School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India.
School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India.
Comput Methods Programs Biomed. 2021 Sep;209:106335. doi: 10.1016/j.cmpb.2021.106335. Epub 2021 Aug 5.
Epilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework.
In this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture.
The automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures.
Assessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.
据世界卫生组织估计，癫痫是一种中枢神经系统疾病，全球约有 5000 万人患有这种疾病。脑电图（EEG）是分析和研究癫痫发作时大脑神经元活动细微变化的最常见和非侵入性方法之一。可以对这些变化进行分析，开发出一种自动化系统，以确定即将发生的癫痫发作的可能性。由于癫痫发作的多变性影响了患者的正常生活，因此开发新方法的进展将提高生活质量，并为医疗领域提供帮助。该方法的目的是避免脑电图通道选择，并使用所有输入脑电图通道特征来设计一个通用的癫痫发作检测框架。
在这项工作中，提出了一种长短期记忆网络，该网络不复杂，具有从非侵入性和侵入性脑电图记录中有效检测癫痫发作的能力。所提出的框架简单有效，设计能力强，可以使用原始脑电图信号检测癫痫发作。此外，还采用了一种通用方法，该方法与通道无关，使得可以通过所提出的架构有效地检测大脑任何半球的 EEG 信号。
该自动癫痫检测系统在波士顿儿童医院-麻省理工学院数据集上实现了 99.9%的高癫痫检测灵敏度和 0.003 次/小时的低假阳性率。而在伯尔尼大学神经内科睡眠-觉醒-癫痫中心数据集上，灵敏度为 99.4%，假阳性率为 0.006 次/小时。所提出模型的收敛分析为高效检测癫痫发作提供了大量的可靠性和正确性。
在非侵入性和侵入性 EEG 信号上评估所提出的框架表明，该框架适用于不同类型的 EEG 记录，因为不同的指标给出了令人满意的结果。由于该框架简单，不需要任何额外的参数优化技术，因此可以减少处理开销而不影响准确性。因此，它可以作为一种有效的监测癫痫发作的方法。
