Performance Evaluation and Metrics for Seizure Prediction Models

EasyChair Preprint 14062

Abstract

Seizure prediction models play a crucial role in managing and improving the quality of life for individuals with epilepsy. However, the effectiveness of these models heavily relies on accurate performance evaluation and the use of appropriate metrics. This abstract provides an overview of performance evaluation and metrics for seizure prediction models.

The abstract begins by highlighting the importance of performance evaluation and metrics in the context of seizure prediction models. It emphasizes the need for reliable measures to assess the predictive capabilities of these models and guide their development.

The abstract then introduces various performance evaluation metrics commonly used in the assessment of seizure prediction models. Metrics such as true positive, true negative, false positive, false negative, sensitivity, specificity, accuracy, precision, and F1 score are discussed. These metrics provide quantitative measures of the model's performance in predicting seizures and non-seizures, allowing for a comprehensive evaluation.

Keyphrases: Hyperparameter Tuning, Overfitting, Underfitting, bias-variance tradeoff, cross-validation, model complexity, performance evaluation, regularization

@booklet{EasyChair:14062,
  author    = {Docas Akinyele},
  title     = {Performance Evaluation and Metrics for Seizure Prediction Models},
  howpublished = {EasyChair Preprint 14062},
  year      = {EasyChair, 2024}}