COMPARATIVE EFFICIENCY OF COMPUTERIZED ANALYSIS OF RESPIRATORY NOISE SPECTRUM ENERGETIC CHARACTERISTIC IN THREE POINTS FOR DIAGNOSIS OF BRONCHOOBSTRUCTIVE SYNDROME IN PEDIATRIC BRONCHIAL ASTHMA

Abstract

Aim. The present work was aimed at development of the technique for computerized diagnosis of pediatric bronchial asthma based on analysis of respiratory noises. Materials and methods. Computerized system for respiratory noise recording was used to receive sound signals from three points located in the mouth, above the trachea and above the right lung (on the front surface of the chest) in 51 pupils (aged 11,2 ± 3,2 years) suffering from bronchial asthma and in 22 healthy volunteers (aged 11,6 ± 2,5 years). Record of patient’s respiratory noises was fulfilled using electronic device with subsequent computational investigation of specific spectral characteristics of this sound. The technique for computerized diagnosis, permitting to perfect the possibilities of respiratory noise processing by means of fast Fourier transport (FFT), is offered in the paper. The suggested technique can be used for diagnosis of bronchoobstructive syndrome (BOS) in children with bronchial asthma. Results. There were suggested empirical criteria for balancing parameters of FFT spectrum, which allow us to develop software for automatic diagnosis of pediatric bronchial asthma. It was also indicated that computerized analysis of the respiratory noise spectrum power is of great diagnostic value (AUV varies from 0.783 to 0.895). The offered approach can be used for diagnosis of bronchial asthma (mainly in the oral cavity points and above the trachea) and for differential diagnosis between BA and other pulmonary diseases in the point above the right upper lobe. Conclusions. The suggested approach to analysis of respiratory noises can become one of additional techniques for BOS diagnosis. It can be used for remote monitoring of asthma patients in the regime of real time, as well as for control of treatment efficiency. Application with program can be inserted into the smartphone or low-cost embedded system for contactless analysis of respiratory noises that is important for remote diagnosis.

Keywords

Bronchial asthma / children / respiratory noises / wheezing / computerized analysis

References

[1] Фурман Е. Г., Яковлева Е. В., Малинин С. В., Фурман Г., Соколовский В. Компьютерный анализ дыхательных шумов при бронхиальной астме у детей. Современные технологии в медицине 2014; 6 (1): 83-88.

[2] Bahoura M., Lu X. Separation of crackles from vesicular sounds using wavelet packet transform. Acoustics, Speech and Signal Processing ICASSP 2006; 2: 1076-1079.

[3] Brand P. L. P., Baraldi E., Bisgaard H., Boner A.L., Castro-Rodriguez J. A., Custovic A. et al. Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach, ERS Task Force Report. Eur. Respir. J. 2008; 32: 1096-1110.

[4] Charbonneau G., Ademovic E., Cheetham B. M. G., Malmberg L. P., Vanderschoot J., Sovijarvi A. R. A. Basic techniques for respiratory sound recordings. Eur. Respir. Rev. 2000; 77 (10): 625-635.

[5] Earis J. E., Cheetham B. M. G. Current methods used for computerized respiratory sound analysis. Eur. Respir. Rev. 2000; 77 (10): 586-590.

[6] Fenton T. R., Pasterkamp H., Tal A., Chernick V. Automated spectral characterization of wheezing in asthmatic children. IEEE Trans. on Biomedical Engineering 1985; 32: 50-55.

[7] Furman E., Yakovleva E., Malinin S., Furman G., Sokolovsky V., Meerovich V. A new modality using breath sound analysis in pediatric asthma. Clinical and Translational Allergy 2014; 105.

[8] Gavriely N. Breath sounds methodology. Boca Raton: CRC Press 1995.

[9] Global initiative for asthma (GINA) 2012, available at: www.ginasthma.org.

[10] Global strategy for the diagnosis and management of asthma in children 5 years and younger (GINA), 2009, available at: www.ginasthma.org.

[11] Gurung A., Scrafford C. G., Tielsch J. M., Levine O. S., Checkley W. Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. Respir. Med. 2011; 105: 1396-1403.

[12] Habukawa C., Murakami K., Horii N., Yamada M., Nagasaka Y. A new modality using breath sound analysis to evaluate the control level of asthma. Allergol. Int. 2013; 62: 29-35.

[13] Habukawa C., Nagasaka Y., Murakami K., Takemura T. High-pitched breath sounds indicate airflow limitation in asymptomatic asthmatic children. Respirology 2009; 14: 399-403.

[14] Hadjileontiadis L., Panoulas K., Penzel T., Gross V., Panas S. On applying continuous wavelet transform in wheeze analysis. Engineering in Medicine and Biology Society IEEE. 2004; 2: 3832-3835.

[15] Hadjileontiadis L. J., Tolias Y. A., Panas S. M. Intelligent system modelling of bioacoustic signals using advanced signal processing techniques. Intelligent Systems: Technology and Applications. Vol. III. Signal, image, and speech processing. Ed. C. T. Leondes. Boca Raton: CRC Press Inc., FL 2002; 103-156.

[16] Kosasih K., Abeyratne U. R., Swarnkar V. High frequency analysis of cough sounds in pediatric patients with respiratory diseases. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012; 5654-5657.

[17] Mazic J., Sovilj S., Magjarevic R. Analysis of respiratory sounds in asthmatic infants. Polytechnic of Dubrovnik. Measurement Science Review 2003; 3: 11.

[18] Reichert S., Gass R., Hajjam A., Brandt C., Nguyen E., Baldassari K., Andres E. The ASAP project: A first step to an auscultation’s school creation. Respiratory Medicine CME 2 2009; 7-14.

[19] Reichert S., Gass R., Brandt C., Andres E. Analysis of respiratory sounds: state of the art. Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine 2008; 45-58.

[20] Saeed S., Body R. Towards evidence based emergency medicine: best BETs from the Manchester Royal Infirmary. Auscultating to diagnose pneumonia. Emerg. Med. J. 2007; 24: 294-296.

[21] Sovijärvi A. R. A., Vanderschoot J., Earis J. E. Standardization of computerized respiratory sound analysis. Eur. Respir. Rev. 2000; 10: 585-595.

[22] Sovijarvi A. R. A., Dalmasso F., Vanderschoot J., Malmberg L. P., Righini G., Stoneman S. A. T. Definition of terms for applications of respiratory sounds. Eur. Respir. Rev. 2000; 10: 597-610.

[23] Sovijarvi A. R. A., Malmberg L. P., Charbonneau G., Vanderschoot J., Dalmasso F., Sacco C., Rossi M., Earis J. R. Characteristics of breath sounds and adventitious respiratory sounds. Eur. Respir. Rev. 2000; 10: 591-596.

[24] Swarnkar V., Abeyratne U. R., Chang A. B., Amrulloh Y. A., Setyati A., Triasih R. Automatic identification of wet and dry cough in pediatric patients with respiratory diseases. Ann. Biomed. Eng. 2013; 41 (5): 1016-1028.

[25] Tolias Y. A., Hadjileontiadis L. J. Wheeze detection based on time-frequency analysis of breath sounds. Computers in Biology and Medicine 2007; 37: 1073-1083.

[26] Yi G. A. A software toolkit for respiratory analysis. MIT Computer Sound and Artificial Intelligence Laboratory 2004; 1: 215-216.

RIGHTS & PERMISSIONS

Furman E.G., Rocheva E.V., Malinin S.V., Furman G.B., Sokolovsky V.L.