Multilayer weighted integrated self‐learning algorithm for automatic diagnosis of epileptic electroencephalogram signals. (14th October 2020)

• Record Type: Journal Article
• Authors: Zhao, Jian; Zhao, Di; Shi, Lijuan; Kuang, Zhejun; Jing, Weipeng; Wang, Huihui
• Other Names: Lv Zhihan guestEditor.; Lloret Jaime guestEditor.; Song Houbing guestEditor.
• Is Part Of: Computational intelligence. Volume 38:Number 1(2022)
• Page Start: 3
• Page End: 19
• Publication Date: 2020-10-14
• Subjects: automatic epilepsy diagnosis -- EEG -- machine learning -- semi‐supervised learning; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12414
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Epilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95.