Optimum Path Forest Classifier Applied To Laryngeal Pathology Detection

Optimum path forest-based classifiers are a novel approach for supervised pattern recognition. The OPF classifier differs from traditional approaches by not estimating probability density functions of the classes neither assuming samples linearity, and creates a discrete optimal partition of the feature space, in which the decision boundary is obtained by the influence zones of the most representative samples of the training set. Due to the large number of applications in biomedical signal processing involving pattern recognition techniques, specially voice disorders identification, we propose here the laryngeal pathology detection by means of OPF. Experiments were performed in three public datasets against SVM, and a comparison in terms of accuracy rates and execution times was also regarded.249252A.A. Spadotto, J.P. Papa, A.R. Gatto, P.C. Cola, J.C. Pereira, R.C. Guido, and A.O. Schelp, Denoising swallowing sound to improve the evaluators qualitative analysis., Computers and Electrical Engineering: Advances on Computer-based Biological Signal Processing Techniques, 34, no. 2, pp. 148-153, 2008Spadotto, A.A., Pereira, J.C., Guido, R.C., Papa, J.P., Falcão, A.X., Gatto, A.R., Cola, P.C., Shelp, A.O., Oropharyngeal dysphagia identification using wavelets and optimum path forest (2008) Proceedings of the 3th IEEE International Symposium on Communications, Control and Signal Processing, , to appearHadjitodorov, S.T., Boyanov, B., Teston, B., Laryngeal pathology detection by means of class-specific neural maps (2000) IEEE Transactions on Information Technology in Biomedicine, 4 (1), pp. 68-73Boyanov, B., Hadjitodorov, S.T., Acoustic analysis of pathological voices. a voice analysis systemfor the screening of laryngeal diseases (1997) IEEE Transactions on Engineering in Medicine and Biology Magazine, 16 (4), pp. 74-82Godino-Llorente, J.I., Vilda, P.G., Senz-Lechn, N., Blanco-Velasco, M., Craz-Roldn, F., Ferrer-Ballester, M.A., (2005) Support vector machines applied to the detection of voice disorders, 3817, pp. 219-230Perrin, E., Berger-Vachon, C., Kauffmann, I., Collet, L., (2006) Acoustical recognition of laryngeal pathology using the fundamental frequency and the first three formants of vowels, 35 (4), pp. 361-368Mezzalama, M., Prinetto, P., Morra, B., (2006) Experiments in automatic classification of laryngeal pathology, 21 (5), pp. 603-611Hadjitodorov, S.T., Ivanov, T., Boyanov, B., Analysis of dysphony using objective voice parameter (1993) Proceedings of the II Balkan Conference on Operational Research, pp. 911-917Schlotthauer, G., Torres, M.E., Jackson-Menaldi, C., Automatic diagnosis of pathological voices (2006) Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing, pp. 150-155Boser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for-optimal margin classifiers (1992) Proc. 5th Workshop on Computational Learning Theory, pp. 144-152. , New York, NY, USA, ACM PressDuan, K., Keerthi, S.S., Which is the best multiclass svm method? an empirical study (2005) Multiple Classifier Systems, pp. 278-285Papa, J.P., Falcão, A.X., Miranda, P.A.V., Suzuki, C.T.N., Mascarenhas, N.D.A., Design of robust pattern classifiers based on optimum-path forests (2007) Mathematical Morphology and its Applications to Signal and Image Processing (ISMM), pp. 337-348. , MCT/INPEPapa, J.P., Falcão, A.X., Suzuki, C.T.N., Mascarenhas, N.D.A., A discrete approach for supervised pattern recognition (2008) 12th International Workshop on Combinatorial Image Analysis (IWCIA), 4958, pp. 136-147. , SpringerJ.A. Montoya-Zegarra, J.P. Papa, N.J. Leite, R.S. Torres, and A.X. Falcão, Rotation-invariant texture recognition, in 3rd International Symposium on Visual Computing, Lake Tahoe, Nevada, CA, USA, Nov 2007, Part II, LNCS 4842, pp. 193-204, SpringerFalcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on PAMI, 26 (1), pp. 19-29. , JanAllène, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R., Some links between min-cuts, optimal spanning forests and watersheds (2007) Proceedings of the ISMM'08, pp. 253-264Hadjitodorov, S.T., Mitev, P., Boyanov, B., (2005) Laryngeal databases, , http://www.informatics.bangor.ac.uk/~kuncheva, Available inCohen, J., A coefficient of agreement for nominal scales (1960) Educational and Psychological Measurement, 20, pp. 37-46Chang, C.C., Lin, C.J., (2001) LIBSVM: A library for support vector machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, Software available at ur

Oropharyngeal Dysphagia Identification Using Wavelets And Optimum Path Forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.735740Logemann, J.A., (1983) Evaluation and treatment of swallowing disorders, , College- Hill PressPlatt, J., (2001) Dysphagia Management for Long-Term Care: A Manual for Nurses and Other Healthcare Professionals, Clinical and Educational ServicesFurkim, A.M., Silva, R.G., (1999) Programas de reabilitacão em disfagia neurogênica, , Frôntis EditorialChen, M.Y.M., Ott, D.J., Peele, V.N., Gelfand, D.W., Oropharynx in pacients with cerebrovascular disease: Evaluation with videofluoroscopy (1990) Radiology, 176, pp. 38-47J.B. Palmer and A.S. Duchane, Rehabilitation of swallowing disorders due to stroke, hys. Med. Rehab.Clin. N. Amer, 2, 1991Horner, J., Buoyer, F.G., Alberts, M.J., Helms, M.J., Dysphagia folowing brain stem stroke: Clinical correlates and outcome (1991) Arch Neurol, 48, pp. 1170-1173Buchhloz, D.W., (1997) Neurologic disordes of swallowing, pp. 37-62. , ppVeis, S.L., Logemann, J.A., Swallowing disorders in persons with cerebrovascular accident (1985) Arch Phys Med Rehabil, 66, pp. 372-376Gordon, C., Hewer, R.L., Wade, D.T., Dysphagia in acute stroke (1987) Brazilian Medical Journal, pp. 3411-3414Silva, R.G., Disfagia neurogênica em adultos: Uma proposta para avaliacão clínica (1999) Disfagias Orofaríngeas, pp. 35-47Cichero, J.A.Y., Murdoch, B.E., The physiologic cause of swallowing sounds: Answers from heart sounds and vocal tract acoustics (1998) Dysphagia, 13, pp. 39-52Youmans, S.R., Stierwalt, J.A.G., The physiologic cause of swallowing sounds: Answers from heart sounds and vocal tract acousticsan acoustic profile of normal swallowing (2005) Dysphagia, 20, pp. 195-209Lazareck, L.J., Moussavi, Z., Classification of normal and dysphagic swallows by acoustical means (2004) IEEE Transactions on Biomedical Engineering, 51Kuncheva, L.I., (2004) Combining Pattern Classifiers: Methods and Algorithms, , Wiley-InterscienceAboofazeli, M., Moussavi, Z., Automated extraction of swallowing sounds using a wavelet-based filter (2006) Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE, pp. 5607-5610Papa, J.P., Falcão, A.X., Miranda, P.A.V., Suzuki, C.T.N., Mascarenhas, N.D.A., Design of robust pattern classifiers based on pptimum-path forests (2007) Mathematical Morphology and its Applications to Signal and Image Processing (ISMM), pp. 337-348. , MCT/INPEPapa, J.P., Falcão, A.X., Suzuki, C.T.N., Mascarenhas, N.D.A., A discrete approach for supervised pattern recognition (2008) Proceedings of the 12th International Workshop on Combinatorial Image Analysis, , Accepted for publicationBoser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for optimal margin classifiers (1992) Proc. 5th Workshop on Computational Learning Theory, pp. 144-152. , New York, NY, USA, ACM PressJ.A. Montoya-Zegarra, J.P. Papa, N.J. Leite, R.S. Torres, and A.X. Falcão, Rotation-invariant texture recognition, in 3rd International Symposium on Visual Computing, Lake Tahoe, Nevada, CA, USA, Nov 2007, Part II, LNCS 4842, pp. 193-204, SpringerVapnik, V.N., An overview of statistical learning theory (1999) Neural Networks, IEEE Transactions on, 10 (5), pp. 988-999Bazaraa, M.S., Sherali, H.D., Shetti, C.M., (2006) Nonlinear programming theory and algorithms, , Wiley-InterscienceBurges, C.J.C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 121-167Falcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on PAMI, 26 (1), pp. 19-29. , JanCormen, T., Leiserson, C., Rivest, R., (1990) Introduction to Algorithms, , MITAllène, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R., Some links between min-cuts, optimal spanning forests and watersheds (2007) Proceedings of the 8th Int. Symposium on Mathematical Morphology, pp. 253-264Addison, P.S., (2002), Institute of Physics Publishing Bristol and PhiladelphiaMallat, S.G., A theory for multiresolution signal decomposition: The wavelet representation (1989) IEEE Transactions on Pattern Analysis and Machine Intelligence, 11 (7), pp. 674-693Daubechies, I., The wavelet transform, time-frequency localization and signal analysis (1990) IEEE Transactions on Information Theory, 36 (5), pp. 961-1005I. Daubechies, Philadelphia, PA: Soc. Indus. Applied Math., 1992Daubechies, I., Orthonormal bases of compactly supported wavelets ii, variations on a theme (1993) SIAM J. Math. Anal, 24 (2), p. 499519Spadotto, A.A., Papa, J.P., Gatto, A.R., Cola, P.C., Pereira, J.C., Guido, R.C., Schelp, A.O., Denoising swallowing sound to improve the evaluators qualitative analysis (2007) Computers and Electrical Engineering, , accepted for publicationChang, C.C., Lin, C.J., (2001) LIBSVM: A library for support vector machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, Software available at ur