Al-Bakri Amir F, Villamar Mauricio F, Haddix Chase, Bensalem-Owen Meriem, Sunderam Sridhar
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2422-2425. doi: 10.1109/EMBC.2018.8512785.
There is resurgent interest in the role played by autonomic dysfunction in seizure generation. Advances in wearable sensors make it convenient to track many autonomic variables in patient populations. This study assesses peri-ictal changes in surrogate measures of autonomic activity for their predictive value in epilepsy patients. We simultaneously recorded fronto-central surface EEG and submental EMG to score vigilance state, intracranial EEG (iEEG) to compute several electrophysiological variables (EV), and measurements (heart rate, blood volume pulse, skin impedance, and skin temperature) relevant to autonomic function (AV) using a wrist-worn sensor from three patients. A naïve Bayes classifier was trained on these features and tested using five-fold cross- validation to determine whether preictal and interictal sleep (or wake) epochs could be distinguished from each other using either AV or EV features. Of 16 EV features, beta power, gamma power (30-45 Hz and 47-75 Hz), line length, and Teager energy showed significant differences for preictal versus interictal sleep (or wake) state in each patient (t test: $p<0.001$). At least one AV was significantly different in each patient for interictal and preictal sleep (or wake) segments ($p<0.001$). Using AV features, the classifier labeled preictal sleep epochs with 84% sensitivity, 79% specificity, and 64% kappa; and 78%, 80% and 55% respectively for preictal wake epochs. Using EV, the classifier labeled preictal sleep epochs with 69% sensitivity, 64% specificity, and 33% kappa; and 15%, 93% and 10% respectively for preictal wake epochs.
自主神经功能障碍在癫痫发作产生中所起的作用再次引起了人们的关注。可穿戴传感器的进步使得追踪患者群体中的许多自主神经变量变得方便。本研究评估了癫痫患者发作期周围自主神经活动替代指标的变化，以探讨其预测价值。我们同时记录了额中央表面脑电图和颏下肌电图以评估警觉状态，记录颅内脑电图（iEEG）以计算几个电生理变量（EV），并使用腕部佩戴的传感器测量了三名患者与自主神经功能（AV）相关的指标（心率、血容量脉搏、皮肤阻抗和皮肤温度）。基于这些特征训练了朴素贝叶斯分类器，并使用五折交叉验证进行测试，以确定是否可以使用AV或EV特征将发作前和发作间期的睡眠（或清醒）时段区分开来。在16个EV特征中，β功率、γ功率（30 - 45Hz和47 - 75Hz）、线长度和蒂杰能量在每名患者的发作前与发作间期睡眠（或清醒）状态之间显示出显著差异（t检验：$p < 0.001$）。在每名患者中，至少有一个AV在发作间期和发作前睡眠（或清醒）时段存在显著差异（$p < 0.001$）。使用AV特征时，分类器对发作前睡眠时段的标记灵敏度为84%，特异性为79%，kappa值为64%；对发作前清醒时段的标记分别为78%、80%和55%。使用EV时，分类器对发作前睡眠时段的标记灵敏度为69%，特异性为64%，kappa值为33%；对发作前清醒时段的标记分别为15%、93%和10%。
