Comparison of 2D-CNN, LSTM, and GRU for cardiovascular disease diagnosis

Yoon, Taeyoung; Yeo, Chaeyun; Lee, Heechang; Kim, Siwoo; Ji, Yebin; Oh, HyeonYoung; Kang, Daesung

doi:10.1109/icce-asia57006.2022.9954699

Cited by 1 publication

(3 citation statements)

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“…Convolutional neural networks (CNN) are also widely used for diagnosing CVDs [ 10 , 11 , 12 , 13 , 14 , 15 ]. The one-dimensional CNN (1D CNN) model exploits the one-dimensional (1D) structure of the signal, so it can be used on these ECG data without transformation.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, there have been numerous studies conducted to detect CVDs using two-dimensional CNN (2D CNN) with ECG signals [ 14 , 15 , 16 , 17 , 18 ]. In order to apply a 1D ECG signal to a 2D CNN, the 1D signal needs to be transformed into a two-dimensional (2D) image.…”

Section: Introductionmentioning

confidence: 99%

“…As another method, we can convert the 1D ECG signal into a spectrogram through a short-time Fourier transform (STFT) or a scalogram using wavelet transform. Yildirim et al fine-tuned 2D CNN models (AlexNet, VGGNet, ResNet, and DenseNet) with spectrogram images to identify diabetes mellitus and Yoon et al applied a pretrained ResNet-50 model to the ECG scalograms to classify four rhythms [ 14 , 15 ]. We refer to the converted scalograms as scalogram images.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases

Yoon

Kang

2023

JPM

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Background: 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. Methods: 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. Results: 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. Conclusion: The proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs.

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