Classification of auditory brainstem response using wavelet decomposition and SVM network

• Autorzy: Dobrowolski A. , Suchocki M. , Tomczykiewicz K. , Majda-Zdancewicz E.

Identyfikatory

DOI

10.1016/j.bbe.2016.01.003

• Języki publikacji: EN

Abstrakty

EN

In electrophysiological hearing assessment and diagnosis of brain stem lesions are most often used auditory brainstem evoked potentials of short latency. They are characterized by successively arranged maxima as a function of time, called waves. Morphology of the course, in particular, the timing and amplitude of each wave, allow neurologist diagnosis, which is not an easy task. Neurologist requires experience, attention and very good perception. In order to support the diagnostic process, the authors have developed an algorithm implementing the automated classification of auditory evoked potentials to the group of pathological and physiological cases. The sensitivity and specificity of group numbering of 130 cases are respectively 95% and 98% and classification accuracy is equal to 97%. The procedures developed by the authors for generation of distinctive features based on wavelet decomposition with a SVM network-based classifier have been integrated into a diagnostic application directly interoperable with Nicolet Viking Select (Natus Medical Inc., USA) system data files.

Słowa kluczowe

EN

auditory brainstem response; brainstem auditory evoked; potentials; wavelet decomposition; support vector machine

PL

słuchowy potencjał wywołany; pień mózgu; dekompozycja falkowa; maszyna wektorów nośnych

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 2
• Strony: 427--436
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Dobrowolski A.

• Military University of Technology, Faculty of Electronics, Institute of Electronic Systems, Kaliskiego 2, 00-908 Warsaw, Poland

autor

Suchocki M.

• Military University of Technology, Faculty of Electronics, Institute of Electronic Systems, Kaliskiego 2, 00-908 Warsaw, Poland

autor

Tomczykiewicz K.

• Military Institute of Health Service, Department of Neurology, Warsaw, Poland

autor

Majda-Zdancewicz E.

• Military University of Technology, Faculty of Electronics, Institute of Electronic Systems, Kaliskiego 2, 00-908 Warsaw, Poland

Bibliografia

• [1] Regan D. Evoked potentials in psychology, sensory, physiology and clinical medicine. Springer; 2012.
• [2] Yamada T, Meng E. Practical guide for clinical neurophysiologic testing: EEG. Lippincott Williams & Wilkins; 2010.
• [3] Chiappa KH. Evoked potentials in clinical medicine. New York: Reven Press; 1990.
• [4] Colon EJ, Visser SL. Evoked potential manual: a practical guide to clinical applications. 2nd rev. ed. Kluwer Academic Publishers; 1990.
• [5] Regan D. Evoked potentials and evoked magnetic fields in science and medicine, human brain electrophysiology. Elsevier; 1989.
• [6] Burkard RF, Don M, Eggermont JJ. Auditory evoked potentials, basic principles and clinical application. Lippincott Williams & Wilkins; 2006.
• [7] Pearl PL, Emsellem HA. Neuro-logic: a primer on localization. Demos Medical Publishing; 2014.
• [8] Augustyniak P. Time-frequency modelling and discrimination of noise in the electrocardiogram. Physiol Meas 2003;24(3):753–67.
• [9] Beynon AJ, Snik AF. Use of the event-related P300 potential in cochlear implant subjects for the study of strategy-dependent speech processing. Int J Audiol 2004;43: S44–7.
• [10] Legatt AD. Brainstem auditory evoked potentials (BAEPs). Encyclopedia of the neurological sciences. Elsevier; 2014.
• [11] Binnie CD, Cooper R, Mauguiere F, Osselton J, Prior PF, Tedman BM.2nd ed. EMG nerve conduction and evoked potentials clinical neurophysiology, vol. 1, 2nd ed. Elsevier; 2004. p. 357–66.
• [12] Valderrama JT, Alvarez I, de la Torre A, Segura JC, Sainz M, Vargas JL. Recording of auditory brainstem response at high stimulation rates using randomized stimulation and averaging. Acoust Soc Am 2012.
• [13] Valderrama JT, de la Torre A, Alvarez I, Segura JC, Thornton ARD, Sainz M, et al. Automatic quality assessment and peak identification of auditory brainstem responses with fitted parametric peaks. Comput Meth Programs Biomed 2014;114:262–75.
• [14] Picton TW. Human Auditory evoked potentials. Plural Publishing Inc; 2010.
• [15] Tsutsui T, Ohno M, Symon L, Wang A. Combined measurement of brainstem auditory and somatosensory evoked potentials in a surgically treated brainstem hematoma. Elsevier; 2004.
• [16] Jiang ZD, Wu YY, Wilkinson AR. Age-related changes in BAER at different click rates from neonates to adults. Acta Paediatr 2009.
• [17] Picton TW, Hillyard SA, Krausz HI, Galambos R. Human auditory evoked potentials. I: Evaluation of components. Elsevier; 2003.
• [18] Izworski A, Tadeusiewicz R, Skarżyński H, Kochanek K, Bułka J, Wochlik I. Automatic analysis and recognition of the auditory brainstem response signals. 18th International Congress on Acoustics; 2004.
• [19] Dobrowolski A, Suchocki M, Tomczykiewicz K. Computer analysis of auditory brainstem evoked potentials. 15th IEEE SPA Conference; 2011. pp. 132–7.
• [20] Dobrowolski A, Wierzbowski M, Tomczykiewicz K. Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders. Comput Meth Programs Biomed 2012;107(3):393–403.
• [21] Tomczykiewicz K, Dobrowolski A, Wierzbowski M. Evaluation of motor unit potential wavelet analysis in the electrodiagnosis of neuromuscular disorders. Muscle Nerve 2012;46(1):63–9.
• [22] Wójtowicz B, Dobrowolski A, Tomczykiewicz K. Fall detector using discrete wavelet decomposition and SVM classifier. Metrol Meas Syst 2015;22(2):303–14.
• [23] Daubechies I. The wavelet transform, time-frequency localizations and signal analysis. IEEE Trans Inf Theory 1990;36(5):961–1005.
• [24] Jensen A, Cour-Harbo A. Riples in mathematics the discrete wavelet transform. Springer; 2001.
• [25] Mallat SG. A wavelet tour of signal processing. Academic Press; 1999.
• [26] Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2:121–67.
• [27] Acir N, Özdamar Ö, Güzelis C. Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection. Eng Appl Artif Intell 2006;19:209–18.
• [28] Acir N, Erkan Y, Bahtiyar YA. Auditory brainstem response classification for threshold detection using estimated evoked potential data: comparison with ensemble of averaged data. Neural Comput Appl 2013;22:859–67.
• [29] Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157–82.
• [30] Vapnik VN. The nature of statistical learning theory. 2nd ed. Springer; 1999.
• [31] Li H, Jiang T. A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs. J Comput Biol 2005;12(6):702–18.
• [32] McCullagh P, Wang H, Zheng H, Lightbody G, McAllister G. A comparison of supervised classification methods for auditory brainstem response determination. Stud Health Technol Inf 2007;129:1289–93.
• [33] Davey R, McCullagh P, Lightbody G, McAllister G. Auditory brainstem response classification: a hybrid model using time and frequency features. Artif Intell Med 2007;40: 1–14.
• [34] Rahbar S, Abolhassani MD, Arabalibeik H, Jafari AH. Auditory brainstem response classification using wavelet transform and multilayer feed-forward networks. International Summer School and Symposium on Medical Devices and Biosensors; 2007.
• [35] Matlab 7.13.0, getting started guide. The MathWorks Inc; 2011.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.