SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms (2402.13005v3)

Abstract: The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

• The paper introduces SzCORE, a framework that standardizes EEG-based seizure detection evaluation using uniform data formats and benchmarking protocols.
• It implements rigorous evaluation methodologies through cross-validation for both personalized and subject-independent models, ensuring objective performance metrics.
• The framework’s open-source library and standardized benchmark promote transparency and reproducibility, fostering community engagement in seizure management research.

Comprehensive Evaluation Framework for EEG-Based Seizure Detection: Presenting SzCORE

Introduction to SzCORE

The SzCORE framework aims to standardize the validation of EEG-based automated seizure detection algorithms. This novel framework introduces a structured approach for comparing the performance of these algorithms across different datasets. By addressing the current lack of common research practices in dataset standardization, evaluation methodology, and performance metric uniformity, SzCORE proposes solutions that could significantly enhance research efficiency and algorithmic accuracy in this crucial area of medical technology.

EEG Datasets and Data Format

A critical part of SzCORE is the establishment of standards for EEG datasets, enforceable through specified recording requirements and uniform data formats. This involves:

• Adhering to international recording standards for EEG data, ensuring compatibility and comparability across studies.
• Standardizing EEG data storage, particularly advocating for the use of .edf files arranged according to the 10-20 system.
• Proposing a universal seizure annotation format, achieved by aligning with BIDS-EEG specifications and the HED-SCORE nomenclature.

Evaluation Methodology

SzCORE distinguishes between personalized and subject-independent models for evaluation purposes. The framework emphasizes the importance of:

• Utilizing cross-validation techniques to ascertain the performance of algorithms with an emphasis on the independence of training and test datasets.
• Applying time-series cross-validation for personalized models, ensuring temporal separation between training and evaluation data.
• Leveraging leave-one-subject-out or k-fold cross-validation for subject-independent models, maintaining subject independence and avoiding overestimation of model performance.

Performance Metrics

The framework introduces two sets of scoring methodologies: sample-based and event-based scoring. Key considerations include:

• Transparency in comparing algorithm effectiveness by employing universally understood and clinically relevant metrics such as sensitivity, precision, and F1-score.
• Addressing the clinical community's needs through event-based scoring while catering to the machine learning community's requirement for detailed sample-based analysis.

Benchmarking and Open-source Library

The 10-20 EEG seizure detection benchmark, as defined by SzCORE, outlines the datasets, tasks, and evaluation metrics for algorithm comparison. Supporting this benchmark, an open-source library facilitates conversion of EEG data and annotations to the standardized format besides computing algorithm performance using the prescribed metrics.

Discussion on Framework Implications

SzCORE offers a robust solution to the fragmented landscape of EEG-based seizure detection algorithm evaluation. By advocating for standard data formats, evaluation methodologies, and performance metrics, the framework provides a common ground for future research. The setup paves the way for more reproducible, transparent, and comparable studies in this domain. Crucially, the proposed benchmark encourages continuous community engagement and algorithmic improvement through a dedicated online platform.

Future Prospects

Although SzCORE is positioned to significantly impact the field of automated seizure detection, its flexibility allows for future expansion to include novel EEG recording techniques and broader neurological monitoring applications. The active participation of the research community in refining and expanding the framework could foster advancements in both the technological and clinical aspects of seizure management.

Closing Thoughts

The introduction of the SzCORE framework represents a significant step towards standardizing the evaluation of EEG-based seizure detection algorithms. By fostering an environment of transparency, comparability, and reproducibility, SzCORE not only aids the scientific community in advancing research but also contributes to the broader goal of enhancing patient care for individuals with epilepsy.