Abstract
Computer-aided diagnosis (CAD) systems have allowed to enhance the performance of conventional, medical diagnosis procedures in different scenarios. Particularly, in the context of voice pathology detection, the use of machine learning algorithms has proved to be a promising and suitable alternative. This work proposes the implementation of two well known classification algorithms, namely artificial neural networks (ANN) and support vector machines (SVM), optimized by particle swarm optimization (PSO) algorithm, aimed at classifying voice signals between healthy and pathologic ones. Three different configurations of the Saarbrucken voice database (SVD) are used. The effect of using balanced and unbalanced versions of this dataset is proved as well as the usefulness of the considered optimization algorithm to improve the final performance outcomes. Also, proposed approach is comparable with state-of-the-art methods.
H.J. Areiza-Laverde—This work is carried out under grants provided by Programa Nacional de Jóvenes Investigadores e Innovadores – COLCIENCIAS – Announcement 775 of 2017.
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References
Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inf. Sci. 415, 190–198 (2017)
Al-nasheri, A., Muhammad, G., Alsulaiman, M., Ali, Z.: Investigation of voice pathology detection and classification on different frequency regions using correlation functions. J. Voice 31(1), 3–15 (2017)
Al-nasheri, A., et al.: An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification. J. Voice 31(1), 113–e9 (2017)
Ali, F.: Voice recognition anatomy, processing, uses and application in C (2017)
AlZubaidi, A.K., Sideseq, F.B., Faeq, A., Basil, M.: Computer aided diagnosis in digital pathology application: review and perspective approach in lung cancer classification. In: 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), pp. 219–224. IEEE (2017)
Barry, W., Pützer, M.: Saarbrucken voice database. Institute of Phonetics, Universität des Saarlandes (2007). http://www.stimmdatenbank.coli.uni-saarland.de
Béranger, J.: Big Data and Ethics: The Medical Datasphere. Elsevier, New York City (2016)
Castro-Ospina, A., Castro-Hoyos, C., Peluffo-Ordonez, D., Castellanos-Dominguez, G.: Novel heuristic search for ventricular arrhythmia detection using normalized cut clustering. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7076–7079. IEEE (2013)
Chiu, C.C., et al.: State-of-the-art speech recognition with sequence-to-sequence models. arXiv preprint arXiv:1712.01769 (2017)
Harar, P., Alonso-Hernandezy, J.B., Mekyska, J., Galaz, Z., Burget, R., Smekal, Z.: Voice pathology detection using deep learning: a preliminary study. In: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), pp. 1–4. IEEE (2017)
Hemmerling, D., Skalski, A., Gajda, J.: Voice data mining for laryngeal pathology assessment. Comput. Biol. Med. 69, 270–276 (2016)
Ibrahim, S., Djemal, R., Alsuwailem, A.: Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern. Biomed. Eng. 38(1), 16–26 (2018)
Lytras, M.D., Papadopoulou, P.: Applying Big Data Analytics in Bioinformatics and Medicine. IGI Global, Pennsylvania (2017)
Martínez, D., Lleida, E., Ortega, A., Miguel, A., Villalba, J.: Voice pathology detection on the Saarbrücken voice database with calibration and fusion of scores using MultiFocal toolkit. In: Torre Toledano, D., et al. (eds.) IberSPEECH 2012. CCIS, vol. 328, pp. 99–109. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35292-8_11
Mendoza, L., Peña, J., Muñoz-Bedoya, L., Velandia-Villamizar, H.: Speech subvocal signal processing using packet wavelet and neuronal network. TecnoLógicas, 655–667 (2013). https://doi.org/10.22430/22565337.371
Muhammad, G., Alhamid, M.F., Hossain, M.S., Almogren, A.S., Vasilakos, A.V.: Enhanced living by assessing voice pathology using a co-occurrence matrix. Sensors 17(2), 267 (2017)
Muhammad, G., et al.: Voice pathology detection using interlaced derivative pattern on glottal source excitation. Biomed. Signal Process. Control 31, 156–164 (2017)
Muhammad, G., et al.: Automatic voice pathology detection and classification using vocal tract area irregularity. Biocybern. Biomed. Eng. 36(2), 309–317 (2016)
Orozco-Naranjo, A.J., Muñoz-Gutiérrez, P.A.: Detection of pathological and normal heartbeat using wavelet packet, support vector machines and multilayer perceptron. Tecno Lógicas 31, 73–91 (2013)
Parascandolo, P., Cesario, L., Vosilla, L., Viano, G.: Computer aided diagnosis: state-of-the-art and application to musculoskeletal diseases. In: Magnenat-Thalmann, N., Ratib, O., Choi, H.F. (eds.) 3D Multiscale Physiological Human, pp. 277–296. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6275-9_12
Schalkoff, R.J.: Artificial Neural Networks, vol. 1. McGraw-Hill, New York (1997)
Schilling, R.J., Harris, S.L.: Fundamentals of Digital Signal Processing Using MATLAB. Cengage Learning, Boston (2011)
Semmlow, J.L., Griffel, B.: Biosignal and Medical Image Processing. CRC Press, Boca Raton (2014)
Shinohara, S., et al.: Multilingual evaluation of voice disability index using pitch rate. ASTESJ 2(3), 765–772 (2017)
Shriberg, L.D., et al.: A diagnostic marker to discriminate childhood apraxia of speech from speech delay: II. Validity studies of the pause marker. J. Speech Lang. Hear. Res. 60(4), S1118–S1134 (2017)
Summers, R.M.: Deep learning and computer-aided diagnosis for medical image processing: a personal perspective. In: Lu, L., Zheng, Y., Carneiro, G., Yang, L. (eds.) Deep Learning and Convolutional Neural Networks for Medical Image Computing. ACVPR, pp. 3–10. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-42999-1_1
von Tscharner, V.: Time-frequency and principal-component methods for the analysis of emgs recorded during a mildly fatiguing exercise on a cycle ergometer. J. Electromyogr. Kinesiol. 12(6), 479–492 (2002)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999). https://doi.org/10.1007/978-1-4757-3264-1
Verde, L., De Pietro, G., Sannino, G.: Voice disorder identification by using machine learning techniques. IEEE Access 6, 16246–16255 (2018)
Wojcicki, K.: HTK MFCC MATLAB. MATLAB Central File Exchange (2011)
Acknowledgements
This work was partially supported by the grants provided by Programa Nacional de Jóvenes Investigadores e Innovadores – COLCIENCIAS – Announcement 775 of 2017 and the support for Instituto Tecnológico Metropolitano from Medellin-Colombia.
Also, authors specially thank the support given by the SDAS Research Group.
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Areiza-Laverde, H.J., Castro-Ospina, A.E., Peluffo-Ordóñez, D.H. (2018). Voice Pathology Detection Using Artificial Neural Networks and Support Vector Machines Powered by a Multicriteria Optimization Algorithm. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_13
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