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

Previous studies have indicated that, during diastole, the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable. To detect such sounds, recordings of diastolic heart sound segments were analyzed using four signal processing techniques: the fast Fourier transform (FFT) autoregressive (AR), autoregressive moving-average (ARMA), and minimum-norm (eigenvector) methods. To further enhance the diastolic heart sounds and reduce background noise, an adaptive filter was used as a preprocessor. The power ratios of the FFT method and the poles of the AR, ARMA, and eigenvector methods were used to diagnose patients as having diseased or normal arteries using a blind protocol without prior knowledge of the actual disease states of the patients to guard against human bias. Of 80 cases, results showed that normal and abnormal records were correctly distinguished in 56 using the fast Fourier transform (FFT), in 63 using the AR, in 62 using the ARMA method, and in 67 using the eigenvector method. These results confirm that high-frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis. >

Noninvasive acoustical detection of coronary artery disease: a comparative study of signal processing methods

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

Comparison of AR-based algorithms for respiratory sounds classification

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.

Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng

The source of lung sounds in the airway is modeled as a white noise source consisting of one or a combination of the following sources: random white noise sequence, periodic train of impulses, and impulsive bursts of energy. Acoustic transmission through the lung parenchyma and chest wall is modeled as an all-pole filter. Using this method, the source and transmission characteristics of lung sounds are estimated separately, based on the lung sounds at the chest wall. To illustrate the potential validity of the model, lung sound segments in known disease conditions were selected from teaching tapes and the source and transmission characteristics were estimated by applying the model. The estimated characteristics were found to be consistent with current knowledge of the generation and transmission of lung sounds in the known conditions. >

Autoregressive modeling of lung sounds: characterization of source and transmission

Nonfiltered (NF) lung sounds from the apical area of the heart along with lung volumes and ECG signals were recorded from 5 normal subjects. The signals were digitized and subjected to three methods o

Reproducibility of the vesicular breath sounds in normal subjects.

Lung sounds were recorded from five normal male subjects during tidal breathing. Simultaneous electrocardiograms were recorded and used as index signals to generate simulated heart sounds for digital subtraction from recorded lung sounds to obtain purer lung sounds. Five random breaths from each subject were analyzed. Sound signals were band-pass filtered 25 to 1,000 Hz (antialiasing), digitized at 3,000 Hz, and then subjected to (1) direct fast Fourier transform (FFT) without filtering (NF); (2) digital high-pass filtering at 75 Hz and subsequent FFT (75 HzF); (3) adaptive filtering and subsequent FFT (AF). The FFT algorithms of all lung sounds were characterized by mean, median, and mode frequencies. The mean, median, and mode of NF were lower than those of 75 HzF (64.98 +/- 4.04 versus 150.42 +/- 17.49, mean +/- SE, p less than 0.003; 44.57 +/- 2.06 versus 111.81.5.78, p less than 0.0003; 36.81 +/- 1.77 versus 86.16 +/- 3.13, p less than 0.0001) and those of AF (64.98 +/- 4.04 versus 96.87 +/- 11.58, p less than 0.01; 44.57 +/- 2.06 versus 68.23 +/- 10.44, p less than 0.05; 36.81 +/- 1.78 versus 52.24 +/- 8.97, p less than 0.06). The mean, median, and mode of AF were lower than those of 75 HzF (96.87 +/- 11.58 versus 150.42 +/- 17.49, p less than 0.02; 68.23 +/- 10.44 versus 111.81 +/- 5.77, p less than 0.007; 52.24 +/- 8.97 versus 86.16 +/- 3.73, p less than 0.01). The results indicated that by filtering out low frequency heart sounds, the frequency spectrum of lung sounds was moved upward.(ABSTRACT TRUNCATED AT 250 WORDS)

Characteristics of normal lung sounds after adaptive filtering.

Unfiltered breath sounds (NF) from the apical area of the heart, lung volume and ECG signals were recorded in 5 normal subjects. The signals were digitized and subjected to three methods of heart soun

Vijay K. Iyer

Papers

Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng

Autoregressive modeling of lung sounds: characterization of source and transmission

Reproducibility of the vesicular breath sounds in normal subjects.

Characteristics of normal lung sounds after adaptive filtering.

Inspiratory and expiratory vesicular breath sounds.