An Application of Deep Learning Model to Specify Cardiovascular Diseases via Analyzing ECG Diagrams

* Reference for image: A. J. Huber, A. H. Leggett, and A. K. Miller, “Electrocardiogram: Blog chronicles value of old, but still vital cardiac test,” Scope, 19-Dec-2017. [Online]. Available:
https://scopeblog.stanford.edu/2016/09/21/electrocardiogram-blog-chronicles-value-of-old-but-still-vital-cardiac-test/. [Accessed: 24-May-2022].
Figure1: Segments In ECG Signal [12]
Figure2: Normal ECG vs. ECGs for CVDs [12]
Figure3: CNN model layers [3]
Figure4: Explanations for five categories [3]
Figure5: CNN-LSTM Architecture from Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets [5]
Figure6: ECG Samples For The Five Categories We Aim To Classify [3]
Figure7: Sample ECGs from MIT-BIH Arrhythmia database [8]
Figure8: The original input ECG diagram
Figure9: ECG after denoising and oversampling
Figure7: Visualization of the Pan-Tompkins Algorithms for R peak detection
Figure8: The layers in the CNN model Figure9: Visualization of CNN model [3]
Figure9: Visualization of CNN model [3]
Figure 10: The layers in the LSTM_CNN combination model

Methodology

Figure 11: Training loss for CNN on denoised dataset (0.006459 after 20 epochs)
Figure 12: Training accuracy for CNN on denoised dataset (99.93% after 20 epochs)
Figure 13: Training loss for CNN-LSTM on denoised dataset (0.014350 after 20 epochs)
Figure 14: Training accuracy for CNN-LSTM on denoised dataset (99.82% after 20 epochs)
Figure 15: Training loss for CNN on noisy dataset (0.006163 after 20 epochs)
Figure 16: Training accuracy for CNN on noisy dataset (99.94% after 20 epochs)
Figure 17: Training loss for CNN-LSTM on noisy dataset (0.013887 after 20 epochs)
Figure 18: Training accuracy for CNN-LSTM on noisy dataset (99.83% after 20 epochs)
Table 1: Reported accuracies for our CNN and CNN-LSTM models on normal and denoised train and test sets. To compare the effect of noises in the input dataset, we also include the results of inputs without denoising. The negligible change in accuracy after denoising indicates that the transformation had a negligible effect on model performance.
Table 2: Reported test accuracies for CNN and CNN-LSTM models on different arrhythmia classes.
Table 3: Comparison of average sensitivity, specificity, and positive predictive (PPV) values across different models.

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