Wearable devices for out‐of‐hospital cardiac arrest: A population survey on the willingness to adhere DOI Creative Commons
Saud Lingawi, Jacob Hutton, Mahsa Khalili

et al.

Journal of the American College of Emergency Physicians Open, Journal Year: 2024, Volume and Issue: 5(5)

Published: Aug. 25, 2024

When an out-of-hospital cardiac arrest (OHCA) occurs, the first step in chain of survival is detection. However, 75% OHCAs are unwitnessed, representing largest barrier to activating survival. Wearable devices have potential be "artificial bystanders," detecting OHCA and alerting 9-1-1. We sought understand factors impacting users' willingness for continuous use a wearable device through online survey inform future these systems automated

Language: Английский

Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection DOI Creative Commons
Mahsa Khalili, Saud Lingawi, Jacob Hutton

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 5, 2024

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential "witness" activate emergency services. In this study, we used an arterial occlusion model simulate investigated the ability infrared photoplethysmogram (PPG) sensors, often utilized in wearable devices, differentiate normal pulsation, pulseless (i.e., resembling arrest), non-physiologic off-body) states. Across classification models trained evaluated on three anatomical locations, higher performances were observed finger (macro average F1-score 0.964 fingertip 0.954 base) compared wrist 0.837). The wrist-based model, which was using all PPG measurements, including both high- low-quality recordings, achieved macro precision recall 0.922 0.800, respectively. This represents most common form factor wearables, could only capture about 43.8% events. However, tested exclusively high-quality recordings outcomes 0.975 fingertip, 0.973 base, 0.934 wrist). had highest performance pulselessness from pulsation off-body measurements 0.978 0.972, able identify 93.7% states event), 0.4% false positive rate. All relied combination time-, power spectral density (PSD)-, frequency-domain features cardiac, recordings. our best represented idealized detection condition, relying ensuring data training evaluation machine learning algorithms. While 90.7% considered high quality, 53.2% passed quality criteria. Our findings have implications adapting provide detection, involving advancements hardware software ensure real-world settings, as well development factors that enable acquisition more consistently. Given these improvements, demonstrate feasibly be made available anyone PPG-based wearables.

Language: Английский

Citations

2

The association of non-prescription drug use preceding out-of-hospital cardiac arrest and clinical outcomes DOI Creative Commons

Valerie Mok,

Morgan Haines, Armin Nowroozpoor

et al.

Resuscitation, Journal Year: 2024, Volume and Issue: 202, P. 110313 - 110313

Published: July 10, 2024

Language: Английский

Citations

1

Wearable devices for out‐of‐hospital cardiac arrest: A population survey on the willingness to adhere DOI Creative Commons
Saud Lingawi, Jacob Hutton, Mahsa Khalili

et al.

Journal of the American College of Emergency Physicians Open, Journal Year: 2024, Volume and Issue: 5(5)

Published: Aug. 25, 2024

When an out-of-hospital cardiac arrest (OHCA) occurs, the first step in chain of survival is detection. However, 75% OHCAs are unwitnessed, representing largest barrier to activating survival. Wearable devices have potential be "artificial bystanders," detecting OHCA and alerting 9-1-1. We sought understand factors impacting users' willingness for continuous use a wearable device through online survey inform future these systems automated

Language: Английский

Citations

0