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Measurement of Respiratory Acoustical Signals
Measurement of Respiratory Acoustical Signals* Comparison of Sensors Hans Pasterkamp, M.D.; SteveS. Kraman, M.D., F.C.C.P.; Fbul D. DeFrain, M.S.E.E.; and George R. Wodicka, Ph.D. We assessed the performance of three air-coupled and four contact sensors under standardized conditions oflung sound recording. Recordings were obtained from three of the investigators at the best site on the posterior lower chest as determined by auscultation. Lung sounds were band-pass filtered between 100 and 2,000 Hz and sampled simultaneously with calibrated airflow at a rate of 10 kHz. Fourier techniques were used for power spectral analysis. Average spectra for inspiratory sounds at Oows of 2±0.5 Us were referenced against background noise at zero Oow. Aircoupled and contact sensors had comparable maximum signal-to-noise ratios and gave similar values for most
spectral parameters. Unexpectedly, less sensitivity (lower signal-to-noise ratio) at high frequencies was observed in the air-coupled devices. Sensor performance needs to be characterized in studies of lung sounds. We suggest that lung sound spectra should be averaged at known airflows over several breaths and that aU measurements should be reported relative to sounds recorded at zero Oow. (Chest 1993; 104:1518-25)
Acoustical signals from the respiratory system traditionally are assessed by subjective auscultation. During recent years, there has been an increasing number of publications on computer-assisted acquisition and analysis of these signals. Digital signal proc-
erated the introduction of respiratory sound analysis systems into clinical laboratories. Unfortunately, investigators around the world use various methods that differ from the choice of sound sensors, through the sampling and processing of sound signals, to the measurement and presentation of results. The need for standardization in this area recently has been emphasized. 5 Microphones and other sound sensors are fundamental parts in any recording of respiratory acoustical signals. Advantages and disadvantages of air-coupledversus contact-type sensors previously have been described but not formally compared in actual recording of respiratory sounds. 6 We, therefore, decided to evaluate the relative performance of a representative selection of sensors in situ. Also, the effect of different techniques for processing of the recorded respiratory sounds was assessed, and suggestions of informative methods for presentation of the acoustical data were put forth.
For editorial comment see page 1320 essing techniques have been shown to provide details on respiratory sounds that surpass the limits ofhuman auditory perception. Studies on the frequency content and distribution of normal respiratory sounds indicate a potential value for clinical application. During bronchial provocation for example, inspiratory sounds are said to show an increase in median frequency that correlates with the degree of airftow obstruction even in the absence of wheeze, both over the lung 1 and at the trachea. 2 Another application for respiratory acoustical measurements is in upper and central airway obstruction where a close correlation between sound spectra and airway patency has been described in physical models3 and also observed clinically.4 Advances in microprocessor technology have accel*From the Department of Pediatri<:s, University of Manitoba, Winnipeg, Canada (Dr. Pasterkamp); the VA Medical Center, Lexington, Ky (Dr. Kraman); and the School of Electrical Engineering, Purdue University, West Lafayette, lnd (Mr. DeFrain and Dr. Wodicka). This study was supported in part by a grant from the Whitaker Foundation and a National Sciem:e Fimndation Young Investigator Award BCS-9257488 to Dr. Wodicka. Dr. Pasterkamp is supported by the Children's Hospital of Winnipeg Research Foundation. This study was presented in part at the 17th International Conference on Lung Sounds, August, 1992, Helsinki, Finland. Manuscript received December 3, 1992; revision accepted Fehmary 26, 1993. RerJrint requests: Dr. ltJsterkamp, CN503-840 Sherbrook Street, Winnipeg, Manitoba, Canadn R3A lSI
1518
F w=highest frequency at which the lung sound signal reaches background noise level.
SUBJECTS AND METHODS
We used seven sensors that are representative of those <.'Ommonly used for respiration acoustic studies (Table 1). Three of us served as subjects for the recording of lung sounds after giving informed consent. The study protocol was approved by the Purdue University Committee on the use of human subjects. All three participants were healthy male nonsmokers, ranging in age from 24 to 47 years, in height from 166 to 183 em, and in weight from 62 to 83 kg. None of the subjects had a respiratory tract infection during the month before the study. The recording oflung sounds took place at the Schl of Electrical Engineering, Purdue University. The subjects sat in a S(lUndpmof chamber and breathed through a calibrated pneumotachograph while they observed the Row signal on an oscilloscope. The target Row range was set at 2 ± 0.5 Us. The point of maximum S(JI.JRd intensity over the posterior lower chest was identified by auscultation before the experiment and marked for successive placement of Measulllllltll1t of Respiratory Acoustical Signals (Paslerllamp et 81)
Table 1-Dimensiom of Sound Semon Sensors AiM:OUpled Sony ECM 155* Sony ECM77* Radio Shack No. 33-1052t Contact HP21050:j: Siemens EMT25C§ PPG No. 20111 FYSPac2,
Height, mm
Weight, g
Diameter, mm
12.0 12.0 8.9
1.7 1.5 2.0
5.6 5.6 7.6
26.0 13.0 8.0 5.1
52.2 15.4 9.9 2.1
14.0 28.0 28.0 20.0
*Sony Corp., Montvale, NJ. tRadio Shack, Tandy Corp., Fort Worth, Tex. :!:Hewlett-Packard, Waltham, Mass. §Siemens, Iselin, NJ. I!PPG (phonopneumography) sensor, Technion University, Haifa, Israel. 'IFYSPac2, University of Brussels, Belgium . the different sensors. The sensors were attached to the skin with double-sided adhesive tape rings. Contact sensors were affixed directly while aiM:OUpled sensors were first placed in coupling chambers. The plastic chamber for the Sony microphones weighed 3.3 g and had internal dimensions of 8-mm height and 10-mm diameter. External height was 18 mm and diameter, 20 mm . The chamber cavity was vented to ambient pressure by a lateral bore hole. The <:oupler for the Radio Shack microphone was a closed-cell foam disk, 8 mm in height and 31 mm in diameter, with a central hole of 5.5 mm in diameter for placement of the microphone. The Hewlett-Packard sensor was taped to the chest surf
before spectral computation. The fast Fourier transforms were calculated at successive 100-ms epochs, resulting in a 50 percent overlap of adjacent windows. The effective frequency resolution of the spectral estimates was approximately 5 Hz. All samples of lung sounds that occurred at flows of 1.5 to 2.5 Us were used fi>r calculation of average spectra within this How gate. The average power spectra of background noise were similarly calculated from samples within a Row gate of 0.0 to 0.1 Us during late expiration. Digital respirosonogr.tms7 were used for inspection of the recorded signals. These sonograms (.'()mbine spectrographic representation of lung sounds with a plot of calibrated air How. 1h compare lung sound spectra, we chose as parameters the quartile frequencies (below which 25, 50, and 75 percent of spectral power were contained) and the spectral edge frequency (below which 99 percent of spectral power was found). The upper and lower limits of the band containing lung sounds were defined as the frequencies above and below 200 Hz at which the lung sound signal dropped to less than 3 decibel (dB) above background noise. We calculated the maximum signal-to-noise ratio (in decibels) and the frequency band where this occurred. We applied linear regression analysis to the sound spectra between 300 and 700 Hz on a log-log plot of power against frequency to determine sound attenuation (decibeVoctave). Differences between individual sensors were assessed by repeated measures analysis of variance and Newman-Keuls multiple comparison tests.• Statistical significantoe was accepted when probability was less than 0.05. RESULTS
On average, the length of recording was 20.8 s (range, 16.0 to 30.3 s) for each subject and sensor, and contained six inspirations (range, 4 to 9). The average number of spectra within the target flow range was 34 (range, 18 to 57). We computed the background noise spectra from an average of31 samples (range, 9 to 70). The slopes of the spectral curves of inspiratory sounds recorded with air-coupled microphones were steeper compared with those recorded with contact sensors (Table 2). A significantly greater sound attenuation was observed with the Sony electret microphones (p<0.05), while the attenuation was least with the FYSPac2 contact sensor (p<0.05). Figure 1 illustrates these findings in one of the subjects. The background noise spectra were subtracted from the lung sound signal spectra in this plot. Sudden dips in
Table 2-Performance of Sound Semora Subject 2
Subject 1 Sensors AiM:OUpled Sony EMC155 Sony ECM77 Radio Shack No. 33-1052 Contact HP 21050 Siemens EMT25C PPG No. 201 FYSPac2
Subject 3
SIN*
Slopet
F..:t:
SIN
Slope
F.,
SIN
Slope
F.,
41.4 42.2 38.6
-25.2 -26.7 -16.9
1,250 1,010 1,370
29.8 28.3 25.3
-22.3 -25.4 -19.1
930 785 655
24.6 23.1 23.3
-21.1 -23.8 -17.6
1,105 850
42.0 33.0 41.1 40.3
-15.9 -16.1 -16.0 -11.4
1,610 1,560 >2,000 >2,000
24 .5 16.4 25.0 26.0
-18.1 -14.3 -17.5 -12.4
1,280 560
28.9 22.3 26.2 25.5
-14.2 -15.3 -15.3 -13.7
1,880 920 >2,000
955
765
925
935
*SIN (dB)= 10 X log (signal power/noise power)= maximum signal-to-noise mtio. tSiope (dB/octave)= linear regression of log-log spectral plot from 300 to 700 Hz. :j:F., =highest frequency at which the lung sound signal reaches background noise level. CHEST I 104 I 5 I NOVEMBER, 1993
1519
0.01
Subject No.3 0.001
-
0.0001 1E-5
N
-J:
lt)
~
1E-6
•
~
Q)
~ a..
1E-7 air coupled - o- Sony ECM155 _ .. _ Radio Shack contact - FYSPac2 -•- PPGI¥201 - .- EMT25C
1E-8 1E-9 1E-10 0
500
1000
1500
2000
Frequency (Hz) Frc:uRE l. Comparison in situ of two aiN:oupled and three contact sensors, plotting difference between signals and noise F... lowest frequency at which the lung sound signal reaches background noise level; Q1 , second quartile frequency (median frequency); F..,., frequency at which the ratio of signal to noise is greatest.
the spectral curves and missing data points above 1,000 Hz indicate, therefore, that the lung sounds were close to or at background noise level. The
difference in spectral slopes is most evident between 500 and 1,000 Hz. The greatest signal hand width in this case was obtained with the PPG sensor. For the
meant SO
v
1st quartile (0 1)
•
median (02)
0
-
N
J:
. :f·
:2:
I
I
l
I I
I I
I
I I
I
100~-------------------------------------------Siemens Sony Radio Shack FYSPac2 HP PPG Sony EMT25C ECM77 #33-1052 21050 #201 ECM155 Frc:liRE 2. Measurements on average lung sound spectra, obtained with seven sensors at inspiratory ftows of2:t0.5 Us.
1520
Measurement o1 Respiratory Acous1lcal Signals (Paaterlcamp et al}
EMT25C (#2) - . - EMT25C (#9) - • - PPG#201
---o-
1500
2000
Frequency (Hz) 3. Comparison in situ of three contact sensors, showing a significant difference in effective sensitivity (maximum signal-to-noise ratio) between two sensors of the same make and model (Siemens FIGURE
EMT25C).
FYSPac2 sensor, the spectral peak near 2,000 Hz depicts the mechanical resonance of this device. The FYSPac2 transducer gave significantly higher values for all spectral parameters compared with those of the other sensors (Fig 2). The first quartile was higher with the Sony and Radio Shack microphones compared with the HP, PPG, and Siemens sensors (p<0.05). The median frequency was also higher with the aiNX>upled microphones compared with the HP and PPG contact sensors (p<0.05). There was a trend toward higher values for the highest frequency at which the lung sound signal reaches background noise level (Fh;) with contact sensors, but the difference did not reach statistical significance (0.1>p<0.05). The frequency at which the ratio of signal-to-noise is greatest was not different between sensors. There was no major difference in the maximum signal-to-noise ratios between aiNX>upled and contact sensors (Table 2). The Siemens accelerometer had a lower sensitivity (p<0.05) which was also evident as a lesser sound quality on listening. This particular sensor (No. 9) was later compared with another accelerometer of the same make and model (No. 2) and to the PPG sensor. The subject for this comparison was a healthy 31-year-old male nonsmoker. The recording was made over the right posterior lower lobe. Power spectra were averaged from 100 samples at inspiratory target flows and from 140 samples of background noise as described previously. The plot of signal-to-noise ratio against frequency (Fig 3) shows a
significant reduction of sensitivity in the Siemens sensor used for the measurements in Table 2, reflecting either a characteristic of this particular sensor or alternatively some damage sustained during transportation. Figure 4 illustrates our observations in one subject (No. 1) who had particularly loud inspiratory sounds at the recording site. These spectra were computed from data obtained with the PPG sensor (Table 1). The log-linear plot of power against frequency on the left provides detail at higher frequencies and illustrates the effect of airflow. In this case, the average spectrum at higher flows (from 2.0 to 2.5 Us) shows increased power compared with the average spectrum at lower flows (from 1.5 to 2.0 Us), particularly at frequencies above 500Hz. Relative to background noise, there is still substantial power at 2,000 Hz. Beyond 2,000 Hz and also below 100 Hz, the attenuating effect of the band-pass filter is obvious. The log-log plot on the right was obtained from the same recording. It has less detail at higher frequencies but allows visual separation of spectral parameters and appreciation of spectral slope. This plot shows the smoothing effect of averaging the sound spectra from seven inspirations compared with a single inspiration. The coefficient of variance from breath to breath in this case was 3.2 percent for the median frequency, 22.4 percent for the frequency at maximum power, and 4.4 percent for the spectral edge frequency. The slope of the power spectrum above 300 Hz shows a CHEST I 104 I 5 I NOVEMBER, 1993
1521
Q2
0 .01
•
noise (n=33) --1.5 to 2.0 Vs (n=15) 2.0 to 2.5 Vs (n=12)
0.001
SE
Fh.I
••
•
Fmax
0 .0001
-N
J:
1E-5
L()
~
1E-6
~
(I)
~ 0
1E-7
a.. 1E-8 1E-9
0
500
1000
1500
2000
100
2500 10
1000
Frequency (Hz) 4. Power spectra of inspiratory sounds recorded over the posterior left lower lobe in subject l. N =number of epochs used for <.~lmputation of average Fourier spectra. F•• and Fh, indicate the lower and upper limits of the frequency band where the inspiratory lung sounds were above background noise (sound at zero air flow). Q, =second quartile (median) frequency; F _=frequency at maximum signal-to-noise ratio; SE,.. =spectral edge (frequency below which 99% of spectral power is found). FIGliRE
linear decline of 16 dB/octave in this case. The lower limit of the lung sound signal had a coefficient of variance of 55.8 percent while the coefficient of variance of the upper limit (Fh;) was 7.4 percent. Figure 5 is taken from the same recording and demonstrates the effect of data window functions. The effect is most clearly seen at frequencies below 100 Hz and above 1,000 Hz when the spectra are estimated without rounding at the edges of the data segment, ie, with a rectangular window. There is an artifact9 of spectral leakage which causes the signal to remain above background noise level from 0 to 2,400 Hz. This artifact is avoided when the Hanning window is applied. Another effect of the Hanning window is a reduction in the power of the spectrum . Figure 5 also shows that the average spectrum during expiration is not simply a mirror image of the inspiratory spectrum. At the same airflow, maximum signal-to-noise ratio is less during expiration, and a spectral peak at approximately 700 Hz appears in this case. Figure 6 shows respirosonograms of recordings with an air-coupled microphone and a contact sensor in another subject (No. 2). Frequencies above 800 Hz were registered only with the contact sensor. Back1522
ground noise at zero airflow also had slightly greater power between 100 and 300Hz when recorded with the contact compared with the air-coupled sensor in this case. DISCUSSION
Our observations on inspiratory lung sounds confirm once again their well-known spectral characteristics, 10 showing 99 percent of sound intensity below 600 Hz and greatest amplitudes between 100 and 300 Hz. Expiratory sounds at the same airflows were quieter and not necessarily similar to inspiratory sounds. For this comparison of sensors, however, we concentrated on data acquired during inspiration. Unexpectedly we found steeper spectral slopes with air-coupled microphones compared with contact sensors. Forgacs 11 mentioned an attenuation of 10 to 20 dB/octave for lung sounds above 200 Hz. Using a contact sensor (HP 21050) over the posterior base of the right lung, Gavriely et al 12 measured spectral slopes of inspiratory sounds between - lO and - 16 dB/octave in normal subjects. Our findings are in general agreement with these authors. However, under identical conditions and in the same individual, the spectral roll-off could be twice as much with some air-coupled microphones Measurement of Respiratory Acoustical Signals (Pasterlcamp at al)
80 inspiration 60 40
--
20
co ~
~
- - - - 6 - Rectangular window ···· ....... noise
0
- - Hanning window .... ......... noise
Q)
~
a..
20 40 60 expiration
80
10
100
1000
Frequency (Hz) FIGURE 5. Difference of signal and noise spectra, depending on the window function (rectangular or Hanning) used before Fourier transformation. Note the dissimilarity of sound spectra during inspiration and expiration at the same airflows.
compared with some contact sensors. The manufacturers' specifications for the electret 0
rw~V r-'~,•J,rc;f Sony6'CWT7
f\
.,
\
:''\
~
\/
~
\/
f'\
,.-)
'
v
I
,r-
tn.p.
•xp.
HP21050
Subject No. 2
FIGURE 6. Comparison in situ of one ai~pled and one contact sensor. The respirosonogram displays time on the horizontal and frequency on the vertical axis while sound intensity is shown on a scale from black (loudest) to white (lowest). Calibrated airftow is plotted at the top. Epochs of 1()()-ms duration that fell within the flow gate of 1.5 to 2.5 Us are marked as solid black squares on the horizontal axis.
microphones used in our study indicate that, if tested under free field conditions, their frequency responses extend beyond 10 kHz and therefore exceed by far the range of interest for respiratory acoustical measurements. The most likely explanation for their relatively inferior frequency response in situ is a selective damping of sounds within the coupling chambers when they are in their measurement position near the chest wall . Druzgalski and coworkers6 found no effect on the frequency response below 2,500 Hz when they placed an electret microphone (Sony ECM50) in a conical coupling cavity of slightly larger dimension than the one used with the Sony microphones in our study. However, their sound source was a loudspeaker, and they did not compare respiratory sound measurements at the chest surface. For the microphones we used double-sided adhesive tape only and did not apply additional loads to fasten the plastic coupler to the chest wall. We also vented the coupler cavity to equalize with atmospheric pressure,l3 and we observed effective sensitivities (maximum signal-to-noise ratios) that were among the highest of all sensors tested. It is therefore unlikely that tension of the underlying skin created a pseudodiaphragm. Even if this had occurred, we would expect attenuation of low rather than high frequencies. The CHEST I 104 I 5 I NOVEMBER, 1993
1523
other air-coupled microphone (Radio Shack) was attached with a closed-cell foam disk and showed less attenuation at high frequencies but still more than the contact sensors. We did not formally assess the effect of different couplers on the frequency response of the electret microphones in this study, but we believe that better designs could possibly be achieved . One may argue that the performance of air-coupled and contact sensors is quite similar in measurements of lung sounds below the spectral edge frequency and that a greater sensitivity of contact sensors to high frequency components is of questionable clinical importance. However, there is new evidence that high frequency sounds travel a longer distance through airways before they propagate through lung tissue to the chest wall. 14 It may therefore be possible to selectively assess small airways by measuring sound transmission at higher frequencies. 15 Respiratory sounds below 250 Hz, on the other hand, may be significantly affected by resonances of the thoracic cavity 16 and therefore less informative of sound propagation within airways. The band-pass filter in our study reduced artifacts from heart and muscle sounds below 100Hz but also energy arising from respiratory sounds. We, therefore, cannot comment on the performance of air-coupled versus contact sensors in measurements of very low frequency lung sounds. However, both types of sensors have been used for the recording of low frequency heart sounds.6 The choice of sensors for respiratory acoustical measurements will be influenced not only by their effective sensitivity and frequency response but also by their size, durability, and cost. Air-coupled electret microphones have a clear advantage in these areas. The Sony and Radio Shack microphones were quite sturdy despite their small size. Compared with the contact sensors which cost between $100 and $400, the electret microphones are generally available for less than $100. We observed a susceptibility to artifacts by noise from cable movements with the contact sensors. The PPG sensor had a high effective sensitivity and responded well over a broad frequency range but also was quite sensitive to extraneous noise. The Hewlett-Packard sensor was large and cumbersome to attach. Its frequency response and effective sensitivity were comparable to those of the PPG sensor. The Siemens sensor was similar in size to the PPG sensor but was less sensitive at frequencies above 1,200 Hz. Subsequent comparisons of this sensor against another of the same make and model showed a substantial reduction in effective sensitivity, suggesting either variable production quality or damage sustained during transport. This emphasizes the need for benchtop standardization before any transducer is used in respiratory acoustical studies. The FYSPac2 sensor is an extremely lightweight and delicate device that 1524
exerts very little load on the chest issue. It, therefore, has been used for in situ calibration of microphones and stethoscopes with artificially induced lung sounds. 17 Fragility, however, makes it unsuitable for use in a clinical environment. In our study, we calibrated different microphones and sensors in situ, using naturally occurring lung sounds. In order to do this, we had to keep individual recording sites constant and we had to standardize by airflow. Flow-gated sampling of respiratory sounds has been described by O'Donnell and Kraman. 111 We suggest that calibrated air flow should be recorded under most circumstances in studies of respiration acoustics. Normalizing air flow to body weight allows the comparison of lung sounds from infancy to adulthood.19 While there may be an effect of a pneumotachograph on tracheal sounds at higher air flows, this does not appear to affect sounds recorded at the chest. 20 Since tracheal sounds may extend well above 2 kHz, 21 we recommend sampling rates of at least 5 kHz for measurements of respiratory sounds. A lowpass filter is necessary in these studies to avoid aliasing. One should be aware that most filters do not roll off sharply enough to guarantee no aliasing effect when the cutoff is selected at half the sampling frequency. We used a cutoff that was very conservatively set at one fifth of the sampling rate. Since there is a well recognized contribution of sounds from the cardiovascular system 22 and from muscles 23 to the low frequency band of acoustical signals at the chest wall, a high-pass filter, such as in this study, often is used. Spectral analysis by fast Fourier transformation is the most common technique in respiratory acoustical studies. Averaging only a few fast Fourier transforms over short time intervals leaves much variability in the power spectrum. For comparison of respiratory sounds in various clinical situations, we therefore advise those performing these measurements to average flow-gated samples over several breaths. Variability may be reduced by narrowing the flow-gate and by averaging over a larger number ofbreaths. Assessing breath-by-breath variability of spectral parameters will help to identify clinical abnormalities and artifacts. Some parameters such as the median frequency tend to be quite stable, 24 while others such as the frequency at maximum power or the limits of the lung sound band width are more variable in our experience. This is explained by the time-varying contribution of heart sounds and muscle noise in the low frequency band, by extraneous noises at high frequencies, and by the occurrence of random peaks in normal respiratory sounds. We selected individual recording sites over the posterior lower lobes based on auscultatory detection of maximum inspiratory lung sound intensity. For each subject, the chosen site remained the same during all Measurement of Respiratory Acoustical Signals (Pasterl
recordings. While tracheal sounds would have provided greater signal-to-noise ratio and signal band width , we wanted to compare lung sound sensors under typical conditions, ie, attached to the chest wall. Intrasubject variation of lung sound amplitudes at various sites over the chests of normal subjects has been documented, but no systemic difference exists between both posterior lower lobes. 1 ~ The consistently increased signal-to-noise ratio in subject 1 may relate to airway geometry and sound generation or to local sound transmission. In any case, standardization by airflow guaranteed that for each subject the same lung sounds were presented to each sensor under comparison. Until the ideal sensor for respiratory acoustical measurements has been found, different types and models are going to be used. We strongly believe that quality control has to include auditory verification of the digitization process, using digital-to-analog playback of the acquired data in order to avoid artifacts. Respirosonograms (Fig 6) can be helpful as visual correlates to the acoustic perception. Reports on respiratory sounds should always provide a comparison of the respiratory sound signal under study to the background noise measured with the sensor in situ at zero airflow. This information may be presented by plotting the signal-to-noise ratio as a function of frequency (Fig 3), by subtracting noise from signal (Fig 1), or by simultaneously plotting signal and noise (Fig 4). We prefer the latter since it provides a clear indication of the effective signal band width. A mirror image plot of inspiratory and expiratory spectra facilitates the recognition of dissimilarities (Fig 5). The choice of log-linear or log-log plots of power against frequency depends on the feature of interest. Sound attenuation per octave is obviously seen best on loglog displays, but if sounds at high frequencies are to be examined, the log-linear display is preferable (Fig 4). As respiratory acoustical measurements expand from research to clinical laboratories, standardization becomes increasingly important. We hope that observations such as those presented herein will help to put in perspective the important information that can be gained from respiratory sounds. ACKNOWLEDGMENTS: We would like to thank Dr. Ignacio Sanchez for his help and participation as a study subject. We gratefuly acknowledge the technical assistance of Mr. Yuns Oh and secretarial help from Mrs. Doris Jensen.
REFERENCES 1 Anderson K, Aitken S, Carter R, MacLeod JE , Moran F.
Variation of breath sound and airway caliber induced by histamine challenge. Am Rev Respir Dis 1990; 141 :1147-50 2 Spence DPS, Bentley S, Evans DH, Morgan MDL. Effect of methacholine induced bronchoconstriction on the spectral characteristics of breath sounds in asthma. Thorax 1992; 47:680-83 3 Coleman RF, Schechter GL. A basic model to study acoustic evaluation of airway obstruction. Arch Orolaryngol Head Neck Surg 1991; 117:1144-49 4 Pasterkamp H, Sanchez I. Tracheal sounds in upper airway obstruction. Chest 1992; 102:963-65 · 5 Mussel MJ. The need for standards in recording and analysing respiratory sounds. Med Bioi Eng Comput 1992; 30:129-39 6 Druzgalski CK, Donnerberg RL, Campbell RM . Techniques of recording respiratory sounds. J Clin Engineer 1980; 5:321-30 7 Pasterkamp H, Carson C, Daien D. Oh Y. Digital respirosonography: new images of lung sounds. Chest 1989; 96:1405-12 8 Hassard TH . Analysis of variance, Understanding biostatistics. Chap 5. St. Louis: Mosby Year Book, 1991; 75-97 9 Cohen A. Biomedical signal processing (vol2). Compression and automatic recognition. Boca Raton, Fla: CRC Press, 1986; 13951 10 Kraman SS. Vesicular (normal) lung sounds: how are they made, where do they come from , and what do they mean? Semin Respir Med 1985; 6:183-91 11 Forgacs P: Lung sounds. London: Bailliere Tindall, 1978; 11 12 Gavriely N, Palti Y, Alroy G. Spectral characteristics of normal breath sounds. J Appl Physiol1981; 50:307-14 13 Ploysongsang Y, Martin RR, Ross WRD, Loudon RG, Macklem PT. Breath sounds and regional ventilation. Am Rev Respir Dis 1977; 146:187-99 14 Wodicka GR, Aguirre A. DeFrain PO, Shannon DC. Phase delay of pulmonary acoustic transmission from trachea to chest wall. IEEE Trans Biomed Eng 1992; 39:1053-59 15 Goncharolf V, Jacobs JE, Cugell OW Wideband acoustic transmission of human lungs. Med Bioi Eng Comput 1989; 27:51319 16 Wodicka GR, Shannon DC. Transfer function of sound transmission in subglottal human respiratory system at low frequencies. J Appl Physiol1990; 69:2126-30 17 Vermarien H. The use of artificially induced lung sounds for the calibration in situ of microphones and stethoscopes for heart and lung sounds. Arch Int Physiol Biochim 1985; 93:33-34 18 O'Donnell OM, Kraman SS. Vesicular lung sound amplitude mapping by automated How-gated phonopneumography. J Appl Physiol1982; 53:603-09 19 Powell RE, Sanchez I, Pasterkamp H. Characteristics of Howstandardized lung sounds in newborns compared to adults [abstract) . Pediatr Res 1992; 31:320A 20 Mussell MJ, Miyamoto Y. Comparison of normal respiratory sounds recorded from the chest and trachea at various respiratory air How levels. Front Med Bioi Eng 1992; 4!73-85 21 Sanchez I, Pasterkamp H. Tracheal sound spectra depend on body height. Am Rev Respir Dis (in press) 22 Pasterkamp H, Fenton R, Tal A, Chernick V. Interference of cardiovascular sounds with phonopneumography in children. Am Rev Respir Dis 1985; 131:61-64 23 Kraman SS. Does the vesicular lung sound come only from the lungs? Am Rev Respir Dis 1983; 128:622-26 24 Ploysongsang Y, Iyer VK, Ramamoorthy PA. Reproducibility of the vesicular breath sounds in normal subjects. Respiration 1991; 58:158-62
CHEST I 104 I 5 I NOVEMBER, 1993
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spectral parameters. Unexpectedly, less sensitivity (lower signal-to-noise ratio) at high frequencies was observed in the air-coupled devices. Sensor performance needs to be characterized in studies of lung sounds. We suggest that lung sound spectra should be averaged at known airflows over several breaths and that aU measurements should be reported relative to sounds recorded at zero Oow. (Chest 1993; 104:1518-25)
Acoustical signals from the respiratory system traditionally are assessed by subjective auscultation. During recent years, there has been an increasing number of publications on computer-assisted acquisition and analysis of these signals. Digital signal proc-
erated the introduction of respiratory sound analysis systems into clinical laboratories. Unfortunately, investigators around the world use various methods that differ from the choice of sound sensors, through the sampling and processing of sound signals, to the measurement and presentation of results. The need for standardization in this area recently has been emphasized. 5 Microphones and other sound sensors are fundamental parts in any recording of respiratory acoustical signals. Advantages and disadvantages of air-coupledversus contact-type sensors previously have been described but not formally compared in actual recording of respiratory sounds. 6 We, therefore, decided to evaluate the relative performance of a representative selection of sensors in situ. Also, the effect of different techniques for processing of the recorded respiratory sounds was assessed, and suggestions of informative methods for presentation of the acoustical data were put forth.
For editorial comment see page 1320 essing techniques have been shown to provide details on respiratory sounds that surpass the limits ofhuman auditory perception. Studies on the frequency content and distribution of normal respiratory sounds indicate a potential value for clinical application. During bronchial provocation for example, inspiratory sounds are said to show an increase in median frequency that correlates with the degree of airftow obstruction even in the absence of wheeze, both over the lung 1 and at the trachea. 2 Another application for respiratory acoustical measurements is in upper and central airway obstruction where a close correlation between sound spectra and airway patency has been described in physical models3 and also observed clinically.4 Advances in microprocessor technology have accel*From the Department of Pediatri<:s, University of Manitoba, Winnipeg, Canada (Dr. Pasterkamp); the VA Medical Center, Lexington, Ky (Dr. Kraman); and the School of Electrical Engineering, Purdue University, West Lafayette, lnd (Mr. DeFrain and Dr. Wodicka). This study was supported in part by a grant from the Whitaker Foundation and a National Sciem:e Fimndation Young Investigator Award BCS-9257488 to Dr. Wodicka. Dr. Pasterkamp is supported by the Children's Hospital of Winnipeg Research Foundation. This study was presented in part at the 17th International Conference on Lung Sounds, August, 1992, Helsinki, Finland. Manuscript received December 3, 1992; revision accepted Fehmary 26, 1993. RerJrint requests: Dr. ltJsterkamp, CN503-840 Sherbrook Street, Winnipeg, Manitoba, Canadn R3A lSI
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F w=highest frequency at which the lung sound signal reaches background noise level.
SUBJECTS AND METHODS
We used seven sensors that are representative of those <.'Ommonly used for respiration acoustic studies (Table 1). Three of us served as subjects for the recording of lung sounds after giving informed consent. The study protocol was approved by the Purdue University Committee on the use of human subjects. All three participants were healthy male nonsmokers, ranging in age from 24 to 47 years, in height from 166 to 183 em, and in weight from 62 to 83 kg. None of the subjects had a respiratory tract infection during the month before the study. The recording oflung sounds took place at the Sch
Table 1-Dimensiom of Sound Semon Sensors AiM:OUpled Sony ECM 155* Sony ECM77* Radio Shack No. 33-1052t Contact HP21050:j: Siemens EMT25C§ PPG No. 20111 FYSPac2,
Height, mm
Weight, g
Diameter, mm
12.0 12.0 8.9
1.7 1.5 2.0
5.6 5.6 7.6
26.0 13.0 8.0 5.1
52.2 15.4 9.9 2.1
14.0 28.0 28.0 20.0
*Sony Corp., Montvale, NJ. tRadio Shack, Tandy Corp., Fort Worth, Tex. :!:Hewlett-Packard, Waltham, Mass. §Siemens, Iselin, NJ. I!PPG (phonopneumography) sensor, Technion University, Haifa, Israel. 'IFYSPac2, University of Brussels, Belgium . the different sensors. The sensors were attached to the skin with double-sided adhesive tape rings. Contact sensors were affixed directly while aiM:OUpled sensors were first placed in coupling chambers. The plastic chamber for the Sony microphones weighed 3.3 g and had internal dimensions of 8-mm height and 10-mm diameter. External height was 18 mm and diameter, 20 mm . The chamber cavity was vented to ambient pressure by a lateral bore hole. The <:oupler for the Radio Shack microphone was a closed-cell foam disk, 8 mm in height and 31 mm in diameter, with a central hole of 5.5 mm in diameter for placement of the microphone. The Hewlett-Packard sensor was taped to the chest surf
before spectral computation. The fast Fourier transforms were calculated at successive 100-ms epochs, resulting in a 50 percent overlap of adjacent windows. The effective frequency resolution of the spectral estimates was approximately 5 Hz. All samples of lung sounds that occurred at flows of 1.5 to 2.5 Us were used fi>r calculation of average spectra within this How gate. The average power spectra of background noise were similarly calculated from samples within a Row gate of 0.0 to 0.1 Us during late expiration. Digital respirosonogr.tms7 were used for inspection of the recorded signals. These sonograms (.'()mbine spectrographic representation of lung sounds with a plot of calibrated air How. 1h compare lung sound spectra, we chose as parameters the quartile frequencies (below which 25, 50, and 75 percent of spectral power were contained) and the spectral edge frequency (below which 99 percent of spectral power was found). The upper and lower limits of the band containing lung sounds were defined as the frequencies above and below 200 Hz at which the lung sound signal dropped to less than 3 decibel (dB) above background noise. We calculated the maximum signal-to-noise ratio (in decibels) and the frequency band where this occurred. We applied linear regression analysis to the sound spectra between 300 and 700 Hz on a log-log plot of power against frequency to determine sound attenuation (decibeVoctave). Differences between individual sensors were assessed by repeated measures analysis of variance and Newman-Keuls multiple comparison tests.• Statistical significantoe was accepted when probability was less than 0.05. RESULTS
On average, the length of recording was 20.8 s (range, 16.0 to 30.3 s) for each subject and sensor, and contained six inspirations (range, 4 to 9). The average number of spectra within the target flow range was 34 (range, 18 to 57). We computed the background noise spectra from an average of31 samples (range, 9 to 70). The slopes of the spectral curves of inspiratory sounds recorded with air-coupled microphones were steeper compared with those recorded with contact sensors (Table 2). A significantly greater sound attenuation was observed with the Sony electret microphones (p<0.05), while the attenuation was least with the FYSPac2 contact sensor (p<0.05). Figure 1 illustrates these findings in one of the subjects. The background noise spectra were subtracted from the lung sound signal spectra in this plot. Sudden dips in
Table 2-Performance of Sound Semora Subject 2
Subject 1 Sensors AiM:OUpled Sony EMC155 Sony ECM77 Radio Shack No. 33-1052 Contact HP 21050 Siemens EMT25C PPG No. 201 FYSPac2
Subject 3
SIN*
Slopet
F..:t:
SIN
Slope
F.,
SIN
Slope
F.,
41.4 42.2 38.6
-25.2 -26.7 -16.9
1,250 1,010 1,370
29.8 28.3 25.3
-22.3 -25.4 -19.1
930 785 655
24.6 23.1 23.3
-21.1 -23.8 -17.6
1,105 850
42.0 33.0 41.1 40.3
-15.9 -16.1 -16.0 -11.4
1,610 1,560 >2,000 >2,000
24 .5 16.4 25.0 26.0
-18.1 -14.3 -17.5 -12.4
1,280 560
28.9 22.3 26.2 25.5
-14.2 -15.3 -15.3 -13.7
1,880 920 >2,000
955
765
925
935
*SIN (dB)= 10 X log (signal power/noise power)= maximum signal-to-noise mtio. tSiope (dB/octave)= linear regression of log-log spectral plot from 300 to 700 Hz. :j:F., =highest frequency at which the lung sound signal reaches background noise level. CHEST I 104 I 5 I NOVEMBER, 1993
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0.01
Subject No.3 0.001
-
0.0001 1E-5
N
-J:
lt)
~
1E-6
•
~
Q)
~ a..
1E-7 air coupled - o- Sony ECM155 _ .. _ Radio Shack contact - FYSPac2 -•- PPGI¥201 - .- EMT25C
1E-8 1E-9 1E-10 0
500
1000
1500
2000
Frequency (Hz) Frc:uRE l. Comparison in situ of two aiN:oupled and three contact sensors, plotting difference between signals and noise F... lowest frequency at which the lung sound signal reaches background noise level; Q1 , second quartile frequency (median frequency); F..,., frequency at which the ratio of signal to noise is greatest.
the spectral curves and missing data points above 1,000 Hz indicate, therefore, that the lung sounds were close to or at background noise level. The
difference in spectral slopes is most evident between 500 and 1,000 Hz. The greatest signal hand width in this case was obtained with the PPG sensor. For the
meant SO
v
1st quartile (0 1)
•
median (02)
0
-
N
J:
. :f·
:2:
I
I
l
I I
I I
I
I I
I
100~-------------------------------------------Siemens Sony Radio Shack FYSPac2 HP PPG Sony EMT25C ECM77 #33-1052 21050 #201 ECM155 Frc:liRE 2. Measurements on average lung sound spectra, obtained with seven sensors at inspiratory ftows of2:t0.5 Us.
1520
Measurement o1 Respiratory Acous1lcal Signals (Paaterlcamp et al}
EMT25C (#2) - . - EMT25C (#9) - • - PPG#201
---o-
1500
2000
Frequency (Hz) 3. Comparison in situ of three contact sensors, showing a significant difference in effective sensitivity (maximum signal-to-noise ratio) between two sensors of the same make and model (Siemens FIGURE
EMT25C).
FYSPac2 sensor, the spectral peak near 2,000 Hz depicts the mechanical resonance of this device. The FYSPac2 transducer gave significantly higher values for all spectral parameters compared with those of the other sensors (Fig 2). The first quartile was higher with the Sony and Radio Shack microphones compared with the HP, PPG, and Siemens sensors (p<0.05). The median frequency was also higher with the aiNX>upled microphones compared with the HP and PPG contact sensors (p<0.05). There was a trend toward higher values for the highest frequency at which the lung sound signal reaches background noise level (Fh;) with contact sensors, but the difference did not reach statistical significance (0.1>p<0.05). The frequency at which the ratio of signal-to-noise is greatest was not different between sensors. There was no major difference in the maximum signal-to-noise ratios between aiNX>upled and contact sensors (Table 2). The Siemens accelerometer had a lower sensitivity (p<0.05) which was also evident as a lesser sound quality on listening. This particular sensor (No. 9) was later compared with another accelerometer of the same make and model (No. 2) and to the PPG sensor. The subject for this comparison was a healthy 31-year-old male nonsmoker. The recording was made over the right posterior lower lobe. Power spectra were averaged from 100 samples at inspiratory target flows and from 140 samples of background noise as described previously. The plot of signal-to-noise ratio against frequency (Fig 3) shows a
significant reduction of sensitivity in the Siemens sensor used for the measurements in Table 2, reflecting either a characteristic of this particular sensor or alternatively some damage sustained during transportation. Figure 4 illustrates our observations in one subject (No. 1) who had particularly loud inspiratory sounds at the recording site. These spectra were computed from data obtained with the PPG sensor (Table 1). The log-linear plot of power against frequency on the left provides detail at higher frequencies and illustrates the effect of airflow. In this case, the average spectrum at higher flows (from 2.0 to 2.5 Us) shows increased power compared with the average spectrum at lower flows (from 1.5 to 2.0 Us), particularly at frequencies above 500Hz. Relative to background noise, there is still substantial power at 2,000 Hz. Beyond 2,000 Hz and also below 100 Hz, the attenuating effect of the band-pass filter is obvious. The log-log plot on the right was obtained from the same recording. It has less detail at higher frequencies but allows visual separation of spectral parameters and appreciation of spectral slope. This plot shows the smoothing effect of averaging the sound spectra from seven inspirations compared with a single inspiration. The coefficient of variance from breath to breath in this case was 3.2 percent for the median frequency, 22.4 percent for the frequency at maximum power, and 4.4 percent for the spectral edge frequency. The slope of the power spectrum above 300 Hz shows a CHEST I 104 I 5 I NOVEMBER, 1993
1521
Q2
0 .01
•
noise (n=33) --1.5 to 2.0 Vs (n=15) 2.0 to 2.5 Vs (n=12)
0.001
SE
Fh.I
••
•
Fmax
0 .0001
-N
J:
1E-5
L()
~
1E-6
~
(I)
~ 0
1E-7
a.. 1E-8 1E-9
0
500
1000
1500
2000
100
2500 10
1000
Frequency (Hz) 4. Power spectra of inspiratory sounds recorded over the posterior left lower lobe in subject l. N =number of epochs used for <.~lmputation of average Fourier spectra. F•• and Fh, indicate the lower and upper limits of the frequency band where the inspiratory lung sounds were above background noise (sound at zero air flow). Q, =second quartile (median) frequency; F _=frequency at maximum signal-to-noise ratio; SE,.. =spectral edge (frequency below which 99% of spectral power is found). FIGliRE
linear decline of 16 dB/octave in this case. The lower limit of the lung sound signal had a coefficient of variance of 55.8 percent while the coefficient of variance of the upper limit (Fh;) was 7.4 percent. Figure 5 is taken from the same recording and demonstrates the effect of data window functions. The effect is most clearly seen at frequencies below 100 Hz and above 1,000 Hz when the spectra are estimated without rounding at the edges of the data segment, ie, with a rectangular window. There is an artifact9 of spectral leakage which causes the signal to remain above background noise level from 0 to 2,400 Hz. This artifact is avoided when the Hanning window is applied. Another effect of the Hanning window is a reduction in the power of the spectrum . Figure 5 also shows that the average spectrum during expiration is not simply a mirror image of the inspiratory spectrum. At the same airflow, maximum signal-to-noise ratio is less during expiration, and a spectral peak at approximately 700 Hz appears in this case. Figure 6 shows respirosonograms of recordings with an air-coupled microphone and a contact sensor in another subject (No. 2). Frequencies above 800 Hz were registered only with the contact sensor. Back1522
ground noise at zero airflow also had slightly greater power between 100 and 300Hz when recorded with the contact compared with the air-coupled sensor in this case. DISCUSSION
Our observations on inspiratory lung sounds confirm once again their well-known spectral characteristics, 10 showing 99 percent of sound intensity below 600 Hz and greatest amplitudes between 100 and 300 Hz. Expiratory sounds at the same airflows were quieter and not necessarily similar to inspiratory sounds. For this comparison of sensors, however, we concentrated on data acquired during inspiration. Unexpectedly we found steeper spectral slopes with air-coupled microphones compared with contact sensors. Forgacs 11 mentioned an attenuation of 10 to 20 dB/octave for lung sounds above 200 Hz. Using a contact sensor (HP 21050) over the posterior base of the right lung, Gavriely et al 12 measured spectral slopes of inspiratory sounds between - lO and - 16 dB/octave in normal subjects. Our findings are in general agreement with these authors. However, under identical conditions and in the same individual, the spectral roll-off could be twice as much with some air-coupled microphones Measurement of Respiratory Acoustical Signals (Pasterlcamp at al)
80 inspiration 60 40
--
20
co ~
~
- - - - 6 - Rectangular window ···· ....... noise
0
- - Hanning window .... ......... noise
Q)
~
a..
20 40 60 expiration
80
10
100
1000
Frequency (Hz) FIGURE 5. Difference of signal and noise spectra, depending on the window function (rectangular or Hanning) used before Fourier transformation. Note the dissimilarity of sound spectra during inspiration and expiration at the same airflows.
compared with some contact sensors. The manufacturers' specifications for the electret 0
rw~V r-'~,•J,rc;f Sony6'CWT7
f\
.,
\
:''\
~
\/
~
\/
f'\
,.-)
'
v
I
,r-
tn.p.
•xp.
HP21050
Subject No. 2
FIGURE 6. Comparison in situ of one ai~pled and one contact sensor. The respirosonogram displays time on the horizontal and frequency on the vertical axis while sound intensity is shown on a scale from black (loudest) to white (lowest). Calibrated airftow is plotted at the top. Epochs of 1()()-ms duration that fell within the flow gate of 1.5 to 2.5 Us are marked as solid black squares on the horizontal axis.
microphones used in our study indicate that, if tested under free field conditions, their frequency responses extend beyond 10 kHz and therefore exceed by far the range of interest for respiratory acoustical measurements. The most likely explanation for their relatively inferior frequency response in situ is a selective damping of sounds within the coupling chambers when they are in their measurement position near the chest wall . Druzgalski and coworkers6 found no effect on the frequency response below 2,500 Hz when they placed an electret microphone (Sony ECM50) in a conical coupling cavity of slightly larger dimension than the one used with the Sony microphones in our study. However, their sound source was a loudspeaker, and they did not compare respiratory sound measurements at the chest surface. For the microphones we used double-sided adhesive tape only and did not apply additional loads to fasten the plastic coupler to the chest wall. We also vented the coupler cavity to equalize with atmospheric pressure,l3 and we observed effective sensitivities (maximum signal-to-noise ratios) that were among the highest of all sensors tested. It is therefore unlikely that tension of the underlying skin created a pseudodiaphragm. Even if this had occurred, we would expect attenuation of low rather than high frequencies. The CHEST I 104 I 5 I NOVEMBER, 1993
1523
other air-coupled microphone (Radio Shack) was attached with a closed-cell foam disk and showed less attenuation at high frequencies but still more than the contact sensors. We did not formally assess the effect of different couplers on the frequency response of the electret microphones in this study, but we believe that better designs could possibly be achieved . One may argue that the performance of air-coupled and contact sensors is quite similar in measurements of lung sounds below the spectral edge frequency and that a greater sensitivity of contact sensors to high frequency components is of questionable clinical importance. However, there is new evidence that high frequency sounds travel a longer distance through airways before they propagate through lung tissue to the chest wall. 14 It may therefore be possible to selectively assess small airways by measuring sound transmission at higher frequencies. 15 Respiratory sounds below 250 Hz, on the other hand, may be significantly affected by resonances of the thoracic cavity 16 and therefore less informative of sound propagation within airways. The band-pass filter in our study reduced artifacts from heart and muscle sounds below 100Hz but also energy arising from respiratory sounds. We, therefore, cannot comment on the performance of air-coupled versus contact sensors in measurements of very low frequency lung sounds. However, both types of sensors have been used for the recording of low frequency heart sounds.6 The choice of sensors for respiratory acoustical measurements will be influenced not only by their effective sensitivity and frequency response but also by their size, durability, and cost. Air-coupled electret microphones have a clear advantage in these areas. The Sony and Radio Shack microphones were quite sturdy despite their small size. Compared with the contact sensors which cost between $100 and $400, the electret microphones are generally available for less than $100. We observed a susceptibility to artifacts by noise from cable movements with the contact sensors. The PPG sensor had a high effective sensitivity and responded well over a broad frequency range but also was quite sensitive to extraneous noise. The Hewlett-Packard sensor was large and cumbersome to attach. Its frequency response and effective sensitivity were comparable to those of the PPG sensor. The Siemens sensor was similar in size to the PPG sensor but was less sensitive at frequencies above 1,200 Hz. Subsequent comparisons of this sensor against another of the same make and model showed a substantial reduction in effective sensitivity, suggesting either variable production quality or damage sustained during transport. This emphasizes the need for benchtop standardization before any transducer is used in respiratory acoustical studies. The FYSPac2 sensor is an extremely lightweight and delicate device that 1524
exerts very little load on the chest issue. It, therefore, has been used for in situ calibration of microphones and stethoscopes with artificially induced lung sounds. 17 Fragility, however, makes it unsuitable for use in a clinical environment. In our study, we calibrated different microphones and sensors in situ, using naturally occurring lung sounds. In order to do this, we had to keep individual recording sites constant and we had to standardize by airflow. Flow-gated sampling of respiratory sounds has been described by O'Donnell and Kraman. 111 We suggest that calibrated air flow should be recorded under most circumstances in studies of respiration acoustics. Normalizing air flow to body weight allows the comparison of lung sounds from infancy to adulthood.19 While there may be an effect of a pneumotachograph on tracheal sounds at higher air flows, this does not appear to affect sounds recorded at the chest. 20 Since tracheal sounds may extend well above 2 kHz, 21 we recommend sampling rates of at least 5 kHz for measurements of respiratory sounds. A lowpass filter is necessary in these studies to avoid aliasing. One should be aware that most filters do not roll off sharply enough to guarantee no aliasing effect when the cutoff is selected at half the sampling frequency. We used a cutoff that was very conservatively set at one fifth of the sampling rate. Since there is a well recognized contribution of sounds from the cardiovascular system 22 and from muscles 23 to the low frequency band of acoustical signals at the chest wall, a high-pass filter, such as in this study, often is used. Spectral analysis by fast Fourier transformation is the most common technique in respiratory acoustical studies. Averaging only a few fast Fourier transforms over short time intervals leaves much variability in the power spectrum. For comparison of respiratory sounds in various clinical situations, we therefore advise those performing these measurements to average flow-gated samples over several breaths. Variability may be reduced by narrowing the flow-gate and by averaging over a larger number ofbreaths. Assessing breath-by-breath variability of spectral parameters will help to identify clinical abnormalities and artifacts. Some parameters such as the median frequency tend to be quite stable, 24 while others such as the frequency at maximum power or the limits of the lung sound band width are more variable in our experience. This is explained by the time-varying contribution of heart sounds and muscle noise in the low frequency band, by extraneous noises at high frequencies, and by the occurrence of random peaks in normal respiratory sounds. We selected individual recording sites over the posterior lower lobes based on auscultatory detection of maximum inspiratory lung sound intensity. For each subject, the chosen site remained the same during all Measurement of Respiratory Acoustical Signals (Pasterl
recordings. While tracheal sounds would have provided greater signal-to-noise ratio and signal band width , we wanted to compare lung sound sensors under typical conditions, ie, attached to the chest wall. Intrasubject variation of lung sound amplitudes at various sites over the chests of normal subjects has been documented, but no systemic difference exists between both posterior lower lobes. 1 ~ The consistently increased signal-to-noise ratio in subject 1 may relate to airway geometry and sound generation or to local sound transmission. In any case, standardization by airflow guaranteed that for each subject the same lung sounds were presented to each sensor under comparison. Until the ideal sensor for respiratory acoustical measurements has been found, different types and models are going to be used. We strongly believe that quality control has to include auditory verification of the digitization process, using digital-to-analog playback of the acquired data in order to avoid artifacts. Respirosonograms (Fig 6) can be helpful as visual correlates to the acoustic perception. Reports on respiratory sounds should always provide a comparison of the respiratory sound signal under study to the background noise measured with the sensor in situ at zero airflow. This information may be presented by plotting the signal-to-noise ratio as a function of frequency (Fig 3), by subtracting noise from signal (Fig 1), or by simultaneously plotting signal and noise (Fig 4). We prefer the latter since it provides a clear indication of the effective signal band width. A mirror image plot of inspiratory and expiratory spectra facilitates the recognition of dissimilarities (Fig 5). The choice of log-linear or log-log plots of power against frequency depends on the feature of interest. Sound attenuation per octave is obviously seen best on loglog displays, but if sounds at high frequencies are to be examined, the log-linear display is preferable (Fig 4). As respiratory acoustical measurements expand from research to clinical laboratories, standardization becomes increasingly important. We hope that observations such as those presented herein will help to put in perspective the important information that can be gained from respiratory sounds. ACKNOWLEDGMENTS: We would like to thank Dr. Ignacio Sanchez for his help and participation as a study subject. We gratefuly acknowledge the technical assistance of Mr. Yuns Oh and secretarial help from Mrs. Doris Jensen.
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