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An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning.
Computational and Mathematical Methods in Medicine ( IF 2.809 ) Pub Date : 2020-08-03 , DOI: 10.1155/2020/5046315
Yufeng Yao 1, 2 , Zhiming Cui 3
Affiliation  

Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients’ health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.

中文翻译:

一种基于改进归纳转移学习的癫痫自动检测方法。

癫痫病是由脑神经元突然异常放电引起的暂时性脑功能障碍引起的一种慢性疾病。癫痫发作具有突发性和反复性的特点,严重危害患者的健康,认知等。在目前情况下,脑电图在临床诊断中对癫痫的诊断,判断和定性定位起着至关重要的作用。是各种癫痫发作的一种不可缺少的检测手段。对癫痫患者脑电信号的研究可以为深入了解其发病机理提供强有力的基础和有用的信息。虽然,基于机器学习的智能分类技术已被广泛用于癫痫脑电信号的分类,并显示出其有效性。事实上,很难确保始终有足够的EEG数据可用于现实生活中的模型训练,这将影响算法的性能。有鉴于此,为了减少数据不足对算法检测性能的影响,在DLSR和归纳转移学习的基础上,提出了一种新的基于区分最小二乘回归的归纳转移学习方法。 。并且,它被用于促进癫痫脑电信号识别的适应性和准确性。该方法继承了DLSR的优点。通过扩展不同类之间的间隔,它可能更适合分类方案。同时,它可以同时使用目标域的数据和源域的知识,这有助于获得更好的性能。结果表明,与其他几种代表性方法相比,改进后的方法在脑电信号识别中具有更多的优势。
更新日期:2020-08-03
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