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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
xxxxx完成签到,获得积分0
1秒前
tong完成签到,获得积分10
1秒前
MFNM完成签到,获得积分10
1秒前
务实的数据线完成签到,获得积分10
1秒前
Xavier完成签到,获得积分10
2秒前
2秒前
何浏亮完成签到,获得积分10
3秒前
时年发布了新的文献求助10
3秒前
称心不尤完成签到 ,获得积分10
4秒前
苏满天完成签到,获得积分10
4秒前
6秒前
魁梧的笑阳完成签到 ,获得积分10
7秒前
qq158014169完成签到 ,获得积分10
7秒前
Luckyhai完成签到,获得积分10
7秒前
沙克几十块完成签到,获得积分10
8秒前
学茶小白完成签到,获得积分10
8秒前
8秒前
9秒前
JevonCheung完成签到 ,获得积分10
9秒前
人参跳芭蕾完成签到 ,获得积分10
9秒前
cc2713206完成签到,获得积分0
9秒前
sino-ft发布了新的文献求助10
10秒前
张文静完成签到,获得积分10
10秒前
10秒前
ccc应助豆浆来点蒜泥采纳,获得10
10秒前
ww完成签到,获得积分10
11秒前
11秒前
采纳慢直接拉黑完成签到 ,获得积分10
11秒前
青珊完成签到,获得积分10
12秒前
研友_Z1xNWn完成签到,获得积分10
13秒前
小揭发布了新的文献求助10
13秒前
lucky完成签到 ,获得积分10
14秒前
威威发布了新的文献求助10
14秒前
昭昭完成签到,获得积分10
14秒前
ttkd11完成签到,获得积分10
15秒前
致橡树给致橡树的求助进行了留言
16秒前
依稀过往间完成签到 ,获得积分10
16秒前
GAW完成签到,获得积分10
18秒前
blUe完成签到,获得积分10
18秒前
橘子树完成签到,获得积分10
18秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 800
Conference Record, IAS Annual Meeting 1977 610
Interest Rate Modeling. Volume 3: Products and Risk Management 600
Interest Rate Modeling. Volume 2: Term Structure Models 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3555935
求助须知:如何正确求助?哪些是违规求助? 3131542
关于积分的说明 9391519
捐赠科研通 2831325
什么是DOI,文献DOI怎么找? 1556415
邀请新用户注册赠送积分活动 726573
科研通“疑难数据库(出版商)”最低求助积分说明 715890