Robust peak recognition in intracranial pressure signals.
Robust peak recognition in intracranial pressure signals.
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DOI：
10.1186/1475-925x-9-61
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发表时间：
2010-10-19
影响因子：
3.9
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Scalzo F;Asgari S;Kim S;Bergsneider M;Hu X
颅内压脉冲（ICP）的波形形态是监测和预测颅内和脑血管病理生理变化的重要指标。虽然目前的ICP脉冲分析框架在大多数脉冲上提供了令人满意的结果，但我们观察到其中几个的性能在异常或更具挑战性的脉冲上显着恶化。本文提供了两个贡献这个问题。首先，它介绍了MOCAIP++，一个通用的ICP脉冲处理框架，概括了MOCAIP（形态聚类和ICP脉冲分析）。它的优点是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。其次，它调查的效果，自动识别，挑战性的脉冲到训练集的峰值识别模型。在ICP信号的大数据集上以及在采样的具有挑战性的ICP脉冲的代表性集合上的实验表明，这两种贡献是互补的，并且显著提高了临床条件下的峰值识别性能。所提出的框架允许提取关于具有挑战性的脉冲上的ICP波形形态的更可靠的统计数据，以调查这些脉冲对患者状况的预测能力。
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.