Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography

脑磁图 脑功能偏侧化 支持向量机 颞叶 癫痫 脑电图 静息状态功能磁共振成像 心理学 物理 听力学 模式识别(心理学) 人工智能 核磁共振 神经科学 计算机科学 医学
作者
Bhargava Gautham,Joydeep Mukherjee,N. Mariyappa,Raghavendra Kenchaiah,Ravindranadh Chowdary Mundlamuri,Ajay Asranna,Viswanathan G. Lakshminarayanapuram,Rose Dawn Bharath,Jitender Saini,Chandana Nagaraj,Sandhya Mangalore,Karthik Kulanthaivelu,Nishanth Sadashiva,Anita Mahadevan,R. Jamuna,Keshav Kumar,Arivazaghan Arimappamagan,Bhaskara Rao Malla,Sanjib Sinha
出处
期刊:Biomedical Signal Processing and Control [Elsevier]
卷期号:72: 103294-103294 被引量:2
标识
DOI:10.1016/j.bspc.2021.103294
摘要

Lateralization of seizure focus in temporal lobe epilepsy (TLE) is a prime step in pre-surgical evaluation requiring prolonged seizure monitoring using video EEG and manual inspection of recordings. This study uses phase amplitude coupling (PAC) in resting state magnetoencephalography to automatically lateralize TLE focus. Fifty-four patients with drug resistant TLE and 21 healthy controls who underwent MEG were considered for the study. Classification was carried out for PAC calculated for source transformed resting state of controls vs left TLE (LTLE)/right TLE (RTLE) and LTLE vs RTLE between beta, low-gamma and high-gamma as high frequency (HF) bands and low frequency (LF) 1–13 Hz, with decision tree (DT), support vector machines (SVM) and naïve Bayes with feature selection by chi-square test. Further, lateralization classification was also calculated with LF sub-bands (delta, theta, alpha). PAC was higher in the TLE compared to controls. LTLE and RTLE showed differences in low gamma-alpha and high gamma-delta coupling (p < 0.05). Accuracy was highest with SVM between controls and LTLE in the low gamma-LF (92.92%, AUC-1), between controls and RTLE in DT and SVM (93.54%, AUC-0.97, 1) in the low gamma-LF band and in low gamma-delta band in SVM (92.04%, AUC-1) between LTLE and RTLE. PAC shows distinct patterns of coupling in each subject group. Feature selection showed involvement of major network hubs and resting state networks. SVM showed best classification potential in the low gamma band. PAC in resting state MEG can supplement pre-surgical evaluation in drug resistant TLE.
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