Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation

Figures

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  Figure 1. Plots of ECG signals for (a) cardiac arrhythmia (ARR), (b) congestive heart failure (CHF), and (c) normal sinus rhythm (NSR), respectively.

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  Figure 2. Spectrograms of the ECG signals for cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR), respectively.

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  Figure 3. Scalograms of the ECG signals for cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR), respectively.

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  Figure 4. A block diagram representation of the arrythmia detection process using ECG signals.

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  Figure 5. Plots of (a) accuracy and (b) loss function for the training and validation data for the proposed small CNN.

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  Figure 6. Plots of (a) accuracy and (b) loss function for the training and validation data for the GoogLeNet model.

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  Figure 7. Visualization of activation patterns for first convolution layer weights in (a) GoogLeNet and (b) proposed SmallNet.

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  Figure 8. Visualization of activation patterns for first convolution layer weights in (a) GoogLeNet and (b) proposed SmallNet.

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  Figure 9. Visualization of strongest activation channels for ARR classification in proposed SmallNet and GoogLeNet.

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  Figure 10. Confusion matrices for training data—comparative analysis of GoogLeNet and proposed SmallNet in cardiac arrhythmia classification.

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  Figure 11. Confusion matrices for testing data—comparative analysis of GoogLeNet and proposed SmallNet in cardiac arrhythmia classification.

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  Figure 12. Comparison of ROC curves for the proposed small CNN and GoogLeNet.