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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Ročník 2023; s. 1 - 7
Hlavní autori: Pale, Una, Teijeiro, Tomas, Atienza, David
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2023
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC40787.2023.10340493