JMIR Publications

ABSTRACT

Background:

In the event of cardiac arrest, providing immediate, high-quality cardiopulmonary resuscitation (CPR) and applying a defibrillator are crucial for patient care. High-quality CPR is defined by chest compressions at a rate of 100–120 per minute and a compression depth of 50–60 mm. However, during an emergency, monitoring the count and depth of compressions poses a significant challenge for individuals administering CPR.

Objective:

This study introduces a neural network model designed to predict and assess the quality of CPR utilizing accelerometer data from a participant’s smartwatch.

Methods:

This research involved collecting real-world chest compression data from 83 participants performing CPR on a mannequin, with accelerometer data captured via smartwatches worn by the participants. This data was employed to train the model against a gold-standard dataset from the mannequin. The accelerometer-derived compression data were aligned with those from the mannequin dataset. Subsequently, the data were segmented into five-second intervals to facilitate training the neural network models.

Results:

Throughout the study, 1,226 neural network models were developed, incorporating variations in hyperparameters and the dataset. The optimal model demonstrated the capability to accurately predict the number of compressions and the average compression depth within a five-second interval, achieving an accuracy of ±3.8 mm and an average deviation in compression count of 0.8.

Conclusions:

The study validates the efficacy of a neural network model in accurately predicting CPR metrics, outperforming other models discussed in the literature and involving a considerably large participant base. Clinical Trial: The ethics application for this research received approval from TBRHSC (REB 2022519), allowing the collection and use of participant data for research purposes. Furthermore, all participants gave written consent for their data to be collected and used in this study.