目的:左心室肥厚(LVH)可损害射血功能并增加心力衰竭的风险。因此,通过筛查早期发现至关重要。本研究旨在提出一种新的方法,以使用二维(2D)卷积神经网络(CNN)使用12导联心电图(ECG)波形增强LVH检测。
方法:利用42,127对心电图经胸超声心动图数据,我们将原始数据预处理为来自每个ECG导联的单次拍摄图像,并进行导联选择以优化LVH诊断.我们提出的一次性筛选方法,在预处理期间实施,允许将任何长度的波形源数据叠加到单帧图像上,从而解决了一维(1D)方法的局限性。我们开发了具有2D-CNN结构的深度学习模型和用于LVH检测的机器学习模型。为了评估我们的方法,我们还将我们的结果与常规ECG标准以及使用1D-CNN方法的先前研究的结果进行了比较,利用东京大学医院的相同数据集进行LVH诊断。
结果:对于LVH检测,2D-CNN模型的受试者工作特征曲线下平均面积(AUROC)为0.916,显着高于使用逻辑回归和随机森林方法获得的结果,以及两个常规ECG标准(AUROC分别为0.766、0.790、0.599和0.622)。合并其他元数据,如心电图测量数据,进一步将平均AUROC提高到0.921。该模型的性能在两个不同的注释标准中保持稳定,并显示出优于先前研究中使用的1D-CNN模型的性能的显着优势(AUROC为0.807)。
结论:本研究引入了一种稳健且计算效率高的方法,该方法优于先前研究中用于LVH检测的1D-CNN模型。我们的方法可以将任何长度的波形转换为固定大小的图像,并利用选定的ECG导联,确保在计算资源有限的环境中的适应性。所提出的方法有望作为早期诊断的工具融入临床实践。通过促进早期治疗和管理可能会提高患者的预后。
OBJECTIVE: Left ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN).
METHODS: Utilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis.
RESULTS: For LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model\'s performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807).
CONCLUSIONS: This study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.