Objective: This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds.

Methods and material: Review of the current medical and technological literature using Pubmed and personal experience.

Results: The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms…

Conclusion: The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools.

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Analysis of Respiratory Sounds: State of the Art

Respiratory sounds are always contaminated by heart sound interference. An essential preprocessing step in some of the heart sound cancellation methods is localizing primary heart sound components. Singular spectrum analysis (SSA), a powerful time series analysis technique, is used in this paper. Despite the frequency overlap of the heart and lung sound components, two different trends in the eigenvalue spectra are recognizable, which leads to find a subspace that contains more information about the underlying heart sound. Artificially mixed and real respiratory signals are used for evaluating the performance of the method. Selecting the appropriate length for the SSA window results in good decomposition quality and low computational cost for the algorithm. The results of the proposed method are compared with those of well-established methods, which use the wavelet transform and entropy of the signal to detect the heart sound components. The proposed method outperforms the wavelet-based method in terms of false detection and also correlation with the underlying heart sounds. Performance of the proposed method is slightly better than that of the entropy-based method. Moreover, the execution time of the former is significantly lower than that of the latter.

Localizing Heart Sounds in Respiratory Signals Using Singular Spectrum Analysis

When recording lung sounds, an incessant noise source occurs due to heart sounds. This noise source severely contaminates the breath sound signal and interferes in the analysis of lung sounds. In this paper, an adaptive heart-noise reduction method, based on fourth-order statistics (FOS) of the recorded signal, without requiring recorded "noise-only" reference signal, is presented. This algorithm uses adaptive filtering to preserve the entire spectrum. Furthermore, the proposed filter is independent of Gaussian uncorrelated noise and insensitive to the step-size parameter. It converges fast with small excess errors and, due to the narrowband nature of heart noise (HN), it requires a very small number of taps. Results from experiments with healthy subjects indicate a local HN reduction equal to or greater than 90%.

Adaptive reduction of heart sounds from lung sounds using fourth-order statistics

The relationship between respiratory sounds and flow is of great interest for researchers and physicians due to its diagnostic potentials. Due to difficulties and inaccuracy of most of the flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibrating the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is novel in terms of being independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. After removing the effects of heart sounds (which distort the low-frequency components of tracheal sounds) on the calculated entropy of the tracheal sounds, the performance of the method at different frequency ranges were investigated. Also, the performance of the proposed method was tested using 6 different segment sizes for entropy calculation and the best segment sizes during inspiration and expiration were found. The method was tested on data of 10 healthy subjects at five different flow rates. The overall estimation error was found to be 8.3 /spl plusmn/ 2.8% and 9.6 /spl plusmn/ 2.8% for inspiration and expiration phases, respectively.

A robust method for estimating respiratory flow using tracheal sounds entropy

Heart sounds produce an incessant noise during lung sounds recordings. This noise severely contaminates the breath sounds signal and interferes in the analysis of lung sounds. In this paper, the use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is presented. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. In addition, the use of hard thresholding in the wavelet transform domain results in a separation of the nonstationary part of the input signal (heart sounds) from the stationary one (lung sounds). Thus, the location of the heart sound noise (1st and 2nd heart sound peaks) is automatically detected, without requiring any noise reference signal. Experimental results have shown that the implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of the superimposed heart sound noise, producing an almost noise-free output signal. Due to its simplicity and its fast implementation the method can easily be used in clinical medicine.

A wavelet-based reduction of heart sound noise from lung sounds

Auscultation of the chest is an attractive diagnostic method used by physicians, owing to its simplicity and noninvasiveness. Hence, there is interest in lung sound analysis using time and frequency domain techniques to increase its usefulness in diagnosis. The sounds recorded or heard are, however, contaminated by incessant heart sounds which interfere in the diagnosis based on, and analysis of, lung sounds. A common method to minimize the effect of heart sounds is to filter the sound with linear high-pass filters which, however, also eliminates the overlapping spectrum of breath sounds. In this work we show how adaptive filtering can be used to reduce heart sounds without significantly affecting breath sounds. The technique is found to reduce the heart sounds by 50?80 percent.

Hohg Fan

Papers

Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng