Noise reduction in intracranial pressure signal using causal shape manifolds.
Noise reduction in intracranial pressure signal using causal shape manifolds.
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DOI：
10.1016/j.bspc.2016.03.003
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发表时间：
2016-07
影响因子：
5.1
通讯作者：
Scalzo F
中科院分区：
文献类型：
--
作者：
Rajagopal A;Hamilton RB;Scalzo F
我们提出了迭代/因果子空间跟踪框架(I/CST)来降低连续监测的准周期生物信号中的噪声。通过投影到在其上执行概率跟踪的简化空间来实现噪声信号(例如，节拍)的基本段的信号重构。该方法的吸引力在于，子空间或流形是通过结合时间、形态和信号高度约束来学习的，因此具有相似形状且在时间和高度上接近的分段样本在子空间表示中也是接近的。对该算法在颅内压(ICP)信号上的有效性的评估是对其如何在临床条件下对常规采集的生物信号进行操作的实际例证。该系统的重建准确性是根据理想的20分钟的颅内压记录进行评估的，该记录是根据监测各种与颅内压相关的情况的患者的平均颅内压建立的。在不同水平的加性高斯白噪声(AWGN)和泊松噪声过程中测试了地面真实信号的重建精度，测得平均信噪比(SNR)分别显著提高了758%和396%。
We present the Iterative/Causal Subspace Tracking framework (I/CST) for reducing noise in continuously monitored quasi-periodic biosignals. Signal reconstruction of the basic segments of the noisy signal (e.g. beats) is achieved by projection to a reduced space on which probabilistic tracking is performed. The attractiveness of the presented method lies in the fact that the subspace, or manifold, is learned by incorporating temporal, morphological, and signal elevation constraints, so that segment samples with similar shapes, and that are close in time and elevation, are also close in the subspace representation. Evaluation of the algorithm’s effectiveness on the intracranial pressure (ICP) signal serves as a practical illustration of how it can operate in clinical conditions on routinely acquired biosignals. The reconstruction accuracy of the system is evaluated on an idealized 20-min ICP recording established from the average ICP of patients monitored for various ICP related conditions. The reconstruction accuracy of the ground truth signal is tested in presence of varying levels of additive white Gaussian noise (AWGN) and Poisson noise processes, and measures significant increases of 758% and 396% in the average signal-to-noise ratio (SNR).
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DOI：
10.1109/tbme.2008.2008636
发表时间：
2009-03
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Hu X;Xu P;Scalzo F;Vespa P;Bergsneider M
通讯作者：
Bergsneider M
影响因子：
3.2
作者：
Scalzo, Fabien;Xu, Peng;Asgari, Shadnaz;Bergsneider, Marvin;Hu, Xiao
通讯作者：
Hu, Xiao
DOI：
10.1097/ta.0b013e3181493080
发表时间：
2007-12-01
影响因子：
--
作者：
Faul, Mark;Wald, Marlena M.;Sattin, Richard W.
通讯作者：
Sattin, Richard W.
影响因子：
14.9
作者：
Shynk, John J.
通讯作者：
Shynk, John J.
影响因子：
8.5
作者：
Choi, Samjin;Adnane, Mourad;Park, Hun-Kuk
通讯作者：
Park, Hun-Kuk