The complexity of intracranial pressure as an indicator of cerebral autoregulation

Intracranial Pressure (ICP) is one of the main neuromonitories used today to guide the treatment of acute neurological patients in the Intensive Care Unit (ICU). Within this article the complexity of periods of intracranial hypertension is evaluated and compared with periods of stable intracranial tension. Using the multiparameter intelligent monitoring in intensive care III (MIMIC-III) database from the Beth Israel Deaconess Medical Center the complexity of periods of stable intracranial tension and high intracranial hypertension are evaluated using two quantifiers: the Permutation Entropy and their respective number of missing patterns. Both indicate a loss of complexity in hypertension signals. A physiological explanation of this loss of complexity is given using a dynamical model of the Cerebral Autoregulation and Cerebral Hemodynamics.Fil: Ciarrocchi, Nicolás. Hospital Italiano; ArgentinaFil: Quiroz, Nicolas. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Traversaro Varela, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina. Universidad Nacional de Lanús; ArgentinaFil: San Roman, Juan Eduardo. Instituto Universidad Escuela de Medicina del Hospital Italiano; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Goldemberg, Fernando. University of Chicago; Estados UnidosFil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentin

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than 90% and p(s1)>p(s720). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than 95%, and PE is not sensitive to changes in ICP and p(s720)>p(s1). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than 90% and p(s1)>p(s720). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than 95%, and PE is not sensitive to changes in ICP and p(s720)>p(s1). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool

Using EEG total energy as a noninvasively tracking of intracranial (and cerebral perfussion) pressure in an animal model: A pilot study

Purpose: This study aims to describe the total EEG energy during episodes of intracranial hypertension (IH) and evaluate its potential as a classification feature for IH. New methods: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH. Results: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7. Comparison with existing methods: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance. Conclusion: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than (Formula presented.) and (Formula presented.). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than (Formula presented.), and PE is not sensitive to changes in ICP and (Formula presented.). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool.Fil: Pose, Fernando Ezequiel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Ciarrocchi, Nicolas Marcelo. Hospital Italiano; ArgentinaFil: Videla, Carlos. Hospital Italiano; ArgentinaFil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentin

Using entropies to monitoring intracranial pressure, evidence from an animal model

Intracranial hypertension (ICH) is associated with worse neurological outcomes and increased mortality. Therefore, its correct monitoring is very important in neurological intensive care and the operating room. There have been successful attempts to use entropic quantifiers to monitor intracranial pressure (ICP);however, they have not been compared against each other to analyze their properties. In this study, we conducted an animal experiment on intracranial hypertension and analyzed the data to determine the efficacy of the most commonly used entropies in literature, namely, Approximate Entropy, Sample Entropy, Permutation Entropy, and Wavelet Entropy. Our analysis revealed that Wavelet Entropy exhibited the best early warning properties, detecting a median insult value of 10.34 ml, 147 s before the ICP reached 20 mmHg, when the ICP median value was 8.37 mmHg. Although all the entropies showed a decomplexing effect on the ICP signal, Wavelet Entropy was the most sensitive, possibly due to the frequency-dependent nature of brain compliance.Fil: Pose, Fernando Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional e Ingeniería Biomédica - Hospital Italiano. Instituto de Medicina Traslacional e Ingeniería Biomédica.- Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional e Ingeniería Biomédica; ArgentinaFil: Videla, Carlos. Hospital Italiano; ArgentinaFil: Campanini Scigliano, Giovanni Denis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional e Ingeniería Biomédica - Hospital Italiano. Instituto de Medicina Traslacional e Ingeniería Biomédica.- Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional e Ingeniería Biomédica; ArgentinaFil: Ciarrocchi, Nicolas Marcelo. Hospital Italiano; ArgentinaFil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional e Ingeniería Biomédica - Hospital Italiano. Instituto de Medicina Traslacional e Ingeniería Biomédica.- Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional e Ingeniería Biomédica; Argentin