PDF(Pubmed)
Abstract：
The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.
摘要：
癫痫的诊断仍然代表着一个复杂的过程，误诊达40%。我们的目标是建立一个自动化的工作流程，帮助临床医生诊断颞叶癫痫（TLE）。我们假设，与经典使用的功能连接度量（ImaginaryCoherence；ImCoh）相比，神经元雪崩（NA）代表了更好地封装丰富的大脑动力学的特征。我们从静息状态脑电图的来源估计中分析了大规模激活爆发（NA）。使用支持向量机，与对照组相比,TLE的分类准确率为0.86±0.08(SD),曲线下面积为0.93±0.07.与ImCoh相比，NA特征的使用将诊断预测的准确性提高了约16％。分类精度随着信号持续时间的增加而增加，在5分钟的记录达到一个平台。总结一下,NA代表自动癫痫识别的可解释特征，与病理相关区域的内在神经元时间尺度有关。
