Signal Domain in Respiratory Sound Analysis: Methods, Application and Future Development

The development of digital signal processing technology encourages researchers to develop better methods for automatic lungs sound recognition system than the existing ones. Lung sounds were originally assessed manually according to doctor's expertise. Signal processing techniques are intended to reduce subjectivity factor. Signal processing techniques for lung sound recognition are developed by researchers based on their point of view to the lung sounds. Several researchers developed signal processing methods in a time domain. Meanwhile, other researchers developed signal processing techniques in a frequency domain or combined some signal domains. This paper describes the sensor used, the dataset used and the characteristics of extraction techniques as well as the classifier in the system developed by the previous researchers. In the final section, we describe some possible development of the future potential application of lung sound analysis.

[1]  Yasemin P. Kahya,et al.  Online classification of lung sounds using DSP , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[2]  Goutam Saha,et al.  Detection of Lungs Status Using Morphological Complexities of Respiratory Sounds , 2014, TheScientificWorldJournal.

[3]  Jithendra Vepa,et al.  Lung sound analysis for wheeze episode detection , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Semra Içer,et al.  Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds , 2014, Digit. Signal Process..

[5]  Bulent Sankur,et al.  Statistical analysis of lung sound data , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[6]  Nizamettin Aydin,et al.  Pulmonary crackle detection using time-frequency and time-scale analysis , 2013, Digit. Signal Process..

[7]  C. Scheffer,et al.  Analysis of adventitious lung sounds originating from pulmonary tuberculosis , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Rajkumar Palaniappan,et al.  Machine learning in lung sound analysis: a systematic review , 2013 .

[9]  Azadeh Yadollahi,et al.  Formant analysis of breath and snore sounds , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  R. Jane,et al.  Analysis of Wheezes in Asthmatic Patients during Spontaneous Respiration , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Ratko Magjarević,et al.  Analysis of Respiratory Sounds in Asthmatic Infants , 2003 .

[12]  R. Murphy,et al.  State of the Art Lung Sounds 1 ’ 2 , 2001 .

[13]  Tülay Yildirim,et al.  Classification of normal and abnormal lung sounds using wavelet coefficients , 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).

[14]  L.J. Hadjileontiadis,et al.  Adaptive reduction of heart sounds from lung sounds using fourth-order statistics , 1997, IEEE Transactions on Biomedical Engineering.

[15]  Leontios J. Hadjileontiadis,et al.  Wheeze detection based on time-frequency analysis of breath sounds , 2007, Comput. Biol. Medicine.

[16]  Stavros M. Panas,et al.  WED: An efficient wheezing-episode detector based on breath sounds spectrogram analysis , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[17]  Mounya Elhilali,et al.  A multiresolution analysis for detection of abnormal lung sounds , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  M. Mejia-Avila,et al.  Adventitious lung sounds imaging by ICA-TVAR scheme , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  Jeffrey J Ward,et al.  R.A.L.E Lung Sounds 3.1 Professional Edition , 2005 .

[20]  R. Jane,et al.  Algorithm for time-frequency detection and analysis of wheezes , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[21]  Raimes Moraes,et al.  Characterization of crackles from patients with fibrosis, heart failure and pneumonia. , 2013, Medical engineering & physics.

[22]  Boualem Boashash,et al.  Time-Frequency Signal Analysis and Processing: A Comprehensive Reference , 2015 .

[23]  Bülent Sankur,et al.  Two-stage classi cation of respiratory sound patterns , 2003 .

[24]  G. N. Webb,et al.  The acoustic basis of the chest examination; studies by means of sound spectrography. , 1955, American review of tuberculosis.

[25]  D. Mannino,et al.  International variation in the prevalence of COPD (The BOLD Study): a population-based prevalence study , 2007, The Lancet.

[26]  Mounya Elhilali,et al.  Characterization of noise contaminations in lung sound recordings , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[27]  Sridhar Krishnan,et al.  Adventitious Sounds Identification and Extraction Using Temporal–Spectral Dominance-Based Features , 2011, IEEE Transactions on Biomedical Engineering.

[28]  Ramón González-Camarena,et al.  Crackle sounds analysis by empirical mode decomposition. Nonlinear and nonstationary signal analysis for distinction of crackles in lung sounds. , 2007, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[29]  Benjin Wang,et al.  The Research of Lung Sound Signals Based on Cepstrum Analysis , 2012, International Conference on Biomedical Engineering and Biotechnology.

[30]  E. H. Dooijes,et al.  Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra , 2000, IEEE Transactions on Biomedical Engineering.

[31]  Feng Jin,et al.  New approaches for spectro-temporal feature extraction with applications to respiratory sound classification , 2014, Neurocomputing.

[32]  Jingping Xu,et al.  A cepstral method for analysis of acoustic transmission characteristics of respiratory system , 1998, IEEE Transactions on Biomedical Engineering.

[33]  F. K. Lam,et al.  Crackle detection and classification based on matched wavelet analysis , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[34]  A. Desai Narasimhalu,et al.  Multimedia databases , 1996, Multimedia Systems.

[35]  Mekki Ksouri,et al.  A new scheme for automatic classification of pathologic lung sounds , 2012 .

[36]  John L. Semmlow,et al.  Biosignal and Medical Image Processing , 2004 .

[37]  Leontios J. Hadjileontiadis.  A Texture-Based Classification of Crackles and Squawks Using Lacunarity , 2009, IEEE Transactions on Biomedical Engineering.

[38]  Rajkumar Palaniappan,et al.  Respiratory sound classification using cepstral features and support vector machine , 2013, 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS).

[39]  S. Charleston-Villalobos,et al.  Analysis of discontinuous adventitious lung sounds by Hilbert-Huang spectrum , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  S C Villalobos,et al.  CRACKLE SOUNDS ANALYSIS BY EMPIRICAL MODE DECOMPOSITION , 2007 .

[41]  Y.P. Kahya,et al.  Multi-channel Classification of Respiratory Sounds , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[42]  Chengli Que,et al.  Identification of Velcro rales based on Hilbert-Huang transform , 2014 .

[43]  Wiesława Kuniszyk-Jóźkowiak,et al.  Analysis of lung auscultatory phenomena using the Wigner-Ville Distribution , 2012 .

[44]  Kenneth Sundaraj,et al.  A survey on automated wheeze detection systems for asthmatic patients. , 2012, Bosnian journal of basic medical sciences.

[45]  H. Krim,et al.  Wheeze Detection and Location using Spectro-temporal Analysis of Lung Sounds , 2013, 2013 29th Southern Biomedical Engineering Conference.

[46]  L.J. Hadjileontiadis,et al.  Multimedia database "Marburg Respiratory Sounds (MARS)" , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[47]  A. Bohadana,et al.  Fundamentals of lung auscultation. , 2014, The New England journal of medicine.

[48]  Amjad Hashemi,et al.  Classification of Wheeze Sounds Using Wavelets and Neural Networks , 2022 .

[49]  Jen-Chien Chien,et al.  A Study of Heart Sound and Lung Sound Separation by Independent Component Analysis Technique , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[50]  N. Malmurugan,et al.  Neural classification of lung sounds using wavelet coefficients , 2004, Comput. Biol. Medicine.

[51]  Y.P. Kahya,et al.  Classifying Respiratory Sounds with Different Feature Sets , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[52]  Yasemin P. Kahya,et al.  Classification of respiratory signals by linear analysis , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[53]  Zahra Moussavi,et al.  Analysis and classification of swallowing sounds using reconstructed phase space features , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[54]  Zahra Moussavi,et al.  The fractality of lung sounds: A comparison of three waveform fractal dimension algorithms , 2005 .

[55]  Shoichi Matsunaga,et al.  Robust classification between normal and abnormal lung sounds using adventitious-sound and heart-sound models , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[56]  Kenneth Sundaraj,et al.  Computer-based Respiratory Sound Analysis: A Systematic Review , 2013 .

[57]  Achmad Rizal,et al.  Lung Sound Recognition Using Spectrogram and Adaptive Resonance Theory 2 Neural Network (ART2) , 2008 .

[58]  Yashar Sarbaz,et al.  Classification of normal and abnormal lung sounds using neural network and support vector machines , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[59]  Pedro Mayorga,et al.  Modified classification of normal Lung Sounds applying Quantile Vectors , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[60]  H. Melbye,et al.  [Auscultation of the lungs--still a useful examination?]. , 2001, Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke.

[61]  Sao-Jie Chen,et al.  WHEEZE RECOGNITION BASED ON 2D BILATERAL FILTERING OF SPECTROGRAM , 2006 .

[62]  José Antonio Fiz,et al.  Estimation of instantaneous frequency from empirical mode decomposition on respiratory sounds analysis , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[63]  Daniel Sánchez Morillo,et al.  Computerized analysis of respiratory sounds during COPD exacerbations , 2013, Comput. Biol. Medicine.

[64]  E. Andrès,et al.  Analysis of Respiratory Sounds: State of the Art , 2008, Clinical medicine. Circulatory, respiratory and pulmonary medicine.

[65]  J. E. Earis,et al.  Current methods used for computerized respiratory sound analysis , 2004 .

[66]  R.M. Carey,et al.  Distinguishing between asthma and pneumonia through automated lung sound analysis , 2005, Proceedings of the IEEE 31st Annual Northeast Bioengineering Conference, 2005..

[67]  Noman Qaid Al-Naggar.  A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation , 2013 .

[68]  Aintree Chest,et al.  Current methods used for computerized respiratory sound analysis , 2000 .

[69]  Z. Moussavi,et al.  Variance fractal dimension trajectory as a tool for hear sound localization in lung sounds recordings , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[70]  Peter Hult,et al.  Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction , 2005, IEEE Signal Processing Letters.

[71]  Farook Sattar,et al.  Automatic wheeze detection using histograms of sample entropy , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[72]  H. Pasterkamp,et al.  Respiratory sounds. Advances beyond the stethoscope. , 1997, American journal of respiratory and critical care medicine.

[73]  Z. Moussavi,et al.  Automated classification of swallowing and breadth sounds , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.