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Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

ACADEMIC EMERGENCY MEDICINE 2012; 19:79–84 © 2012 by the Society for Academic Emergency Medicine

Abstract

Objectives:  Although congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and asthma patients typically present with abnormal auscultatory findings on lung examination, respiratory sounds are not normally subjected to rigorous analysis. The aim of this study was to evaluate in detail the distribution of respiratory sound intensity in CHF, COPD, and asthma patients during acute exacerbation.

Methods:  Respiratory sounds throughout the respiratory cycle were captured and displayed using an acoustic-based imaging technique. Breath sound distribution was mapped to create a gray-scale sequence of two-dimensional images based on intensity of sound (vibration). Consecutive CHF (n = 22), COPD (n = 19), and asthma (n = 18) patients were imaged at the time of presentation to the emergency department (ED). Twenty healthy subjects were also enrolled as a comparison group. Geographical area of the images and respiratory sound patterns were quantitatively analyzed.

Results:  In healthy volunteers and COPD patients, the median (interquartile range [IQR]) geographical areas of the vibration energy images were similar, at 75.6 (IQR = 6.0) and 75.8 (IQR = 10.8) kilopixels, respectively (p > 0.05). Compared to healthy volunteers and COPD patients, areas for CHF and asthma patients were smaller, at 66.9 (IQR = 9.9) and 53.9 (IQR = 15.6) kilopixels, respectively (p < 0.05). The geographic area ratios between the left and right lungs for healthy volunteers and CHF and COPD patients were 1.0 (IQR = 0.2), 1.0 (IQR = 0.2), and 1.0 (IQR = 0.1), respectively. Compared to healthy volunteers, the geographic area ratio between the left and right lungs for asthma patients was 0.5 (IQR = 0.4; p < 0.05). In healthy volunteers and CHF patients, the ratios of vibration energy values at peak inspiration and expiration (peak I/E ratio) were 4.6 (IQR = 4.4) and 4.7 (IQR = 3.5). In marked contrast, the peak I/E ratios of COPD and asthma patients were 3.4 (= 2.1) and 0.1 (IQR = 0.3; p < 0.05), respectively.

Conclusions:  The pilot data generated in this study support the concept that relative differences in respiratory sound intensity may be useful in distinguishing acute dyspnea caused by CHF, COPD, or asthma.

Dyspnea is one of the leading cause of visits to the emergency department (ED).1 Distinguishing dyspnea caused by cardiac or respiratory reasons at the time of presentation may be difficult.2,3 Symptoms of acute and chronic cardiac and respiratory illnesses overlap. Patients with exacerbations of congestive heart failure (CHF), asthma, and chronic obstructive pulmonary disease (COPD) usually present with abnormal auscultatory findings. Diagnostic difficulty has been reported, particularly in the prehospital setting.4,5

Respiratory sounds are generally considered to provide clinically relevant information in asthma, COPD, and CHF. Although the abnormal respiratory sounds are auscultated, respiratory sounds are not normally subjected to rigorous analysis. In the past decade, there have been attempts to refine noninvasive acoustic data to better detect and monitor pulmonary abnormalities through the use of computerized lung sound analysis.6 The theory behind this type of analysis is that diseases affecting the lungs would result in alterations of lung vibration that may be too subtle to be detected on the skin surface using conventional methods. These altered vibrations may be due to changes in amount of vibration created due to increases or decreases in airflow or changes in the transmission of vibrations through the diseased lung parenchyma or pleural space and heterogeneity of disease throughout the lung. Computerized vibration imaging technology is able to record lung vibrations and convert the signals to a dynamic image of the lung in near real time.6–11 This was a pilot study with the aim to investigate in detail the distribution of respiratory sound intensity in CHF, COPD, and asthma patients during acute exacerbations.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Study Design

This was a prospective pilot study. The study protocol was approved by the institutional review board, and informed consent was obtained from all patients and healthy volunteers.

Study Setting and Population

Consecutive patients 18 to 85 years of age who presented to the ED with acute dyspnea were identified and enrolled in the ED of an academic hospital with 40,000 annual patients. The respiratory measurements were taken, and then patients were divided into groups based on their discharge diagnosis (based on history and physical examination) of CHF, COPD, or asthma. Patients were excluded from the study if they were diagnosed with more than one of the diagnoses of interest or some other etiology of their dyspnea was identified, such as pneumonia, pulmonary embolism, etc. (Figure 1). Patients with hemodynamic instability or who were deemed unable to be seated without assistance were also excluded. Some patients who were admitted to the hospital due to nonresponse to the therapy in the ED had some additional objective measurements, such as echocardiography and pulmonary function testing (Table 1). Results of chest radiographs were based on official radiology reports. Healthy subjects with no known cardiopulmonary disease and normal chest radiographs (as per official report) were enrolled as a control group.

image

Figure 1.  Study design and protocol. CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; VRI = vibration response imaging.

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Table 1.    Subject Characteristics
Patient GroupsHealthy (n = 20)CHF (n = 22)COPD (n = 19)Asthma (n = 18)
  1. CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease.

Age (yr), mean (±SD)47 (±13)54 (±17)49 (±14)42 (±11)
Male14131610
Female 5 9 3 8
Chest x-ray20221918
Echocardiography 021 6 1
Pulmonary function testing 0 212 6

Study Protocol

Recording Procedure.  Respiratory sound data were acquired on the day of presentation to the ED. All recordings were obtained with the subjects in a seated position. Respiratory sounds were captured using a vibration response imaging device (Deep Breeze, Or-Akiva, Israel). This is a noninvasive computerized acoustic-based imaging technique that displays the geographic distribution of vibration energy of respiratory sounds throughout the respiratory cycle.12,13 With this technique, 36 sensors (two arrays, one over each lung) are adhered to the patient’s back by a computer-controlled low vacuum and used to record respiratory vibrations. Subjects are instructed to take deep, comfortable breaths during 12 seconds of recording. The recorded signal is amplified and filtered to minimize cardiac and other nonrespiratory vibration frequencies.

Recorded lung sounds (measured as vibration energy) can be displayed in different formats. First, the input from the sensors can be displayed as a video made by serial two-dimensional images (Figure 2). In this format, an image very similar to a ventilation scan is generated, with depiction of the relative spatial distribution of sound intensities. Increased sound intensity is represented as an increase in darkness. Each frame of the video represents 0.17 seconds worth of data. The maximal energy frame (MEF) was the frame in the video sequence that usually provided the most information on the distribution of lung vibration and usually approximated peak inspiration. The image from this frame was used for the area measurements. The image represented the relative distribution of vibration energy, not the absolute energy. Either the graphical data can be expressed as the sum of vibration energy from both lungs, or each lung can be represented. For each respiratory cycle, there are two vibration peaks, one during inspiration and one during expiration. The inspiratory peak will be referred to as the peak inspiratory vibration (PIV), and the expiratory peak will be referred to as the peak expiratory vibration (PEV).10 A graphed linear summation of total vibration energy is generated in addition to the two-dimensional image (Figure 2) and was used to choose breath cycles. Numerical data of peak vibration energy during inspiration and exhalation were obtained using proprietary software (Deep Breeze, Or-Akiva, Israel). The ratios of PIV/PEV (peak I/E vibration ratio) during each respiratory cycle were calculated and then averaged for each patient. High-energy artifacts from background noise due to patient movement against matrix framework are occasionally encountered and easily identified in the image. These images were excluded from analysis.

image

Figure 2.  Representative vibration energy images. (A) Healthy volunteer. The images for the normal patients showed that right and left lungs viewed together have peripheral smooth, rounded, and uninterrupted contours. Planar distribution, area, size, and intensity of the right and left lung images are similar and encompass the entire imaging field. (B) CHF patient. The image is also smooth and rounded, but for this patient, the image is smaller in size, likely due to pulmonary edema, particularly at the bases. (C) COPD patient. In this image, different areas of each lung demonstrate peaks in sound energy at different times. Because of this asynchrony, the contours of the lung periphery on the planar images are not smooth, but have a “bumpy-lumpy” appearance. (D) Asthma patient. Left and right lungs demonstrate peaks in sound energy at different times. Because of this asynchrony, the contours of the lung periphery on the planar images are not even and smooth.

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Four groups were analyzed and compared: CHF, COPD, asthma, and normal controls. Three methods of analysis were used. The first was visual description of the videos. The second was analysis of the area of the MEF image during inspiration as measured using the software Image J (a public domain Java image processing program inspired by NIH Image), which objectively provides a digital measurement of the number of pixels.12 The third was the analysis of numerical vibration energy data, PIV, and PEV.

Data Analysis

The Wilcoxon signed rank test for paired and unpaired data (SPSS 15.0, SPSS, Inc., Chicago, IL) was used for analysis for exploratory purposes only. Each disease state (CHF, COPD, and asthma) was compared to healthy volunteers. Medians with interquartile ranges (IQRs) are reported. A p value less than 0.05 was considered statistically significant.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

A convenience sample of 87 patients and 20 healthy subjects was enrolled in the study. Of the patients, 22 had CHF, 19 had COPD, and 18 were classified as having asthma. Eleven patients were excluded from analysis due to diagnoses of multiple etiologies of dyspnea, and 17 patients were also excluded due to diagnosis of other etiologies (pneumothorax, pneumonia, and others) of dyspnea (Figure 1).

Dynamic and Static Image Features

Peak inspiratory images for each of the study groups are presented in Data Supplements S1–S4 (available as supporting information in the online version of this paper). The static and dynamic images had differences as described below. In all subjects, the dynamic images “build and fade” twice for every respiratory cycle, once for inspiration and again for exhalation. In the healthy control subjects, the maximal vibration intensity in inspiration is visually greater than that in exhalation. Sound patterns develop synchronously in both lungs. Right and left lungs viewed together have peripheral smooth, rounded, and uninterrupted contours. Planar distribution, area, size, and intensity of the right and left lung images are similar. The planar sound images for the CHF patients were visually similar to those of the healthy controls. In the subjects with COPD, the dynamic images showed asynchrony within each lung, and different areas of each lung demonstrated peaks in sound energy at different times. Because of this asynchrony, the contours of the lung periphery on the planar images are not smooth, but have a “bumpy-lumpy” appearance (“disco” lung). In the subjects with asthma, the dynamic images showed asynchrony between lungs, and maximal vibration energy occurred not during inspiration, as with normal subjects, but during expiration.

Geographic Area and Vibration Energy of Respiratory Sounds

The mean geographic areas of each image were calculated. In healthy volunteers and COPD patients, the median (IQR) geographical areas of the vibration energy image were similar, at 75.6 (IQR = 6.0) and 75.8 (IQR = 10.8) kilopixels, respectively (p > 0.05). Compared to healthy volunteers and COPD patients, areas for CHF and asthma patients were smaller, at 66.9 (IQR = 9.9) and 53.9 (IQR = 15.6) kilopixels, respectively (p < 0.05; Figure 3).

image

Figure 3.  Geographical area of VRI images during maximal inspiration in healthy volunteers, CHF, COPD, and asthma exacerbation. Boxes show median and IQRs and I bars represent highest and lowest values. *Lower area values compared to healthy volunteers (p < 0.05). CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; IQR = interquartile range; VRI = vibration response imaging.

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The geographic area ratios between left and right lung of each image were calculated in each group. The geographic area ratios between left and right lung for healthy volunteers, CHF patients, and COPD patients were 1.0 (IQR = 0.2), 1.0 (IQR = 0.2), and 1.0 (IQR = 0.1), respectively. For healthy volunteers, the geographic area ratio between left and right lung for asthma patients was 0.5 (IQR = 0.4; p < 0.05; Figure 4).

image

Figure 4.  Left and right lung geographical area ratio of VRI images during maximal inspiration in healthy volunteers, CHF, COPD, and asthma exacerbation. *Lower geographical area ratio when compared to normal controls (p < 0.05). CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease.

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The PIV and PEV values were calculated for each group and expressed as the peak I/E vibration ratio for each patient. In healthy volunteers and CHF patients, the peak I/E vibration ratios were similar, at 4.6 (4.4) and 4.7 (3.5), respectively (p > 0.05). In marked contrast, the peak I/E vibration ratios were 3.4 (2.1) and 0.1 (0.3; p < 0.05) in patients with COPD and asthma, respectively. These differed significantly from the other two groups (p < 0.05; Figure 5).

image

Figure 5.  Peak I/E vibration ratio in health, CHF, COPD, and asthma. *Lower I/E peak vibration ratio when compared to normal controls (p < 0.05). CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; peak I/E = peak inspiration/peak respiration.

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Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

In this initial exploratory study, we evaluated the visual display of respiratory sound patterns in patients with acute CHF, asthma, and COPD exacerbation. Several findings differentiate CHF, COPD, and asthma. The first is the typical image features that include shape, MEF image area, disturbed development of image, and inspiratory and expiratory phases, which are visually evident on both the dynamic and the static images. The second is the distribution of vibration energy between inspiration and exhalation. The third is the geographical area of VRI images during maximal inspiration. Each of the above differences has a clear physiologic basis.

The pathophysiology of acute CHF might include gravity-driven maldistribution of pulmonary edema.12–16 The decreased area (distribution of vibration) seen peripherally in CHF patients is likely due to decreased transmission of breath sounds to peripheral lung tissue in the presence of pulmonary edema. This would in turn result in a smaller image due to decreased homogeneity of vibration intensity. Lower airflow peripherally due to edema could also result in a smaller image.

Inhomogeneity of airflow has been studied by documenting the anatomic results of the inhomogeneity in COPD patients.17–21 Computed tomography and positron emission tomography scanning have been used to demonstrate inhomogeneity, with a mosaic pattern considered to represent differences in regional ventilation due to regional differences in degree of air flow obstruction. In COPD patients, the change in inspiratory and expiratory phases reflects the prolongation of exhalation due to airflow obstruction.

In asthma patients, there is asynchrony between left and right lungs. The theory behind this finding is not clear. Focal air trapping, structural inhomogeneity, and asynchronous airflow are all different parts of the spectrum of obstructive airway diseases and their physiologic sequelae. Besides those, we bring up a hypothesis that is due to body “self-regulation.” In other words, because of the narrowed airways, the obstructive air in expiration cannot all go through one bronchus (especially in the main bronchus) at the same time, and a single bronchus cannot receive air from two lung areas at the same time, but instead is self-regulated as the obstructive air has to go one side first and another later. This phenomenon is just like road traffic: when several lanes merge into one lane on the road, cars are supposed to go one by one into one lane.

Compared to healthy volunteers, the geographic area of the image in CHF is smaller, and there is no difference in peak I/E vibration ratio between the two lungs. In COPD, there is no difference in geographic area of the image, but there is a significant decrease in peak I/E vibration ratio. In asthma, the geographic area of the image is much smaller, and the peak I/E ratio is even further decreased between the two lungs.

The dynamic images (online video supplements) showed asynchrony within each lung (disco lung) in COPD patients, asynchrony between left and right lungs in asthmatic patients, and synchrony between two lungs with smaller images in CHF patients. This is more dramatic when patients have more severe disease. Most imaging modalities document anatomy, whereas vibration imaging documents the geometric distribution of physiologic measurements of lung sound. This technique has documented asynchrony between lungs and has the capacity to document changes in inhomogeneity over time. Therefore, this acoustic-based sound analysis technique may be complementary to standard chest x-ray. In this study of patients presenting to the ED with acute dyspnea, we have shown that the use of this imaging technique can help to distinguish those patients who have CHF, COPD, and asthma from healthy controls. Although these results are not surprising given knowledge of pathophysiology, the ability to have a noninvasive and objective assessment of these differences may prove to be clinically useful in determining underlying physiology in patients presenting to the ED with acute dyspnea.

Limitations

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

This was a pilot study, and the study sample size was relatively small. This study did not examine the ability of the study measurements to affect treatment decisions or outcomes. The study employs comparisons that were made using the Wilcoxon signed rank tests, as there were four groups. We did a separate test for each pairwise comparison. This would increase the risk of a Type I error. We did not adjust for multiple comparisons because of the exploratory nature of the study.

Conclusions

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

The pilot data generated in this study support the concept that relative differences in the study measurement characteristics may be useful in distinguishing acute dyspnea caused by congestive heart failure and chronic obstructive pulmonary disease or asthma. Further study is necessary to determine this technology’s clinical value in the ED.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

The authors thank Y. M. Zhao (Biostatistics Group of Medicine, Peking University Third Hospital) for his statistical analysis assistance. Also, the authors Joseph E. Parrillo, MD, and R. Phillip Dellinger, MD, for their constructive input as well as T. Bartter at Cooper University Hospital, Camden, NJ.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations
  7. Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Data Supplement S1. Healthy video.

Data Supplement S2. Asthma video.

Data Supplement S3. CHF video.

Data Supplement S4. COPD video.

Please note: Wiley Periodicals Inc. are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

FilenameFormatSizeDescription
ACEM_1255_sm_DataS1-healthyvideo.avi62KSupporting info item
ACEM_1255_sm_DataS2-asthmavideo.avi536KSupporting info item
ACEM_1255_sm_DataS3-chfvideo.avi345KSupporting info item
ACEM_1255_sm_DataS4-copdvideo.avi433KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.