Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection.

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

Wearable devices for out-of-hospital cardiac arrest: A population survey on the willingness to adhere.

Objectives: When an out-of-hospital cardiac arrest (OHCA) occurs, the first step in the chain of survival is detection. However, 75% of OHCAs are unwitnessed, representing the largest barrier to activating the chain of survival. Wearable devices have the potential to be "artificial bystanders," detecting OHCA and alerting 9-1-1. We sought to understand factors impacting users' willingness for continuous use of a wearable device through an online survey to inform future use of these systems for automated OHCA detection. Methods: Data were collected from October 2022 to June 2023 through voluntary response sampling. The survey investigated user convenience and perception of urgency to understand design preferences and willingness to adhere to continuous wearable use across different hypothetical risk levels. Associations between categorical variables and willingness were evaluated through nonparametric tests. Logistic models were fit to evaluate the association between continuous variables and willingness at different hypothetical risk levels. Results: The survey was completed by 359 participants. Participants preferred hand-based devices (wristbands: 87%, watches: 86%, rings: 62%) and prioritized comfort (94%), cost (83%), and size (72%). Participants were more willing to adhere at higher levels of hypothetical risk. At the baseline risk of 0.1%, older individuals with prior wearable use were most willing to adhere to continuous wearable use. Conclusion: Individuals were willing to continuously wear wearable devices for OHCA detection, especially at increased hypothetical risk of OHCA. Optimizing willingness is not just a matter of adjusting for user preferences, but also increasing perception of urgency through awareness and education about OHCA.

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review.

Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.

The effect of recognition on survival after out-of-hospital cardiac arrest and implications for biosensor technologies.

BACKGROUND: Biosensor technologies have been proposed as a solution to provide recognition and facilitate earlier responses to unwitnessed out-of-hospital cardiac arrest (OHCA) cases. We sought to estimate the effect of recognition on survival and modelled the potential incremental impact of increased recognition of unwitnessed cases on survival to hospital discharge, to demonstrate the potential benefit of biosensor technologies. METHODS: We included cases from the British Columbia Cardiac Arrest Registry (2019-2020), which includes Emergency Medical Services (EMS)-assessed OHCAs. We excluded cases that would not have benefitted from early recognition (EMS-witnessed, terminal illness, or do-not-resuscitate). Using a mediation analysis, we estimated the relative benefits on survival of a witness recognizing vs. intervening in an OHCA; and estimated the expected additional number of survivors resulting from increasing recognition alone using a bootstrap logistic regression framework. RESULTS: Of 13,655 EMS-assessed cases, 11,412 were included (6314 EMS-treated, 5098 EMS-untreated). Survival to hospital discharge was 191/8879 (2.2%) in unwitnessed cases and 429/2533 (17%) in bystander-witnessed cases. Of the total effect attributable to a bystander witness, recognition accounted for 84% (95% CI: 72, 86) of the benefit. If all previously unwitnessed cases had been bystander witnessed, we would expect 1198 additional survivors. If these cases had been recognized, but no interventions performed, we would expect 912 additional survivors. CONCLUSION: Unwitnessed OHCA account for the majority of OHCAs, yet survival is dismal. Methods to improve recognition, such as with biosensor technologies, may lead to substantial improvements in overall survival.

Evaluation of transcutaneous near-infrared spectroscopy for early detection of cardiac arrest in an animal model.

Sudden cardiac arrest (SCA) is a leading cause of mortality worldwide. The SCA-to-resuscitation interval is a key determinant of patient outcomes, highlighting the clinical need for reliable and timely detection of SCA. Near-infrared spectroscopy (NIRS), a non-invasive optical technique, may have utility for this application. We investigated transcutaneous NIRS as a method to detect pentobarbital-induced changes during cardiac arrest in eight Yucatan miniature pigs. NIRS measurements during cardiac arrest were compared to invasively acquired carotid blood pressure and partial oxygen pressure (PO2) of spinal cord tissues. We observed statistically significant decreases in mean arterial pressure (MAP) 64.68 mmHg ± 13.08, p < 0.0001), spinal cord PO2 (38.16 mmHg ± 20.04, p = 0.0028), and NIRS-derived tissue oxygen saturation (TSI%) (14.50% ± 3.80, p < 0.0001) from baseline to 5 min after pentobarbital administration. Euthanasia-to-first change in hemodynamics for MAP and TSI (%) were similar [MAP (10.43 ± 4.73 s) vs TSI (%) (12.04 ± 1.85 s), p = 0.3714]. No significant difference was detected between NIRS and blood pressure-derived pulse rates during baseline periods (p > 0.99) and following pentobarbital administration (p = 0.97). Transcutaneous NIRS demonstrated the potential to identify rapid hemodynamic changes due to cardiac arrest in periods similar to invasive indices. We conclude that transcutaneous NIRS monitoring may present a novel, non-invasive approach for SCA detection, which warrants further investigation.

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

Aim: Cardiac arrest (CA) is the cessation of circulation to vital organs that can only be reversed with rapid and appropriate interventions. Sensor technologies for early detection and activation of the emergency medical system could enable rapid response to CA and increase the probability of survival. We conducted a systematic review to summarize the literature surrounding the performance of sensor technologies in detecting OHCA. Methods: We searched the academic and grey literature using keywords related to cardiac arrest, sensor technologies, and recognition/detection. We included English articles published up until June 6, 2022, including investigations and patent filings that reported the sensitivity and specificity of sensor technologies to detect cardiac arrest on human or animal subjects. (Prospero# CRD42021267797). Results: We screened 1666 articles and included four publications examining sensor technologies. One tested the performance of a physical sensor on human participants in simulated CA, one tested performance on audio recordings of patients in cardiac arrest, and two utilized a hybrid design for testing including human participants and ECG databases. Three of the devices were wearable and one was an audio detection algorithm utilizing household smart technologies. Real-world testing was limited in all studies. Sensitivity and specificity for the sensors ranged from 97.2 to 100% and 90.3 to 99.9%, respectively. All included studies had a medium/high risk of bias, with 2/4 having a high risk of bias. Conclusions: Sensor technologies show promise for cardiac arrest detection. However, current evidence is sparse and of high risk of bias. Small sample sizes and databases with low external validity limit the generalizability of findings.