Vakitbilir Nuray, Raj Rahul, Griesdale Donald E G, Sekhon Mypinder, Bernard Francis, Gallagher Clare, Thelin Eric P, Froese Logan, Stein Kevin Y, Kramer Andreas H, Aries Marcel J H, Zeiler Frederick A
Department of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Department of Neurosurgery, University of Helsinki, 00100 Helsinki, Finland.
Sensors (Basel). 2025 Apr 27;25(9):2762. doi: 10.3390/s25092762.
Therapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. This study uses the CAnadian High-Resolution TBI (CAHR-TBI) dataset to examine the impact of temporal resolution changes (1 min to 24 h) on autoregressive integrated moving average (ARIMA) modeling for raw and derived cerebral physiologic signals. Stationarity tests indicated that the majority of the signals required first-order differencing to address persistent trends. A grid search identified optimal ARIMA parameters (,,) for each signal and resolution. Subgroup analyses revealed population-specific differences in temporal structure, and small-scale forecasting using optimal parameters confirmed model adequacy. Variations in optimal structures across signals and patients highlight the importance of tailoring ARIMA models for precise interpretation and performance. Findings show that both raw and derived indices exhibit intrinsic ARIMA components regardless of resolution. Ignoring these features risks compromising the significance of models developed from such data. This underscores the need for careful resolution considerations in temporal modeling for TBI care.
创伤性脑损伤（TBI）急性期的治疗管理依赖于持续的多模态脑生理监测，以检测和预防继发性损伤。这些高分辨率数据流来自各种侵入性/非侵入性传感器技术，给临床医生带来了挑战，因为它们难以整合到管理算法和预后模型中。数据缩减技术，如移动平均滤波器，简化了数据，但可能无法解决统计自相关问题，还可能引入新特性，影响模型的效用和解释。本研究使用加拿大高分辨率TBI（CAHR-TBI）数据集，研究时间分辨率变化（1分钟至24小时）对原始和派生脑生理信号的自回归积分移动平均（ARIMA）建模的影响。平稳性测试表明，大多数信号需要进行一阶差分以解决持续趋势。网格搜索为每个信号和分辨率确定了最佳ARIMA参数（p、d、q）。亚组分析揭示了时间结构上的人群特异性差异，使用最佳参数进行的小规模预测证实了模型的充分性。不同信号和患者的最佳结构差异突出了为精确解释和性能量身定制ARIMA模型的重要性。研究结果表明，无论分辨率如何，原始指标和派生指标均呈现内在的ARIMA成分。忽视这些特征可能会损害从此类数据开发的模型的重要性。这强调了在TBI护理的时间建模中仔细考虑分辨率的必要性。
