Abstract
Stroke has been one of the diseases with high incidence in the world, which is the main reason for adult deformity. It’s significant to diagnose stroke quickly and accurately. Currently the main diagnostic method of acute ischemic stroke is also Computed Tomography. Meanwhile, electroencephalogram (EEG) is an electrophysiological manifestation that directly reflects brain activity. Through the analysis of EEG, a large amount of physiological and pathological information can be found. Using qEEG as an indicator for the diagnosis of stroke patients has become a novel and prospective method. This paper proposed a classification method for stroke patients and healthy controls using Quadratic Discriminant Analysis. Four simple features of task-EEG which are RPR of Beta, Delta, DAR and DTABR were obtained by Welch’s method. The classification results showed the certain potential for qEEG as an diagnosis method.
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References
Whiteford, H.A., et al.: Global burden of disease attributable to mental and substance use disorders: findings from the global burden of disease study 2010. Lancet 382(9904), 1575–1586 (2013)
Johnson, C.O., et al.: Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 18(5), 439–458 (2019)
Huang, C.-Y., et al.: Improving the utility of the brunnstrom recovery stages in patients with stroke: validation and quantification. Medicine 95(31), e4508 (2016)
Signe Brunnstrom, S.: Motor testing procedures in hemiplegia: based on sequential recovery stages. Phys. Ther. 46(4), 357–375 (1966)
Fugl-Meyer, A.R., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scandinavian J. Rehabi. Med. 7(1), 13–31 (1975)
Weeren, A.J.T.M., van Putten, M.J.A.M., Sheorajpanday, R.V.A., Nagels, G., De Deyn, P.P.: Reproducibility and clinical relevance of quantitative EEG parameters in cerebral ischemia: a basic approach. Clinical Neurophysiol. 120, 845–855 (2009)
Minfen, S., Zhiwei, L.: Classification of mental task EEG signals using wavelet packet entropy and SVM. In: 8th International Conference on Electronic Measurement and Instruments. IEEE (2007)
Finnigan, S., van Putten, M.J.: EEG in Ischaemic stroke: quantitative EEG can uniquely inform (sub-) acute prognoses and clinical management. Clin. Neurophysiol. 124, 10–19 (2013)
Foreman, B., Claassen, J.: Quantitative EEG for the detection of brain ischemia. In: Vincent, J.L. (ed.) Annual Update in Intensive Care and Emergency Medicine 2012. Annual Update in Intensive Care and Emergency Medicine, vol 2012. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-25716-2_67
Nair, G.J., Sita, J.: Feature extraction and classification of EEG signals for mapping motor area of the brain. In: International Conference on Control Communication and Computing (ICCC), pp. 463–468. IEEE (2013)
Chen, K., Ye, F., Ding, J.S., et al.: Investigation of corticomuscular functional coupling during hand movements using vine copula. Brain Sci. 12, 754 (2022)
Welch, P.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)
Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recogn. 3(2), 145–180 (2016)
Badri, C., Rahma, O.N., Wijaya, S.K.: Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. In: International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM), pp. 180–186. IEEE (2017)
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Pan, X., Chang, H., Liu, H. (2022). A Classification Method for Acute Ischemic Stroke Patients and Healthy Controls Based on qEEG. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_49
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DOI: https://doi.org/10.1007/978-3-031-13822-5_49
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