I am working with ECGs and trying to use a CNN model to perform binary classification. The goal is to classify 30s ECGs to detect a specific disease. I am using CNN and converting ECGs to images (scalograms). Unfortunately, the amout of with the specific disease are not enough thus I am plannign to split the 30s ecgs to multiple 10s ecgs (overlapping) becuase I have a larger dataset with 10s ecgs thus I can train my model.
I have trained my model on a dataset and tested on a different dataset that has 10s long ecgs. The model perfomed well. Eventhough, that the 10s dataset has a ratio of 1:9, I have created a balance training dataset and then tested on the testing dataset. The model had a good F1 score.
However, when I tried to classify 30s ecgs using the method described above most of the data are classifies as label 1 (diseas detected) and almost none as label 0.
PS: I convert the ecgs to RGB images (scalograms), which give information about the time-frequency domain PS 2: My dataset is about 90% of images of label 0 (both 10s dataset and 30s dataset) however, when tested on the 30s dataset most of the data are classified as label 1. Need to mention that when trained I carefully choosed data to create a balanced dataset for training.
Does anyone has any suggestions?
Thank you in advance.