Research / UCLA Neurosurgery Neural Systems and Dynamics Laboratory (NSDL)

NSDL was established in 2006. Our primary mission is to conduct translational biomedical informatics research.
We are uniquely positioned for this mission because our lab is tightly integrated with clinical programs in our Neurosurgery department including Adult Hydrocephalus and Neurocritical Care and it can meanwhile draw engineering and technical talents from upper UCLA campus. We understand that translational biomedical informatics is a diverse field in terms of clinical applications but it has a methodological core that concerns with advancing techniques of integrating and analyzing data from multi-scale biological and physiological sources. Therefore, we strive to contribute to specific clinical areas of caring neurosurgical patients while developing generalizable technologies.
Currently, we are pursuing research projects to build a system capable of producing forecasts of various clinical events in neurocritical care units so that they can be proactively managed, elucidating the pathophysiology behind the impairment of cerebrospinal fluid circulation to guide better treatment of normal pressure hydrocephalus, and using approaches of metabolomics to extract biomarkers of metabolic disturbance after brain injury. With our inter-disciplinary effort, we will augment the primary role of UCLA Neurosurgery as a spot of patient care to enable it also function as a “factory” of validated and generalizable translational biomedical informatics tools and systems.
Members
Principal Investigators
Senior Advisors
- Neil Martin, M.D., Professor, UCLA Neurosurgery
- Ali Sayed, Ph.D., Professor, UCLA Eletrical Engineering
- Valeriy Nenov, Ph.D., Ajunct Professor, UCLA Neurosurgery
- Saleem Hasan, M.S., President, Pulse Biomedical Inc.
Post Graduate Research Fellows
Graduate Students
Undergraduate Students
Visiting Scholars
- XingMing Guo, Professor, Chongqing University, China
Visiting Students
Lab Alumni
Active Extramural Grants
- “Continuous-Signal Driven Predictive Models in Neurological Intensive Care Units”; NINDS; 2009-2013; PI: Hu X
- “Intracranial Pressure Latency as a Biomarker of Cerebral Vasculature Status”; NINDS; 2008-2010; PI: Hu X
- “Data Mining Based Noninvasive Intracranial Pressure Assessment”; NINDS; 2007-2010; PI: Hu X
- “A Data Fusion Method for Bedside Monitoring of Lumped Cerebral Arterial Radii”; NINDS; 2007-2010; PI: Hu X
- “In Vivo CSF Shunt Hydrodynamics in Hydrocephalus”; NINDS; 2007-2011; PI: Bergsneider M
Publications
- Hu X and Nenov V. A single-lead ECG enhancement algorithm using a regularized data-driven filter. IEEE Transactions on Biomedical Engineering 2006; 53: 347-351.
- Hu X, Nenov V, Glenn TC, Bergsneider M, and Martin N. A Framework of Noninvasive Intracranial Pressure Assessment via Data Mining of Cerebral Hemodynamic Signals. Biomedical Signal Processing & Control 2006;1: 64-77.
- Hu X, Nenov V, Glenn TC, Steiner LA, Czosnyka M, Bergsneider M, and Martin N. Nonlinear analysis of cerebral hemodynamic and intracranial pressure signals for characterization of autoregulation. IEEE Transactions on Biomedical Engineering 2006; 53: 195-209.
- Hu X, Alwan AA, Rubinstein EH, and Bergsneider M. Reduction of compartment compliance increases venous flow pulsatility and lowers apparent vascular compliance: Implications for cerebral blood flow hemodynamics. Medical Engineering & Physics 2006; 28: 304-314.
- Hu X, Nenov V, Bergsneider M, Glenn TC, Paul V, and Martin N Estimation of Hidden State Variables of the Intracranial System Using Constrained Nonlinear Kalman Filters. IEEE Transactions on Biomedical Engineering 2007; 54(4): 597-610.

- Hu X, Nenov V, Paul V, and Bergsneider M. Characterization of Interdependency between Intracranial Pressure and Heart Variability Signals: a Causal Spectral Measure and a Generalized Synchronization Measure. IEEE Transactions on Biomedfical Engineering 2007; 54(8):1407-17.

- Vespa PM, Miller C, Hu X, Nenov V, Buxey F, Martin NA. Intensive care unit robotic telepresence facilitates rapid physician response to unstable patients and decreased cost in neurointensive care. Surg Neurol. 2007 Apr;67(4):331-7.
- Hu X, Miller C, Vespa P and Bergsneider M Adaptive computation of approximate entropy and its application in integrative analysis of irregularity of heart rate variability and intracranial pressure signals. Medical engineering & physics(2008) 30 631-9.

- Hu X, Xu P, Lee D J, Vespa P, Baldwin K and Bergsneider M. An algorithm for extracting intracranial pressure latency relative to electrocardiogram R wave. Physiological measurement (2008) 29 459-71.

- Hu X, Xu P, Lee DJ, Paul V, Bergsneider M. (2008) Morphological changes of intracranial pressure pulses are correlated with acute dilatation of ventricles. Acta Neurochir Suppl 102:131-136
- Cattivelli FS, Sayed AH, Hu X, Lee D, Vespa P. (2008) Mathematical models of cerebral hemodynamics for detection of vasospasm in major cerebral arteries. Acta Neurochir Suppl 102:63-69

- Bergsneider M, Miller C, Vespa P M and Hu X 2008 Surgical management of adult hydrocephalus Neurosurgery 62 Suppl 2 SHC643-SHC59; discussion SHC59-SHC60
- Xu P., Bergsneider M, and Hu X, Pulse onset detection using neighbor pulse-based signal enhancement. Med Eng Phys, 2009. 31(3): p. 337-345.
- Hu X, Subudhi AW, Xu P, Asgari S, Roach RC, Bergsneider M. Inferring cerebrovascular changes from latencies of systemic and intracranial pulses: a model-based latency subtraction algorithm. J Cereb Blood Flow Metab, 2009; 29:688-697
- Hu X, Xu P, Scalzo F, Vespa P, Bergsneider M. Morphological clustering and analysis of continuous intracranial pressure. IEEE Trans Biomed Eng, 2009; 56:696-705
- Scalzo F, Xu P, Asgari S, Bergsneider M, Hu X. Regression Analysis for Peak Designation in Intracranial Pressure Signals. Medical & Biological Engineering Computing. Med Biol Eng Comput. 2009 September; 47(9): 967-977.
- Xu P, Kasprowicz M, Bergsneider M, Hu X. Improve non-Invasive Intracranial Pressure Assessment with Non-Linear Kernel Regression. IEEE Transactions on Information Technology in Biomedicine. (Accepted)
- Sapo M, Wu SZ, Shadnaz A, McNair N, Buxey F, Martin N, Hu X. A Comparison Study of Vital Signs Charted by Nurses with Automated Acquired Values. Journal of Clinical Monitoring and Computing. (Accepted)
- Asgari, S., et al., A subspace decomposition approach toward recognizing valid pulsatile signals. Physiol Meas, 2009. 30(11): p. 1211-1225.
- Asgari S, Bergsneider M, Hu X. A Robust Approach towards Recognizing Valid Arterial Blood Pressure Pulses. IEEE Transactions on Information Technology in Biomedicine (Accepted).
- Hu X, Xu P, Asgari S, Paul V, Bergsneider M. Forecasting ICP Elevation Based on Prescient Changes of Intracranial Pressure Waveform Morphology. IEEE Transactions on Biomedical Engineering. (Accepted).
- Hu X, Xu P, Wu SZ. Asgari S, Bergsneider M. A Data Mining Framework for Time Series Estimation Journal of Biomedical Informatics. (Accepted).
Research Demonstrations (To be constructed)
Pending and Granted Patents
- “Clinical information system”, with Nenov V et al.
- “Multi Automated Severity Scoring”, with Martin N et al.
- “Data mining based Noninvasive Intracranial Pressure Assessment”, with Nenov V et al.
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“Morphological Clustering and Analysis of Intracranial Pressure”, with Bergsneider M
25 Keywords Describing our Research:
Biomedical Informatics, Clinical Translational Science, Critical Care, Brain Injury, Stroke, Hydrocephalus, Ischemia, Subarachnoid Hemorrhage, Cerebral Ischemia, Seizure, Intracranial Pressure, EEG, ECG, Metabolomics, Proteomics, CT Perfusion Imaging, MR Perfusion Imaging, Clinical Forecasting, Multimodality Monitoring, Noninvasive, Data Mining, System Identification, Mathematical Model, HRV, Pulse Wave
Related Links
UCLA Hydrocephalus Program UCLA Neurocritical Care Program UCLA BIRC UCLA EE Adaptive Systems Lab Physionet NINDS
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