Department of Neurosurgery, Geffen School of Medicine, Neural Systems and Dynamic Lab, University of California, Los Angeles, CA, USA.
Biomed Eng Online. 2010 Oct 19;9:61. doi: 10.1186/1475-925X-9-61.
The waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.
This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.
Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.
The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.
颅内压脉冲（ICP）的波形形态是监测和预测关键颅内和脑血管病理生理变化的重要指标。虽然当前的 ICP 脉冲分析框架在大多数脉冲上都能取得令人满意的结果，但我们观察到，其中一些框架在异常或更具挑战性的脉冲上的性能显著恶化。
本文针对这一问题提出了两项贡献。首先，它引入了 MOCAIP++，这是一个通用的 ICP 脉冲处理框架，它对 MOCAIP（ICP 脉冲的形态聚类和分析）进行了扩展。它的优势在于集成了几种峰识别方法来描述 ICP 形态，并利用不同的 ICP 特征来提高峰识别的准确性。其次，它研究了将自动识别的具有挑战性的脉冲纳入峰识别模型训练集的效果。
在一个大型 ICP 信号数据集以及具有代表性的抽样挑战性 ICP 脉冲集合上的实验表明，这两个贡献是互补的，可以显著提高临床条件下的峰识别性能。
所提出的框架允许从具有挑战性的脉冲中提取更可靠的 ICP 波形形态统计信息，以研究这些脉冲对患者病情的预测能力。
