![]() ![]() The acoustic sensor used in this study was developed by our colleagues at the Metropolitan Autonomous University at Mexico City, Mexico, and have been successfully applied for respiratory sound acquisitions. The importance of this shape is that it provides an efficient transducer of air pressure fluctuations from the skin over the trachea to the microphone. The tracheal sounds were collected using an acoustical sensor, which contained a subminiature electret microphone BT-21759-000 (Knowles Electronics, Itasca, IL, USA) placed in a plastic bell, which consisted of a conical coupler chamber, in accordance to previous findings. In this study, two signals were acquired simultaneously: tracheal sounds and Respitrace signal. ![]() Repeated experiments were performed to investigate if the models for fitting data obtained during the first day of collecting signals could be successfully used on the data from the remaining days. As a figure of merit, the normalized root-mean-squared errors (NRMSEs) were calculated in both cases. For testing the proposed method and comparing it with SE method, we collected signals from healthy and non-smoker volunteers for six days, for a total of 30 recordings. In addition, we estimated volumes by obtaining Shannon entropy (SE) from the same tracheal sounds, and compared them to reference volumes. The estimated volumes were compared to peak-to-peak volumes obtained from a Respitrace signal, which was considered as a reference. In this study, we explore the possibility to estimate tidal volume using BFD, which, to the best of our knowledge, was not used for respiratory sound analysis. None of these efforts was concerned with the tidal volume estimation using fractal analysis. Due to this fact, some past studies investigated and showed successful applications of fractal analysis on tracheal and lung sounds. Tracheal sounds, as part of respiratory sounds, are non-stationary and stochastic signals. In this paper, we propose the use of blanket fractal dimension (BFD) for estimating the tidal volume from tracheal sounds acquired by a commercially available Android smartphone. In addition, with an extensive growth of electronic devices and their computational capabilities, the development of portable tidal volume estimation systems is now possible. Therefore, there is a need for a miniature monitoring device that can be used in everyday situations and not only in clinical and/or research settings. However, these methods require the use of specialized equipment, and cannot be easily applied in nonclinical settings. Various methods exist for measuring the tidal volume, such as spirometry, whole-body plethysmography, inductance plethysmography, and electrocardiography. The average value is about 500 mL per breath at rest. Tidal volume is defined as the volume of air exchanged in one breath, and is commonly measured at the mouth. It plays an important role for both healthy people and people with respiratory diseases, hence measuring and checking volume’s values can be helpful, especially in assessing risky situations involving respiratory failure. Tidal volume is one of the parameters for monitoring respiratory activity. Respiratory activity is one of the vital signs, and as such requires an adequate attention. 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. The smallest NRMSE error of 15.877% ± 9.246% (mean ± standard deviation) was obtained with the BFD and exponential model. The results show that the BFD outperformed the SE (at least twice smaller NRMSE was obtained). The evaluation of the performed estimation, using BFD and SE methods, was quantified by the normalized root-mean-squared error (NRMSE). 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 estimated volumes were compared to the true values, obtained with a Respitrace system, which was considered as a reference. Thus, the total number of recordings was 30. 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. We collected tracheal sounds with a Samsung Galaxy S4 smartphone, from five ( N = 5) healthy volunteers. In this paper, we propose the use of blanket fractal dimension (BFD) to estimate the tidal volume from smartphone-acquired tracheal sounds. ![]()
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