PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Mathematical methods of signal analysis applied in medical diagnostic

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Digital signal processing, such as filtering, information extraction, and fusion of various results, is currently an integral part of advanced medical therapies. It is especially important in neurosurgery during deep-brain stimulation procedures. In such procedures, the surgical target is accessed using special electrodes while not being directly visible. This requires very precise identification of brain structures in 3D space throughout the surgery. In the case of deep-brain stimulation surgery for Parkinson’s disease (PD), the target area—the subthalamic nucleus (STN)—is located deep within the brain. It is also very small (just a few millimetres across), which makes this procedure even more difficult. For this reason, various signals are acquired, filtered, and finally fused, to provide the neurosurgeon with the exact location of the target. These signals come from preoperative medical imaging (such as MRI and CT), and from recordings of brain activity carried out during surgery using special brain-implanted electrodes. Using the method described in this paper, it is possible to construct a decision-support system that, during surgery, analyses signals recorded within the patient’s brain and classifies them as recorded within the STN or not. The constructed classifier discriminates signals with a sensitivity of 0.97 and a specificity of 0.96. The described algorithm is currently used for deep-brain stimulation surgeries among PD patients.
Rocznik
Strony
449--462
Opis fizyczny
Bibliogr. 33 poz., tab., wykr.
Twórcy
  • Bioinformatics and Machine Recognition Department, Research and Academic Computer Network, Kolska 12, 01-045 Warsaw, Poland
Bibliografia
  • [1] Anderson, P.B. and Rogers, M.H. (2009). Deep Brain Stimulation: Applications, Complications and Side Effects, Nova Biomedical Books, New York, NY.
  • [2] Apostolidis-Afentoulis, V. and Lioufi, K.I. (2015). SVM classification with linear and RBF kernels, http://www.academia.edu/13811676/SVM_Classificat ion_with_Linear_and_RBF_kernels.
  • [3] Cagnan, H., Dolan, K., He, X., Contarino, M.F., Schuurman, R., Van Den Munckhof, P., Wadman, W.J., Bour, L. and Martens, H.C. (2011). Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity, Journal of Neural Engineering 8(4): 046006.
  • [4] Ciecierski, K.A., Raś, Z.W. and Przybyszewski, A.W. (2014a). Foundations of automatic system for intrasurgical localization of subthalamic nucleus in Parkinson patients, Web Intelligence and Agent Systems 12(1): 63–82.
  • [5] Ciecierski, K., Mandat, T., Rola, R., Raś, Z.W. and Przybyszewski, A.W. (2014b). Computer aided subthalamic nucleus (STN) localization during deep brain stimulation (DBS) surgery in Parkinson’s patients, Annales Academiae Medicae Silesiensis 5(68): 275–283.
  • [6] Dietterich, T.G. (2000). Ensemble methods in machine learning, in J. Kittler and F. Rodi (Eds), Multiple Classifier Systems, Springer, Berlin, pp. 1–15, DOI: 10.1007/3-540-45014-91.
  • [7] Duch, W., Adamczak, R. and Diercksen, G.H.F. (2000). Classification, association and pattern completion using neural similarity based methods, International Journal of Applied Mathematics and Computer Science 10(4): 747–766.
  • [8] Freeman, E.A. and Moisen, G.G. (2008). A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa, Ecological Modelling 217(1–2): 48–58.
  • [9] Ho, A.L., Ali, R., Connolly, I.D., Henderson, J.M., Dhall, R., Stein, S.C. and Halpern, C.H. (2018). Awake versus asleep deep brain stimulation for Parkinson’s disease: A critical comparison and meta-analysis, Journal of Neurology, Neurosurgery & Psychiatry 89(7): 687–691.
  • [10] Hutchison, W.D., Allan, R.J., Opitz, H., Levy, R., Dostrovsky, J.O., Lang, A.E. and Lozano, A.M. (1998). Neurophysiological identification of the subthalamic nucleus in surgery for Parkinson’s disease, Annals of Neurology 44(4): 622–628.
  • [11] Israel, Z. and Burchiel, K.J. (2011). Microelectrode Recording in Movement Disorder Surgery, Thieme, Stuttgart.
  • [12] Jeleń, Ł., Fevens, T. and Krzyżak, A. (2008). Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies, International Journal of Applied Mathematics and Computer Science 18(1): 75–83, DOI: 10.2478/v10006-008-0007-x.
  • [13] Jensen, A. and la Cour-Harbo, A. (2001). Ripples in Mathematics: The Discrete Wavelet Transform, Berlin/Heidelberg.
  • [14] Kano, T., Katayama, Y., Kobayashi, K., Kasai, M., Oshima, H., Fukaya, C. and Yamamoto, T. (2006). Detection of boundaries of subthalamic nucleus by multiple-cell spike density analysis in deep brain stimulation for Parkinson’s disease, in J.W. Chang et al. (Eds), Advances in Functional and Reparative Neurosurgery, Springer, Vienna, pp. 33–35.
  • [15] Koch, C. (2004). Biophysics of Computation: Information Processing in Single Neurons, Oxford University Press, Oxford.
  • [16] Levy, R., Hutchison, W.D., Lozano, A.M. and Dostrovsky, J.O. (2000). High-frequency synchronization of neuronal activity in the subthalamic nucleus of parkinsonian patients with limb tremor, Journal of Neuroscience 20(20): 7766–7775.
  • [17] Lewicki, M.S. (1998). A review of methods for spike sorting: The detection and classification of neural action potentials, Network: Computation in Neural Systems 9(4): R53–R78.
  • [18] Mallet, L., Schüpbach, M., N’Diaye, K., Remy, P., Bardinet, E., Czernecki, V., Welter, M.-L., Pelissolo, A., Ruberg, M., Agid, Y. and Yelnik, J. (2007). Stimulation of subterritories of the subthalamic nucleus reveals its role in the integration of the emotional and motor aspects of behavior, Proceedings of the National Academy of Sciences 104(25): 10661–10666.
  • [19] Mandat, T.S., Hurwitz, T. and Honey, C.R. (2006). Hypomania as an adverse effect of subthalamic nucleus stimulation: Report of two cases, Acta Neurochirurgica 148(8): 895–898.
  • [20] Mandat, T., Tykocki, T., Koziara, H., Koziorowski, D., Brodacki, B., Rola, R., Bonicki, W. and Nauman, P. (2011). Subthalamic deep brain stimulation for the treatment of Parkinson disease, Neurologia i Neurochirurgia Polska 45(1): 32–36.
  • [21] Moran, A., Bar-Gad, I., Bergman, H. and Israel, Z. (2006). Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure, Movement Disorders 21(9): 1425–1431.
  • [22] Nieuwenhuys, R., Voogd, J. and Van Huijzen, C. (2007). The Human Central Nervous System: A Synopsis and Atlas, Springer, Berlin.
  • [23] Novak, P., Daniluk, S., Ellias, S.A. and Nazzaro, J.M. (2007). Detection of the subthalamic nucleus in microelectrographic recordings in Parkinson disease using the high-frequency (>500 Hz) neuronal background, Journal of Neurosurgery 106(1): 175–179.
  • [24] Parent, A. and Hazrati, L.-N. (1995). Functional anatomy of the basal ganglia. II: The place of subthalamic nucleus and external pallidium in basal ganglia circuitry, Brain Research Reviews 20(1): 128–154.
  • [25] Saleh, S., Swanson, K.I., Lake, W.B. and Sillay, K.A. (2015). Awake neurophysiologically guided versus asleep MRI-guided STN DBS for Parkinson disease: A comparison of outcomes using levodopa equivalents, Stereotactic and Functional Surgeny 93(6): 419–426.
  • [26] Schaltenbrand, G. (1977). Atlas for Stereotaxy of the Human Brain, Georg Thieme, Stuttgart.
  • [27] Schiaffino, L., Muñoz, A.R., Martínez, J.G., Villora, J.F., Gutiérrez, A. and Torres, I.M. (2016). STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery, Journal of Physics: Conference Series 705(1): 012050.
  • [28] Shamir, R.R., Zaidel, A., Joskowicz, L., Bergman, H. and Israel, Z. (2012). Microelectrode recording duration and spatial density constraints for automatic targeting of the subthalamic nucleus, Stereotactic and Functional Neurosurgery 90(5): 325–334.
  • [29] Smith, S.W. (1997). The Scientist & Engineer’s Guide to California Technical, Digital Signal Processing, Publishing, San Diego, CA.
  • [30] Temel, Y., Blokland, A., Steinbusch, H.W.M. and Visser-Vandewalle, V. (2005). The functional role of the subthalamic nucleus in cognitive and limbic circuits, Progress in Neurobiology 76(6): 393–413.
  • [31] Valsky, D., Marmor-Levin, O., Deffains, M., Eitan, R., Blackwell, K. T., Bergman, H. and Israel, Z. (2017). Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery, Movement Disorders 32(1): 70–79.
  • [32] Williams, C.K.I. (2003). Learning with kernels: Support vector machines, regularization, optimization, and beyond, Journal of the American Statistical Association 98(462): 489–489.
  • [33] Zaidel, A., Spivak, A., Shpigelman, L., Bergman, H. and Israel, Z. (2009). Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a hidden Markov model, Movement Disorders 24(12): 1785–1793.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1c2ac7ad-6721-4fcb-84b2-a09ae9a9ba28
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.