Detection of Intracranial Hypertension using Deep Learning.
Detection of Intracranial Hypertension using Deep Learning.
复制标题
使用深度学习检测颅内高压。
DOI：
10.1109/icpr.2016.7900010
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Scalzo,Fabien
中科院分区：
文献类型：
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作者：
Quachtran,Benjamin;Hamilton,Robert;Scalzo,Fabien
颅内高血压是一种以脑内压力升高为特征的疾病，通常在神经重症监护中进行监测，只有在发生升高后才能诊断。这种基于反应的治疗方法使患者在错误检测的情况下面临更高的额外并发症风险。颅内高压的检测一直是最近许多研究的主题，试图准确地表征高血压的原因，特别是检查波形形态。我们研究了深度学习（一种分层形式的机器学习）的使用，以建模高血压和波形形态之间的关系，使我们能够准确地检测高血压的存在。来自60名患者的数据显示了半小时时间跨度内的颅内压水平，用于评估模型。我们将每个患者的记录分为30秒段内的平均标准化搏动，为每个搏动分配高（即大于15 mmHg）或低颅内压的标签。对该模型进行了测试，以预测颅内压升高的存在。在我们的数据集上，发现该算法在检测颅内高压方面的准确率为92.05 ± 2.25%。
Intracranial Hypertension, a disorder characterized by elevated pressure in the brain, is typically monitored in neurointensive care and diagnosed only after elevation has occurred. This reaction-based method of treatment leaves patients at higher risk of additional complications in case of misdetection. The detection of intracranial hypertension has been the subject of many recent studies in an attempt to accurately characterize the causes of hypertension, specifically examining waveform morphology. We investigate the use of Deep Learning, a hierarchical form of machine learning, to model the relationship between hypertension and waveform morphology, giving us the ability to accurately detect presence hypertension. Data from 60 patients, showing intracranial pressure levels over a half hour time span, was used to evaluate the model. We divided each patient's recording into average normalized beats over 30 sec segments, assigning each beat a label of high (i.e. greater than 15 mmHg) or low intracranial pressure. The model was tested to predict the presence of elevated intracranial pressure. The algorithm was found to be 92.05 ± 2.25% accurate in detecting intracranial hypertension on our dataset.