An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

Abstract

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.

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

“…EEG classification, feature extraction, and artifact removal have been considered vital steps of signal processing analysis for a robust EEG‐BCI system (Mudgal et al, 2020; Osalusi et al, 2018). Much research has been done on the EEG classification using signal processing, Machine Learning (ML), Deep Learning (DL), meta‐learning, and Transfer Learning (TL) architectures for Automatic Epilepsy Diagnosis (AED) (Abdulhay et al, 2020; Hassan et al, 2020; Kemal Kiymik et al, 2004; Rajendra Acharya et al, 2018; Subasi, 2007; Yao & Cui, 2020).…”