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

Abstract

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.

Respiratory acoustic thoracic imaging (RATHI): Assessing deterministic interpolation techniques

“…Therefore it seems necessary to enhance the SNR of the LS signals by cancelling the heart sounds and other interference (CHARLESTON et al, 1997).…”

Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction

“…An observed signal is a projection from this multivariate state space onto a one-dimensional time series. can be considered as a set of scalar measurements (1) from which a sequence of -dimensional vectors can be constructed using Takens' delay embedding theorem (2) where is a delay parameter, and is the embedding dimension [9]. The purpose of the embedding is to unfold the projection back into a reconstructed state space that is dynamically and topologically equivalent to the state space that generated the process [10].…”

“…The latter sounds can be reduced with adequate and firm microphone placement and by using sound proof rooms, but HS noise is unavoidable. HS and LS have overlapping frequency spectra, and even though highpass filtering is often employed to reduce HS, this results in loss of important signal information [2]. Previous approaches to HS cancellation from recorded LS include wavelet-based methods [2], adaptive filtering techniques [3], and fourth-order statistics [4], all resulting in reduced but still audible HS.…”

“…HS and LS have overlapping frequency spectra, and even though highpass filtering is often employed to reduce HS, this results in loss of important signal information [2]. Previous approaches to HS cancellation from recorded LS include wavelet-based methods [2], adaptive filtering techniques [3], and fourth-order statistics [4], all resulting in reduced but still audible HS. Recent studies indicate that cutting out segments containing HS followed by interpolation of the missing data yields promising results [5], [6].…”

Separating heart sound from lung sound using Non-Local Means filter

“…Several different techniques have been implemented to remove (reduce) the level of respiratory sound signals (RSSs) within total sound recording. High pass filtering [1], wavelet based methods [2], adaptive filtering techniques [3], fourthorder statistics [4]. Promising results can be achieved by cutting out heart sound segments and interpolating the missing data [5] and two-channel handling of lung sound [6].…”