Rao Gaurav, Savage David W, Erickson Gabrielle, Kyryluk Nathan, Lingras Pawan, Mago Vijay
Department of Mathematics and Computing, Faculty of Science, Saint Mary's University, Halifax, NS, Canada.
Emergency Medicine, Faculty of Family and Emergency Medicine, NOSM University, Thunder Bay, ON, Canada.
JMIR Mhealth Uhealth. 2025 May 5;13:e57469. doi: 10.2196/57469.
Sudden cardiac arrest is a major cause of mortality, necessitating immediate and high-quality cardiopulmonary resuscitation (CPR) for improved survival rates. High-quality CPR is defined by chest compressions at a rate of 100-120 per minute and a depth of 50-60 mm. Monitoring and maintaining these parameters in real time during emergencies remain a challenge.
This study introduces a neural network model designed to predict and assess CPR quality using accelerometer data from a smartwatch.
The study involved 83 participants performing CPR on mannequins, with accelerometer data collected via smartwatches worn by the participants. These data were aligned with gold-standard data from the mannequins. The accelerometer-derived compression data were segmented into 5-second intervals for training the neural network models. A total of 1226 neural network models were developed, incorporating variations in hyperparameters and dataset configurations to optimize performance.
The optimal model demonstrated the capability to accurately predict the number of compressions and the average compression depth within a 5-second interval. The model achieved an accuracy of ±3.8 mm for compression depth and an average deviation of 0.8 compressions. The results indicated that the neural network model could accurately assess CPR quality metrics, surpassing other models discussed in the literature. The large and diverse dataset used in this study contributed to the robustness and reliability of the model.
This study validates the efficacy of a neural network model in accurately predicting CPR metrics using smartwatch accelerometer data. The model outperforms previous methods and shows promise for real-time feedback during CPR. Future work involves deploying the model directly on smartwatches for real-time application, potentially improving sudden cardiac arrest survival rates through immediate and accurate feedback on CPR quality.
心脏骤停是主要的死亡原因，需要立即进行高质量的心肺复苏（CPR）以提高生存率。高质量心肺复苏的定义是每分钟进行100 - 120次胸外按压，按压深度为50 - 60毫米。在紧急情况下实时监测和维持这些参数仍然是一项挑战。
本研究介绍一种神经网络模型，该模型旨在使用智能手表的加速度计数据预测和评估心肺复苏质量。
该研究让83名参与者对人体模型进行心肺复苏，通过参与者佩戴的智能手表收集加速度计数据。这些数据与人体模型的金标准数据进行比对。将从加速度计得出的按压数据按5秒间隔进行分段，用于训练神经网络模型。共开发了1226个神经网络模型，纳入超参数和数据集配置的变化以优化性能。
最优模型显示出能够准确预测5秒间隔内的按压次数和平均按压深度。该模型在按压深度方面的准确率达到±3.8毫米，平均偏差为0.8次按压。结果表明，该神经网络模型能够准确评估心肺复苏质量指标，优于文献中讨论的其他模型。本研究中使用的大量且多样的数据集有助于提高模型的稳健性和可靠性。
本研究验证了神经网络模型使用智能手表加速度计数据准确预测心肺复苏指标的有效性。该模型优于先前的方法，并显示出在心肺复苏过程中提供实时反馈的潜力。未来的工作包括将该模型直接部署到智能手表上以进行实时应用，有可能通过对心肺复苏质量的即时和准确反馈提高心脏骤停的生存率。
