Sensor technologies to detect out-of-hospital cardiac arrest: A systematic review of diagnostic test performance

Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project

“… 4 A technical solution to automatically detect OHCA and to alert EMS will for the first time provide victims of unwitnessed OHCA a realistic chance of survival in good condition, and will likely also markedly decrease the time to treatment in cases of witnessed OHCA. A very recent systematic review described the diagnostic test performance of different sensor technologies that could potentially be used for the detection of cardiac arrest, 18 but to the best of our knowledge, a technology that actually detects OHCA and alerts EMS has not been previously developed. Chan and colleagues have trained a support vector machine algorithm to reliably detect agonal breathing 11 ; however, a large proportion of patients with OHCA do not have agonal breathing, and the study was not designed to include automatic alerting of EMS.…”

The Latest in Resuscitation Research: Highlights From the 2022 American Heart Association’s Resuscitation Science Symposium

“… 35 Dr Perkins highlighted 2 systematic reviews, 1 focused on wearable technologies for cardiac arrest detection, and another on dispatching citizen responders. 36 , 37 …”

Wolf Creek XVII part 3: Automated cardiac arrest diagnosis

“…Wearable technologies could theoretically use photoplethysmography (PPG), ECG, temperature and accelerometer sensors to detect cardiac arrest events and the cessation of circulation. 15 The audio sensors (i.e., speakers and microphones) of smart devices have been shown capable of detecting agonal breathing, a respiratory manifestation of cardiac arrest 16 as well as dangerous breathing patterns (e.g., apnea, hypopnea) using sonar. 17 Research to date shows promising results, with some technologies showing a sensitivity and specificity greater than 99% 18 , 19 in controlled settings.…”

“…Current research in automated cardiac arrest detection mostly involves feasibility studies, whose primary focus is sensitivity, with small populations or retrospective studies using data to create a model to diagnose cardiac arrest. 15 Prospective studies with larger populations and real-world data from patients in the preclinical setting are important to measure real-world performance of these systems with respect to sensitivity and specificity. There are several barriers to conducting this research, for example false positive rates, which we will discuss in the section on barriers to translation.…”