- Validation of the effect of maxblur pooling method for improving temporal shift-invariance of Convolutional Neural Network (CNN) in bio-signals classification tasks.
- About maxblur pooling, please refer to Making Convolutional Networks Shift-Invariant Again
- Paper
- Best Paper Award in Biosignals2020
Max Pooling | Maxblur Pooling |
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- We used MIT-BIH Atrial fibrillation (AF) Dataset
- The dataset contains 23 records of 10 hour ECG with heart beat annotation and AF annotation
- 100 RRI as a Segment
- Do 2-class classification Task (Normal / AF)
- Accuracy : classification accuracy on test data
- Consistency : how often the model predict the same label given 2 different shift to the same input
Data Augmentation | 1-Layer | 2-Layer | 3-Layer |
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Yes | ![]() |
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No | ![]() |
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- Accuracy : classification accuracy on test data
- Robustness : classification accuracy on crashed test data
Data Augmentation | 1-Layer | 2-Layer | 3-Layer |
---|---|---|---|
Yes | ![]() |
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No | ![]() |
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filter size | baseline = max | baseline = avg |
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7 | ![]() |
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