Recommendations Related To Wheeze Sound Data Acquisition

In the field of computerized respiratory sounds,a reliable data set with a sufficient number of subjects is required for the development of wheeze detection algorithm or for further analysis.Validated and accurate data is a critical issue in the field of research.In this study,the protocol related to wheeze sound data acquisition is discussed.Previously,most articles focused on wheeze detection or its parametric analysis,but no consideration was given to data acquisition.Second major purpose of this study is to exhibit particulars of our dataset which was attained for future analysis.We compile a database with a sufficient and reliable number of cases with all essential details,in contrast to commercially available wheeze sound data used for research,freely available online data on websites and data used to train medical students for auscultation

The origin of Korotkoff sounds and the accuracy of auscultatory blood pressure measurements

This study explores the hypothesis that the sharper, high frequency Korotkoff sounds come from resonant motion of the arterial wall, which begins after the artery transitions from a buckled state to an expanding state. The motion of one mass, two nonlinear springs, and one damper, driven by transmural pressure under the cuff, are used to model and compute the Korotkoff sounds according to principles of classical Newtonian physics. The natural resonance of this spring-mass-damper system provides a concise, yet rigorous, explanation for the origin of Korotkoff sounds. Fundamentally, wall stretching in expansion requires more force than wall bending in buckling. At cuff pressures between systolic and diastolic arterial pressure, audible vibrations (\u3e 40 Hz) occur during early expansion of the artery wall beyond its zero pressure radius after the outward moving mass of tissue experiences sudden deceleration, caused by the discontinuity in stiffness between bucked and expanded states. The idealized spring-mass-damper model faithfully reproduces the time domain waveforms of actual Korotkoff sounds in humans. Appearance of arterial sounds occurs at or just above the level of systolic pressure. Disappearance of arterial sounds occurs at or just above the level of diastolic pressure. Muffling of the sounds is explained by increased resistance of the artery to collapse, caused by downstream venous engorgement. A simple analytical model can define the physical origin of Korotkoff sounds, suggesting improved mechanical or electronic filters for their selective detection, and confirming the disappearance of the Korotkoff sounds as the optimal diastolic endpoint

Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informativ

Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severit

Childhood Asthma in the Midwest

In spite of the National Asthma Education and Prevention Program guidelines outlining how to diagnose, treat, and educate asthmatics, asthma morbidity and mortality rates are still mounting. Furthermore, the minority population has disproportionately higher rates of unfavorable outcomes from asthma, thereby diminishing their quality of life. The study\u27s theoretical framework was based on the health belief model and explored associations of asthma control with self-efficacy and asthma education. Few studies focus on asthma inequity. The purpose of this quantitative study was to assess relationships between asthma control, race, asthma education, and healthcare utilization amongst asthmatic children residing in the Midwest. Secondary data from the Centers for Disease Control\u27s Behavioral Risk Factor Surveillance System\u27s Asthma Call-back Survey were used (n=477,221). Participant characteristics were examined using descriptive statistics. A sequence of bivariate and logistic regression analysis was used to test each hypothesis. The findings revealed significant associations amongst asthma control, race, asthma education, and healthcare utilization. In addition, children with uncontrolled asthma have greater visits to the emergency department and to their pediatrician\u27s office due to their asthma symptoms. Moreover, the study results indicated that African American children experienced uncontrolled asthma at a higher rate when compared to other children, consequently decreasing their quality of life. The study showed the need for policy change to expand funding and programs aimed at decreasing uncontrolled asthma by improving asthma education, especially in African American communities, in hope of empowering asthmatics to play a vital role in their health and increasing their quality of life

Soft Stethoscope for Detecting Asthma Wheeze in Young Children

Asthma is a chronic disease that is commonly suffered by children. Asthmatic children have a lower quality of life than other children. Physicians and pediatricians recommend that parents record the frequency of attacks and their symptoms to help manage their children's asthma. However, the lack of a convenient device for monitoring the asthmatic condition leads to the difficulties in managing it, especially when it is suffered by young children. This work develops a wheeze detection system for use at home. A small and soft stethoscope was used to collect the respiratory sound. The wheeze detection algorithm was the Adaptive Respiratory Spectrum Correlation Coefficient (RSACC) algorithm, which has the advantages of high sensitivity/specificity and a low computational requirement. Fifty-nine sound files from eight young children (one to seven years old) were collected in the emergency room and analyzed. The results revealed that the system provided 88% sensitivity and 94% specificity in wheeze detection. In conclusion, this small soft stethoscope can be easily used on young children. A noisy environment does not affect the effectiveness of the system in detecting wheeze. Hence, the system can be used at home by parents who wish to evaluate and manage the asthmatic condition of their children

Soft Stethoscope for Detecting Asthma Wheeze in Young Children

Asthma is a chronic disease that is commonly suffered by children. Asthmatic children have a lower quality of life than other children. Physicians and pediatricians recommend that parents record the frequency of attacks and their symptoms to help manage their children’s asthma. However, the lack of a convenient device for monitoring the asthmatic condition leads to the difficulties in managing it, especially when it is suffered by young children. This work develops a wheeze detection system for use at home. A small and soft stethoscope was used to collect the respiratory sound. The wheeze detection algorithm was the Adaptive Respiratory Spectrum Correlation Coefficient (RSACC) algorithm, which has the advantages of high sensitivity/specificity and a low computational requirement. Fifty-nine sound files from eight young children (one to seven years old) were collected in the emergency room and analyzed. The results revealed that the system provided 88% sensitivity and 94% specificity in wheeze detection. In conclusion, this small soft stethoscope can be easily used on young children. A noisy environment does not affect the effectiveness of the system in detecting wheeze. Hence, the system can be used at home by parents who wish to evaluate and manage the asthmatic condition of their children