Jamshidi Simin, Espinoza Arturo I, Heinzman Jonathan T, May Patrick, Uc Ergun Y, Narayanan Nandakumar S, Dasgupta Soura
Department of Computer and Electrical Engineering, College of Engineering, University of Iowa, Iowa City, IA.
Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, IA.
medRxiv. 2025 Jul 8:2025.07.07.25331047. doi: 10.1101/2025.07.07.25331047.
Parkinson's disease (PD) increases mortality is difficult to predict because of its heterogeneity and the availability of very few reliable which prognostic markers.
We used electroencephalography (EEG) and the Linear Predictive Coding EEG Algorithm for PD (LEAPD) for binary classification of 3-year mortality status and correlation between LEAPD indices and time to death.
2-minutes resting-state EEG from 94 PD patients (59 channels, 22 deceased within 3 years of recording) was used for binary classification of 3-year mortality status. Single-channel classification using a balanced dataset of 44 was performed using leave-one-out cross-validation (LOOCV). Robustness was evaluated by truncating the recordings. LOOCV Spearman's correlation coefficient (ρ) was obtained between LEAPD indices and time to death. Optimum hyperparameters obtained from a balanced training dataset of 30 were tested on the remaining 64 patients by 10,000 randomized comparisons of 7 vs 7, using 5 channel combinations Hyperparameters for the best ρ, using the same training dataset were for the out-of-sample correlation for the remaining 7 deceased.
In LOOCV analysis several channels yielded 100% accuracy with robust performance from five. The correlations ranged between ρ = -0.59 to -0.86; were significant after adjusting for age, cognitive and motor impairment. Out-of-sample testing using the best-performing 5-channel combination yielded a mean accuracy of 83%. Out-of-sample Spearman's ρ was -0.82.
LEAPD provides a robust approach for binary classification of mortality in PD from resting-state EEG. LEAPD indices correlate with survival duration, independent of clinical predictors, suggesting potential utility as a continuous neurophysiological biomarker.
帕金森病（PD）导致的死亡率增加难以预测，因为其具有异质性且可靠的预后标志物极少。
我们使用脑电图（EEG）和帕金森病线性预测编码脑电图算法（LEAPD）对3年死亡状态进行二元分类，并分析LEAPD指标与死亡时间之间的相关性。
对94例PD患者（59个通道，其中22例在记录后的3年内死亡）进行2分钟静息状态脑电图检查，用于3年死亡状态的二元分类。使用44个平衡数据集进行单通道分类，采用留一法交叉验证（LOOCV）。通过截断记录来评估稳健性。获得LEAPD指标与死亡时间之间的LOOCV斯皮尔曼相关系数（ρ）。从30个平衡训练数据集中获得的最佳超参数，通过对剩余64例患者进行10000次7对7的随机比较，使用5种通道组合进行测试。使用相同的训练数据集，针对最佳ρ的超参数用于其余7例死亡患者的样本外相关性分析。
在LOOCV分析中，几个通道的准确率达到100%，其中五个通道表现稳健。相关性范围在ρ = -0.59至 -0.86之间；在调整年龄、认知和运动障碍后具有显著性。使用表现最佳的5通道组合进行样本外测试，平均准确率为83%。样本外斯皮尔曼ρ为 -0.82。
LEAPD为从静息状态脑电图对PD死亡率进行二元分类提供了一种稳健的方法。LEAPD指标与生存持续时间相关，独立于临床预测因素，表明其作为连续神经生理生物标志物的潜在效用。
