A seizure detection method based on hypergraph features and machine learning. (August 2022)

• Record Type: Journal Article
• Authors: Gao, Xiang; Zhu, Yue; Yang, Yufang; Zhang, Fang; Zhou, Fan; Tian, Xiang; Xu, Kedi; Chen, Yaowu
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Seizure detection -- Hypergraph features -- Random forest -- Support vector machines (SVM) -- Magnitude-squared coherence (MSC); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103769
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Magnitude-squared coherence method was used to find the frequency band and channels. Hypergraph feature coefficients were used to distinguish different signals. Machine learning methods were used to classify the seizure signals. Individually adjust parameters for different patients adaptively. Abstract: Effective feature extraction is the key to successful seizure detection. A good feature should be adaptable, noise-resistant, and informative. In this study, a seizure detection method based on hypergraph features and machine learning was proposed. During the preprocessing step, the magnitude-squared coherence (MSC) method was used to select the useful channels and frequency band. The hypergraph method was introduced to represent the correlation network based on the MSC result. Hypergraphs can show relationships between channels, and they have higher dimensional properties than normal graphs. To quantify the characteristics of these channels, three feature coefficients, which are HG-Independent(HG-Ind), HG-Dependent(HG-Dep), and HG-Share(HG-Sha), were derived and input as features into the machine learning algorithm. Two machine learning algorithms, random forest (RF) and support vector machine (SVM), were tested on the data of eight patients in our study. According to the results, RF had a better performance than SVM, and the group of three features performed better than the single feature. Three additional methods from other papers, extra-trees, random forest based on spectral correlation, and neural networks, were compared with the HG-RF, and the HG-RF had the best performance, with an accuracy of 96.30 % . This method showed resistance to noise and adaptability for different patient data. In addition, this method facilitated a balance between effectiveness and efficiency and provided a new idea for online detection of epilepsy.