NeuroHelp Ltd., Ramat-Gan 5252181, Israel.
Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
Sensors (Basel). 2023 Jun 21;23(13):5805. doi: 10.3390/s23135805.
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.
癫痫是一种常见的神经障碍疾病，由于癫痫发作的不可预测性，极大地影响了患者的生活质量。开发一种可靠且易于使用的可穿戴脑电图系统，能够检测和预测癫痫发作，有可能彻底改变癫痫护理。然而，优化这种系统的电极配置（对于平衡准确性和实用性至关重要）仍有待探索。本研究通过开发一种优化癫痫检测机器学习算法的电极配置的系统方法来解决这一差距。我们的方法应用于 158 名癫痫患者的长时间注释脑电图记录的广泛数据库。评估了从一个到十八个的多种电极配置，以确定最佳电极数量。结果表明，随着电极数量的减少，性能最初保持不变，但在大约八个电极时，性能下降。随后，使用计算密集型工作流程对所有八电极配置进行了全面分析，以确定最佳配置。这种方法可以为 EEG 系统的机械设计过程提供信息，在检测准确性、易用性和便携性之间取得平衡。此外，该框架还有可能优化其他机器学习应用中的硬件。该研究朝着开发高效的可穿戴脑电图系统以进行癫痫检测迈出了重要一步。
