arrest using the photoplethysmography sensor signals acquired from a commercial smartwatch. The algorithm accurately identified the moment of cardiac arrest in concordance with the clinical reference signals. Informed consent was obtained for this research from a legal representative.
Conclusion
Although this controlled end-of-life setting does not represent the circumstances of an OHCA, this case demonstrates the feasibility of detecting true cardiac arrest using a commercial available smartwatch. Prospective studies in real-world OHCA populations are needed to assess clinical performance and practical applicability.
Publication
First automated detection of a cardiac arrest using a commercially available smartwatch: a case report
van den Beuken WMF, Tuinman PR, Nideröst B, Goossen SA, van Schuppen H, Loer SA, Schwarte LA, Schober P. Resuscitation Plus. 2026;28:101247.
DOI: 10.1016/j.resplu.2026.101247
© 2026 The Authors. Published by Elsevier B.V. This is an open access article under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Automated cardiac arrest detection and emergency service alerting using device-independent smartwatch technology: proof-of-principle
Wisse M.F. van den Beuken, Beat Nideröst, Sebastiaan A. Goossen, Tom A. Kooy, Derya Demirtas, Daryl Autar, Stephan A. Loer, Susanne Eberl, Vokko P. van Halm, Bernd E. Winkler, Hans van Schuppen, Pieter Roel Tuinman, Lothar A. Schwarte, Patrick Schober
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.
Publication
Automated cardiac arrest detection and emergency service alerting using device-independent smartwatch technology: proof-of-principle
van den Beuken WMF, Nideröst B, Goossen SA, Kooy TA, Demirtas D, Autar D, Loer SA, Eberl S, van Halm VP, Winkler BE, van Schuppen H, Tuinman PR, Schwarte LA, Schober P. Resuscitation. 2025;213:110657.
DOI: 10.1016/j.resuscitation.2025.110657
© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Investigating Users’ Attitudes Toward Automated Smartwatch Cardiac Arrest Detection: Cross-Sectional Survey Study
Wisse M F van den Beuken, MD; Hans van Schuppen, MD, PhD; Derya Demirtas, MMath, PhD; Vokko P van Halm, MD, PhD; Patrick van der Geest, MD, PhD; Stephan A Loer, MD, PhD; Lothar A Schwarte, MD, PhD; Patrick Schober, MD, PhD
DOI:10.2196/57574
Abstract
Background
Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality in the developed world. Timely detection of cardiac arrest and prompt activation of emergency medical services (EMS) are essential, yet challenging. Automated cardiac arrest detection using sensor signals from smartwatches has the potential to shorten the interval between cardiac arrest and activation of EMS, thereby increasing the likelihood of survival.
Objective
This cross-sectional survey study aims to investigate users’ perspectives on aspects of continuous monitoring such as privacy and data protection, as well as other implications, and to collect insights into their attitudes toward the technology.
Methods
We conducted a cross-sectional web-based survey in the Netherlands among 2 groups of potential users of automated cardiac arrest technology: consumers who already own a smartwatch and patients at risk of cardiac arrest. Surveys primarily consisted of closed-ended questions with some additional open-ended questions to provide supplementary insight. The quantitative data were analyzed descriptively, and a content analysis of the open-ended questions was conducted.
Results
In the consumer group (n=1005), 90.2% (n=906; 95% CI 88.1%-91.9%) of participants expressed an interest in the technology, and 89% (n=1196; 95% CI 87.3%-90.7%) of the patient group (n=1344) showed interest. More than 75% (consumer group: n= 756; patient group: n=1004) of the participants in both groups indicated they were willing to use the technology. The main concerns raised by participants regarding the technology included privacy, data protection, reliability, and accessibility.
Conclusions
The vast majority of potential users expressed a strong interest in and positive attitude toward automated cardiac arrest detection using smartwatch technology. However, a number of concerns were identified, which should be addressed in the development and implementation process to optimize acceptance and effectiveness of the technology.
Publication
Investigating Users’ Attitudes Toward Automated Smartwatch Cardiac Arrest Detection: Cross-Sectional Survey Study
© Wisse M F van den Beuken, Hans van Schuppen, Derya Demirtas, Vokko P van Halm, Patrick van der Geest, Stephan A Loer, Lothar A Schwarte, Patrick Schober. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 25.7.2024. DOI:10.2196/57574
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.
Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project
Patrick Schober, Wisse M.F. van den Beuken, Beat Nideröst, Tom A. Kooy, Steve Thijssen, Carolien S.E. Bulte, Bregje A.A. Huisman, Pieter R. Tuinman, Alexander Nap, Hanno L. Tan, Stephan A. Loer, Gaby Franschman, Roelof G. Lettinga, Derya Demirtas, Susanne Eberl, Hans van Schuppen, Lothar A. Schwarte
Abstract
Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Immediate detection and treatment are of paramount importance for survival and good quality of life. The first link in the ‘chain of survival’ after OHCA – the early recognition and alerting of emergency medical services – is at the same time the weakest link as it entirely depends on witnesses. About one half of OHCA cases are unwitnessed, and victims of unwitnessed OHCA have virtually no chance of survival with good neurologic outcome. Also in case of a witnessed cardiac arrest, alerting of emergency medical services is often delayed for several minutes. Therefore, a technological solution to automatically detect cardiac arrests and to instantly trigger an emergency response has the potential to save thousands of lives per year and to greatly improve neurologic recovery and quality of life in survivors.
The HEART-SAFE consortium, consisting of two academic centres and three companies in the Netherlands, collaborates to develop and implement a technical solution to reliably detect OHCA based on sensor signals derived from commercially available smartwatches using artificial intelligence. In this manuscript, we describe the rationale, the envisioned solution, as well as a protocol outline of the work packages involved in the development of the technology.
Publication
Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project
Schober P, van den Beuken WMF, Nideröst B, Kooy TA, Thijssen S, Bulte CSE, Huisman BAA, Tuinman PR, Nap A, Tan HL, Loer SA, Franschman G, Lettinga RG, Demirtas D, Eberl S, van Schuppen H, Schwarte LA. Resuscitation Plus. 2022;12:100324.
DOI: 10.1016/j.resplu.2022.100324
© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the Creative Commons Attribution 4.0 International License (CC BY 4.0).