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Optimal selection of mother wavelet for accurate infant cry classification

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Abstract

Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.

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References

  1. Saraswathy J, Hariharan M, Yaacob S, Khairunizam W (2012) Automatic classification of infant cry: a review. In: Proceedings of the 2012 international conference on biomedical engineering (ICOBE-2012), pp 543–548

  2. Lederman D (2002) Automatic classification of infants’ cry. Dissertation, University of Negev, Department of Electrical and Computer Engineering

  3. Boukydis CFZ, Lester BM (1985) Infant crying. Plenum press, New York

    Google Scholar 

  4. Lederman D, Cohen A, Zmora E, Wermke K, Hauschildt S, Stellzig-Eisenhauer A (2008) Classification of cries of infants with cleft-palate using parallel hidden Markov models. Med Biol Eng Comput 46:965–975

    Article  PubMed  Google Scholar 

  5. Michelsson K, Kaskinn H, Aulanko R, Rinne A (1984) Sound spectrographic cry analysis of infants with hydrocephalus. Acta Paediatr Scand 73:65–68

    Article  PubMed  CAS  Google Scholar 

  6. Colton RH, Steinschneider A (1981) The cry characteristics of an infant who died of sudden infant death syndrome. J Speech Hear Dis 46:359–363

    Article  CAS  Google Scholar 

  7. Ruiz Diaz MA, Reyes Garcia CA, Altamirano Robles LC, Xaltena Altamirano JE (2012) Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis. Biomed Signal Process Control 7:43–49

    Article  Google Scholar 

  8. Verduzco-Mendoza A, Arch-Tirado E, Reyes-Garcia CA, Leybon-Ibarra J, Licona-Bonilla J (2012) Spectrographic infant cry analysis in newborns with profound hearing loss and perinatal high-risk newborns. Cir Cir 80:3–10

    PubMed  Google Scholar 

  9. Hariharan M, Sin Chee L, Yaacob S (2012) Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network. J Med Syst 36:1309–1315

    Article  PubMed  CAS  Google Scholar 

  10. Office of Health and Nutrition CFP, Health and Nutrition, Bureau for Global Programs Field Support and Research, U.S. (2004) Detecting and treating newborn asphyxia, maternal neonatal & health, pp 1–2. http://pdf.usaid.gov/pdf_docs/Pnacy993.pdf Accessed 18 June 2012

  11. Cunningham M, Edward O (2003) Hearing assessment in infants and children: recommendations beyond neonatal screening. Am Acad Pediatr Clin Rep 111:436–440

    Google Scholar 

  12. Rosales-Perez A, Reyes-Garcia CA, Gomez-Gil P (2011) Genetic fuzzy relational neural network for infant cry classification. Lect Notes Comput Sci 6718:288–296

    Article  Google Scholar 

  13. Zabidi A, Mansor W, Khuan LY, Yassin IM, Sahak R (2011) Binary particle swarm optimization for selection of features in the recognition of infants cries with asphyxia. In: Proceedings of the IEEE international colloquium on signal processing and its applications, pp 272–276

  14. Hariharan M, Yaacob S, Awang SA (2011) Pathological infant cry analysis using wavelet packet transform and probabilistic neural network. Exp Syst Appl 38:15377–15382

    Article  Google Scholar 

  15. Rosales-Perez A, Reyes-Garcia CA, Gonzalez JA, Arch-Tirado E (2012) Infant cry classification using genetic selection of a fuzzy model. Lect Notes Comput Sci 7441:212–219

    Article  Google Scholar 

  16. Hariharan M, Saraswathy J, Sindhu R, Wan Khairunizam, Yaacob S (2012) Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks. Exp Syst Appl 39:9515–9523

    Article  Google Scholar 

  17. Hariharan M, Sindhu R, Sazali Yaacob (2012) Normal and hypoacoustic infant cry signal using time-frequency analysis and general regression neural network. Comput Methods Programs Biomed 108:559–569

    Article  PubMed  CAS  Google Scholar 

  18. http://ingenieria.uatx.mx/orionfrg/cry/. Accessed 7 May 2010

  19. Reyes-Galaviz OF, Cano-Ortiz S, Reyes-Garcia C, y Electronica CO, Puebla M (2009) Evolutionary-neural system to classify infant cry units for pathologies identification in recently born babies. In: Proceedings of the 8th Mexican international conference on artificial intelligent, pp 330–335

  20. Verduzco-Mendoza A, Arch-Tirado E, Reyes-Garcia CA, Leybon-Ibarra J, Licona-Bonilla J (2009) Qualitative and quantitative crying analysis of new born babies delivered under high risk gestation. Multimodal Signals Lect Notes Artif Intell 5398:320–327

    Google Scholar 

  21. Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process 16:275–287

    Article  Google Scholar 

  22. Kumari A, Bisht M (2013) Optimal wavelet filter maximizes the cross correlation coefficient with an ECG signal. Int J Innov Technol Res 1(2):191–193

    Google Scholar 

  23. Matlab® Documentation, version 7.0, Release 14, 2004. The MathWorks, Inc

  24. Kale VN, Khalsa NN (2010) Performance evaluation of various wavelets for image compression of natural and artificial images. Int J Comput Sci Commun 1(1):179–184

    Google Scholar 

  25. Goudarzi MM, Taheri A, Pooyan M, Mahboobi R (2006) Multiwavelet and biological signal processing. Int J Inf Technol 2(4):264–272

    Google Scholar 

  26. Martis RJ, Acharya UR, Ray AK, Chakraborty C (2011) Application of higher order cumulants to ECG signals for the cardiac health diagnosis. In: Proceedings of the 33rd international conference on IEEE EMBS, pp 1697–1700

  27. Gandhi T, Panigrahi BK, Anand S (2011) A comparative study of wavelet families for EEG signal classification. Neurocomputing 74:3051–3057

    Article  Google Scholar 

  28. Nagaria B, Hashmi F, Dhakad P (2011) Comparative analysis of fast wavelet transform for image compression for optimal image quality and higher compression ratio. Int J Eng Sci Technol 3:4014–4019

    Google Scholar 

  29. Chaudhari A, Chaudhary P, Cheeran AN, Aswani Y (2012) Improving signal to noise ratio of low-dose CT image using wavelet transform. Int J Comput Sci Eng 4:779–789

    Google Scholar 

  30. Hariharan M, Fook CY, Sindhu R, Ilias B, Yaacob S (2012) A comparative study of wavelet families for classification of wrist motions. Comput Electr Eng 38:1798–1807

    Article  Google Scholar 

  31. Murugesapandian P, Yaacob S, Hariharan M (2008) Feature extraction based on Mel-scaled wavelet packet transform for the diagnosis of voice disorders. Proc IFMBE 2008 21:790–793

    Article  Google Scholar 

  32. Burrus CS, Ramesh A, Gopinath, Guo H (1998) Introduction to wavelets and wavelet transforms: a primer. Prentice Hall, Englewood Cliffs

    Google Scholar 

  33. Gopinath B, Shanthi N (2013) Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images. Australas Phys Eng Sci Med. doi:10.1007/s13246-013-0199-8

    PubMed  Google Scholar 

  34. Wang Y, Yang Z, Hao D, Zhang S, Yang Y, Zeng Y (2013) Measurement of subcutaneous adipose tissue thickness by near-infrared. Australas Phys Eng Sci Med. doi:10.1007/s13246-013-0196-y

    Google Scholar 

  35. Mahmoodian H (2012) Predicting the continuous values of breast cancer relapse time by type-2 fuzzy logic system. Australas Phys Eng Sci Med 35:193–204

    Article  PubMed  Google Scholar 

  36. Hariharan M, Paulraj MP, Yaccob S (2010) Time-domain features and probabilistic neural network for the detection of vocal fold pathology. Malays J Comput Sci 23:60–67

    Google Scholar 

  37. Hariharan M, Paulraj MP, Yaacob S (2011) Detection of vocal fold paralysis and oedema using time-domain features and probabilistic neural network. Int J Biomed Eng Technol 6:46–57

    Article  Google Scholar 

  38. Specht DF (1990) Probabilistic neural networks. IEEE Trans Neural Netw 3:109–118

    Article  Google Scholar 

  39. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576

    Article  PubMed  CAS  Google Scholar 

  40. Heaton Jeff (2008) Introduction to neural networks for Java, 2nd edn. Heaton Research, St. Louis

    Google Scholar 

  41. Cohen I (2001) Enhancement of speech using bark-scaled wavelet packet decomposition. In: Proceedings of the 7th European conference speech, communication and technology, 2nd INTERSPEECH Event, Aalborg, Denmark, Sept 3–7

  42. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence, vol 2, Montreal, Quebec, Canada, pp 1137–1143

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Acknowledgments

The Baby Chillanto Data Base is a property of the Instituto Nacional de Astrofisica Optica y Electronica –CONACYT, Mexico. We like to thank Dr. Carlos A. Reyes-Garcia, Dr. Emilio Arch-Tirado and his INR-Mexico group, and Dr. Edgar M. Garcia-Tamayo for their dedication of the collection of the Infant Cry data base. The authors would like to thank Dr. Carlos Alberto Reyes-Garcia, Researcher, CCC-Inaoep, Mexico for providing infant cry database. All authors declare that they have no financial or any commercial conflicts of interest.

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Authors and Affiliations

  1. School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600, Arau, Perlis, Malaysia

    J. Saraswathy, M. Hariharan, Wan Khairunizam & Sazali Yaacob

  2. Department of Pediatrics, Hospital Sultanah Bahiyah, 05460, Alor Setar, Kedah, Malaysia

    Thiyagar Nadarajaw

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  1. J. Saraswathy
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  2. M. Hariharan
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  3. Thiyagar Nadarajaw
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  4. Wan Khairunizam
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  5. Sazali Yaacob
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Correspondence to J. Saraswathy.

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Saraswathy, J., Hariharan, M., Nadarajaw, T. et al. Optimal selection of mother wavelet for accurate infant cry classification. Australas Phys Eng Sci Med 37, 439–456 (2014). https://doi.org/10.1007/s13246-014-0264-y

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