Myocardial Infarction Detection On Ptb
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| CNN | 93.5% | Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals | - |
| Deep residual CNN | 95.9% | ECG Heartbeat Classification: A Deep Transferable Representation | - |
| ConvNetQuake | 99.43% | Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms | - |
| T-wave + Total Integral | 94.7% | A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal | - |
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