Robust Peak Recognition in Intracranial Pressure Signals

<p>Abstract</p> <p>Background</p> <p>The waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.</p> <p>Methods</p> <p>This paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.</p> <p>Results</p> <p>Experiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.</p> <p>Conclusion</p> <p>The proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.</p

Monitoring of Intracranial Pressure in Patients with Traumatic Brain Injury

Since Monro published his observations on the nature of the contents of the intracranial space in 1783 there has been investigation of the unique relationship between the contents of the skull and the intracranial pressure (ICP). This is particularly true following traumatic brain injury (TBI), where it is clear that elevated ICP due to the underlying pathological processes is associated with a poorer clinical outcome. Consequently, there is considerable interest in monitoring and manipulating ICP In patients with TBI.The two techniques most commonly used in clinical practice to monitor ICP are via an intraventricular or intraparenchymal catheter with a microtransducer system. Both of these techniques are invasive and are thus associated with complications such as haemorrhage and infection. For this reason, significant research effort has been directed towards development of a non-invasive method to measure ICP. These include imaging based studies using computed tomography (CT) and magnetic resonance imaging (MRI), transcranial Doppler sonography (TCD), near-infrared spectroscopy (NIRS), tympanic membrane displacement (TMD), visual-evoked potentials (VEPs), measurements of optic nerve sheath diameter (ONSD) and other measurements of the optic nerve, retina, pupil and ophthalmic artery.The principle aims of ICP monitoring in TBI are to allow early detection of secondary haemorrhage or ischaemic processes and to guide therapies that limit intracranial hypertension and optimise cerebral perfusion. However, information from the ICP value and the ICP waveform can also be used to estimate intracranial compliance, assess cerebrovascular pressure reactivity and attempt to forecast future episodes of intracranial hypertension

The Recurrence-Based Analysis of Intracranial Pressure

Modern computational approaches tied together with the power of mathematical science has pushed us closer to reach a deeper understanding of complex dynamical systems. Real-world biological and physiological systems now can be studied on account of the accessibility to fast, cheap and powerful computers. In particular, the field of neuroscience and brain data analysis has grown significantly in the recent years. Recurrence plots (RPs) are a relatively new approach for the analysis of nonlinear, non-stationary and noisy data. Rooted in topological properties of the system, RP visualizes the recurrence states of the dynamical system. Armed with the recurrence quantification measures, RP is even more rigorous in exploring and quantifying real-world dynamical system. In the present work, we benefit from the RP and RQA methods to study the behavior of intracranial pressure (ICP) waveforms. ICP is defined as the fluid pressure inside the skull which carries important information associated with the status of the patient. Our main goal is to detect sudden changes or extreme regime changing in these signals. Patterns appearing in RP can shed light on fundamental characteristics of the system. Our results suggest distinguishable patterns in the RPs of some subjects which are not detectable in the raw ICP signals. This work sets up the workflow for using RP analysis in online ICP monitoring of brain-injured patients

Multimodal and autoregulation monitoring in the neurointensive care unit

Given the complexity of cerebral pathology in patients with acute brain injury, various neuromonitoring strategies have been developed to better appreciate physiologic relationships and potentially harmful derangements. There is ample evidence that bundling several neuromonitoring devices, termed “multimodal monitoring,” is more beneficial compared to monitoring individual parameters as each may capture different and complementary aspects of cerebral physiology to provide a comprehensive picture that can help guide management. Furthermore, each modality has specific strengths and limitations that depend largely on spatiotemporal characteristics and complexity of the signal acquired. In this review we focus on the common clinical neuromonitoring techniques including intracranial pressure, brain tissue oxygenation, transcranial doppler and near-infrared spectroscopy with a focus on how each modality can also provide useful information about cerebral autoregulation capacity. Finally, we discuss the current evidence in using these modalities to support clinical decision making as well as potential insights into the future of advanced cerebral homeostatic assessments including neurovascular coupling

Physiological and pharmacological modelling in neurological intensive care and anaesthesia

Mathematical models of physiological processes can be used in critical care and anaesthesia to improve the understanding of disease processes and to guide treatment. This thesis provides a detailed description of two studies that are related through their shared aim of modelling different aspects of brain physiology. The Relationship Between Transcranial Bioimpedance and Invasive Intracranial Pressure Measurement in Traumatic Brain Injury Patients (BioTBI) Study describes an attempt to model intracranial pressure (ICP) in patients admitted with severe traumatic brain injury (TBI). It is introduced with a detailed discussion of the monitoring and modelling of ICP in patients with TBI alongside the rationale for considering transcranial bioimpedance (TCB) as a non-invasive approach to estimating ICP. The BioTBI Study confirmed a significant relationship between TCB and invasively measured ICP in ten patients admitted to the neurological intensive care unit (NICU) with severe TBI. Even when using an adjusted linear modelling technique to account for patient covariates, the magnitude of the relationship was small (r-squared = 0.32) and on the basis of the study, TCB is not seen as a realistic technique to monitor ICP in TBI. Target controlled infusion (TCI) of anaesthetic drugs exploit known pharmacokinetic pharmacodynamic (PKPD) models to achieve set concentrations in the plasma or an effect site. Following a discussion of PKPD model development for the anaesthetic drug propofol, the Validation Study of the Covariates Model (VaSCoM) describes a joint PKPD study of the Covariates Model. Pharmacokinetic validation of plasma concentrations predicted by the model in forty patients undergoing general anaesthesia confirmed a favourable overall bias (3%) and inaccuracy (25%) compared to established PKPD models. The first description of the pharmacodynamic behaviour of the Covariates Model is provided with an estimated rate constant for elimination from the effect site compartment (ke0) of 0.21 to 0.27 min-1