A method of estimating intracranial decompensation in man

Szewczykowski, J; Dytko, P; Kunicki, A; Korsak-Sliwka, J; Sliwka, S; Dziduszko, J; Grochowski, W; Augustyniak, B

doi:10.3171/jns.1976.45.2.0155

Cited by 18 publications

(5 citation statements)

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“…Since the late 1970s, methods of estimating intracranial decompensation and elastance capacity have been suggested. Authors have proposed that changes of slope in mean ICP and standard deviation regression plots (Szewczykowski and others 1976), and in the intersection between the linear slopes of baseline ICP and ICP pulse-amplitude (Szewczykowski and others 1977) are indicative of alterations in intracranial dynamics. More recently, secondary indices have been primarily derived from the mean values and the mean wave amplitudes of ICP.…”

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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“…Subordinate indices were found to be helpful in identify the threshold and commencement of intracranial disorder in 70s along with waveform analysis. In order to estimate the intracranial instant capacity, the joint point between ICP pulse amplitude and the linear slopes of baseline ICP [50] and plots of standard deviation regression and mean ICP slope variations [51] were considered to indicate the variations of intracranial dynamics. The mean wave amplitudes and mean values of ICP were used as secondary indices [10].…”

Section: Brain Compliance and Pressure Reactivity Indexmentioning

confidence: 99%

Non-Invasive Modeling of Intracranial Hypertension from Physiological Channels

Naraei¹

2022

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Intracranial pressure (ICP), the pressure within the cranium reflects three elements: cerebrospinal fluid, brain tissue and blood pressure. High ICP (above 20 mmHg) is called intracranial hypertension (ICH) which is due to the tumour, swelling or the internal bleeding of brain and may cause secondary damage to the brain. ICP is a crucial parameter in diagnosis of brain injuries. Two models which utilize machine learning techniques to anticipate ICH and assist in clinical decision making were developed in the present thesis. ICP can be monitored through the invasive techniques (i.e., inserting an intraventricular catheter through the skull). Despite the high accuracy, the episodes of ICH can also be manually identified only after placement of catheter which is accompanied by lots of technical difficulties. Furthermore, the ICP signal might not be available continuously or may include unwanted noise that could introduce more complication to the diagnosis and treatment procedure. Considering the difficulties of the invasive techniques, a non-invasive model, capable to predict the ICH helps to save time, estimate the missing ICPs, predict the ICP in advance and accelerate medical intervention. The present thesis introduces two machine learning models to resolve the current limitations: 1- Non-invasive prediction of ICP labels 10 minutes in advance where the status of ICP (normal / ICH) is predicted based on the two components extracted from the physiological signals such as mean arterial blood pressure and respiration rate. 2- Wavelet – clustering where a machine learning solution for ICP estimation using a hybrid wavelet clustering is proposed. The episodes of ICP and derived from ICP (such as cerebral perfusion pressure) are excluded from the second model.

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“…Стабильность церебральной гемодинамики обусловлена функционированием сложной, многокомпонентной системы регуляции мозгового кровотока [12][13][14]. Помимо количе-ственной оценки каждого из внутричерепных объе-мов (мозговая ткань, кровь и ликвор), существенное значение имеет суммарная характеристика кранио-вертебральных объемных соотношений (КВОС), которая определяется как «теснота» (tightness) [15][16][17] всей цереброспинальной системы в целом. С этой целью изучается зависимость между дополни-тельным объемом, вводимым в ликворное про-странство, и возникающими при этом изменениями внутричерепного давления (ВЧД).…”

Section: методика собственных исследованийunclassified

“…С этой целью изучается зависимость между дополни-тельным объемом, вводимым в ликворное про-странство, и возникающими при этом изменениями внутричерепного давления (ВЧД). В качестве до-полнительного объема обычно используется сте-рильный физраствор, вводимый эндолюмбально, причем одновременно оцениваются изменения ВЧД (инфузионный тест) [15][16][17][18]. К сожалению, инвазивность этого метода значительно ограничи-вает возможности его клинического применения.…”

Section: методика собственных исследованийunclassified