Intracranial pressure model in intensive care unit using a simple recurrent neural network through time

Shieh, Jiann-Shing; Chou, Chi-Fong; Huang, Sheng-Jean; Kao, Ming‐Chien

doi:10.1016/j.neucom.2003.10.006

Cited by 28 publications

(10 citation statements)

References 39 publications

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“…Mariak and others (2000) have investigated the usefulness of algorithms in classifying and discriminating changes in the global properties of ICP. In addition, the simple recurrent neural network through time was modeled to provide ICP values as an output, when computing the significant contributing factors of alterations in physical state, measured by noninvasive methods in head-injured patients (Shieh and others 2004). Advancements in this field have presented a neural network algorithm as a more reliable method to predict future mean ICP over previous algorithms for its ability to accurately perceive variations in small ICP time frames that are segmented from the whole time series (Zhang and others 2011).…”

Section: Current and Forthcoming Advanced Techniques Of Icp Analysismentioning

confidence: 99%

Analysis of Intracranial Pressure

2013

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The monitoring of intracranial pressure (ICP) is an important tool in medicine for its ability to portray the brain's compliance status. The bedside monitor displays the ICP waveform and intermittent mean values to guide physicians in the management of patients, particularly those having sustained a traumatic brain injury. Researchers in the fields of engineering and physics have investigated various mathematical analysis techniques applicable to the waveform in order to extract additional diagnostic and prognostic information, although they largely remain limited to research applications. The purpose of this review is to present the current techniques used to monitor and interpret ICP and explore the potential of using advanced mathematical techniques to provide information about system perturbations from states of homeostasis. We discuss the limits of each proposed technique and we propose that nonlinear analysis could be a reliable approach to describe ICP signals over time, with the fractal dimension as a potential predictive clinically meaningful biomarker. Our goal is to stimulate translational research that can move modern analysis of ICP using these techniques into widespread practical use, and to investigate to the clinical utility of a tool capable of simplifying multiple variables obtained from various sensors.

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Section: Current and Forthcoming Advanced Techniques Of Icp Analysismentioning

confidence: 99%

Analysis of Intracranial Pressure

2013

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“…RNNs have been used in a number of interesting applications including associative memories, spatiotemporal pattern classification, control, optimization, forecasting and generalization of pattern sequences (Gü ler et al, 2005;Petrosian, Prokhorov, Lajara-Nanson, & Schiffer, 2001;Shieh, Chou, Huang, & Kao, 2004). Fully recurrent networks use unconstrained fully interconnected architectures and learning algorithms that can deal with time-varying input and/or output in non-trivial ways.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Multiclass support vector machines for diagnosis of erythemato-squamous diseases

íbeylï

2008

Expert Systems with Applications

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“…RNNs can perform highly non‐linear dynamic mappings and thus have temporally extended applications, whereas multilayer feedforward networks are confined to performing static mappings (Elman, 1990; Saad et al , 1998; Gupta & McAvoy, 2000; Gupta et al , 2000). RNNs have been used in a number of interesting applications including associative memories, spatiotemporal pattern classification, control, optimization, forecasting and generalization of pattern sequences (Petrosian et al , 2000, 2001; Shieh et al , 2004; Güler & Übeyli, 2006b).…”

Section: Rnnsmentioning

confidence: 99%

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Übeylı

2008

Expert Systems

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Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal-to-noise ratio screening method. The application results of the signal-tonoise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non-salient input features.

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Intracranial pressure model in intensive care unit using a simple recurrent neural network through time

Cited by 28 publications

References 39 publications

Analysis of Intracranial Pressure

Analysis of Intracranial Pressure

Multiclass support vector machines for diagnosis of erythemato-squamous diseases

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

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