Bayesian tracking of intracranial pressure signal morphology

Artificial Intelligence in Medicine

Volumn 54, Issue 2, 2012, Pages 115-123

  Scalzo, Fabien ^a   Asgari, Shadnaz ^a   Kim, Sunghan ^a   Bergsneider, Marvin ^a   Hu, Xiao ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian inference; Belief propagation; Brain injury; Dynamic markov model; Graphical model; Hydrocephalus; Intracranial pressure; Probabilistic tracking; Waveform morphology

Indexed keywords

BAYESIAN INFERENCE; BELIEF PROPAGATION; BRAIN INJURY; GRAPHICAL MODEL; HYDROCEPHALUS; INTRA-CRANIAL PRESSURE; WAVE FORMS;

ALGORITHMS; BAYESIAN NETWORKS; GRAPHIC METHODS; MARKOV PROCESSES; MORPHOLOGY; PATIENT MONITORING; PATIENT TREATMENT; RANDOM VARIABLES;

INFERENCE ENGINES;

ARTICLE; BAYESIAN LEARNING; ELECTROCARDIOGRAPHY; HUMAN; INTRACRANIAL PRESSURE; INTRACRANIAL PRESSURE MONITORING; MAJOR CLINICAL STUDY; NONPARAMETRIC TEST; PRIORITY JOURNAL; WAVEFORM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; ELECTROCARDIOGRAPHY; EQUIPMENT DESIGN; HUMANS; INTRACRANIAL PRESSURE; MARKOV CHAINS; MONITORING, PHYSIOLOGIC; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; PREDICTIVE VALUE OF TESTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS; TRANSDUCERS, PRESSURE;

EID: 84855934613     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.08.007     Document Type: Article

References (27)

1. Piper I. Intracranial pressure and elastance. Head injury: pathophysiology and management of severe closed injury 1997, Chapman & Hall Medical, p. 101-20, [chapter 6].
2. Balestreri M., Czosnyka M., Steiner L., Schmidt E., Smielewski P., Matta B., et al. Intracranial hypertension: what additional information can be derived from ICP waveform after head injury?. Acta Neurochir (Wien) 2004, 146(2):131-141.
3. Czosnyka M., Guazzo E., Whitehouse M., Smielewski P., Czosnyka Z., Kirkpatrick P., et al. Significance of intracranial pressure waveform analysis after head injury. Acta Neurochir (Wien) 1996, 138(5):531-541.
4. Contant C.F., Robertson C.S., Crouch J., Gopinath S.P., Narayan R.K., Grossman R.G. Intracranial pressure waveform indices in transient and refractory intracranial hypertension. J Neurosci Methods 1995, 57(l):15-25.
5. Takizawa H., Gabra-Sanders T., Miller J.D. Changes in the cerebrospinal fluid pulse wave spectrum associated with raised intracranial pressure. Neuro-surgery 1987, 20(3):355-361.
6. Cardoso E.R., Reddy K., Bose D. Effect of subarachnoid hemorrhage on intracranial pulse waves in cats. J Neurosurg 1988, 69(5):712-718.
7. Cardoso E.R., Rowan J.O., Galbraith S. Analysis of the cerebrospinal fluid pulse wave in intracranial pressure. J Neurosurg 1983, 59(5):817-821.
8. Portnoy H.D., Chopp M. Cerebrospinal fluid pulse wave form analysis during hypercapnia and hypoxia. Neurosurgery 1981, 9(1):14-27.
9. Chopp M., Portnoy H.D. Systems analysis of intracranial pressure, comparison with volume-pressure test and csf-pulse amplitude analysis. J Neurosurg 1980, 53(4):516-527.
10. Hu X., Glenn T., Scalzo F., Bergsneider M., Sarkiss C., Martin N., et al. Intracranial pressure pulse morphological features improved detection of decreased cerebral blood flow. Physiol Meas 2010, 31:679-695.
11. Hu X., Xu P., Asgari S., Vespa P., Bergsneider M. Forecasting icp elevation based on prescient changes of intracranial pressure waveform morphology. IEEE Trans Biomed Eng 2010, 57(5):1070-1078.
12. Aboy M., McNames J., Thong T., Tsunami D., Ellenby M., Goldstein B. An automatic beat detection algorithm for pressure signals. IEEE Trans Biomed Eng 2005, 52(10):1662-1670.
13. Hu X., Xu P., Scalzo F., Vespa P., Bergsneider M. Morphological clustering and analysis of continuous intracranial pressure. IEEE Trans Biomed Eng 2009, 56(3):696-705.
14. Eide P. A new method for processing of continuous intracranial pressure signals. Med Eng Phys 2006, 28(6):579-587.
15. Ellis T., McNames J., Aboy M. Pulse morphology visualization and analysis with applications in cardiovascular pressure signals. IEEE Trans Biomed Eng 2007, 54(9):1552-1559.
16. Sudderth E.B., Ihler A.T., Freeman W.T., Willsky A.S. Nonparametric belief propagation. IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR), vol. 1 2003, 605-612.
17. Eide P.K. Comparison of simultaneous continuous intracranial pressure (ICP) signals from ICP sensors placed within the brain parenchyma and the epidural space. Med Eng Phys 2008, 30:34-40.
18. Eide P.K., Saehle T. Is ventriculomegaly in idiopathic normal pressure hydro-cephalus associated with a transmantle gradient in pulsatile intracranial pressure?. Acta Neurochir (Wien) 2010, 152:989-995.
19. Wagshul M.E., Eide P.K., Madsen J.R. The pulsating brain: a review of experimental and clinical studies of intracranial pulsatility. Fluids Barriers CNS 2011, 8:5.
20. Hu X., Xu P., Lee D., Vespa P., Bergsneider M. An algorithm of extracting intracranial pressure latency relative to electrocardiogram r wave. Physiol Meas 2008, 29:459-471.
21. Afonso V.X., Tompkins W.J., Nguyen T.Q., Luo S. Ecg beat detection using filter banks. IEEE Trans Biomed Eng 1999, 46(2):192-202.
22. Scalzo F., Xu P., Asgari S., Bergsneider M., Hu X. Regression analysis for peak designation in pulsatile pressure signals. Med Biol Eng Comput 2009, 47(9):967-977.
23. Scalzo F., Asgari S., Kim S., Bergsneider M., Hu X. Robust peak recognition in intracranial pressure signals. Biomed Eng Online 2010, 9:61.
24. Kaufman L., Rousseeuw P.J. Finding groups in data: an introduction to cluster analysis. Wiley series in probability and mathematical statistics 2005, Wiley, Hoboken, NJ.
25. Sudderth E.B., Mandel M.I., Freeman W.T., Willsky A.S. Visual hand tracking using nonparametric belief propagation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop (CVPRW); vol. 1 2004, p189.
26. Bernier O., Cheung-Mon-Chan P., Bouguet A. Fast nonparametric belief propagation for real-time stereo articulated body tracking. Comput Vis Image Underst 2009, 113(1):29-47.
27. Scalzo F., Piater J.H. Adaptive patch features for object class recognition with learned hierarchical models. IEEE conference on Computer Vision and Pattern Recognition Workshop (CVPRW), vol. 1 2007, 1-8.