End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.2. Methods
2.2.1. ECG Image Generation
2.2.2. CNN Model Construction and Classification
3. Experiments
3.1. Experimental Setup
- Setting 1:
- Setting 2:
3.2. Evaluation Indices
4. Experimental Results
4.1. Classification Results for Setting 1
4.1.1. MI Detection Results
4.1.2. MI Localization Results
4.2. Classification Results for Setting 2
4.2.1. MI Detection Results
4.2.2. MI Localization Results
5. Discussion
5.1. Results of Setting 1
5.2. Results of Setting 2
5.3. Study Advantages and Limitations
- The proposed model does not require complicated preprocessing, e.g., noise reduction, trend removal, beat segmentation, and feature selection.
- With the proposed model, it is possible to detect and localize MI by comprehensively checking the characteristics of the ECG images for each lead (similar to the diagnoses of medical professionals).
- It is possible to misclassify ECG images with extremely strong noise and trends.
- It is possible to misclassify ECG images with multiple beats or ECG images with most of the beats missing.
5.4. Discussion for Practical Application of the Model
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Class (Abbreviation) | Number of Subjects | Number of ECG Data | Number of ECG Image Sets |
---|---|---|---|
Normal (N) | 51 | 74 | 4837 |
Anterior (A) | 17 | 47 | 2812 |
Anterior–Lateral (AL) | 14 | 39 | 2580 |
Anterior–Septal (AS) | 27 | 77 | 4620 |
Inferior (I) | 30 | 87 | 5268 |
Inferior–Lateral (IL) | 23 | 55 | 3315 |
Inferior–Posterior (IP) | 1 | 1 | 38 |
Inferior–Posterior–Lateral (IPL) | 8 | 19 | 1118 |
Lateral (L) | 1 | 3 | 180 |
Posterior (P) | 1 | 4 | 240 |
Posterior–Lateral (PL) | 2 | 5 | 300 |
Total | 175 | 411 | 25,308 |
Layer | Number of Input Nodes | Number of ECG Output Nodes | Kernel Size | Batch Normalization | Activation Function |
---|---|---|---|---|---|
Convolution 1 | True | ReLU | |||
Convolution 2 | Ture | ReLU | |||
Pooling 1 | False | - | |||
Convolution 3 | True | ReLU | |||
Convolution 4 | True | ReLU | |||
Pooling 2 | False | - | |||
Convolution 5 | True | ReLU | |||
Convolution 6 | True | ReLU | |||
Pooling 3 | False | - | |||
The flattened vectors of the 12 leads are concatenated | |||||
Fully connected 1 | 3456 | 2048 | - | True | ReLU |
Fully connected 2 | 2048 | 1024 | - | True | ReLU |
Fully connected 3 | 1024 | 11 | - | False | SoftMax |
Loss function | Cross-entropy loss | ||||
Optimizer | Adam |
Predicted Class | |||
---|---|---|---|
N | MI | ||
True Class | N | 4822 | 15 |
MI | 31 | 20,440 |
Index | Score |
---|---|
Sensitivity | 0.9985 |
Specificity | 0.9969 |
Accuracy | 0.9982 |
Predicted Class | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
N | A | AL | AS | I | IL | IP | IPL | L | P | PL | Accuracy | ||
True class | N | 4818 | 1 | 0 | 5 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0.9961 |
A | 2 | 2782 | 3 | 15 | 7 | 3 | 0 | 0 | 0 | 0 | 0 | 0.9893 | |
AL | 3 | 5 | 2551 | 17 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0.9888 | |
AS | 3 | 11 | 4 | 4590 | 9 | 1 | 0 | 1 | 0 | 1 | 0 | 0.9935 | |
I | 6 | 5 | 0 | 5 | 5243 | 6 | 0 | 2 | 1 | 0 | 0 | 0.9953 | |
IL | 1 | 1 | 3 | 1 | 14 | 3286 | 0 | 9 | 0 | 0 | 0 | 0.9913 | |
IP | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 1.0000 | |
IPL | 0 | 0 | 1 | 1 | 4 | 6 | 0 | 1106 | 0 | 0 | 0 | 0.9893 | |
L | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 180 | 0 | 0 | 1.0000 | |
P | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 236 | 0 | 0.9833 | |
PL | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 297 | 0.9900 | |
Total | 0.9928 |
Predicted Class | |||
---|---|---|---|
N | MI | ||
True Class | N | 3718 | 1119 |
MI | 390 | 19,623 |
Index | Score |
---|---|
Sensitivity | 0.9805 |
Specificity | 0.7687 |
Accuracy | 0.9393 |
Predicted Class | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
N | A | AL | AS | I | IL | IPL | PL | Accuracy | ||
True class | N | 4012 | 24 | 21 | 85 | 515 | 132 | 26 | 22 | 0.8294 |
A | 91 | 1524 | 646 | 329 | 177 | 45 | 0 | 0 | 0.5420 | |
AL | 79 | 311 | 1692 | 329 | 152 | 16 | 1 | 0 | 0.6558 | |
AS | 156 | 140 | 731 | 3239 | 207 | 145 | 1 | 1 | 0.7011 | |
I | 471 | 158 | 167 | 75 | 3876 | 429 | 90 | 2 | 0.7358 | |
IL | 118 | 64 | 10 | 113 | 790 | 2098 | 92 | 30 | 0.6329 | |
IPL | 16 | 3 | 8 | 0 | 190 | 256 | 622 | 23 | 0.5564 | |
PL | 13 | 0 | 9 | 4 | 59 | 59 | 6 | 150 | 0.5000 | |
Total | 0.6927 |
Author (Year) | Methods | MI Detection Results | MI Localization Results |
---|---|---|---|
Arif et al., 2012 [10] | k-NN | Sensitivity = 99.97% | Accuracy = 98.8% |
Specificity = 99.9% | |||
Safdarian et al., 2014 [11] | • Probabilistic Neural Network (PNN) | Accuracy = 94% | Accuracy = 76% |
• k-NN | |||
• Multilayer Perceptron (MLP) | |||
• Naive Bayes Classification | |||
Sharma et al., 2015 [12] | • SVM-Lin | Accuracy = 96% | Accuracy = 99.58% |
• SVM-RBF | Sensitivity = 93% | ||
• k-NN | Specificity = 99% | ||
Acharya et al., 2016 [13] | k-NN | Accuracy = 98.8% | Accuracy = 98.74% |
Sensitivity = 99.45% | Sensitivity = 99.55% | ||
Specificity = 96.27% | Specificity = 99.16% | ||
Baloglu et al., 2019 [14] | Deep CNN | N/A | Accuracy = 99.78% |
Sugimoto et al., 2019 [15] | • Convolutional autoencoder • k-NN | Accuracy = 99.87% | Accuracy = 99.88% |
Sensitivity = 99.91% | Sensitivity = 99.12% | ||
Specificity = 99.59% | Specificity = 99.92% | ||
Cao et al., 2022 [17] | • SENet • Grad-CAM | Accuracy = 99.98% | Accuracy = 99.79% |
Sensitivity = 99.94% | Sensitivity = 99.88% | ||
Specificity = 99.94% | Specificity = 99.98% | ||
Proposed model | CNN | Accuracy = 99.82% | Accuracy = 99.28% |
Sensitivity = 99.85% | Sensitivity = 99.21% | ||
Specificity = 99.69% | Specificity = 99.61% |
Author (Year) | Methods | MI Detection Results | MI Localization Results |
---|---|---|---|
Fu et al., 2020 [30] | MLA-CNN-BiGRU | Accuracy = 96.50% | Accuracy = 62.94% |
Sensitivity = 97.10% | Sensitivity = 63.97% | ||
Specificity = 93.34% | Specificity = 63.00% | ||
Han et al., 2020 [31] | ML-ResNet | Accuracy = 95.49% | Accuracy = 55.74% |
Sensitivity = 94.85% | Sensitivity = 47.58% | ||
Specificity = 97.37% | Specificity = 55.37% | ||
Proposed model | CNN | Accuracy = 93.93% | Accuracy = 69.27% |
Sensitivity = 98.05% | Sensitivity = 65.96% | ||
Specificity = 76.87% | Specificity = 82.94% |
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Uchiyama, R.; Okada, Y.; Kakizaki, R.; Tomioka, S. End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing. Bioengineering 2022, 9, 430. https://doi.org/10.3390/bioengineering9090430
Uchiyama R, Okada Y, Kakizaki R, Tomioka S. End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing. Bioengineering. 2022; 9(9):430. https://doi.org/10.3390/bioengineering9090430
Chicago/Turabian StyleUchiyama, Ryunosuke, Yoshifumi Okada, Ryuya Kakizaki, and Sekito Tomioka. 2022. "End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing" Bioengineering 9, no. 9: 430. https://doi.org/10.3390/bioengineering9090430