Analysis of quiet spontaneous breathing as measured by spirometer and electrical impedance plethysmograph

COMPUTERS

AND

BIOMEDICAL

RESEARCH

6,74-89 (1973)

Analysis of Quiet Spontaneous Breathing as Measured Spirometer and Electrical Impedance Ptethysmograph ROMESH WADHWANI

by

AND RICHARD L. LONG~NI

Medical Systems E~in~e~ing Laboratory, C~rneg~e-~~iio~ Pittsburgh, Pennsylvania 15.213

Unii~er~ity,

Received August 28, 1972

A spirometer and a guard-ring type three-electrode impedance plethysmograp~ (ZPG) were used simultaneously to monitor quiet spontaneous breathing. Measurements were made at 4 thoracic electrode locations on each of 14 male subjects: 7 normal and 7 with chronic obstructive lung disease (COLD). Simple features, developed by computer analysis for each inspiratory and each expiratory phase, includeleast-squares binomial tits to the data. The results indicate that the waveforms are easity parametri~d. The features appear to be of potential diagnostic value. A cardiac-related signal superimposed in the ZPG waveform on the much larger breathing signal, was extracted by subtracting out the least-squares binomial fit. Optimally the cardiac-related signals so obtained resemble very closely the cardiac-pump stroke volume waveforms.

I. INTRODUCTION In the last three decades physiologists have developed several pulmonary function tests. Some of these tests (I) require relatively simple equipment and enjoy widespread clinical usage. These pulmonary function tests, however, require that the subject breathe in a forced, nonspontaneous manner. The transient nature of the pulmonary response under forced conditions can complicate the task of interpretation, and much depends on the cooperation of the subject. Often healthy individuals can and do simulate pathological conditions. Where the patient receives financial benefits for such performance, the subjects become apt pupils. Then, again, in severe cases, for example patients in intensive care, forced br#thing can cause inconvenience and even distress. For this type of case the tests are, of course, impractical. The development of pulmonary function tests under quiet breathing conditions may be an answer to some of these problems. Pathological conditions are hard to fake and, where the patient is in an intensive care situation, the measurements would not create a hardship for him. The spirometer, a commonly used clinical instrument for the measure~~ent of changes in lung air volume, cannot be used continuously or even long enough to Copyright All rights

Q 1973 by Academic Press, Inc. of reproduction in any form reserved.

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establish a stable state, and also it puts a load on the patient. Spirometrically determined patterns in quiet breathing are usually examined for such features as the breathing rate, amplitude, relative durations of the inspiratory and expiratory phases, etc. While these features are certainly valuable, further analysis of quiet breathing spirometric waveforms could add to the number of useful features available to the pulmonary physiologist. We have modified a standard spirometer to make it suitable for these purposes and the results of just such an anlaysis, performed on an IBM 360~67, are part of this paper. Two of the several features examined by computer appear to be of value. Fortunately, these features are easily obtained from a strip-chart recording of the breathing pattern, and no computer is really necessary. Very limited tests on seven normal subjects and seven subjects with chronic obstructive lung disease indicate that these two features will probably allow near 100 % separation between normals and abnormals. [2]. Electrical Impedance Plethysmography (ZPG) has been investigated by several researchers as a potentially valuable noninvasive, nonpatient-stressing technique for monitoring respiration {J-13). Others have attempted to determine cardiac output from thoracic impedance changes (13-23). In these cases varying degrees of success have been reported for the experimental correlations, but the actual physiological sources of the impedance variation have not been established. In fact, it appears that the materials, the impedance of which is being measured, depend on the specific ZPG system used. Thus, interpretation is not straightforward and cannot be found in any simple application of first principles (24), but must be found by correlation and other pattern recognition means. ZPG measurements are subject to error due to various artifacts originating at or near the skin-electrode interface, etc. These errors have been avoided to different degrees by different kinds of ZPG systems: high-frequency (e.g., 100 kHz)-to minimize skin resistance effects (by using the skin-electrode system as a capacitor); low-frequency (e.g., below 1 kHz)-to minimize reactive electrical currents; 2-electrode; 3-electrode, guard ring; or 4-electrode to reduce various artifacts. The electrical impedance plethysmograph used in this work is a 3-electrode guard-ring type, operated at 100 kHz (9). It is worth noting that, whatever the type of ZPG used, artifacts do exist and these are usually far less pronounced in quiet breathing than in forced breathing. The thoracic electrical impedance waveform can be considered as a sum of three components: a signal related to respiration, a signal related to cardiac pumping activity, and noise. During inspiration the lungs fill with air and the thoracic impedance increases, in expiration the impedance decreases; this is the respiratory signal. The cardiac-related signal is probably the result of changes in the pulmonary circulatory system during systole. Under usual conditions of breathing, the cardiac-related signal is only a small fraction of the respiratory signal, their relative amplitudes being dependent on electrode placement and other factors. In cases where it is desired to use the ZPG as a spirometer, or even as a respiration monitor,

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the cardiac-related impedance changes are considered part of the noise which corrupts the signal. Where a cardiac output monitor is desired, the respiratory signal appears as a highly undesirable baseline. As a result, the simultaneous monitoring of both has its problems, though it is highly desirable from an applications standpoint. In our work, quiet breathing has been measured simultaneously by spirometer and electrical impedance plethysmograph. An electrocardiogram is sinlultaneousiy recorded. This data is then analyzed on a digital computer. The results of this analysis include: the development of basic features from the spirometer waveforms, the quantitative comparison of spirometer and ZPG signals, and a means for the ready extraction of cardiac-related signals from the ZPG quiet-breathing signal. The simultaneous monitoring of the respiratory component and the cardiacrelated component of the ZPG signal is thus made possible, under quiet conditions, with minimum inconvenience to the patient.

II. FEATURE EXTRACTION

A. Breathing Phase Ident@cation Measurements were made on 14 subjects, 7 normal and 7 with chronic obstructive lung disease. Information was recorded on 3 channels of a 4-channel cassette recorder. This information (spirometer, ZPG, EKG) was stored in analog form on standard Philips C-80 cassette. Four independent sets of measurements were made on each subject, each set corresponding to the different configuration of ZPG electrodearray. The electrode-arrays have 9 electrode segments each which are electrically connected to allow the array to function as a center and guard-ring electrode or as a reference electrode, while simultaneously allowing the monitoring of the EKG. Figure 1 shows the location of the 4 electrode-arrays used on each subject, and the configurations used for ZPG monitoring. Each of the 4 data-records per subject represent about 45 set of simultaneously monitored spirometer, ZPG, and EKG information. Each such record therefore corresponds to several cycles (inspiration and expiration) of quiet breathing. Prior to analysis on the IBM 340/67, this analog data was first digitized on an analog-to-digita converter with a 99.9% accuracy, After this digitizing procedure, each data-record is a string of 27,000 data-samples (3 channels, 200 samples per set, 45 set). Each data-string must then be analysed to identify the individual breathing cycles, and then features of interest can be extracted from each breathing cycle. Breathing waveform identification analysis of the aggregated spirometer and ZPG data can be done in two ways: entire breathing cycles or individual breathing phases (inspiration, expiration) can be identified. Though the former procedure is computationally faster, the advantages of phase-by-phase identification, for

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BREATHING

feature extraction, are overwhelming. Physiologically, the processes of inspiration and expiration are different. Inspiration is an active phase requiring muscular work. Expiration, for the most part, is a passive process involving elastic recoil of the thoracic cage-abdomen-lung system. Thus, features extracted for each breathing phase can, in some sense, be related to the underlying physiological events. Another advantage of phasewise identification is the consequent simplification in the determination of some features. For example, a least-squares polynomial fit to the breathing trace in inspiration, or in expiration, is much simpler and more meaningful

A

ELECTRODE

CONFIGURATION RECORD NO. Rl R2 R3 R4

t

ELECTRODE

ABCD-

CENTER/GUARD RING ELECTRODE

II

A B C D

REFERENCE ELECTRODE

I

B A D C

i

LOCATION:

Second intercostal Fourth intercostal Sixth intercostal Sixth intercostal

space, in line with right nipple. space,left midaxillary line. space, right midaxillary line. space, left midaxillary line.

FIG. 1. Location of the four 9-segment electrode-arrays, and the configurations monitoring.

used for ZPG

than a fit over the entire breathing cycle. In view of these advantages, breathing patterns in the spirometer and ZPG data were identified phase by phase. The identification subroutine was basically a simple one. Each time the slope of the breathing waveform (i.e., for spirometric data, the flow rate) changed sign a new phase was considered to have begun. Several heuristics were used, however, to minimize the chances of error in identification. Such error is more likely in the analysis of the ZPG data than the spirometer data because of the superimposed cardiac-related signal and also because of a greater noise level. During phase identification a record was also kept of the time correspondence of spirometer and ZPG data.

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B. Feature Selection

Figure 2 is the trace of a typical breathing cycle as monitored by spirometer and ZPG in the experiments performed. The qualitative similarity between the two curves, for both the expiratory phases, is readily seen. All the features discussed below wereindependently derived for each phase of each breathing-cycle, asmeasured by the spirometer and by the electrical impedance plethysmograph. 100 -

SPIROMETER

TIME PHASE

75 -

FIG.

DIFFERENCE

ELECTRICAL

2. Typical breathing cycle as monitored by spirometer and by ZPG.

The obvious features were first calculated: the duration and the amplitude of each phase. For the spirometer signal the amplitude was calibrated in liters. The ZPG amplitude was in arbitrary units proportional to the electrical impedance measured. Denoting the duration of the expiratory phase by E and that of the inspiratory phase by I, the E/I ratio was calculated. The transition points between consecutive phases are not easily identified, but within the limits of this uncertainty, the E/Zratio should have a numerical value about 1.2 (I). Large deviations from this range are indicative of abnormal breathing. The reasons may be pathological or otherwise. An inspiratory phase parameter called the rise time is defined as the time to

ANALYSIS OF QUIET SPONTANEOUS BREATHING

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rise from 10 to 90 “/, of full amplitude. The fall time is the corresponding parameter for the expiratory phase. Both parameters are illustrated in Fig. 3. The choice of these as features was prompted by two considerations. In Fig. 3, it is seen that little change occurs in the pulmonary gas volume in the regions at the start and end of each phase. Thus, some portions of the breathing cycle contribute less to lung ventilation than others. The rise time and fall time are, essentially standard measures of portions which are responsible for 80 ‘A of the ventilation. Secondly, these parameters were required to simplify the task of least-square fitting of simple curves to each phase. For example, the total inspiration phase curve is typically S-shaped. However, in the rise-time period, the curve can be well approximated by a parabolic segment. Once the rise-time (or fall-time) was defined, it was possible to parametrize the breathing phase by the coefficients of the least-squares binomial. Denoting the rise time by RI, and the fall time by RE, the following features were computed: RI/I, RE/E, and RE/RI. These features are examined in more detail in the next section.

T I ME

FIG.

3. Rise-time (RI) in inspiration, fall-time (RI?) in expiration for a typical breathing cycle.

Another interesting aspect of each breathing phase is its curvature. Physiologically, the derivative of the breathing waveform, as measured by the spirometer, is a measure of the pulmonary gas flow-rate. The shape of this breathing curve thus reflects the change in flow-rate with time. There is a significant advantage to be gained in fitting each spirometer phase with a least-squares polynomial; a small number of coefficients can be used to describe the phase (and hence, the time-varying flow-rate) quite accurately. It turns out that both inspiratory and expiratory phases can be approximated by second-degree polynomials with a residual error of typically less than 4%. Each spirometer phase was independently fitted by a polynomial of the tYP : y(t)=u,+a,t+a,tZ. The coefficients a,, (I~, and a2 were obtained as follows : Let n be the number of data points to which least-squares fit is to be made. yi = y(ti) = UO+ Ul t* + a2 t*‘p

i = 1,2, . . ., n,

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1 I ,., 1 . .,y,] = [u,a, a,] f, t2 . . t,, , CY1,,1’2, . in2 [ 1

or using matrix notation,

t,’ t,’

orY-A-T, where Y = [y, y, . * *JJ,J A = [a,, a, a2]

Let T’ be the transpose of matrix T. Therefore, Y.T’ = A.(T.T’). Here (T-T’) is invertible. Finally A = Y*Tf*(T.T’)-‘. In all cases, a, is positive for inspiration and negative for expiration. Since a, is an arbitrary base line parameter, just two features, a, and a,, are sufficient to characterize the shape of each phase. Second-degree polynomial fitting was also done on the data for each ZPG phase, but with an added benefit. The ZPG data includes a relatively small impedance signal related to cardiac pumping action superimposed on the respiratory signal. For electrodes located at the level of the third or fourth intercostal space, on the left midaxillary line, the cardiac-related impedance signal may be as large as 10 y,, of the total impedance change; at the sixth intercostal, on either midaxillary line, the cardiac-related changes are much smaller. Examination of the small cardiac impedance signal with a predominant respiratory baseline is, therefore, difficult. The polynomial least-squares fit is, however, a good approximation to this respiratory baseline. Subtraction of this polynomial fit from the ZPG signal results in the removal of much of the baseline variations, and the cardiac-related signal so obtained has a greatly improved signal-to-noise ratio. Each of the features described above was obtained independently for each breathing phase in the record under examination. This allowed an understanding of changes in the features with each new breathing cycle for any given subject. However, to allow comparisons between different subjects, or between different records for the same subject, the mean value and standard deviation was determined over the number of breathing phases in that record for each feature. The computational procedure used included a number of built-in checks; improperly identified phases. noisy data, etc. were ignored, and only the properly identified phases contributed to the calculation of the mean value and standard deviation.

81 III.

FEATURE ANALYSIS

A. Spirometer

In all, 56 sets of measurements were examined-4 independent sets on each of 14 male subjects. Of these subjects, 7 were normal and 7 had chronic obstructive lung disease (COLD). COLD patients varied in age from 60-85, and had the disease in mild-moderate or severe degrees. Four of the normals were in the age group 55-70; the remaining 6 were 24, 30, and 35, respectively. In terms of body type, 11 were classified as mesomorphs and 3 as endomorphs. The quiet breathing tidal volume for normals and COLD patients varied between 0.5 and 1 liter. Assuming a total dead-space (anatomic plus spirometer hose) of about 0.2 liter, the alveolar ventilation per breathing cycle thus varied from 0.3 to 0.8 liter ofpulmonary gas. The respiration rate had a range of lo-30 breathing cycles per min, with the E/Zratio varying between 0.75 and 1.9 .Typically, COLD patients exhibited a larger E/Z ratio than the normals. The minute volume of breathing was between 7 and 15 liters of pulmonary gas. The rise time ratio (defined as the ratio ~Z~Z) and the fall time ratio (equal to REIE) were compared. The rise-time ratio was, in all cases, between 0.55 and 0.75, while the fall-time ratio varied from 0.45 to 0.75. For any given record on any subject, typically the fall-time ratio was less than the rise-time ratio. Both ratios were characteristically larger for COLD patients than the subjects with normal lungs. An insight into the physiological significance of the rise-time and fall-time ratios is now provided. Assume each record is a regular quiet-breathing sequence consisting of a number of identical breathing cycles as illustrated in Fig. 3. Using standard notation for the parameters, the measured minute volume, AG’, is given by NV=

k-A/(Z+

E) liters/min,

where k is a constant depending on the units of A, Z, and E. The gas flow-rates in the rise-time and fall-time regions are usually higher than those in the remaining regions of the breathing cycle. A different (and larger) minute volume, Mvl, would be obtained for a hypothetical situation in which the subject would be allowed to breathe only along the rise-time and fall-time segments of the breathing curve, i.e., in the regions with higher gas flow-rates. In this hypothetical constrained case, the respiratory amplitude A = 0.8 and the inspiratory and expiratory phases are of duration RI and RE, respectively. The constraint may require that more muscular work be done and a different breathingpattern followed, but with no significant change in the dynamic properties of the pulmonary system. In this case, MV, = k*O.S(RZ+ RE) liters/min. The minute volume ratio (MVR) is defined as the ratio MV/MV, and is given by MVR = ~V/~V~

= 0.8(RZ -I- RE)/(Z + E).

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Finally, MVR = @fll) t 1 + REIRI) o.8’------~~(1 -t- E/I)

*

In most cases the ratio RE/RI is smaller than E/I, and the minute volume ratio can approach unity only when the value of Rr~~approaches 0.8, i.e., in the case in which breathing volume changes linearly with time. In all cases in which the breathing curve in the rise-time and fall-time regions is concave or convex, the value of RI/I is less than 0.8. Comparing COLD patients with normal subjects, the former group generally had a larger value for the ratio (1 + R~lRI~l~~ f E/f), in addition to the larger RI/I value. Thus, COLD patients have a larger MVR than the normals. Least-squares binomials were fitted to each breathing phase, with a residual error of less than 4 y0 of the total signal energy in each case. The coefficient a, had values from 4.8-8.6 liters/set in inspiration, and -5-16 liters,/sec in expiration. The negative sign merely indicates the reversal of gas flow. For all normals, a, has larger values in expiration than inspiration. Four COLD patients, however, exhibited values for a, in inspiration that were close to or greater than those in expiration. The coefficient a, is a measure of gas acceleration or deceleration in the airways. Values obtained ranged from -0.2 to -1.25 liters/se9 in expiration, with the gas usually decelerating towards the end of the breathing phase. Some COLD patients, however, exhibited abnormal values for a, as discussed elsewhere (2). Impedance Plethysmograph The ZPG used did not have an output directly in ohms, but was such that any measurements made were easily convertible into ohms, the units of impedance. in our investigations, however, the emphasis was on changes in impedance in quiet breathing and not the resting base impedance in ohms. Therefore, no systematic effort was made to convert all measurements imo ohms. Typically, the resting thoracic impedance measured was about 500-800 ohms, depending on the electrode position. The measured resting impedance was, in all cases, highest in the R2 configuration, i.e. with the guard-ring electrode at the level of the fourth intercostal space on the left mid-axillary line, and the reference electrode at the level of the second intercostal in line with the right nipple. The resting im~dance was lower with these electrodes reversed-the RI configuration. The R3 configuration (with guard-ring electrode on the right midaxillary line, and reference electrode on the left mida~llary line, both at the level of the sixth intercostal space) and the R4 configuration (electrodes reversed) exhibited resting impedances that were, typically, lower than those measured with the Rl configuration. No effort was made to independently derive the resistive and reactive components of the total measured impedance. Sensitivity is one of the measures used to determine the quality of an electrical impedance plethysmograph as a respiratory monitor; it is defined as the change in 3.

Electrical

ANALYSIS

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impedance as a percentage of resting impedance for a unit change in the volume of the lung gas. In this respect, the ZPG used appears to be superior to others whose performance has been reported in the literature. All the data reported is for typical fractional impedance changes corresponding to the inhalation of 4-5 liters of air, and varies from 4% for 2-electrode systems (25) to 18 % for 4-electrode systems (3), to 35 % for the guard-ring system used (26). In this work, the fractional impedance changes were calculated for the inhalation of 1 liter of air, for each of the 4 configurations. Typically, the percent impedance changes for the four configurations were : Configuration

Rl R2 R3 R4

lo-15 % 10-20x 20-25x 15%.

The same features that were determined from the spirometric data were also computed from the ZPG data, for each breathing phase. The quality of these features was a function of the noise superimposed on the respiratory signal as monitored by the ZPG. For example, the Rl configuration produced a noisier signal than the R4 configuration because of a larger cardiac-related component in the former. The extent to which each configuration produces a ZPG signal more sensitive to impedance changes in the vicinity of the electrodes than to overall thoracic impedance changes must also contribute to differences in the values obtained for features extracted separately from spirometer and ZPG data. Results of the comparison between the features determined from spirometer and ZPG data are summarized below. (i) In every case of spirometer measurement of a breathing record, the fall-time ratio is smaller than the rise-time ratio. The reverse was generally true for ZPG measurements with electrodes in the Rl, R2, and R4 configurations. (ii) The values of the rise-time ratio obtained independently from spirometer and ZPG measurements were generally the same. The fall-time ratio determined from the ZPG was higher than that determined from the spirometer for all normal subjects, but for only some of the COLD patients. (iii) Generally, the E/Z ratio determined from ZPG data was smaller than the spirometrically-measured value. This was due to the duration of the expiratory phase being shorter, and the duration of the inspiratory phase being longer, in the ZPG breathing cycles compared with corresponding spirometer breathing cycles. (iv) To allow a comparison between the rate at which breathing-related changes occur in inspiration and expiration, the ratio of the polynomial coefficients [“llexp/[ullinspwas calculated. For the R2 configuration, this ratio was the same for ZPG and spirometer data. For the Rl configuration of the electrodes, the ZPG value was smaller; for the R3 and R4 configurations, the ZPG values were larger.

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(v) Generally, there was correspondence in the curvature of each breathing phase, as obtained independently from spirometer and ZPG data. For the spirometer, the polynomial coefficient a, always has a larger magnitude in expiration than in inspiration. For the ZPG, the relative values depended on the electrode configuration. Some conclusions may be drawn from these comparisons. Though the spirometer and ZPG breathing waveforms resemble one another, they do differ, and, for any specific configuration of ZPG electrodes, these differences are generally consistent and can be quantitatively expressed. As might be intuitively expected, these differences themselves depend on electrode configuration and are possibly informative about regional impedance changes. They suggest differences in pulmonary gas flow distribution in various parts of the lungs. Such differences are also apparent from one breathing cycle to the next, possibly due to temporal variations in the participation of different segments of the lungs in the respiratory process. Figure 4 is a plot of normalized impedance changes against normalized spirometer volume changes, for different breathing cycles. The temporal variations are not insignificant, even when compared with the differences between alternative configurations. If the ZPG is planned for use as a respiration monitor, two criteria are paramount : convenient electrode location for minimum discomfort to the patient and minimum variation in the impedance-lung volume correlation from one breathing cycle to the next. The problems of linearity and variability between different subjects are of lesser importance, as they can be solved by simple computational means and by calibration, respectively. Based on the two criteria, configurations R3 and R4 appear to he satisfactory for respiratory monitoring, particularly because of the small cardiacrelated component in the ZPG signal. IV. CARDIAC-RELATED

IMPEDANCE SIGNAL

The ZPG signal includes a component related to cardiac pumping activity. This cardiac-related signal has been observed and studied by a large number ofresearchers, several of whom are references (22-23). These researchers used different measurement techniques but, in each case, were able to distinguish an impedance signal synchronous with the electrocardiogram. With transthoracic impedance measurements, under normal conditions of breathing, the impedance changes due to respiration show up as a baseline shift on the cardiac-related impedance signal. This baseline makes the analysis of the cardiac signal difficult, and its elimination is desirable. Some work has been reported on the removal of the respiratory baseline by passive filtering, signal subtraction and other means (22). Calvert and Hart have used Fourier analysis to extract the cardiac-related component from the total thoracic impedance signal (23). Rather than attempting any later elimination of the respiratory baseline, most researchers have tried to take measurements in which the respiratory-related impedance component is small. The most common technique is

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to take measurements during a suspended state of breathing. Alternatively, electrode configurations are used which are minimally affected by impedance changes due to respiration. The former is highly inconvenient for the subject, and it may not even be practical for use in an intensive-care situation. The latter requires special care in placing electrodes as the locations may change from one body to another, and the measured changes are often small. In either case, an improved cardiac-related signal is obtained only by the elimination of the respiratory signal, thus making the simultaneous measurement (and analysis) of the respiratory and cardiac-reiated components impossible.

. FRAME 1, CONFIGURATION F1 FRAME 2,CONFIGURATION 0 FRAME 3,CONFIGURATION x CONFIGURATION B

0 0 D

IO % NORMALIZED

SPIROMETER

VOLUME(l00%~1

liter1

FKG. 4. The relationship between spirometer volume and ZPG impedance, for different breathing cycles (frames) and different locations.

This cardiac-related impedance signal has been used in estimating cardiac stroke volume. The formula commonly used for this purpose assumes that stroke voIume is proportional to the minimum value of the slope of the cardiac-related impedance signal, and to the ejection time. Some researchers claim satisfactory results using the cardiac-related impedance (17), while others get good correlations only in some cases (21). Though the physiological factors,underlying such correlations are not yet completely understood, impedance cardiography offers sufficient promise as a noninvasive technique for the monitoring of cardiac output to warrant further investigation. It is intuitively logical to expect that a cardiac-related signal that is

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less corrupted by noise and respiratory baseline will allow a more accurate determination of cardiac output. In our work, we have considered the least-squares binomial fit to each breathing phaseasagoodapproximationto therespiratorybaseline.Subtractionofthis binomial from the measured ZPG signal, for each breathing phase, results in a signal with a

-

EXPIRATION----+ (Subj~~WW~)

k---

FALLTIME

---+

I

I TIME

2

3

fsecf

FIG. 5. Illustration of procedure used in separating the breathing- and cardiac-related ponents of the ZPG signal.

com-

predominantly cardiac-related component. The technique is simple, computationally cheap, and effective. Also, since the measured ZPG signal contains respiratory and cardiac-related components, it is possible to examine each component i~~~d~lly as desired, This procedure was used to extract the cardiac-related component of the ZPG signal, for each of the 4 electrode configurations on each of the 14 subjects. In every case for which a clean ZPG trace was available, the cardiac component was successfully extracted. The entire procedure is illustrated in Fig. 5 which shows the

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cardiac-related impedance component extracted from the ZPG signal. The inspiratory and expiratory phases have been taken from different subjects, with the electrodes in configuration Rl in both cases to allow the display of a maximum number of cardiac cycles in each phase. The shape of the cardiac component so obtained very closely resembles the cardiac-related impedance measurements made by other researchers using impedance cardiographs under breath-holding conditions.

FIG. 6. ~rdja~-~~t~com~nent N20.

of the ZPG signal, all four electrode configurations. Subject:

Though the cardiac component is never more than a fraction of the respiratory component in any of the four configurations used, the larger the cardiac-component the better. Fig. 6 shows typical ZPG waveforms for each of the 4 configurations on a normal subject, the cardiac-related impedance signal extracted in each case. It is seen that configurations Rl and R2 produce ZPG signals that are richer in the cardiacrelated component, and probably should be preferred where the ZPG is to be used primarily as an impedance cardiograph. It is interesting to note that this extraction technique works even in cases (e.g. configuration R3 and R4) in which the cardiacrelated component is such a small fraction of the total impedance change that it is visually almost im~r#ptible in the ZPG trace.

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No attempt was made to actually calculate the cardiac output from the minimum slope of the cardiac-related signal so extracted. The technique, however, offers promise as an effective means of continuously monitoring the cardiac-related impedance signal without any serious inconvenience to the patient. Simultaneously, the respiratory-related impedance signal can also be observed, analyzed, and used for spirometry, diagnostic, or other purposes. ACKNOWLEDGMENT This work was supported by Pennsylvania Science and Engineering #87 and by NIH under grant #HE 12722.

Foundation

under Grant

REFERENCES 1. COMROE,J. II., FORSTER,R. E., DuBors, A. B., BRISCOE,W. A., ANU CARISE~, E. “The Lung: Clinical Physiology and Pulmonary Function Tests.” Year Book Medical Publishers, Chicago, 1967.

WADHWANI, R. T., LONGINI, R. L., AND WADHWANI, B. Diagnosis of chronic obstructive lung disease (COLD) in quiet spontaneous breathing, in preparation. 3. ALLISON, R. D., HOLMES, E. L., AND NYBOER, J. Volumetric dynamics of respiration as measured by electrical impedance plethysmography. J; A&. P&.s. 19, I66I 73 (1964). 4. GOLDENSOHN,E. S. AND ZABLOW, L. An electrical impedance spirometer. f. Appl. Phys. 14,465 2.

(1959).

5. GEDDES, L. A., HOFF, H. E., HICKMAN, D. M., AND MOORE, A. G. The impedance pneumograph. Aerosp. Med. 33,28-33 (1962). 6. BAKER, L. E., GEDDES,L. A., AND HOFF,H. E. Quantitative evaluation of impedance spirometry in man. Amer. J. Med. Electronics, 4(2), 73-77 (1965). 7. KUBICEK, W. G., KINNEN, E., AND EDIN, A. Calibration of an impedance pneumograph. J. Appl. Physs. 19, 557-560 (1964). 8. HAMILTON, L. H., BEARD, J. D., AND KORY, R. C. impedance measurement of tidal volume and ventilation. J. Appl. Physiol. 20, 565-567 (1967). 9. COOLEY, W. L. AND LONGINI, R. L. A new design for an impedance pneumograph. J. &?I. Phvsiol.

25 (4), 429-432

(1968).

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ANALYSIS OF QUIET SI’C3NTANECWS BIEATHING

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17. KUBICE~,

18. 19. 20. 21.

22.

23. 24. 25.

26.

89