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GillesVandewiele / EHG-Oversampling

Reproducing feature engineering & oversampling experiments on TPEHG DB and assessing the real impact of over-sampling
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Ahmed et al. "A multivariate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis" #22

Closed GillesVandewiele closed 4 years ago

GillesVandewiele commented 4 years ago

Reported result

Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99.

Features

Then both MMFE (Fuzzy Entropy) and MMSE (Sample Entropy) analyses were performed on each one-min epoch (which had 60 × 20 = 1200 samples) and afterwards averaged over the 27 epochs to produce the MMFE or MMSE curves for each record. In this multiscale study, we considered 10 scales for each epoch, so that the coarse graining process of MMFE/MMSE analysis yielded only 120 samples at the highest scale, which however was sufficient for MFSampEn calculation. These MSampEn or MFSampEn values calculated on 10 different coarse-graining scales were used as features in classification stage

Model

Guassian (??) SVM? Is this RBF?

Oversampling

In this study, to solve the class skew problem, the Adaptive Synthetic Sampling (ADASYN) [44,45] technique was used.

Comments

We have no Fuzzy Entropy, but I think it is enough to only consider Sample Entropy

GillesVandewiele commented 4 years ago

For both MSE and MMSE, as the scales are generated using the so-called coarse graining procedure, this reduces the input data length by the scale factor, thereby imposing a limit on the length of input data which can be effectively processed via MSE or MMSE.