Cerebral vasospasm remains the leading cause of morbidity and mortality after aneurysmal subarachnoid hemorrhage (aSAH). Predicting its occurrence in a timely manner for therapy intervention is critically important in improving outcome after aSAH but remains unsatisfactory using existing techniques. The overall goal of this project is to develop and validate a novel data fusion algorithm for predicing vasospasm after aSAH that only requires bedside measurements of arterial blood pressure (ABP), intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The specific objectives of this project are 1) To develop a vasospasm warning generation algorithm based on the lumped proximal (n) and distal (r2) cerebral arterial radii estimated from the data fusion process; 2) To compare the data fusion approach with the existing Transcranial Doppler (TCD)-based vasospasm diagnostic criteria; 3) To compare the data fusion approach with model-independent methods for vasospasm prediction. A constrained nonlinear Kalman Filter is applied in the data fusion process to estimate the lumped proximal (n) and distal (r2) cerebral arterial radii that are two internal state variables of an intracranial pressure dynamic model. It is hypothesized that development of vasospasm will show a consistent trend in the estimated n and/or r2 and that this trend is detectable by a statistical trend detection algorithm. The detection of such a trend will then fire a warning of impending vasospasm. It was estimated that annual number of aSAH patients is about 30,000 in US alone, among which up to 70% can develop angiographic vasospasm with possible vasospasm in small cerebral arteries in remaining cases. Cerebral ischemia due to vasospasm not treated in a timely fashion can lead to devastating outcome. A promising way to improve outcome after aSAH is to detect it even before angiographic evidence and act with interventions. If validated, the proposed data fusion vasospasm assessment method could achieve this goal in a low cost and in a compatible way with the current clinical practice, a desirable feature to gain wide clinical acceptance of a new approach.
| Xu, Peng; Hu, Xiao; Yao, Dezhong (2013) Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial pressure. Comput Biol Med 43:425-33 |
| Kasprowicz, Magdalena; Bergsneider, Marvin; Czosnyka, Marek et al. (2012) Association between ICP pulse waveform morphology and ICP B waves. Acta Neurochir Suppl 114:29-34 |
| Kim, Sunghan; Bergsneider, Marvin; Hu, Xiao (2011) A systematic study of linear dynamic modeling of intracranial pressure dynamics. Physiol Meas 32:319-36 |
| Hu, Xiao; Xu, Peng; Asgari, Shadnaz et al. (2010) Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology. IEEE Trans Biomed Eng 57:1070-8 |
| Xu, Peng; Kasprowicz, Magdalena; Bergsneider, Marvin et al. (2010) Improved noninvasive intracranial pressure assessment with nonlinear kernel regression. IEEE Trans Inf Technol Biomed 14:971-8 |
| Kasprowicz, Magdalena; Asgari, Shadnaz; Bergsneider, Marvin et al. (2010) Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse. J Neurosci Methods 190:310-8 |
| Asgari, Shadnaz; Bergsneider, Marvin; Hu, Xiao (2010) A robust approach toward recognizing valid arterial-blood-pressure pulses. IEEE Trans Inf Technol Biomed 14:166-72 |
| Hu, Xiao; Xu, Peng; Wu, Shaozhi et al. (2010) A data mining framework for time series estimation. J Biomed Inform 43:190-9 |
| Scalzo, Fabien; Xu, Peng; Asgari, Shadnaz et al. (2009) Regression analysis for peak designation in pulsatile pressure signals. Med Biol Eng Comput 47:967-77 |
| Xu, Peng; Bergsneider, Marvin; Hu, Xiao (2009) Pulse onset detection using neighbor pulse-based signal enhancement. Med Eng Phys 31:337-45 |
Showing the most recent 10 out of 22 publications
