Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng

Iyer, Vijay K.; Ramamoorthy, Prabhu; Fan, H.; Ploysongsang, Yongyudh

doi:10.1109/tbme.1986.325693

Cited by 104 publications

(54 citation statements)

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“…First, the interaction between the heart sounds and the respiratory sound is assumed to be additive. Second, since the two sounds are generated by independent sources, they are assumed to be uncorrelated [6], [7]. In addition, the relationship between the heart sounds outside and within the respiratory segment is expressed as in (2) [8], [9].…”

Section: Modeling Of the Acquired Respiratory Signalmentioning

confidence: 99%

“…However, in view of the studies carried out by other researchers [3]- [5], highpass filtering for this application results in loss of important signal information. More recently, various adaptive filtering schemes have been proposed to eliminate this kind of interference [6]- [9]. Adaptive-based approaches are considered to be the most practical, as they do not require any a priori information about the signals.…”

mentioning

confidence: 99%

“…However, they have only been partially successful, as their performance generally depends on accurate time alignment of the reference and primary signals. In [6] the reference signal to the adaptive filter was generated by adding to the acquired electrocardiographic (ECG) signal a delayed version of itself. The approach cannot follow the time variations between the first and second heart sounds or alterations in the 0018-9294/97$10.00 © 1997 IEEE cardiac frequency present in the acquired respiratory signal, because the distance between the original ECG and its delayed version is kept constant throughout the process.…”

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confidence: 99%

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Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme

Charleston

Azimi-Sadjadi²,

González-Camarena³

1997

IEEE Trans. Biomed. Eng.

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Abstract-This paper is concerned with the problem of cancellation of heart sounds from the acquired respiratory sounds using a new joint time-delay and signal-estimation (JTDSE) procedure. Multiresolution discrete wavelet transform (DWT) is first applied to decompose the signals into several subbands. To accurately separate the heart sounds from the acquired respiratory sounds, time-delay estimation (TDE) is performed iteratively in each subband using two adaptation mechanisms that minimize the sum of squared errors between these signals. The time delay is updated using a nonlinear adaptation, namely the Levenberg-Marquardt (LM) algorithm, while the function of the other adaptive system-which uses the block fast transversal filter (BFTF)-is to minimize the mean squared error between the outputs of the delay estimator and the adaptive filter. The proposed methodology possesses a number of key benefits such as the incorporation of multiple complementary information at different subbands, robustness in presence of noise, and accuracy in TDE. The scheme is applied to several cases of simulated and actual respiratory sounds under different conditions and the results are compared with those of the standard adaptive filtering. The results showed the promise of the scheme for the TDE and subsequent interference cancellation.

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Section: Modeling Of the Acquired Respiratory Signalmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme

Charleston

Azimi-Sadjadi²,

González-Camarena³

1997

IEEE Trans. Biomed. Eng.

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“…The experimental results of each subject under the condition of holding breath are summarized in the same table for reference. Note: heart sounds reduction equation (1) (1) where y is the component of raw mixed sounds and x is the component of estimated heart sounds.…”

Section: Max In Data Setmentioning

confidence: 99%

“…Nevertheless, adaptive scheme requires a "noise only" reference signal. In previous research [1], electrocardiographic (ECG) signal can be the reference signal, and this technique reduces the heart sounds by 50-80 percent. However, this technique requires at least two extra sensors to pick up the reference signal such as the lead II ECG.…”

Section: Introductionmentioning

confidence: 99%

Reduction of heart sounds from lung sound recordings by automated gain control and adaptive filtering techniques

Yip

Zhang

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Auscultation is an attractive, simple, and noninvasive method for the diagnosis of cardiovascular and pulmonary disorders. However, heart sounds contaminates severely lung sound recordings. The results of our previous researches indicated that the Laplacian electrocardiographic signal (LECG) could be used as a reference for adaptive filtering to reduce heart sounds. In this paper, an integrated platform including an electronic stethoscope, an automated gain control (AGC), and an adaptive algorithm, has been developed to process the signal in real time. The AGC algorithm allows amplifying the LECG signal in different scales to solve the problem of relative weak LECG signals at right chest. The experimental result shows that the heart-noise reduction at right chest is improved from 43% reported early to 75%. The overall heart sound reduction by our new scheme ranges from 75% to 83% at different chest locations. I INTRODUCTIONIn auscultation, heart sounds is an intrusive noise source in respiratory sounds. Simply using filtering technique cannot reject the unwanted signal, i.e. interference, effectively due to the overlapping in their spectra. Adaptive filtering may be the most suitable method to reduce intelligently the unwanted heart sounds in lung sound recordings. Nevertheless, adaptive scheme requires a "noise only" reference signal. In previous research [1], electrocardiographic (ECG) signal can be the reference signal, and this technique reduces the heart sounds by 50-80 percent. However, this technique requires at least two extra sensors to pick up the reference signal such as the lead II ECG. Another approach is to extract the "noise only" reference signal by a delayed version of original signal [2]. After the complex signal processing [3], a satisfactory result can be obtained. However, this scheme requires huge computation ability. Thus, it is difficult to implement those methods in a stethoscope for the real time applications.In this work, Laplacian ECG (LECG) [4] is used for the 'noise only' reference signal rather than standard ECG. Both the electronic signal -Laplacian ECG, and the acoustic signallung sounds mixed with heart sounds can be picked up by a newly designed stethoscope. A simple adaptive filtering, leastmean-square (LMS) is used to eliminate the interference. All hardware and software designs have been integrated together to make a single device to process the signal in real time (Fig. 1). Fig. 1 Stethoscope block diagramUsing the new stethoscope, lung sounds without heart sound interference have been obtained successfully. Moreover, the LECG waveform can be observed at the same time. II. SIGNAL PRE-PROPRESSINGTwo different signals, acoustic signal and electronic signal are picked up by a new type stethoscope. Raw acoustic signal was band-pass filtered from 25Hz to 1000Hz. The operational amplifier (Linear Technology LT1013) was used with the gain factor of 40. Raw LECG was filtered by a band-pass filter with the bandwidth of 5Hz to 500Hz, and the gain factor of 1000. The high in...

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Bioacoustic Signals

Hadjileontiadis

Rekanos

Panas

2006

Wiley Encyclopedia of Biomedical Engineering

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Biological sounds, such as lung sounds, heart sounds, bowel sounds, and joint sounds, emitted from the human body during its functioning provide an easy and noninvasive way of indicating existing pathology. By applying advanced signal processing techniques to the so‐called bioacoustic signals (BAS), useful information of high diagnostic value can be revealed. The purpose of this review is an in depth exploration of the properties of BAS, starting from their categorization, their characteristics and relation to human pathologies, and moving on to the recording standards that should be followed for their accurate acquisition, and focusing on the most current BAS analysis techniques. The latter refer to important signal processing issues, such as de‐noising, detection, modeling, feature extraction, and classification, involving a variety of methodologies, such as spectral analysis, wavelet transform, higher‐order statistics/spectrum, lower‐order statistics, fractal dimension, higher‐order crossings, reduced interference time‐frequency distributions. As a result, the way important problems in BAS analysis are approached with modern mathematical techniques is presented. Some descriptive examples along with an updated bibliography are provided to the reader as a means for thorough exploitation of BAS, because they cover advanced applications in the field. Consequently, the current pathways and trends in BAS analysis are introduced, giving rise to their objective evaluation and interpretation. However, they reveal the call for a common interest in interdisciplinary undertakings, both by biomedical engineers and by clinicians. Although it is unknown when traditional clinicians accept alternative approaches in everyday clinical practice, the results shown here are the outcome of joined efforts and scientific resources from both engineers and clinical medical doctors, paving the way for an accelerated transition from a traditional to a more enhanced analysis and appreciation of BAS.

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Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng

Cited by 104 publications

References 4 publications

Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme

Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme

Reduction of heart sounds from lung sound recordings by automated gain control and adaptive filtering techniques

Bioacoustic Signals

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