Abstract
Previous work has been demonstrated that tracking features describing the dynamic and time-varying patterns in brain monitoring signals provide additional predictive information beyond that derived from static features based on snapshot measurements. To achieve more accurate predictions of outcomes of patients with traumatic brain injury (TBI), we proposed a statistical framework to extract dynamic features from brain monitoring signals based on the framework of Gaussian processes (GPs). GPs provide an explicit probabilistic, nonparametric Bayesian approach to metric regression problems. This not only provides probabilistic predictions, but also gives the ability to cope with missing data and infer model parameters such as those that control the function’s shape, noise level and dynamics of the signal. Through experimental evaluation, we have demonstrated that dynamic features extracted from GPs provide additional predictive information in addition to the features based on the pressure reactivity index (PRx). Significant improvements in patient outcome prediction were achieved by combining GP-based and PRx-based dynamic features. In particular, compared with the a baseline PRx-based model, the combined model achieved over 30 % improvement in prediction accuracy and sensitivity and over 20 % improvement in specificity and the area under the receiver operating characteristic curve.
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Abad N, Druzgalski C (2009) Medical and engineering support and needs in neurological intensive care units. In: Proceedings of the PAHCE'09, Mexico City, Mexico, pp 157–159
Arindam C, Prasanth BN, Andy JK (2002) A data parallel approach for large-scale Gaussian process modeling. Proceeding of SDM
Balestreri M, Czosnyka M, Steiner LA, Hiler M, Schmidt EA, Matta B, Menon D, Hutchinson P, Pickard JD (2005) Association between outcome, cerebral pressure reactivity and slow ICP waves following head injury. Acta Neurochir Suppl 95:25–28
Catherine JK, Robert LB, Pamela HM (2008) Intracranial pressure variability and long-term outcome following traumatic brain injury. Acta Neurochir Suppl 102:105–108
Clifton L, Clifton DA, Pimentel MA, Watkinson PJ, Tarassenko L (2013) Gaussian processes for personalized e-health monitoring with wearable sensors. IEEE Trans Biomed Eng 60(1):193–197
Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 89:68–102
Czosnyka M, Pickard JD (2004) Monitoring and interpretation of intracranial pressure. J Neurol Neurosurg Psychiatry 75:813–821
Czosnyka M, Hutchinson PJ, Balestreri M, Hiler M, Smielewski P, Pickard JD (2006) Monitoring and interpretation of intracranial pressure after head injury. Acta Neurochir Suppl 96(4):121–125
Dutton R, McCunn M (2003) Traumatic brain injury. Curr Opin Crit Care 9(6):503–509
Edward S, Zoubin G (2005) Sparse Gaussian processes using pseudo-inputs. NIPS
Feng M, Loy LY, Zhang F, Guan CT et al.(2011) Artifact removal for intracranial pressure monitoring Signals. In: A Robust Solution with Signal Decomposition. Proceedings of 33rd international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Guendling K, Smielewski P, Czosnyka M, Lewis P, Nortje J, Timofeev I, Hutchinson PJ, Pickard JD (2006) Use of ICM+ software for on-line analysis of intracranial and arterial pressures in head-injured patients. Acta Neurochir Suppl 96(4):108–113
Hlatky R, Valadka AB, Robertson CS (2005) Intracranial pressure response to induced hypertension: role of dynamic pressure autoregulation. Neurosurgery 57:9117–9123
Hu X, Xu P, Scalzo F, Vespa P, Bergsneider M (2009) Morphological clustering and analysis of continuous intracranial pressure. IEEE Transactions on Biomedical Engineering 56(3):696–705
Hu X, Asgari XP, Vespa P, Bergsneider M (2010) Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology. IEEE Transactions on Biomedical Engineering 57(5):1070–1078
Ivanov PC, Amaral LA, Goldberger AL, Havlin S, Rosenblum MG, Struzik ZR, Stanley HE (1999) Multifractality in human heartbeat dynamics. Nature 399:461–465
Jones P, Andrews P, Midgley S, Anderson S, Piper I, Tocher J, Housley A, Corrie J, Slattery J, Dearden N (1994) Measuring the burden of secondary insults in head-injured patients during intensive care. J Neurosurg Anesthesiol 6(1):4–14
Lee KK, Seow WT, Ng I (2006) Demographical profiles of adult severe traumatic brain injury patients: implications for healthcare planning. Singapore Med J 47(1):31–36
Marmarou A, Anderson JRL, Ward JD, Choi SC, Young HF, Eisenberg HM, Foulkes MA, Marshall LF, Jane JA (1991) Impact of ICP instability and hypotension on outcome in patients with severe heal trauma. J Neurosurg 75:59–66
Marmarou A (2000) Increased intracranial pressure in head injury and influence of blood volume. J Neurotrauma 9:327–333
Rasmussen E, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, ISBN 0-262-18253, us
Roberts S, Osborne M, Ebden M, Reece S, Gibson N, Aigrain S (2012) Gaussian processes for time-series modelling. Philosophical Transactions of the Royal Society (Part A), 371(1984): online
Robertson C, Valadka A, Hannay H, Contant C, Gopinath S, Cormio M, Uzura M, Grossman R (1999) Prevention of secondary ischemic insults after severe head injury. Crit Care Med 27(10):2086–2095
Ross N, Eynon CA (2005) Intracranial pressure monitoring. Curr Anaesth Crit Care 16(4):255–261
Signorini D, Andrews P, Jones P, Wardlaw J, Miller J (1999) Predicting survival using simple clinical variables: a case study in traumatic brain injury. J Neurol Neurosurg Psychiatry 66(1):20–25
Swiercz M, Mariak Z, Krejza J, Lewko J, Szydlik P (2000) Intracranial pressure processing with artificial neural networks: prediction of ICP trends. Acta Neurochir 142(4):531–542
Wald S, Shackford S (1993) The effect of secondary insults on mortality and long-term disability after severe head injury in a rural region without a trauma system. J Trauma 34(3):377–382
Wright W (2007) Multimodal monitoring in the ICU: when could it be useful? J Neurol Sci 261(1–2):10–15
Acknowledgments
Dr Mengling Feng’s fellowship is funded by A*STAR Graduate Scholarship (AGS). Marco A F Pimentel is supported by the RCUK Digital Economy Program grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation) and FCT – Fundação para a Ciência e a Tecnologia – under grant SFRH/DB/79799/2011.
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We declare that we have no conflicts of interest.
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Pimentel, M.A.F., Brennan, T., Lehman, Lw., King, N.K.K., Ang, BT., Feng, M. (2016). Outcome Prediction for Patients with Traumatic Brain Injury with Dynamic Features from Intracranial Pressure and Arterial Blood Pressure Signals: A Gaussian Process Approach. In: Ang, BT. (eds) Intracranial Pressure and Brain Monitoring XV. Acta Neurochirurgica Supplement, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-22533-3_17
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DOI: https://doi.org/10.1007/978-3-319-22533-3_17
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