Heart sound cancellation from lung sound recordings using time-frequency filtering

Abstract

During lung sound recordings, heart sounds (HS) interfere with clinical interpretation of lung sounds over the low frequency components which is significant especially at low flow rates. Hence, it is desirable to cancel the effect of HS on lung sound records. In this paper, a novel HS cancellation method is presented. This method first localizes HS segments using multiresolution decomposition of the wavelet transform coefficients, then removes those segments from the original lung sound record and estimates the missing data via a 2D interpolation in the time-frequency (TF) domain. Finally, the signal is reconstructed into the time domain. To evaluate the efficiency of the TF filtering, the average power spectral density (PSD) of the original lung sound segments with and without HS over four frequency bands from 20 to 300 Hz were calculated and compared with the average PSD of the filtered signals. Statistical tests show that there is no significant difference between the average PSD of the HS-free original lung sounds and the TF-filtered signal for all frequency bands at both low and medium flow rates. It was found that the proposed method successfully removes HS from lung sound signals while preserving the original fundamental components of the lung sounds.

Separating Heart Sound from Lung Sound Using LabVIEW

“…High Pass Filtering (HPF) with an arbitrary cut-off frequency between 70 and 100 Hz is not efficient in this case because lung sounds have major components in that region particularly at low flow rates. Therefore, HS reduction from lung sounds without altering the main characteristic features of the lung sound has been of interest for many researchers [12].…”

Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound

“…before another source can be extracted following the same procedure, equations (6)(7)(8)(9)(10)(11)(12)(13). This extraction is computationally simple when compared with other AJD algorithms [14].…”

“…Although experimental results were promising, the method suffers from high computational load. Time-frequency (TF) filtering techniques have also been proposed for HSS reduction in LSS [8] and [9], of which the technique employed in [9] is computationally efficient [9] but the results are unconvincing. Recently, for the case of only a single measurement sensor, an adaptive line enhancer was employed to mitigate the HSS in LSS, which yielded promising results [10].…”

Spectro-Temporal Analysis of Auscultatory Sounds

“…For lung sound analysis, signal processing strategies based on conventional time, frequency, or time-frequency signal representations have been proposed for heart sound cancelation. Representative strategies include entropy calculation (7) and recurrence time statistics (8) for heart sound detection-and-removal followed by lung sound prediction, adaptive filtering (e.g., (9; 10)), time-frequency spectrogram filtering (11), and time-frequency wavelet filtering (e.g., (12)(13)(14)). Subjective assessment, however, has suggested that due to the temporal and spectral overlap between heart and lung sounds, heart sound removal may result in noisy or possibly "non-recognizable" lung sounds (15).…”