Data-Driven, Label Consistent, Dictionary Learning Methods for Analysis of Biological Datasets

The goal of this thesis is to develop a data-driven, label consistent, and dictionary learning based framework that can be applied on a variety of signal analysis problems. Current methods based on analytical models do not adequately take the variability within and across datasets into consideration when designing signal analysis algorithms. This variability can be added as a morphological constraint to improve the signal analysis algorithms. In particular, this work focuses on three different applications: 1) we present a method for large-scale automated three-dimensional (3-D) reconstruction and profiling of microglia populations in extended regions of brain tissue for quantifying arbor morphology, sensing activation states, and analyzing the spatial distributions of cell activation patterns in tissue; this work provided an opportunity to profile the distribution of microglia in the controlled and device implanted brain. 2) we present a novel morphological constrained spectral unmixing (MCSU) algorithm that combines the spectral and morphological cues in the multispectral image data cube to improve the unmixing quality, this work provided an opportunity to identify new therapeutic opportunities for pancreatic ductal adenocarcinoma (PDAC) from the images collected from humans; and finally, 3) we developed a framework to analyze neuronal response from electroencephalography (EEG) datasets acquired from the infants ranging from 6-24 months. We demonstrated that combining different frequency bands from different spatial locations, yields better classification results, instead of the traditional approach where either one or two frequency bands are used. Using an adaptation of Tibshirani’s Sparse Group LASSO algorithm, we uncovered different spatial and bio markers for understanding a human infant’s brain. These bio-markers can be used for developmental stages of infants and further analysis is required to study the clinical aspects of infant’s social and cognitive development. This work establishes the fundamental mathematical basis for the next generation of algorithms that can leverage the morphological cues from the biological datasets. The algorithm has been embedded into the open source FARSIGHT toolkit with an intuitive graphical user interface.Electrical and Computer Engineering, Department o

Harnessing Big Data in Neurocritical Care in the Era of Precision Medicine

Purpose of review To survey the literature around "big data" and "artificial intelligence" use, its challenges, and examples in clinical and research domains within neurocritical care. Recent findings Innovations in digital data storage and integration technology have motivated the active development of data-driven tools for neurocritical care practice. There has been progress towards harmonization of data dictionaries within the field to boost generalizability of studies. Numerous collaborative groups have cultivated datasets that will enable further study of disease detection, prediction, and management of critically ill neurologic patients. Essential to the effective analysis of big data in neurocritical care are the increasing relationships between clinicians and data scientists. There are multiple challenges related to the efficient and ethical use of big data, including data complexity, database stewardship and governance, data quality, and safe implementation of data-driven conclusions. Continued efforts towards optimally harnessing data will be crucial in neurocritical care. Critically ill neurologic patients generate an abundant amount of data in the course of routine management. Clinical research is evolving from benefiting the "average patient" to aiming to deliver more precise treatment to the individual patient. In neurocritical care, this has manifested through big data-for triage decisions, enhancing workflow, event detection, and outcome prediction

Heart rate and heart rate variability as a prognosticating feature for functional outcome after cardiac arrest: A scoping review

Background: Despite significant progress in cardiopulmonary resuscitation and post-cardiac arrest care, favorable outcome in out-of hospital sudden cardiac arrest patients remains low. One of the main reasons for mortality in these patients is withdrawal of life-sustaining treatment. There is a need for precise and equitable prognostication tools to support families in avoiding premature or inappropriate WLST. Heart rate (HR) and heart rate variability (HRV) have been noted for their association with outcome, and are positioned to be a useful modality for prognostication. Objectives: The aim of this scoping review is to rigorously explore which electrocardiography features have been shown to predict functional outcome in post-cardiac arrest patients. Methods: The search was performed in Pubmed, EMBASE, and SCOPUS for studies published from January 1, 2011, to September 29, 2022, including papers in English or Korean. Results: Seven studies were included with a total of 1359 patients. Four studies evaluated HR, one study evaluated RR inverval, and two studies evaluated HRV. All studies were retrospective, with 3 multi-center and 4 single-center studies. All seven studies were inclusive of patients who underwent targeted temperature management (TTM) after cardiac arrest, and two studies included patients without TTM. Five studies used cerebral performance category to assess functional outcome, two studies used Glasgow outcome score, and one study used modified Rankin scale. Three studies measured outcome at hospital discharge, one study measured outcome at 14 days after return of spontaneous circulation, two studies measured outcome after 3 months, and one after 1 year. In all studies that evaluated HR, lower HR was associated with favorable functional outcome. Two studies found that higher complexity of HRV was associated with favorable functional outcome. Conclusion: HR and HRV showed clear associations with functional outcome in patients after CA, but cinilcial utility for prognostication is uncertain

Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability

Electroencephalography (EEG) has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI) data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data) to evaluate the power spectral density (PSD) content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing). No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

PurposeAccurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone.Methods488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt–Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset.ResultsThe performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset.ConclusionCurrent DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy

Hyperemia in subarachnoid hemorrhage patients is associated with an increased risk of seizures

The association between impaired brain perfusion, cerebrovascular reactivity status and the risk of ictal events in patients with subarachnoid hemorrhage is unknown. We identified 13 subarachnoid hemorrhage (SAH) patients with seizures and 22 with ictal-interictal continuum (IIC), and compared multimodality physiological recordings to 38 similarly poor-grade SAH patients without ictal activity. We analyzed 10,179 cumulative minutes of seizure and 12,762 cumulative minutes of IIC. Cerebrovascular reactivity (PRx) was not different between subjects with seizures, IIC, or controls. Cerebral perfusion pressure (CPP) was higher in patients with seizures [99 ± 6.5,  = .005] and IIC [97 ± 8.5,  = .007] when compared to controls [89 ± 12.3]. DeltaCPP, defined as actual CPP minus optimal CPP (CPPopt), was also higher in the seizure group [8.3 ± 7.9,  = .0003] and IIC [8.1 ± 10.3,  = .0006] when compared to controls [-0.1 ± 5]. Time spent with supra-optimal CPP was higher in the seizure group [342 ± 213 min/day,  = .002] when compared to controls [154 ± 120 min/day]. In a temporal examination, a supra-optimal CPP preceded increased seizures and IIC in SAH patients, an hour before and continued to increase during the events [  < .0001]

An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms

Objective: Intracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter malfunction or routine patient care. Existing methods for artifact detection include threshold-based, stability-based, or template matching, and result in higher false positives (when there is variability in the ICP waveforms) or higher false negatives (when the ICP waveforms lack complete triphasic components but are valid). Approach: We hypothesized that artifact labeling of ICP waveforms can be optimized by an active learning approach which includes interactive querying of domain experts to identify a manageable number of informative training examples. Main results: The resulting active learning based framework identified non-artifactual ICP pulses with a superior AUC of 0.96 + 0.012, compared to existing methods: template matching (AUC: 0.71 + 0.04), ICP stability (AUC: 0.51 + 0.036) and threshold-based (AUC: 0.5 + 0.02). Significance: The proposed active learning framework will support real-time ICP-derived analytics by improving precision of artifact-labelling

Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury

International audienc

Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage

The objective of this study was to examine whether heart rate variability (HRV) measures can be used to detect neurocardiogenic injury (NCI). Three hundred and twenty-six consecutive admissions with aneurysmal subarachnoid hemorrhage (SAH) met criteria for the study. Of 326 subjects, 56 (17.2%) developed NCI which we defined by wall motion abnormality with ventricular dysfunction on transthoracic echocardiogram or cardiac troponin-I > 0.3 ng/mL without electrocardiogram evidence of coronary artery insufficiency. HRV measures (in time and frequency domains, as well as nonlinear technique of detrended fluctuation analysis) were calculated over the first 48 h. We applied longitudinal multilevel linear regression to characterize the relationship of HRV measures with NCI and examine between-group differences at baseline and over time. There was decreased vagal activity in NCI subjects with a between-group difference in low/high frequency ratio (β 3.42, SE 0.92, p = 0.0002), with sympathovagal balance in favor of sympathetic nervous activity. All time-domain measures were decreased in SAH subjects with NCI. An ensemble machine learning approach translated these measures into a classification tool that demonstrated good discrimination using the area under the receiver operating characteristic curve (AUROC 0.82), the area under precision recall curve (AUPRC 0.75), and a correct classification rate of 0.81. HRV measures are significantly associated with our label of NCI and a machine learning approach using features derived from HRV measures can classify SAH patients that develop NCI