Yoon Taeyoung, Kang Daesung
Department of Healthcare Information Technology, Inje University, 197, Inje-ro, Gimhae-si 50834, Republic of Korea.
J Pers Med. 2023 Feb 20;13(2):373. doi: 10.3390/jpm13020373.
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs.
Experiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People's Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensemble method. Logistic regression, support vector machine, random forest, and XGBoost were used as a meta learner by combining the predictions of the base learner. The study introduced a method called multi-modal stacking ensemble, which involves training a meta learner through a stacking ensemble that combines predictions from two modalities: scalogram images and ECG grayscale images.
The multi-modal stacking ensemble with a combination of ResNet-50 and logistic regression achieved an AUC of 0.995, an accuracy of 93.97%, a sensitivity of 0.940, a precision of 0.937, and an F1-score of 0.936, which are higher than those of LSTM, BiLSTM, individual base learners, simple averaging ensemble, and single-modal stacking ensemble methods.
The proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs.
心血管疾病（CVDs）是全球主要的死亡原因。深度学习方法已广泛应用于医学图像分析领域，并在心血管疾病的诊断中显示出有前景的结果。
对查普曼大学和绍兴市人民医院收集的12导联心电图（ECG）数据库进行实验。将每个导联的ECG信号转换为小波尺度图图像和ECG灰度图像，并用于微调每个导联的预训练ResNet-50模型。ResNet-50模型用作堆叠集成方法的基础学习器。通过结合基础学习器的预测结果，将逻辑回归、支持向量机、随机森林和XGBoost用作元学习器。该研究引入了一种称为多模态堆叠集成的方法，该方法通过堆叠集成来训练元学习器，该堆叠集成结合了来自两种模态的预测结果：小波尺度图图像和ECG灰度图像。
ResNet-50和逻辑回归相结合的多模态堆叠集成方法的AUC为0.995，准确率为93.97%，灵敏度为0.940，精确率为0.937，F1分数为0.936，高于长短期记忆网络（LSTM）、双向长短期记忆网络（BiLSTM）、单个基础学习器、简单平均集成和单模态堆叠集成方法。
所提出的多模态堆叠集成方法在诊断心血管疾病方面显示出有效性。
