Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning

Hagar Gelbard-Sagiv, Snir Pardo, Nir Getter, Miriam Guendelman, Felix Benninger, Dror Kraus, Oren Shriki, Shay Ben-Sasson

Research output: Contribution to journalArticlepeer-review

Abstract

Epilepsy, a prevalent neurological disorder, profoundly affects patients’ quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.

Original languageEnglish
Article number5805
JournalSensors
Volume23
Issue number13
DOIs
StatePublished - 1 Jul 2023

Keywords

  • computational efficient
  • continuous EEG monitoring
  • electrode configuration optimization
  • machine learning
  • metric adjustment
  • seizure detection
  • wearable EEG

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry

Fingerprint

Dive into the research topics of 'Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning'. Together they form a unique fingerprint.

Cite this