A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method

Jankovic J (2007) Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79(4):368–376 Khorasani A, Daliri MR (2014) HMM for classification of Parkinson’s disease based on the raw gait data. J Med Syst 38(12):1–6 Langston JW (2002) Parkinson’s disease: current and future challenges. NeuroToxicology 23(4–5):443–450 Pahwa R, Lyons KE (2013) Handbook of Parkinson’s disease, 5th edn. Informa Healthc, USA Lang AE, Lozano AM (1998) Parkinson’s disease—first of two parts. N Engl J Med 339:1044–1053 Schrag A, Ben-Schlomo Y, Quinn N (2002) How valid is the clinical diagnosis of Parkinson‘s disease in the community? J Neurol Neurosurg Psychiatry 7:529–535 Moore DJ, West AB, Dawson VL, Dawson TM (2005) Molecular pathology of Parkinson’s disease. Annu Rev Neurosci 28:57–87 Elbaz A, Bower JH, Maraganore DM, McDonnell SK, Peterson BJ, Ahlskog JE, Schaid DJ, Rocca WA (2002) Risk tables for parkinsonism and Parkinson’s disease. J Clin Epidemiol 55:25–31 Ramaker C, Marinus J, Stiggelbout AM, van Hilten BJ (2002) Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov Disord 17(5):867–876 Ozcift A (2012) SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease. J Med Syst 36(4):2141–2147 Sakar BE (2014) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings, Ph.D. Thesis, Istanbul University, Turkey Darley FL, Aronson AE, Brown JR (1969) Differential diagnostic patterns of dysarthria. J Speech Hear Res 12:246–269 Gamboa J, Jimenez-Jimenez FJ, Nieto A, Montojo J, Orti-Pareja M, Molina JA, García-Albea E, Cobeta I (1997) Acoustic voice analysis in patients with Parkinson‘s disease treated with dopaminergic drugs. J Voice 11:314–320 Ho A, Bradshaw JL, Iansek R (2008) For better or for worse: the effect of Levodopa on Speech in Parkinson‘s disease. Mov Disord 23(4):574–580 Harel B, Cannizzaro M, Snyder PJ (2004) Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: a longitudinal case study. Brain Cogn 56:24–29 Skodda S, Rinsche H, Schlegel U (2009) Progression of dysprosody in Parkinson’s disease over time—a longitudinal study. Mov Disord 24(5):716–722 Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 56(4):1010–1022 Harel B, Cannizzaro M, Snyder PJ (2004) Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: a longitudinal case study. Brain Cogn 56:24–29 Seera M, Lim CP, Tan SC, Loo CK (2015) A hybrid FAM–CART model and its application to medical data classification. Neural Comput Appl. doi:10.1007/s00521-015-1852-9 Sapir S, Ramig L, Spielman J, Fox C (2010) Formant centralization ratio (FCR): a proposal for a new acoustic measure of dysarthric speech. J Speech Lang Hear Res 53:114–125 Cnockaert L, Schoentgen J, Auzou P, Ozsancak C, Defebve L, Grenez F (2008) Low frequency vocal modulations in vowels produced by Parkinsonian subjects. Speech Commun 50:288–300 Erdogdu Sakar B, Isenkul M, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 17(4):828–834 Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 56(4):1015–1022 Shahbaba B, Neal R (2009) Nonlinear models using Dirichlet process mixtures. J Mach Learn Res 10:1829–1850 Das R (2010) A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl 37(2):1568–1572 Guo PF, Bhattacharya P, Kharma N (2010) Advances in detecting Parkinson’s disease. Med Biom 6165:306–314 Luukka P (2011) Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst Appl 38(4):4600–4607 Li DC, Liu CW, Hu SC (2011) A fuzzy-based data transformation for feature extraction to increase classification performance with small medical datasets. Artif Intell Med 52(1):45–52 Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Progr Biomed 104(3):443–451 Spadoto AA, Guido RC, Carnevali FL, Pagnin AF, Falcao AX, Papa JP (2011) Improving Parkinson’s disease identification through evolutionary-based feature selection. In: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC ‘11), pp 7857–7860 Polat K (2012) Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy c-means clustering. Int J Syst Sci 43(4):597–609 Zuo WL, Wang ZY, Liu T, Chen HL (2013) Effective detection of Parkinson’s disease using an adaptive fuzzy k-nearest neighbor approach. Biomed Signal Process Control 8(4):364–373 Sakar CO, Kursun O (2010) Telediagnosis of Parkinson’s disease using measurements of dysphonia. J Med Syst 34(4):591–599 Chen HL, Huang CC, Yu XG, Xu X, Sun X, Wang G, Wang SJ (2013) An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40(1):263–271 Ma C, Ouyang J, Chen HL, Zhao XH (2014) An efficient diagnosis system for Parkinson’s disease using kernel-based extreme learning machine with subtractive clustering features weighting approach. Comput Math Methods Med. doi:10.1155/2014/985789 Parkinsons Dataset. https://archive.ics.uci.edu/ml/datasets/Parkinsons. Accessed 10 Sept 2014 Elbaz A, Bower JH, Maraganore DM, McDonnell SK, Peterson BJ, Ahlskog JE, Schaid DJ, Rocca WA (2002) Risk tables for Parkinsonism and Parkinson’s disease. J Clin Epidemiol 55:25–31 Little MA, McSharry PE, Hunter EJ, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 56:1015–1022 MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability. University of California Press, Berkeley, CA, pp 281–297. MR0214227. Zbl 0214.46201 Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York Yager RR, Filev DP (1994) Generation of fuzzy rules by mountain clustering. IEEE Trans Syst Man Cybern 24:209–219 Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278 Gunes S, Polat K, Yosunkaya S (2010) Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Syst Appl 37(12):7729–7736 Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M (2014) Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput Appl 24(7–8):1917–1928 Hirose A, Shotaro Y (2013) Relationship between phase and amplitude generalization errors in complex and real-valued feed-forward neural networks. Neural Comput Appl 22(7–8):1357–1366 Ceylan R, Ceylan M, Ozbay Y, Kara S (2011) Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease. Expert Syst Appl 38(8):9744–9751 Peker M, Sen B, Delen D (2015) A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Health Inf. doi:10.1109/JBHI.2014.23877952015 Sivachitraa M, Savithab R, Sureshb S, Vijayachitrac S (2015) A fully complex-valued fast learning classifier (FC-FLC) for real-valued classification problems. Neurocomputing 149:198–206 Shogo O, Arima Y, Hirose A (2014) Millimeter-wave security imaging using complex-valued self-organizing map for visualization of moving targets. Neurocomputing 134:247–253 Nitta T (2004) Orthogonality of decision boundaries in complex-valued neural networks. Neural Comput 16(1):73–97 Aizenberg I (2011) Complex-valued neural networks with multi-valued neurons. Springer, Heidelberg, pp 264–265 Nitta T (1997) An extension of the back-propagation algorithm to complex numbers. Neural Network 10:1391–1415 Ozbay Y, Kara S, Latifoglu F, Ceylan R, Ceylan M (2007) Complex-valued wavelet artificial neural network for Doppler signals classifying. Artif Intell Med 40(2):143–156 Nitta T (1993) A back-propagation algorithm for complex numbered neural networks. In: Proceedings of 1993 international joint conference on neural networks, pp 1649–1652 Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46 Astrom F, Koker R (2011) A parallel neural network approach to prediction of Parkinson’s disease. Expert Syst Appl 38(10):12470–12474 Chen HL, Huang CC, Yu XG, Xuc X, Sund X, Wangd G, Wangd SJ (2013) An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40(1):263–271