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

Abstract

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...

Performance Evaluation of Heart Sound Cancellation in FPGA Hardware Implementation for Electronic Stethoscope

“…Normally, adaptive filter can be used to reduce heart sound noise from lung sound as in [12, 14], but, for electronic stethoscope to be used in outpatient clinic settings, it is not convenient because adaptive filter requires an external reference signal. In this work, we choose ALE instead.…”

Fundamentals of Respiratory Sounds and Analysis

“…Two groups, who applied adaptive filtering for heart sound reduction, used ECG signals instead of the heart sound as the copy of the noise input [26,27]. This is highly questionable as the ECG signal is not the noise of the lung sound record and hence even if the filter tries to adapt itself to the ECG signal, the results will not be meaningful.…”

Lung Sounds: An Advanced Signal Processing Perspective

“…To this end, in most cases, the agreement of an automatic method with physician's interpretation is a basic criterion for its acceptance. Through the years, LS analysis has branched out in various directions; the most popular include respiratory flow estimation [26,27], heart sound cancellation [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], DAS detection and denoising [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57], nonlinear analysis of LS [58][59][60][61][62][63][64], feature extraction, and classification [43,52,[65][66][67][68][69][70]…”