Analysis of the distribution of machine learning algorithms

This dataset comprises a comprehensive analysis of state-of-the-art techniques and systems for seizure detection and classification, based on various papers and studies. It integrates detailed metadata on publications, including their year, methodologies, seizure types (both ILAE-2017 and paper-specific), datasets, and biomarker utilization. The dataset also provides performance metrics such as accuracy, sensitivity, specificity, false-positive rates, and AUC-ROC values, alongside additional technical details about machine learning models, feature extraction techniques, and biomarkers.

Key highlights:

1. Publication Metadata: Papers range across years, detailing types (e.g., detection systems) and seizure classifications.
2. Dataset Insights: Analysis includes commonly used datasets like CHB-MIT, SWEC, and TUSZ/TUH, alongside proprietary datasets.
3. Machine Learning and Feature Extraction: Techniques such as CNN, RF, and PCA are documented with their roles in seizure detection.
4. Performance Metrics: Metrics for classification efficacy and false-positive occurrences underline the system reliability.
5. Supplementary Descriptions: Acronyms and key terminologies are detailed to provide clarity on the methodologies and outcomes.

This dataset serves as a valuable resource for understanding trends in seizure detection research, offering insights into the intersection of biomedical signals, machine learning, and feature extraction techniques. It is well-suited for meta-analyses, benchmarking studies, and the development of future seizure detection systems.