A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field. >

/pdf/time-frequency-distributions-a-review-3lxbcw4fg4.pdf

Time-frequency distributions-a review

A tutorial review of both linear and quadratic representations is given. The linear representations discussed are the short-time Fourier transform and the wavelet transform. The discussion of quadratic representations concentrates on the Wigner distribution, the ambiguity function, smoothed versions of the Wigner distribution, and various classes of quadratic time-frequency representations. Examples of the application of these representations to typical problems encountered in time-varying signal processing are provided. >

Linear and quadratic time-frequency signal representations

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.

/pdf/analysis-of-respiratory-sounds-state-of-the-art-24iol5hyut.pdf

Analysis of Respiratory Sounds: State of the Art

An algorithm for simultaneously estimating the range and bearing of multiple near-field sources is presented. The method is based on the application of signal subspace ideas to the spatial Wigner-Ville distribution approach originally presented by B.R. Breed and T.E. Posch (Proc. ICASSP'84, p.41B.9.1, 1984). The principal advantages of using signal-subspace methods are that the range/direction-of-arrival estimates are obtained with improved precision and resolution, and without computation or search of a complicated spectral surface. Additionally, these methods allow a simple and more effective extension of the spatial Wigner-Ville approach to cases in which noise and/or multiple signals are present. Simulations have shown that the algorithm performs well for a wide variety of test cases, and comparisons with the Cramer-Rao bound indicate near-optimal direction-of-arrival estimates. >

/pdf/passive-direction-of-arrival-and-range-estimation-for-near-56eb98dgeb.pdf

Passive direction-of-arrival and range estimation for near-field sources

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

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

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.

The implementation of real-time image encoding requires a high and constant throughput rate not achievable by a SISD machine. With a reasonable size codebook and SIMD machine architecture. Vector Quantization algorithm can be implemented in real-time. But, single stage Vector Quantization requires fairly large-size codebook for good quality image encoding. Multi-Stage Vector Quantization with codebooks of moderate size at each stage has been shown to be an alternative viable approach. The bits per pixel rates for TV quality composite color image encoding using Multi-Stage Vector Quantization are reported. The VQ/MSVQ implementation requires two processors, inner product processor and comparator-address generator. The implementation details of the processors and their throughput rate are described.

Bit serial systolic chip set for real-time image coding

Autoregressive(AR) or linear predictive(LP) modeling and Wigner time-frequency representations have been proposed for non-stationary signal analysis and synthesis, owing to their specific advantages over the short-time Fourier transform, viz. reduced data set characterisation and improved frequency resolution of the former, and the improved time resolution and thence better non-stationary signal representation of the latter. However, the former is limited in time resolution and the latter in frequency resolution and size of characterising data set, depending on the size of the windows that need to be used. This paper investigates the potential combination of the two above methods, with the aim of exploiting their advantages simultaneously and addressing the window size-resolution dilemma. The concept shows good promise to this end, despite the problems of the cross-spectral components and computational complexity, that need to be addressed. Examples of simulated signals are presented to illustrate the advantages of this representation.

Panapakkam A. Ramamoorthy

Papers

Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng

Reproducibility of the vesicular breath sounds in normal subjects.

Characteristics of normal lung sounds after adaptive filtering.

Bit serial systolic chip set for real-time image coding

Autoregressive modeling of the Wigner spectrum