The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.
Department of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China.
Comput Math Methods Med. 2020 Aug 3;2020:5046315. doi: 10.1155/2020/5046315. eCollection 2020.
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.
癫痫是一种由大脑神经元突然异常放电引起的慢性疾病，导致短暂的大脑功能障碍。癫痫发作具有突发性和重复性的特点，严重危害患者的健康、认知等。在当前情况下，脑电图在各种癫痫发作的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，是必不可少的检测手段。对癫痫患者的脑电图信号进行研究，可以为深入了解其发病机制提供有力的依据和有用的信息。尽管基于机器学习的智能分类技术已广泛应用于癫痫脑电图信号的分类，并显示出其有效性。但在实际生活中，很难保证总是有足够的脑电图数据可用于训练模型，这会影响算法的性能。鉴于此，为了降低数据不足对算法检测性能的影响，引入了一种基于判别最小二乘回归 (DLSR) 的归纳迁移学习方法 (DLSR-based inductive transfer learning method)，它是在 DLSR 和归纳迁移学习的基础上提出的。并应用于提高癫痫 EEG 信号识别的适应性和准确性。所提出的方法继承了 DLSR 的优点；通过扩展不同类之间的间隔，它可以更适合分类场景。同时，它可以同时利用目标域的数据和源域的知识，有助于获得更好的性能。实验结果表明，与其他几种代表性方法相比，改进后的方法在 EEG 信号识别方面具有更多优势。
