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

Feasibility of Hidden Markov Models for the Description of Time-Varying Physiologic State After Severe Traumatic Brain Injury.

OBJECTIVES: Continuous assessment of physiology after traumatic brain injury is essential to prevent secondary brain insults. The present work aims at the development of a method for detecting physiologic states associated with the outcome from time-series physiologic measurements using a hidden Markov model. DESIGN: Unsupervised clustering of hourly values of intracranial pressure/cerebral perfusion pressure, the compensatory reserve index, and autoregulation status was attempted using a hidden Markov model. A ternary state variable was learned to classify the patient's physiologic state at any point in time into three categories ("good," "intermediate," or "poor") and determined the physiologic parameters associated with each state. SETTING: The proposed hidden Markov model was trained and applied on a large dataset (28,939 hr of data) using a stratified 20-fold cross-validation. PATIENTS: The data were collected from 379 traumatic brain injury patients admitted to Addenbrooke's Hospital, Cambridge between 2002 and 2016. INTERVENTIONS: Retrospective observational analysis. MEASUREMENTS AND MAIN RESULTS: Unsupervised training of the hidden Markov model yielded states characterized by intracranial pressure, cerebral perfusion pressure, compensatory reserve index, and autoregulation status that were physiologically plausible. The resulting classifier retained a dose-dependent prognostic ability. Dynamic analysis suggested that the hidden Markov model was stable over short periods of time consistent with typical timescales for traumatic brain injury pathogenesis. CONCLUSIONS: To our knowledge, this is the first application of unsupervised learning to multidimensional time-series traumatic brain injury physiology. We demonstrated that clustering using a hidden Markov model can reduce a complex set of physiologic variables to a simple sequence of clinically plausible time-sensitive physiologic states while retaining prognostic information in a dose-dependent manner. Such states may provide a more natural and parsimonious basis for triggering intervention decisions

An Observational Study of Engineering Online Education During the COVID-19 Pandemic

Although online education has become a viable and major component of higher education in many fields, its employment in engineering disciplines has been limited. COVID-19 pandemic compelled the global and abrupt conversion of conventional face-to-face instruction to the online format. The negative impact of such sudden change is undeniable. Urgent and careful planning is needed to mitigate pandemic negative effects on engineering education, especially for vulnerable, disadvantaged, and underrepresented students who have to deal with additional challenges (e.g. digital equity gap). To enhance engineering online instruction during the pandemic era, we conducted an observational study at California State University, Long Beach (a minority-serving institution). 110 faculty and 627 students from six engineering departments participated in our surveys and answered quantitative and qualitative questions to highlight the challenges they experienced during the online instruction in Spring 2020. In this work, we present the results of these surveys in detail and propose solutions to address the identified issues including logistical, technical, learning/teaching challenges, assessment methods, and hands-on training. As the pandemic continues, sharing these results with other educators can help with more effective planning and choice of best practices to improve the online engineering education during COVID-19 and beyond.Comment: 10 pages, 3 figures, 2 table

Synthesis and characterization of electrospun polyvinyl alcohol nanofibrous scaffolds modified by blending with chitosan for neural tissue engineering

Among several attempts to integrate tissue engineering concepts into strategies to repair different parts of the human body, neuronal repair stands as a challenging area due to the complexity of the structure and function of the nervous system and the low efficiency of conventional repair approaches. Herein, electrospun polyvinyl alcohol (PVA)/chitosan nano-fibrous scaffolds have been synthesized with large pore sizes as potential matrices for nervous tissue engineering and repair. PVA fibers were modified through blending with chitosan and porosity of scaffolds was measured at various levels of their depth through an image analysis method. In addition, the structural, physicochemical, biodegradability, and swelling of the chitosan nanofibrous scaffolds were evaluated. The chitosan-containing scaffolds were used for in vitro cell culture in contact with PC12 nerve cells, and they were found to exhibit the most balanced properties to meet the basic required specifications for nerve cells. It could be concluded that addition of chitosan to the PVA scaffolds enhances viability and proliferation of nerve cells, which increases the biocompatibility of the scaffolds. In fact, addition of a small percentage of chitosan to the PVA scaffolds proved to be a promising approach for synthesis of a neural-friendly polymeric blend

Lung Injury Is a Predictor of Cerebral Hypoxia and Mortality in Traumatic Brain Injury.

Background: A major contributor to unfavorable outcome after traumatic brain injury (TBI) is secondary brain injury. Low brain tissue oxygen tension (PbtO2) has shown to be an independent predictor of unfavorable outcome. Although PbtO2 provides clinicians with an understanding of the ischemic and non-ischemic derangements of brain physiology, its value does not take into consideration systemic oxygenation that can influence patients' outcomes. This study analyses brain and systemic oxygenation and a number of related indices in TBI patients: PbtO2, partial arterial oxygenation pressure (PaO2), PbtO2/PaO2, ratio of PbtO2 to fraction of inspired oxygen (FiO2), and PaO2/FiO2. The primary aim of this study was to identify independent risk factors for cerebral hypoxia. Secondary goal was to determine whether any of these indices are predictors of mortality outcome in TBI patients. Materials and Methods: A single-centre retrospective cohort study of 70 TBI patients admitted to the Neurocritical Care Unit (NCCU) at Cambridge University Hospital in 2014-2018 and undergoing advanced neuromonitoring including invasive PbtO2 was conducted. Three hundred and three simultaneous measurements of PbtO2, PaO2, PbtO2/PaO2, PbtO2/FiO2, PaO2/FiO2 were collected and mortality at discharge from NCCU was considered as outcome. Generalized estimating equations were used to analyse the longitudinal data. Results: Our results showed PbtO2 of 28 mmHg as threshold to define cerebral hypoxia. PaO2/FiO2 found to be a strong and independent risk factor for cerebral hypoxia when adjusting for confounding factor of intracranial pressure (ICP) with adjusted odds ratio of 1.78, 95% confidence interval of (1.10-2.87) and p-value = 0.019. With respect to TBI outcome, compromised values of PbtO2, PbtO2/PaO2, PbtO2/FiO2, and PaO2/FiO2 were all independent predictors of mortality while considered individually and adjusting for confounding factors of ICP, age, gender, and cerebral perfusion pressure (CPP). However, when considering all the compromised values together, only PaO2/FiO2 became an independent predictor of mortality with adjusted odds ratio of 3.47 (1.20-10.04) and p-value = 0.022. Conclusions: Brain and Lung interaction in TBI patients is a complex interrelationship. PaO2/FiO2 seems to be a major determinant of cerebral hypoxia and mortality. These results confirm the importance of employing ventilator strategies to prevent cerebral hypoxia and improve the outcome in TBI patients

Regression analysis for peak designation in pulsatile pressure signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm

A novel low-complexity digital filter design for wearable ECG devices - Fig 12

<p>Magnitude responses of the optimally factored-cascade IFIR implementation of H(z) for two different values of post-filter-multiplier: a) β = (1.010100111)₂; b) β = (1.0)₂.</p

Illustration of the performance of the of the proposed filter when the input ECG data (collected with wearable sensors) is contaminated with various levels of DC and 50 Hz noise.

<p>Illustration of the performance of the of the proposed filter when the input ECG data (collected with wearable sensors) is contaminated with various levels of DC and 50 Hz noise.</p

Choice of <i>Delay</i> parameter in the optimally factored-cascade IFIR implementation of <i>H</i>(<i>z</i>).

<p>Choice of <i>Delay</i> parameter in the optimally factored-cascade IFIR implementation of <i>H</i>(<i>z</i>).</p