Gibson C Michael, Mehta Sameer, Ceschim Mariana R S, Frauenfelder Alejandra, Vieira Daniel, Botelho Roberto, Fernandez Francisco, Villagran Carlos, Niklitschek Sebastian, Matheus Cristina I, Pinto Gladys, Vallenilla Isabella, Lopez Claudia, Acosta Maria I, Munguia Anibal, Fitzgerald Clara, Mazzini Jorge, Pisana Lorena, Quintero Samantha
Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Lumen Foundation, Division of Telemedicine & Artificial Intelligence, Miami, FL, USA.
Int J Cardiol. 2022 Jan 1;346:47-52. doi: 10.1016/j.ijcard.2021.11.039. Epub 2021 Nov 18.
While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis.
To develop and validate a machine learning-guided algorithm which uses a single‑lead electrocardiogram (ECG) for STEMI detection to speed diagnosis.
Data was extracted from the Latin America Telemedicine Infarct Network (LATIN), a population-based Acute Myocardial Infarction (AMI) program that provides care to patients in Brazil, Colombia, Mexico, and Argentina through telemedicine.
the first dataset was comprised of 8511 ECGs that were used for various machine learning experiments to test our Deep Learning approach for STEMI diagnosis. The second dataset of 2542 confirmed STEMI diagnosis EKG records, including specific ischemic heart wall information (anterior, inferior, and lateral), was derived from the previous dataset to test the STEMI localization model. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats with fixed window of 0.4 s to the left and 0.9 s to the right of main. Training & Testing: 90% and 10% of the total dataset, respectively, were used for both models.
two 1-D convolutional neural networks were implemented, two classes were considered for first models (STEMI/Not-STEMI) and three classes for the second model (Anterior/Inferior/Lateral) each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each model.
The single‑lead ECG strategy was able to provide an accuracy of 90.5% for STEMI detection with Lead V2, which also yielded the best results overall among individual leads. STEMI Localization model provided promising results for anterior and inferior wall STEMIs but remained suboptimal for Lateral STEMI.
An Artificial Intelligence-enhanced single‑lead ECG is a promising screening tool. This technology provides an autonomous and accurate STEMI diagnostic alternative that can be incorporated into wearable devices, potentially providing patients reliable means to seek treatment early and offers the potential to thereby improve STEMI outcomes in the long run.
虽然ST段抬高型心肌梗死（STEMI）从入院到球囊扩张时间通常低于90分钟，但从症状出现到入院时间仍长达2.5小时，这至少部分归因于诊断延迟。
开发并验证一种机器学习引导算法，该算法使用单导联心电图（ECG）进行STEMI检测以加快诊断速度。
数据取自拉丁美洲远程医疗梗死网络（LATIN），这是一个基于人群的急性心肌梗死（AMI）项目，通过远程医疗为巴西、哥伦比亚、墨西哥和阿根廷的患者提供护理。
第一个数据集由8511份心电图组成，用于各种机器学习实验，以测试我们用于STEMI诊断的深度学习方法。第二个数据集包含2542份确诊STEMI诊断的心电图记录，包括特定缺血性心脏壁信息（前壁、下壁和侧壁），该数据集源自先前的数据集，用于测试STEMI定位模型。预处理：通过小波系统检测QRS波群，将每份心电图记录分割为单个心跳，主波左右两侧固定窗口分别为0.4秒和0.9秒。训练与测试：两个模型分别使用总数据集的90%和10%。
实施了两个一维卷积神经网络，第一个模型考虑两类（STEMI/非STEMI），第二个模型考虑三类（前壁/下壁/侧壁），分别对应受影响的心脏壁。这些个体概率被汇总以生成每个模型的最终标签。
单导联心电图策略使用V2导联进行STEMI检测的准确率为90.5%，在各导联中总体结果也最佳。STEMI定位模型在前壁和下壁STEMI方面取得了有前景的结果，但在侧壁STEMI方面仍不理想。
人工智能增强的单导联心电图是一种有前景的筛查工具。该技术提供了一种自主且准确的STEMI诊断替代方法，可纳入可穿戴设备，有可能为患者提供早期寻求治疗的可靠手段，并有可能从长远改善STEMI治疗结果。
