Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography

Zilin Li; Baotian Zhao; Wenhan Hu; Chao Zhang; Xiu Wang; Jianguo Zhang; Kai Zhang

doi:10.1016/j.seizure.2023.11.005

Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography

Seizure. 2023 Dec:113:58-65. doi: 10.1016/j.seizure.2023.11.005. Epub 2023 Nov 10.

Authors

Zilin Li¹, Baotian Zhao¹, Wenhan Hu², Chao Zhang¹, Xiu Wang¹, Jianguo Zhang², Kai Zhang³

Affiliations

¹ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
² Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
³ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China. Electronic address: zhangkai62035@sina.com.

PMID: 37984126
DOI: 10.1016/j.seizure.2023.11.005

Abstract

Objective: High-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains.

Methods: HFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital.

Results: A dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features.

Conclusion: Our classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.

Keywords: Epilepsy surgery; Epileptogenic zone; High-frequency oscillations; Machine learning; Stereoelectroencephalography.

MeSH terms

Brain
Brain Waves* / physiology
Electroencephalography / methods
Epilepsies, Partial*
Humans
Machine Learning