Week 5: Understanding the Statistics Behind Seizure Prediction

Besides working on the introduction, literature review, and methodology, this week I worked on understanding the statistical side of seizure prediction in order to understand how researchers have evaluated and sampled seizure prediction in the past.

One of the most interesting patterns I found in literature this week was how a method called Leave-One-Subject-Out Cross-Validation (LOSO-CV) was commonly utilized. Instead of using the typical deep learning method of setting aside a certain percentage of the samples as test sets, LOSO-CV proposes (like the name suggests) leaving the data from one subject completely out and setting it aside as a test set. Then, this process is repeated until every subject has been set aside as a test set. LOSO-CV allows researchers to more accurately evaluate how well a model will perform with unseen data.

Many research papers in seizure prediction contained the same evaluation methods, including sensitivity, false prediction rate (FPR), and area under the receiver operating characteristic curve (AUC). Sensitivity assesses how well a model makes a correct prediction. FPR is a measure of how often the model makes a false prediction. In EEG research, FPR is often reported in false positives per hour instead of simply a fraction. Finally, AUC describes the balance between sensitivity and FPR. The receiver operating characteristic curve plots sensitivity versus FPR. Thus, the area under that curve reports how well a classification model can distinguish between categories overall.