Analysis of breath sounds in normal and asthmatic children and adults using computer digitized airway phonopneumography (CDAP)

A novel feature extraction technique for pulmonary sound analysis based on EMD

BACKGROUND AND OBJECTIVE

The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarity of the signal. To overcome this problem we need an automated computer-aided diagnostic system that will be competent in noisy environment. In this paper, a novel feature extraction technique is introduced for discriminating various pulmonary dysfunctions in an automated way based on pattern recognition algorithms.

METHOD

In this work, the disease correlated relevant characteristics of lung sounds signals are identified in terms of statistical distribution parameters: mean, variance, skewness, and kurtosis. These features are extracted from selective morphological components of the mapped signal in the empirical mode decomposition domain. The feature set is fed to the classifier model to differentiate their corresponding classes.

RESULTS

The significance of features developed are validated by conducting several experiments using supervised and unsupervised classifiers. Furthermore, the discriminating power of the proposed features is compared with three types of baseline features. The experimental result is evaluated by statistical analysis and also validated with physicians inference.

CONCLUSIONS

It is found that the proposed features extraction technique is superior to the baseline methods in terms of classification accuracy, sensitivity and specificity. The developed method gives better results compared to baseline methods in any circumstance. The proposed method gives a higher accuracy of 94.16, sensitivity of 100 and specificity of 93.75 for an artificial neural network classifier.

Detection of lungs status using morphological complexities of respiratory sounds

Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

Lung sounds in bronchial asthma

Modern understanding of lung sounds started with a historical article by Forgacs. Since then, many studies have clarified the changes of lung sounds due to airway narrowing as well as the mechanism of genesis for these sounds. Studies using bronchoprovocation have shown that an increase of the frequency and/or intensity of lung sounds was a common finding of airway narrowing and correlated well with lung function. Bronchoprovocation studies have also disclosed that wheezing may not be as sensitive as changes in basic lung sounds in acute airway narrowing. A forced expiratory wheeze (FEW) may be an early sign of airway obstruction in patients with bronchial asthma. Studies of FEW showed that airway wall oscillation and vortex shedding in central airways are the most likely mechanisms of the generation of expiratory wheezes. Studies on the genesis of wheezes have disclosed that inspiratory and expiratory wheezes may have the same mechanism of generation as a flutter/flow limitation mechanism, either localized or generalized. In lung sound analysis, the narrower the airways are, the higher the frequency of breathing sounds is, and, if a patient has higher than normal breathing sounds, i.e., bronchial sounds, he or she may have airway narrowing or airway inflammation. It is sometimes difficult to detect subtle changes in lung sounds; therefore, we anticipate that automated analysis of lung sounds will be used to overcome these difficulties in the near future.

Auscultation revisited: the waveform and spectral characteristics of breath sounds during general anesthesia

Although auscultation is commonly used as a continuous monitoring tool during anesthesia, the breath sounds of anesthetized patients have never been systematically studied. In this investigation we used digital audio technology to record and analyze the breath sounds of 14 healthy adult patients receiving general anesthesia with positive pressure ventilation. Sounds recorded from inside the esophagus were compared to those recorded from the surface of the chest, and corresponding airflow was measured with a pneumotachograph. The sound samples associated with inspiratory and expiratory phases were analyzed in the time domain (RMS amplitude) and frequency domain (peak frequency, spectral edge, and power ratios). There was a positive linear correlation (R2 > 0.9) between inspiratory flow and sound amplitude in the precordial and esophageal samples of all patients. The RMS amplitude of the inspiratory and expiratory sounds was approximately 13 times greater when recorded from inside the esophagus than from the surface of the chest in all patients at all flows (p < 0.001). The peak frequency (Hz) was significantly higher in the esophageal recordings than the precordial samples (298 +/- 9 vs 181 +/- 10, P < 0.0001), as was the 97% spectral edge (Hz) (740 +/- 7 vs 348 +/- 16, P < 0.0001). In the adult population esophageal stethoscopes yield higher frequencies and greater amplitude than precordial stethoscopes. Quantification of lung sounds may provide for improved monitoring and diagnostic capability during anesthesia and surgery.

[A phonogramic study of expiratory wheezing in the asthmatic patient]

The quantitative analysis of expiratory wheezing may offer a new approach for study respiratory function in asthmatics.

METHOD

The sound spectrum during expiration was analyzed in 9 asthmatics with wheezing and 5 normal subjects. Phonographic parameters were then correlated with spirometric results for baseline respiration and deep breathing.

RESULTS

a) Expiratory wheezing is heard in a band of 210 to 280 Hz during deep breathing, and b) the volume in this band correlates positively with mean expiratory flow (VT/TE) and negatively with the slope of the volume/flow curve between 50 and 25% of FVC.

CONCLUSIONS

The degree of air flow limitation in the peripheral airways correlates with the volume of pulmonary sound.

Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary

Articles about medical diagnostic decision support (MDDS) systems often begin with a disclaimer such as, "despite many years of research and millions of dollars of expenditures on medical diagnostic systems, none is in widespread use at the present time." While this statement remains true in the sense that no single diagnostic system is in widespread use, it is misleading with regard to the state of the art of these systems. Diagnostic systems, many simple and some complex, are now ubiquitous, and research on MDDS systems is growing. The nature of MDDS systems has diversified over time. The prospects for adoption of large-scale diagnostic systems are better now than ever before, due to enthusiasm for implementation of the electronic medical record in academic, commercial, and primary care settings. Diagnostic decision support systems have become an established component of medical technology. This paper provides a review and a threaded bibliography for some of the important work on MDDS systems over the years from 1954 to 1993.