Harvard Medical School, Boston, MA, USA.
Epilepsy Behav. 2013 Feb;26(2):143-52. doi: 10.1016/j.yebeh.2012.11.048. Epub 2013 Jan 3.
Methods for rapid and objective quantification of interictal spikes in raw, unprocessed electroencephalogram (EEG) samples are scarce. We evaluated the accuracy of a tailored automated spike quantification algorithm. The automated quantification was compared with the quantification by two board-certified clinical neurophysiologists (gold-standard) in five steps: 1) accuracy in a single EEG channel (5 EEG samples), 2) accuracy in multiple EEG channels and across different stages of the sleep-wake cycles (75 EEG samples), 3) capacity to detect lateralization of spikes (6 EEG samples), 4) accuracy after application of a machine-learning mechanism (11 EEG samples), and 5) accuracy during wakefulness only (8 EEG samples). Our method was accurate during all stages of the sleep-wake cycle and improved after the application of the machine-learning mechanism. Spikes were correctly lateralized in all cases. Our automated method was accurate in quantifying and detecting the lateralization of interictal spikes in raw unprocessed EEG samples.
在原始未处理的脑电图 (EEG) 样本中快速、客观地量化发作间期棘波的方法很少。我们评估了一种定制的自动棘波量化算法的准确性。该自动量化方法与两位经过认证的临床神经生理学家的量化（金标准）进行了五个步骤的比较：1）单个 EEG 通道的准确性（5 个 EEG 样本），2）多个 EEG 通道和不同睡眠-觉醒周期阶段的准确性（75 个 EEG 样本），3）检测棘波侧化的能力（6 个 EEG 样本），4）应用机器学习机制后的准确性（11 个 EEG 样本），以及 5）仅在觉醒期间的准确性（8 个 EEG 样本）。我们的方法在睡眠-觉醒周期的所有阶段都准确，并在应用机器学习机制后得到了改善。在所有情况下，棘波都被正确地侧化。我们的自动方法在量化和检测原始未处理 EEG 样本中的发作间期棘波的侧化方面是准确的。
