Tidal Volume Estimation Using the Blanket Fractal Dimension of the Tracheal Sounds Acquired by Smartphone

In this paper, we propose the use of blanket fractal dimension (BFD) to estimate the tidal volume from smartphone-acquired tracheal sounds. We collected tracheal sounds with a Samsung Galaxy S4 smartphone, from five (N = 5) healthy volunteers. Each volunteer performed the experiment six times; first to obtain linear and exponential fitting models, and then to fit new data onto the existing models. Thus, the total number of recordings was 30. The estimated volumes were compared to the true values, obtained with a Respitrace system, which was considered as a reference. Since Shannon entropy (SE) is frequently used as a feature in tracheal sound analyses, we estimated the tidal volume from the same sounds by using SE as well. The evaluation of the performed estimation, using BFD and SE methods, was quantified by the normalized root-mean-squared error (NRMSE). The results show that the BFD outperformed the SE (at least twice smaller NRMSE was obtained). The smallest NRMSE error of 15.877% ± 9.246% (mean ± standard deviation) was obtained with the BFD and exponential model. In addition, it was shown that the fitting curves calculated during the first day of experiments could be successfully used for at least the five following days.

[1]  Sonia Charleston-Villalobos,et al.  Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients , 2011, Comput. Biol. Medicine.

[2]  B. Mandelbrot.  Stochastic models for the Earth's relief, the shape and the fractal dimension of the coastlines, and the number-area rule for islands. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

[3]  M J Tobin,et al.  Subjective and objective measurement of tidal volume in critically ill patients. , 1985, Chest.

[4]  T. Aljama-Corrales,et al.  Linear and Nonlinear Analysis of Base Lung Sound in Extrinsic Allergic Alveolitis Patients in Comparison to Healthy Subjects , 2013, Methods of Information in Medicine.

[5]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[6]  S. J. Cala,et al.  Chest wall and lung volume estimation by optical reflectance motion analysis. , 1996, Journal of applied physiology.

[7]  Frank H Wilhelm,et al.  Reliability of respiratory tidal volume estimation by means of ambulatory inductive plethysmography. , 2006, Biomedical sciences instrumentation.

[8]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[9]  Zahra Moussavi,et al.  Fundamentals of Respiratory Sounds and Analysis , 2006, Fundamentals of Respiratory Sounds and Analysis.

[10]  S. Kira,et al.  Variability of breath-by-breath tidal volume and its characteristics in normal and diseased subjects. Ventilatory monitoring with electrical impedance pneumography. , 1985, Japanese journal of medicine.

[11]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[12]  Joseph Naor,et al.  Multiple Resolution Texture Analysis and Classification , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Louis-Gilles Durand,et al.  Phonospirometry for noninvasive measurement of ventilation: methodology and preliminary results. , 2002, Journal of applied physiology.

[14]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[15]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[16]  Lauralee Sherwood.  Fundamentals of Human Physiology , 1925, The Indian Medical Gazette.

[17]  Martin J. Tobin,et al.  Subjective and Objective Measurement of Tidal Volume in Critically III Patients , 1985 .

[18]  F. Dalmasso,et al.  Definition of terms for applications of respiratory sounds , 2000 .

[19]  Zahra Moussavi,et al.  A robust method for estimating respiratory flow using tracheal sounds entropy , 2006, IEEE Transactions on Biomedical Engineering.

[20]  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).

[21]  Ki H. Chon,et al.  Physiological Parameter Monitoring from Optical Recordings With a Mobile Phone , 2012, IEEE Transactions on Biomedical Engineering.

[22]  Antonis A Armoundas,et al.  An optimized method for estimating the tidal volume from intracardiac or body surface electrocardiographic signals: implications for estimating minute ventilation. , 2014, American journal of physiology. Heart and circulatory physiology.

[23]  Esther Rodríguez-Villegas,et al.  Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System , 2008, IEEE Transactions on Biomedical Engineering.

[24]  Leontios J Hadjileontiadis,et al.  Empirical mode decomposition and fractal dimension filter. A novel technique for denoising explosive lung sounds. , 2007, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[25]  B. Hök,et al.  Critical review of non-invasive respiratory monitoring in medical care , 2003, Medical and Biological Engineering and Computing.

[26]  Peter Hult,et al.  Chaotic dynamics of respiratory sounds , 2006 .

[27]  Yee Siong Lee,et al.  Monitoring and Analysis of Respiratory Patterns Using Microwave Doppler Radar , 2014, IEEE Journal of Translational Engineering in Health and Medicine.

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

[29]  B. Mandelbrot.  How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[30]  Leontios J. Hadjileontiadis.  A Novel Technique for Denoising Explosive Lung Sounds Emnpirmical Mode Decompiosition and Fractal Dimension Foilter , 2007 .

[31]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[32]  B Bojović,et al.  Non-invasive respiratory monitoring using long-period fiber grating sensors. , 2014, Biomedical optics express.

[33]  Zahra Moussavi,et al.  A robust method for heart sounds localization using lung sounds entropy , 2006, IEEE Transactions on Biomedical Engineering.

[34]  Ana M. Gavrovska,et al.  Identification of fundamental heart sounds from PCG using blanket fractal dimension , 2014, 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO).

[35]  Esther Rodríguez-Villegas,et al.  Automatic lung tidal volumes estimation from tracheal sounds , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[37]  Zahra Moussavi,et al.  Respiratory onset detection using variance fractal dimension , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  C. Druzgalski,et al.  Techniques of Recording Respiratory Sounds , 1980 .

[39]  Hartmut Jürgens,et al.  Chaos and Fractals: New Frontiers of Science , 1992 .

[40]  L.J. Hadjileontiadis,et al.  Detection of explosive lung and bowel sounds by means of fractal dimension , 2003, IEEE Signal Processing Letters.

[41]  Ki H. Chon,et al.  Tracheal Sounds Acquisition Using Smartphones , 2014, Sensors.

[42]  M. Turner,et al.  Analysis of the Limitations of Fractal Dimension Texture Segmentation for Image Characterisation , 2002 .

[43]  Ki H. Chon,et al.  Atrial Fibrillation Detection Using an iPhone 4S , 2013, IEEE Transactions on Biomedical Engineering.

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

[45]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .