Background

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.

Objectives

To develop and validate a machine learning-guided algorithm which uses a single‑lead electrocardiogram (ECG) for STEMI detection to speed diagnosis.

Methods

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. Sample: 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. Classification: 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.

Results

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.

Conclusions

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.

中文翻译：

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使用深度学习方法改进 STEMI 检测的单导联心电图

背景

虽然 ST 段抬高心肌梗塞 (STEMI) 门到球囊的时间通常低于 90 分钟，但症状到门的时间仍然很长，为 2.5 小时，至少部分原因是诊断延迟。

目标

开发和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。

方法

数据来自拉丁美洲远程医疗梗塞网络 (LATIN)，这是一个基于人群的急性心肌梗塞 (AMI) 计划，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。示例：第一个数据集由 8511 个心电图组成，这些心电图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 条已确认的 STEMI 诊断 EKG 记录，包括特定的缺血性心壁信息（前、下和侧向），是从之前的数据集派生的，用于测试 STEMI 定位模型。预处理：通过小波系统检测 QRS 复合波，将每个 EKG 记录分割成单独的心跳，固定窗口左侧为 0.4 秒，主右侧为 0.9 秒。训练和测试：两个模型分别使用了总数据集的 90% 和 10%。分类：实施了两个一维卷积神经网络，第一个模型考虑了两个类别（STEMI/Not-STEMI），第二个模型考虑了三个类别（前/下/外侧），每个类别对应于受影响的心脏壁。汇总这些单独的概率以生成每个模型的最终标签。

结果

单导联心电图策略能够为使用导联 V2 的 STEMI 检测提供 90.5% 的准确度，这在各个导联中也产生了总体上最好的结果。STEMI 定位模型为前壁和下壁 STEMI 提供了有希望的结果，但对于侧向 STEMI 仍然不理想。

结论

人工智能增强的单导联心电图是一种很有前途的筛查工具。该技术提供了一种自主且准确的 STEMI 诊断替代方案，可整合到可穿戴设备中，可能为患者提供可靠的及早寻求治疗的方法，并提供从长远来看改善 STEMI 预后的潜力。