Importance of methodological choices in data manipulation for validating epileptic seizure detection models

Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life. Despite advances in machine learning and IoT, small, non-stigmatizing wearable devices for continuous monitoring and detection in outpatient environment...

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Veröffentlicht in:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Jg. 2023; S. 1 - 7
Hauptverfasser: Pale, Una, Teijeiro, Tomas, Atienza, David
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2023
Schlagworte:
comparability
cross-validation approaches
data selection
Electroencephalography - methods
Epilepsy
Epilepsy - diagnosis
Estimation
Humans
Machine learning
Methodological choices
Neurological diseases
performance metrics
reproducibility
Reproducibility of Results
seizure detection
Seizures - diagnosis
Sociology
Training
Wearable computers
Wearable Electronic Devices
ISSN:2694-0604, 2694-0604
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Zusammenfassung:Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life. Despite advances in machine learning and IoT, small, non-stigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not yet widely available. Part of the reason is the complexity of epilepsy itself, including highly imbalanced data, multimodal nature, and very subject-specific signatures. However, another problem is the heterogeneity of methodological approaches in research, leading to slower progress, difficulty in comparing results, and low reproducibility. Therefore, this article identifies a wide range of methodological decisions that must be made and reported when training and evaluating the performance of epilepsy detection systems. We characterize the influence of individual choices using a typical ensemble random-forest model and the publicly available CHB-MIT database, providing a broader picture of each decision and giving good-practice recommendations, based on our experience, where possible.
Bibliographie:ObjectType-Article-1
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ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC40787.2023.10340493