Optimal control-based bayesian detection of clinical and behavioral state transitions

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 20, Issue 5, 2012, Pages 708-719

  Santaniello, Sabato ^a   Sherman, David L ^a   Thakor, Nitish V ^a   Eskandar, Emad N ^b   Sarma, Sridevi V ^a  

^a Johns Hopkins University   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

Bayesian estimation; neural systems; optimal control; quickest detection (QD)

Indexed keywords

BAYESIAN DETECTION; BAYESIAN ESTIMATIONS; BAYESIAN PARADIGM; BEHAVIORAL STATE; CLINICAL STATE; DETECTION ALGORITHM; EEG RECORDING; EMERGING TOPICS; EPILEPTIC SEIZURES; FALSE POSITIVE; LOSS FUNCTIONS; MEASUREMENT-BASED; MOTOR TASKS; MULTI-CHANNEL; NEURAL PROSTHETIC; NEURAL SYSTEMS; NEUROSTIMULATION; OPTIMAL CONTROLS; PARKINSON'S DISEASE; PHYSICAL IMPAIRMENTS; PROBABILITY OF FALSE POSITIVES; QUICKEST DETECTION; SINGLE UNIT RECORDINGS; STATE EVOLUTIONS; STATE TRANSITIONS; THRESHOLD POLICIES; TIME VARYING; TRANSITION TIME;

BAYESIAN NETWORKS; DRUG DELIVERY; HIDDEN MARKOV MODELS; MAMMALS; NEURODEGENERATIVE DISEASES; PATIENT TREATMENT;

CONTROL;

AGED; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BEHAVIOR; BRAIN; EVOKED MUSCLE RESPONSE; FEMALE; HUMAN; MALE; METHODOLOGY; MIDDLE AGED; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BEHAVIOR; BRAIN; EVOKED POTENTIALS, MOTOR; FEMALE; HUMANS; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84866079024     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2012.2210246     Document Type: Article

References (62)

1. G. B. Wetherhil and D. W. Brown, Statistical Process Control. London, U.K.: Chapman Hall, 1991.
2. R. Trivedi and R. Chandramouli, "Secret key estimation in sequential steganography," IEEE Trans. Signal Process., vol. 53, no. 2, pp. 746-757, 2005. (Pubitemid 40181768)
3. A. G. Tartakovsky, B. L. Rozovskii, R. B. Blazek, and H. Kim, "A novel approach to detection of intrusions in computer networks via adaptive sequential and batch-sequential change-point detection methods," IEEE Trans. Signal Process., vol. 54, no. 9, pp. 3372-3382, Sep. 2006.
4. A. N. Shiryaev, H. Geman, D. Madan, S. Pliska, and T. Vorst, Eds., "Quickest detection problems in the technical analysis of financial data," in Mathematical Finance-Bachelier Congress 2000, Berlin, Germany, 2002, pp. 487-521.
5. E. Andreou and E. Ghysels, "The impact of sampling frequency and volatility estimators on change point tests," J. Financial Econ., vol. 2, pp. 290-318, 2004.
6. M. Pettersson, "Monitoring a freshwater fish population: Statistical surveillance of biodiversity," Environmetrics, vol. 9, pp. 139-150, 1998. (Pubitemid 28178605)
7. T. P.Barnett,D.W. Pierce, andR. Schnur, "Detection of anthropogenic climate change in the world's oceans," Science, vol. 292, pp. 270-274, 2001. (Pubitemid 32295382)
8. G. J. Gibson, A. Kleczkowski, and C. A. Gilligan, "Bayesian analysis of botanical epidemics using stochastic compartmental models," Proc. Natl. Acad. Sci. USA, vol. 101, pp. 12120-12124, 2004. (Pubitemid 39145406)
9. B. K. Ellis et al., "Long-term effects of a trophic cascade in a large lake ecosystem," Proc Natl. Acad. Sci. USA, vol. 108, pp. 1070-1075, 2011.
10. M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes: Theory and Applications. Englewood Cliffs, NJ: Prentice Hall, 1993.
11. H. V. Poor and O. Hadjiliadis, Quickest Detection. Cambridge, U.K.: Cambridge Univ. Press, 2009.
12. N. V. Thakor, Y. S. Zhu, and K. Y. Pan, "Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm," IEEE Trans. Biomed. Eng., vol. 37, no. 9, pp. 837-843, Sep. 1990. (Pubitemid 20368418)
13. N. V. Thakor, A. Natarajan, and G. F. Tomaselli, "Multiway sequential hypothesis testing for tachyarrhythmia discrimination," IEEE Trans. Biomed. Eng., vol. 41, no. 5, pp. 480-487, May 1994. (Pubitemid 24213151)
14. I. Osorio, M. G. Frei, and S. B. Wilkinson, "Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset," Epilepsia, vol. 39, pp. 615-627, 1998. (Pubitemid 28265429)
15. J. Ushiba, Y. Tomita, Y. Masakado, and Y. Komune, "A cumulative sum test for a peri-stimulus time histogram using the Monte Carlo method," J. Neurosci. Methods, vol. 118, pp. 207-214, 2002. (Pubitemid 36157323)
16. A. M. White et al., "Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury," J. Neurosci. Methods, vol. 152, pp. 255-266, 2006. (Pubitemid 43343497)
17. A. J. Yu, "Optimal change-detection and spiking neurons," in Proc. 20th Annu. Conf. Neural Inf. Process. Syst. (NIPS 2006), Vancouver, BC, Canada, 2006, pp. 1545-1552.
18. A. Shoeb et al., "Patient-specific seizure onset detection," Epilepsy Behav., vol. 5, pp. 483-498, 2004. (Pubitemid 39060703)
19. J. Hu, J. Si, B. P. Olson, and J. He, "Feature detection in motor cortical spikes by principal component analysis," IEEE Trans. Neural Syst. Rehab Eng., vol. 13, no. 3, pp. 256-262, Sep. 2005. (Pubitemid 41416007)
20. M. J. van Putten, T. Kind, E. Visser, and V. Lagerburg, "Detecting temporal lobe seizures from scalp EEG recordings: A comparison of various features," Clin. Neurophysiol., vol. 116, pp. 2480-2489, 2005. (Pubitemid 41265977)
21. R.Meier, H. Dittrich, A. Schulze-Bonhage, and A. Aertsen, "Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns," J. Clin. Neurophysiol., vol. 25, pp. 119-131, 2008. (Pubitemid 351787122)
22. M. Tito, M. Cabrerizo, M. Ayala, P. Jayakar, and M. Adjouadi, "Seizure detection: An assessment of time-and frequency-based features in a unified two-dimensional decisional space using nonlinear decision functions," J. Clin. Neurophysiol., vol. 26, pp. 381-391, 2009.
23. J. W. Yoon, S. J. Roberts, M. Dyson, and J. Q. Gan, "Adaptive classification for brain computer interface systems using sequential Monte Carlo sampling," Neural Netw., vol. 22, pp. 1286-1294, 2009.
24. J. J. Baker, E. Scheme, K. Englehart, D. T. Hutchinson, and B. Greger, "Continuous detection and decoding of dexterous finger flexions with implantable myoelectric sensors," IEEE Trans. Neural Syst. Rehab. Eng., vol. 18, no. 4, pp. 424-432, Aug. 2010.
25. A. Temko, E. Thomas, W. Marnane, G. Lightbody, and G. Boylan, "EEG-based neonatal seizure detection with support vector machines," Clin. Neurophysiol., vol. 122, pp. 464-473, 2011.
26. A. Alkan, E. Koklukaya, and A. Subasi, "Automatic seizure detection in EEG using logistic regression and artificial neural network," J. Neurosci. Methods, vol. 148, pp. 167-176, 2005. (Pubitemid 41476228)
27. S. B.Wilson, "A neural network method for automatic and incremental learning applied to patient-dependent seizure detection," Clin. Neurophysiol., vol. 116, pp. 1785-1795, 2005.
28. S. Ghosh-Dastidar, H. Adeli, and N. Dadmehr, "Mixed-band waveletchaos-neural network methodology for epilepsy and epileptic seizure detection," IEEE Trans. Biomed. Eng., vol. 54, no. 9, pp. 1545-1551, Sep. 2007. (Pubitemid 47301028)
29. V. Srinivasan, C. Eswaran, and N. Sriraam, "Approximate entropybased epileptic EEG detection using artificial neural networks," IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 3, pp. 288-295, May 2007. (Pubitemid 46758846)
30. S. Acharya et al., "Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area," IEEE Trans. Neural Syst. Rehab. Eng., vol. 16, no. 1, pp. 15-23, Feb. 2008. (Pubitemid 351264086)
31. S. Ghosh-Dastidar, H. Adeli, and N. Dadmehr, "Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection," IEEE Trans. Biomed. Eng., vol. 55, no. 2, pp. 512-518, Feb. 2008.
32. L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, and A. Pazos, "Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks," J. Neurosci. Methods, vol. 191, pp. 101-109, 2010.
33. D. Sussillo et al., "A recurrent neural network for closed-loop intracortical brain-machine interface decoders," J. Neural Eng., vol. 9, p. 026027, 2012.
34. Y. U. Khan and J. Gotman, "Wavelet based automatic seizure detection in intracerebral electroencephalogram," Clin. Neurophysiol., vol. 114, pp. 898-908, 2003. (Pubitemid 36556101)
35. S. Grewal and J. Gotman, "An automatic warning system for epileptic seizures recorded on intracerebral EEGs," Clin. Neurophysiol., vol. 116, pp. 2460-2472, 2005. (Pubitemid 41265975)
36. M. E. Saab and J. Gotman, "A system to detect the onset of epileptic seizures in scalp EEG," Clin. Neurophysiol., vol. 116, pp. 427-442, 2005. (Pubitemid 40126946)
37. C. Kemere et al., "Detecting neural-state transitions using hidden Markov models for motor cortical prostheses," J. Neurophysiol., vol. 100, pp. 2441-2452, 2008.
38. G. Schalk, P. Brunner, L. A. Gerhardt, H. Bischof, and J. R. Wolpaw, "Brain computer interfaces (BCIs):Detection instead of classification, " J. Neurosci. Methods, vol. 167, pp. 51-62, 2008. (Pubitemid 350186061)
39. P. Miller and D. B. Katz, "Stochastic transitions between neural states in taste processing and decision-making," J. Neurosci., vol. 30, pp. 2559-2570, 2010.
40. A. N. Shiryaev, "On optimum methods in quickest detection problems," Theory Probab. Appl., vol. 8, pp. 22-46, 1963.
41. H. V. Poor, "Quickest detection with exponential penalty for delay," Ann. Statist., vol. 26, pp. 2179-2205, 1998. (Pubitemid 128376932)
42. H. C. Lee, W. van Drongelen, A. B. McGee, D. M. Frim, and M. H. Kohrman, "Comparison of seizure detection algorithms in continuously monitored pediatric patients," J. Clin. Neurophysiol., vol. 24, pp. 137-146, 2007. (Pubitemid 46588234)
43. R. J. Elliott, L. Aggoun, and J. B. Moore, Hidden Markov Models. Estimation and Control. New York: Springer, 1995.
44. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis. Cambridge, U.K.: Cambridge University Press, 1998.
45. J. O. Berger, Statistical Decision Theory and Bayesian Analysis, 2nd ed. New York: Springer, 1985.
46. D. P. Bertsekas, Dynamic Programming and Optimal Control, 3rd ed. Belmont, MA: Athena Scientific, 2005.
47. P. R. Freeman, "The secretary problem and its extensions: A review," Int. Stat. Rev., vol. 51, pp. 189-206, 1983.
48. S. V. Sarma et al., "Using point process models to compare neural spiking activity in the subthalamic nucleus of Parkinson's patients and a healthy primate," IEEE Trans. Biomed. Eng., vol. 57, no. 6, pp. 1297-1305, Jun. 2010.
49. D. L. Sherman et al., "Sinusoidal modeling of ictal activity along a thalamus-to-cortex seizure pathway. I: New coherence approaches," Ann. Biomed. Eng., vol. 32, pp. 1252-1264, 2004.
50. H. Bergman, T. Wichmann, B. Karmon, and M. R. DeLong, "The primate subthalamic nucleus. II. Neuronal activity in the MPTP model of Parkinsonism," J. Neurophysiol., vol. 72, pp. 507-520, 1994. (Pubitemid 24273560)
51. Z. M. Williams, J. S. Neimat, G. R. Cosgrove, and E. N. Eskandar, "Timing and direction selectivity of subthalamic and pallidal neurons in patients with Parkinson's disease," Exp. Brain Res., vol. 162, pp. 407-416, 2005. (Pubitemid 40684776)
52. J. T. Gale, D. C. Shields, F. A. Jain, R. Amirnovin, and E. N. Eskandar, "Subthalamic nucleus discharge patterns during movement in the normal monkey and Parkinsonian patient," Brain Res., vol. 1260, pp. 15-23, 2009.
53. E. S. Page, "Continuous inspection schemes," Biometrika, vol. 41, pp. 100-115, 1954.
54. S. Santaniello, D. L. Sherman, M. A. Mirski, N. V. Thakor, and S. V. Sarma, "A Bayesian framework for analyzing iEEG data from a rat model of epilepsy," in Proc. 33rd IEEE EMBS Annu. Conf. (EMBC 2011), Boston, MA, 2011, pp. 1435-1438.
55. S. V. Sarma and S. Santaniello, "Quickest detection of state-transition in point processes: Application to neuronal activity," in Proc. 18th IFAC World Conference (IFAC 2011), Milan, Italy, 2011, pp. 10021-10026.
56. S. Santaniello et al., "Quickest detection of drug resistant seizures: An optimal control approach," Epilepsy Behav., vol. 22, pp. S49-S60, 2011.
57. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, pp. 861-874, 2006.
58. D. L. Snyder and M. I. Miller, Random Point Processes in Time and Space. New York: Springer, 1991.
59. E. N. Brown, R. Barbieri, U. T. Eden, and L. M. Frank, "Likelihood methods for neural data analysis," in Computational Neuroscience: A Comprehensive Approach, J. Feng, Ed. London, U.K.: CRC, 2003, pp. 253-286.
60. P. McCullagh and J. A. Nelder, Generalized Linear Models, 2nd ed. Boca Raton, FL: CRC, 1990.
61. H. G. Golub and C. F. van Loan, Matrix Computations, 3rd ed. Baltimore, MD: Johns Hopkins Univ. Press, 1996.
62. J. W. Pillow, Y. Ahmadian, and L. Paninski, "Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains," Neural Comput., vol. 23, pp. 1-45, 2011.