ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn FlG. I. Ventilation (upper trace) and electro-oculogram (lower trace) recorded during (a) nonrapid eye movement (Non-REM) sleep and (b) rapid eye movement (REM) sleep
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING
a
, !'i 1
20ml
E
ft 'ft
V A
/ y (J \$ V\i V V
•ifc .S(i
M. K. S. HATHORN B.Sc. M.B. Ph.D.
, .i 1 ,;;t ip nil• 1 « --h /! / nry V
1
ft, L_ PI
ij
A,
\ 1'. \!
I
i
•
Department ofPhysiology The London Hospital Medical College London
,. 1
" I .
'
' , •
^
r
-"It;'" !| l|i
w ;ii in s .}•
'' ' , II nrJit' I1 i!l|
i
•i
1 2 3 4 5 6 7
Some patterns of infantile breathing Relationships within individual respiratory cycles Relationships between successive respiratory cycles Frequency analysis of the rate and depth of breathing Changes in breathing patterns over short time-intervals Discussion Conclusions References
:i i:-ii[iakE f fi.iaaii:iin Jijij, ryj In
20 ml
I—~~
•.•hfelilll
JE Si. L t E i
M'
Most studies on ventilation in the newborn have concerned the infant's responses to experimental procedures such as altering the composition or pressure of inspired air. These investigations have produced much valuable information; experiments concerned with respiratory responses should, however, be based on the characteristics of spontaneous breathing in the unstimulated infant, for these may well determine the outcome of the particular stimulus applied (Jones, Crowell & Kapuniai, 1970). There have been relatively few such investigations. A number of new techniques and approaches have been developed over the past few decades which have opened up new lines of investigation into problems of respiratory control. An example is the demonstration of breathing movements in the fetus by Boddy & Dawes (pp. 3-7 of this Bulletin). Another example is the recognition of changes in breathing patterns m different sleep states (Aserinsky & Kleitman, 1953; Prechtl & Beintema, 1964). This now makes it necessary for the investigator to note the sleep state of the infant whenever he is recording respiratory data. With the introduction of an improved trunk plethysmograph by Cross (1949), it became possible to make accurate measurements in the newborn infant, not only of respiration rate, but also of tidal volume (VT) and hence ventilation on a breath-bybreath basis, without the use of masks which may alter the infant's behaviour, or valves and tubing which increase either dead space or resistance to air flow. The use of the polygraph, and multi-channel tape recordings of changes occurring in important physiological variables, including respiration, in the newborn infant in different sleep states (e.g. Prechtl, Akiyama, Zinkin & Grant, 1968), makes it possible to analyse the data in different ways: playing back the tape on to a slowly moving pen-recorder makes it possible to recognize changes occurring over long time-intervals, while a faster speed facilitates detailed measurements of short-term phenomena. Finally, a number of mathematical and statistical techniques have become available for the analysis of time-series, i.e., a series of events or changes in data occurring over a period of time (Bendat & Piersol, 1966; Cox & Lewis, 1966).
JLJ
3 ^ S3
v^ 10 seconds
The present paper attempts to describe the use of some of these techniques to analyse the patterns of breathing found during different sleep states in some 30 healthy term infants, studied with their mothers' informed consent during the first week after delivery. By "patterns of breathing" is meant the relationships between the rate and depth of breathing, either from cycle to cycle or over longer periods of time. It is hoped that this may help towards an understanding of respiratory control in the newborn infant. 1. Some Patterns of Infantile Breathing Patterns that seem to characterize breathing in the sleeping newborn infant are: (i) regular breathing; (ii) irregular and FlG. 2. Tracings of ventilation in three term infants, before and after the occurrence of sighs (After Cross, 1954) I mpi ration
Each sigh if of the order of 100 ml. Inspiration of air Is depicted in the downwards direction In the tracings
Br.Mcd.Bull. 1975
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn FIG. 3. Compressed section of tracing of ventilation in an infant to demonstrate the effect of the occurrence of sighs
FlG. 4. Computer-drawn graphs of tidal volume (V T ) plotted against instantaneous frequency ( f ) for individual respiratory cycles
•
20 ml
:
•
-
:
"
,
*
•
'
40 10
15
Sighs occurred at A, B, C and D. The arrow indicates the Injection of 20ml of air Into the plethysmograph to test for leaks. After C, the sleep state changed from Non-REM to REM. The portions selected for analysis were B-C and C - D
(ml)
.'V,
For abbreviations, see figure I
more rapid breathing; and (iii) periodic breathing (Deming & Washburn, 1935; Cross, 1954; Prechtl, 1972). Prechtl & Beintema (1964) have related regular breathing to a sleep state characterized by the absence of eye movements and most motor activity apart from startles (non-rapid eye movement (Non-REM) sleep). Irregular and more rapid breathing is accompanied by sporadic movements of the extremities, mouthing, and rapid eye movements (REM) seen beneath the closed lids (REM sleep); see fig. 1. Periodic breathing occurs most commonly during Non-REM sleep, particularly in premature infants. Sudden movements or startles, accompanied by deep sighs, occur at random intervals and are found more frequently during Non-REM sleep; they may herald a change in sleep state or pattern of breathing (fig. 2, 3). The analytical techniques to be described are applicable only to steady-state data whose statistical properties do not alter with time (Bendat & Piersol, 1966; Cox & Lewis, 1966). This severely limits the lengths of trace available (fig. 3). In the comparison of breathing patterns during REM and Non-REM sleep in the term newborn infant, adjacent sections of trace were selected which fulfilled the criterion of a steady state (Hathorn, 1974). 2. Relationships within Individual Respiratory Cycles By measuring the amplitude of each respiratory cycle (VT) and its duration, it is possible to calculate instantaneous frequency (/), where/= 60/duration in seconds, and hence instantaneous pulmonary ventilation (V), where V= VT x / It has been shown by Prechtl et al. (1968), amongst others, that mean / is higher during REM sleep than in Non-REM sleep. In addition, there is a significant increase in mean V during REM sleep, while VT is either unchanged or decreased (Bolton & Herman, 1974; Hathorn, 1974). There is also greater variation in instantaneous V, VT and / during REM than during Non-REM sleep (Hathorn, 1974) (fig. 4). In the adult, Priban (1963) and others have demonstrated a
Vol. 31 No. 1
50
100
VT is plotted against f during (a) Non-REM sleep (correlation coefficient (r) = - 0 . 4 1 0 ) , (b) REM sleep ( r = - 0 . 6 9 4 ) , and (c) periodic breathing (r=0.0005). The three types of breathing demonstrate different distributions of values for VT and f (all drawn to the same scale) For abbreviations, see figure I
negative correlation between the values of VT and/in individual respiratory cycles. This is also true of most newborn infants in both sleep states; in periodic breathing, this relation between VT and / is no longer present, the correlation being usually zero or even positive (fig. 4). The fact that these correlations are only partial, and are not found in all infants, raises the question of whether there are other relationships between the rate and depth of breathing which operate over a longer time-span than does the individual respiratory cycle. 3. Relationships between Successive Respiratory Cycles Priban (1963) found "runs" in the frequency of successive respiratory cycles in adults which could not be accounted for on a random basis, and Lenfant (1967) has described the presence of long-term oscillatory trends in ventilation in conscious adults. Considering the onset of each respiratory cycle as an " event", the intervals between such events may be studied, using methods developed by Cox & Lewis (1966) for the analysis of series of events. In newborn infants, analysis of these intervals suggests the presence of recurrent periodicities in the rate of breathing (Hathorn, 1974). These are present in the apparently regular breathing ofNon-REM sleep as well as in the irregular breathing of REM sleep. The next step is to determine the characteristics of such periodicities in respiratory rate, and their possible occurrence in VT as well.
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathom FIG. 5. Filtered instantaneous respiration rate in an infant showing oscillations in rate (min~') about the mean, during (a) REM sleep and (b) NonREM sleep
4. Frequency Analysis of the Rate and Depth of Breathing Autocorrelation, cross-correlation and spectral (Fourier) analysis1 are techniques extensively used in the physical sciences, and were recently introduced into thefieldof neurophysiology, for the detection of periodicities in data, the isolation of the frequencies present, and for seeking correlations between different periodic phenomena (see Blackman & Tukey, 1959; Bendat & Piersol, 1966). These techniques require either continuous (analogue) data, or data sampled at equi-spaced timeintervals. At first sight, the respiratory trace is itself a continuous signal and could therefore be analysed directly. The near-sinusoidal shape of the individual respiratory cycles, however, would result in a frequency spectrum completely dominated by a single peak at the respiratory frequency of the infant, and the amplitude of this peak would register the average VT over the period analysed (trivial information indeed!). Any long-term fluctuations in rate would be submerged, and oscillations in VT would be averaged out. The method selected to overcome this problem was to treat VT a n d / a s continuous variables, and then to sample them at equi-spaced time-intervals. This procedure is justified when analysing changes over periods longer than the individual cycle. This type of analysis was performed using a computer program which created a continuous histogram of VT o r / o n the ordinates, with the durations of successive respiratory cycles on the abscissae. These histograms were then sampled at equi-spaced time-intervals (Jones, Crowell, Nakagawa & Kapuniai, 1971), the final sampling interval being 0.5 second. Autocovariances and frequency spectra for both VT and / were calculated, using standard computer programs (Bendat & Piersol, 1966). These showed the presence of cyclical changes in both the rate and depth of breathing, particularly in the frequency range below 0.20Hz, i.e., with a period greater than 5 seconds. Comparison of the frequency spectra between different infants, and indeed between successive sets of data in the same infant, however, showed marked instabilities in the verylow-frequency portion of the spectrum. Such instability is commonly found in many types of data and is due to slow or long-term fluctuations in the data being analysed. The standard technique used to deal with this problem in continuous data is to pass the signal through a band-pass filter, which passes only the desired range of frequencies. A digital computer may be programmed as a "digital filter" to perform the same operation on time-sampled data. The problem, however, is to choose the appropriate frequency range. This was done by dividing the time-sampled data into two equal parts and calculating the cross-correlations and a crossspectrum between them, in order to detect those frequencies common to both parts of the data. By use of this method it was found that the consistent frequencies present, in both VT and/, lay between about 0.04Hz and 0.20Hz (i.e., a period of 25 to 5 seconds). A computer program was therefore written to pass all frequencies present in this range, and to suppress all higher and lower frequencies in the data (Blackman & Tukey, 1959). The filtered data showed the presence of oscillations in both
+ 10
o -10
+ 10 0 -10 30
100
Seconds
For abbreviations, see figure I
V-x and / in both sleep states (fig. 5). These oscillations show variation not only in amplitude, but also in the peak-to-peak interval between individual oscillations. In order to estimate mean amplitude and frequency over the duration of the data, frequency spectra were calculated for both VT and/. A typical result is shown in fig. 6. The following may be noted: (i) the amplitudes of the oscillations in VT and / a r e higher in REM than in Non-REM sleep (this has been found in all infants so far studied); (ii) the frequency ranges of the oscillations are similar for VT and / in both sleep states (main peak about 0.08 Hz in this infant); (iii) the frequency peaks are fairly broad, FIG. 6. Frequency spectra for (a) VT and (b) f in REM and Non-REM sleep, plotted on a semilogarithmic scale (infant is the same as in fig. 5)
100.0 r-
0.0
Frequency (Hz)
< Autocorrelation (or autocovariance) functions are calculated by estimating the correlation between a time-sories and a repKca thereof, the latter being displaced one lime-unit (or lag) at a time In relation to the original; this demonstrates the interval over which the periodic wave starta"repcating^' itself. The Fourier transform of these functions provides information on how variation in the data Is distributed between different frequencies. Cruss-correlations add cross-spectra are similarly calculated between two different time-aeries.
Ordinates are presented In terms o f " power", I.e., the distribution of variance according to frequency For abbreviations, see figures I and 4
10 Br. Med. Bull. 1975
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn FIG. 7. Cross-correlations between filtered VT and f during (a) Non-REM sleep and (b) periodic breathing
FIG. 8. Analysis of the rhythm of breathing in an infant during Non-REM sleep, showing onset of periodic breathing
40
SO
140
Seconds
+5
+10
Computer-drawn graph shows, from the top down, instantaneous V, VT and f In an Infant during Non-REM sleep (left), with a sudden transition to periodic breathing (right). The bottom trace shows the running correlation (R) between VT and f over a moving Interval of five seconds; the stippled areas show the positive correlations, the solid black areas the negative ones
Lag (seconds) Note the negative deflection at zero lag during Non-REM sleep (VT and f out of phase) and the predominantly positive deflection ( W and f i n phase) In periodic breathing. " L a g " Is the time over which the cross-correlations i r e calculatea(see footnote I, p. 10)
V: instantaneous pulmonary ventilation; for other abbreviations, see figures I and 4
For abbreviations, see figures I and 4
running correlation shows that Vr and / have a tendency to shift their phase relationship in a semi-periodic manner; this is possibly a "beat" phenomenon.
indicating that the oscillations do not have a single, sharply denned frequency, but instead vary around a mean value; and (iv) the frequency spectrum for/in a number of infants showed a second peak at approximately double the fundamental frequency (during Non-REM sleep in this infant). The relationships between periodicities in VT and / were examined by means of cross-correlations between these two time-series (fig. 7). In most infants, the cross-correlation showed a negative correlation between FT and/in both REM and Non-REM sleep, i.e., a peak in VT coincided with a trough in/. As periodic breathing is uncommon in term newborn infants, relatively few traces have as yet been analysed. These showed similar frequency spectra for VT and / ; cross-correlation, however, showed the oscillations in VT and / to be in phase with each other (fig. 7). 5.
6. Discussion There are a number of well-documented respiratory reflexes in the newborn infant involving stretch or other receptors in the respiratory passages, with a response-time sufficiently rapid to implicate purely neural pathways in the response. On the other hand, respiratory responses to alterations in the composition of inspired air are usually delayed by the time taken for changes in alveolar gas and hence pulmonary blood to reach the chemoreceptors via the blood stream. The low-frequency oscillations in VT and/described in this paper, with periods varying between limits of about 5 and 25 seconds, are compatible with the behaviour of feedback control systems involving chemoreceptor activity, although proof of this is at present lacking. The "transport-lag" of the feedback loop, i.e., the circulation time, may well be itself a timedependent quantity which alters with oscillations in heart rate (Tarlo, Valimaki & Rautaharju, 1971) or changes in blood flow during the respiratory cycle. This could account for some of the variation in cycle length of the oscillations in VT and /. Lenard (1970) hasreviewedsome of the differences found in the newborn in different sleep states. These include a lowamplitude electroencephalogram (EEG) in REM sleep, while during Non-REM sleep the amplitude is higher and may include bursts of "trace alternant"; these consist of bursts of high-amplitude activity occurring at an average interval of 7-9 seconds in infants 2-5 days old (Prechtl et al. 1968). With Prechtl's permission, one of his traces has been measured, and shows a significant cross-correlation between the EEG amplitude and the oscillations in respiratory rate. There arc also changes in reflex activity in different sleep states in the newborn infant (reviewed by Prechtl, 1972).
Changes in Breathing Patterns over Short Time-Intervals
The technique of frequency or spectral analysis is applicable only to data whose statistical properties do not change with time. If there are sudden changes in the pattern of breathing (as infig.2), each portion would have to be analysed separately; analysis of the whole trace would define only the average properties of the data and hence conceal any moment-tomoment changes in, for example, frequency or phase. (It was for this reason that only steady-state data were analysed in this way.) This problem is illustrated by fig. 8, which shows changes in V, VT and / in an infant in Non-REM sleep whose breathing suddenly became periodic. It will be seen that the predominantly negative running correlation between Vr and/during Non-REM sleep changed in a few seconds into a predominantly positive correlation with the onset of periodic breathing. Another point to note is that, in both parts of the trace, the 11
Vol. 31 No. 1
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn Bolton & Herman (1974), however, have shown that the ventilatory response to 100 % oxygen is present in both REM and Non-REM sleep in the newborn infant. Prechtl & Lenard (1967) suggested that the irregularities in respiratory rate and heart rate in REM sleep may be the result of random noise in the central nervous system, consequent upon a change in the pattern of afferent inputs in this sleep state. The present findings indicate that at least part of the variability in ventilation in REM sleep is due to a higher amplitude of the oscillations in Vr and/already present in Non-REM sleep. This could be caused by alterations in damping or effective gain in respiratory control systems, or by alterations in the phase relationships between VT and / in the two sleep states; whatever the explanation, these findings point to the importance of supra-medullary neural influences on the operation of respiratory control mechanisms. The negative correlations between Vr and fin individual respiratory cycles in man (e.g. Priban, 1963) and in the cat (von Euler, Herrero & Wexler, 1970) are often cited as evidence of neural control mechanisms operating within the time-period of the single respiratory cycle, keeping V at a relatively stable level. Although statistically significant in most cases, the correlation coefficients between VT and/are in fact low in many infants, indicating that most of the variation found in VT and in/cannot be accounted for by such a reciprocal relationship between them. One possibility to be considered is that there are different control systems for VT and / ; these two systems would normally be more or less "locked " out of phase with each other, resulting in an over-all negative correlation between the rate and depth of breathing, with a consequent stability in ventilation. Under certain circumstances, however, their phase difference may change, resulting in sudden alterations in the pattern of breathing (fig. 2, 8). Most of the explanations of periodic or Cheyne-Stokes type of breathing implicate fairly gross changes in functional residual capacity, circulating blood volume, circulation time, or changes in respiratory centre sensitivity to COj (see Guyton, Crowell & Moore, 1956; Dowell, Buckley, Cohen, Whalen & Sieker, 1971). In the newborn infant, however, changes in and
out of periodic breathing occur far too rapidly for the above factors to be operating. Chernick & Avery (1966) have shown that increased sensitivity to CO2 does not appear to be a factor in periodic breathing of the premature infant. Here again, sudden shifts in phase between VT and/could provide an explanation for a sudden onset and offset of periodic breathing. As suggested by Chernick, Heldrich & Avery (1964), rapid changes in the pattern of breathing in the newborn may be a feature of immaturity of the developing nervous system. It would be of great interest to perform similar analyses of breathing patterns in premature infants and indeed in the fetus. 7. Conclusions Attempts have been made in recent years to apply the concepts of control engineering to biological systems, and some progress has been made with mathematical models of nerve membranes, muscle control systems, temperature regulation and other homoeostatic mechanisms (Jones, 1969). Attempts have also been made to construct models of the respiratory system (e.g. Grodins, Gray, Schroeder, Norins & Jones, 1954; Fincham & Beishon, 1973). As the latter authors point out, these models apply only to the simpler parts of such systems, and we have little understanding of the central control mechanisms. Analysis of the rhythm of infantile breathing may provide more information on these central respiratory mechanisms and at the same time provide a base-line for further analysis of the infant's responses to respiratory stimuli. Any model of the respiratory system in the newborn infant, to have genuine predictive value, in addition would have to make provision for different sleep states, and for the level of maturation of the central nervous system. While the latter may appear to be of little importance at the moment, we have to recognize that, while the HeringBreuer reflex, for example, is similar in both the newborn and adult rabbit, it is very dissimilar in the newborn and adult human. The human being may need to reorganize his respiratory control in order to be able to solve the problems of vocalization.
REFERENCES
Aserinsky, E. & Kleitman, N. (1953) Science (N. Y.) 118,273-274 Bcndat, J. S. & Piersol, A. G. (1966) Measurement and analysis of random data. Wiley, New York Blackman, R. B. & Tukey, J. W. (1959) The measurement of power spectra: from the point of view of communications engineering. Dover Publications, New York Bolton, D. P. G. & Herman, S. (1974)/. Physiol. (Lond.) 240,67-77 Chernick, V. & Avery, M. E. (1966) / . Appl. Physiol. 21, 434-440 Chernick, V., Heldrich, F. & Avcry, M. E. (1964) / . Pediatr. 64, 330-340 Cox, D. R. & Lewis, P. A. W. (1966) The statistical analysis of series of events. Methuen, London Cross, K. W. (1949) / . Physiol. (Lond.) 109, 459-474 Cross, K. W. (1954) Cold Spring Harbor Symp. Quant. Biol. 19, 126-130 Deming, J. & Washbura, A. H. (1935) Am. J. Dis. Child. 49,108124 Dowell, A. R., Buckley, E., Cohen, R., Whalen, R. E. & Sieker, H. O. (1971) Arch. Intern. Med. 127, 712-726 Euler, C. von, Herrero, F. & Wexler, I. (1970) Respir. Physiol. 10, 93-108 Fincham, B. & Beishon, J. (1973) Systems behaviour. Module 7: The human respiratory system. The Open University Press, Milton Keynes Grodins, F. S., Gray, J. S., Schroeder, K. R., Norins, A. L. & Jones, R. W. (1954) / . Appl. Physiol. 7, 283-308
Guyton, A. C, Crowell, J. W. & Moore, J. W. (1956) Am. J. Physiol. 187, 395-398 Hathorn, M. K. S. (1974) / . Physiol. (Lond.) 243, 101-113 Jones, R. H., Crowell, D. H. & Kapuniai, L. E. (1970) Biometrics, 26, 269-280 Jones, R. H., Crowell, D. H., Nakagawa, J. K. & Kapuniai, L. E. (1971) IEEE Trans. Bio-Med. Eng. BME-18, 360-365 Jones, R. W. (1969) In: Schwan, H. P., ed. Biological engineering, pp. 87-203. McGraw-Hill, New York Lenard, H. G. (1970) Ada Paediatr. Scand. 59, 572-581 Lenfant, C. (1967) /. Appl. Physiol. 22,675-684 Prechtl, H. F. R. (1972) In: Qemente, C. D., Purpura, D. P. & Mayer, F. E., ed. Sleep and the maturing nervous system, pp. 287-301. Academic Press, New York & London Prechtl, H. F. R., Akiyama, Y., Zinkin, P. & Grant, D. K. (1968) In: Keith, R. M. & Bax, M., ed. Studies in infancy, pp. 1-21 (Clinics in Developmental Medicine, no. 27). Heinemann Medical, London Prechtl, H. [F.R.] & Beintema, D. (1964) The neurological examination of the full-term newborn infant (Little Club Clinics in Developmental Medicine, no. 12). Heinemann Medical, London Prechtl, H. F. R. & Lenard, H. G. (1967) Brain lies. 5, 477493 Priban, L P. (1963) /. Physiol. {Lond.) 166,425-W4 Tarlo, P. A., Valunaki, I. & Rautaharju, P. M. (1971) / . Appl. Physiol. 31, 70-75
12
Br. Med. Bull. 1975
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING
a
, !'i 1
20ml
E
ft 'ft
V A
/ y (J \$ V\i V V
•ifc .S(i
M. K. S. HATHORN B.Sc. M.B. Ph.D.
, .i 1 ,;;t ip nil• 1 « --h /! / nry V
1
ft, L_ PI
ij
A,
\ 1'. \!
I
i
•
Department ofPhysiology The London Hospital Medical College London
,. 1
" I .
'
' , •
^
r
-"It;'" !| l|i
w ;ii in s .}•
'' ' , II nrJit' I1 i!l|
i
•i
1 2 3 4 5 6 7
Some patterns of infantile breathing Relationships within individual respiratory cycles Relationships between successive respiratory cycles Frequency analysis of the rate and depth of breathing Changes in breathing patterns over short time-intervals Discussion Conclusions References
:i i:-ii[iakE f fi.iaaii:iin Jijij, ryj In
20 ml
I—~~
•.•hfelilll
JE Si. L t E i
M'
Most studies on ventilation in the newborn have concerned the infant's responses to experimental procedures such as altering the composition or pressure of inspired air. These investigations have produced much valuable information; experiments concerned with respiratory responses should, however, be based on the characteristics of spontaneous breathing in the unstimulated infant, for these may well determine the outcome of the particular stimulus applied (Jones, Crowell & Kapuniai, 1970). There have been relatively few such investigations. A number of new techniques and approaches have been developed over the past few decades which have opened up new lines of investigation into problems of respiratory control. An example is the demonstration of breathing movements in the fetus by Boddy & Dawes (pp. 3-7 of this Bulletin). Another example is the recognition of changes in breathing patterns m different sleep states (Aserinsky & Kleitman, 1953; Prechtl & Beintema, 1964). This now makes it necessary for the investigator to note the sleep state of the infant whenever he is recording respiratory data. With the introduction of an improved trunk plethysmograph by Cross (1949), it became possible to make accurate measurements in the newborn infant, not only of respiration rate, but also of tidal volume (VT) and hence ventilation on a breath-bybreath basis, without the use of masks which may alter the infant's behaviour, or valves and tubing which increase either dead space or resistance to air flow. The use of the polygraph, and multi-channel tape recordings of changes occurring in important physiological variables, including respiration, in the newborn infant in different sleep states (e.g. Prechtl, Akiyama, Zinkin & Grant, 1968), makes it possible to analyse the data in different ways: playing back the tape on to a slowly moving pen-recorder makes it possible to recognize changes occurring over long time-intervals, while a faster speed facilitates detailed measurements of short-term phenomena. Finally, a number of mathematical and statistical techniques have become available for the analysis of time-series, i.e., a series of events or changes in data occurring over a period of time (Bendat & Piersol, 1966; Cox & Lewis, 1966).
JLJ
3 ^ S3
v^ 10 seconds
The present paper attempts to describe the use of some of these techniques to analyse the patterns of breathing found during different sleep states in some 30 healthy term infants, studied with their mothers' informed consent during the first week after delivery. By "patterns of breathing" is meant the relationships between the rate and depth of breathing, either from cycle to cycle or over longer periods of time. It is hoped that this may help towards an understanding of respiratory control in the newborn infant. 1. Some Patterns of Infantile Breathing Patterns that seem to characterize breathing in the sleeping newborn infant are: (i) regular breathing; (ii) irregular and FlG. 2. Tracings of ventilation in three term infants, before and after the occurrence of sighs (After Cross, 1954) I mpi ration
Each sigh if of the order of 100 ml. Inspiration of air Is depicted in the downwards direction In the tracings
Br.Mcd.Bull. 1975
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn FIG. 3. Compressed section of tracing of ventilation in an infant to demonstrate the effect of the occurrence of sighs
FlG. 4. Computer-drawn graphs of tidal volume (V T ) plotted against instantaneous frequency ( f ) for individual respiratory cycles
•
20 ml
:
•
-
:
"
,
*
•
'
40 10
15
Sighs occurred at A, B, C and D. The arrow indicates the Injection of 20ml of air Into the plethysmograph to test for leaks. After C, the sleep state changed from Non-REM to REM. The portions selected for analysis were B-C and C - D
(ml)
.'V,
For abbreviations, see figure I
more rapid breathing; and (iii) periodic breathing (Deming & Washburn, 1935; Cross, 1954; Prechtl, 1972). Prechtl & Beintema (1964) have related regular breathing to a sleep state characterized by the absence of eye movements and most motor activity apart from startles (non-rapid eye movement (Non-REM) sleep). Irregular and more rapid breathing is accompanied by sporadic movements of the extremities, mouthing, and rapid eye movements (REM) seen beneath the closed lids (REM sleep); see fig. 1. Periodic breathing occurs most commonly during Non-REM sleep, particularly in premature infants. Sudden movements or startles, accompanied by deep sighs, occur at random intervals and are found more frequently during Non-REM sleep; they may herald a change in sleep state or pattern of breathing (fig. 2, 3). The analytical techniques to be described are applicable only to steady-state data whose statistical properties do not alter with time (Bendat & Piersol, 1966; Cox & Lewis, 1966). This severely limits the lengths of trace available (fig. 3). In the comparison of breathing patterns during REM and Non-REM sleep in the term newborn infant, adjacent sections of trace were selected which fulfilled the criterion of a steady state (Hathorn, 1974). 2. Relationships within Individual Respiratory Cycles By measuring the amplitude of each respiratory cycle (VT) and its duration, it is possible to calculate instantaneous frequency (/), where/= 60/duration in seconds, and hence instantaneous pulmonary ventilation (V), where V= VT x / It has been shown by Prechtl et al. (1968), amongst others, that mean / is higher during REM sleep than in Non-REM sleep. In addition, there is a significant increase in mean V during REM sleep, while VT is either unchanged or decreased (Bolton & Herman, 1974; Hathorn, 1974). There is also greater variation in instantaneous V, VT and / during REM than during Non-REM sleep (Hathorn, 1974) (fig. 4). In the adult, Priban (1963) and others have demonstrated a
Vol. 31 No. 1
50
100
VT is plotted against f during (a) Non-REM sleep (correlation coefficient (r) = - 0 . 4 1 0 ) , (b) REM sleep ( r = - 0 . 6 9 4 ) , and (c) periodic breathing (r=0.0005). The three types of breathing demonstrate different distributions of values for VT and f (all drawn to the same scale) For abbreviations, see figure I
negative correlation between the values of VT and/in individual respiratory cycles. This is also true of most newborn infants in both sleep states; in periodic breathing, this relation between VT and / is no longer present, the correlation being usually zero or even positive (fig. 4). The fact that these correlations are only partial, and are not found in all infants, raises the question of whether there are other relationships between the rate and depth of breathing which operate over a longer time-span than does the individual respiratory cycle. 3. Relationships between Successive Respiratory Cycles Priban (1963) found "runs" in the frequency of successive respiratory cycles in adults which could not be accounted for on a random basis, and Lenfant (1967) has described the presence of long-term oscillatory trends in ventilation in conscious adults. Considering the onset of each respiratory cycle as an " event", the intervals between such events may be studied, using methods developed by Cox & Lewis (1966) for the analysis of series of events. In newborn infants, analysis of these intervals suggests the presence of recurrent periodicities in the rate of breathing (Hathorn, 1974). These are present in the apparently regular breathing ofNon-REM sleep as well as in the irregular breathing of REM sleep. The next step is to determine the characteristics of such periodicities in respiratory rate, and their possible occurrence in VT as well.
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathom FIG. 5. Filtered instantaneous respiration rate in an infant showing oscillations in rate (min~') about the mean, during (a) REM sleep and (b) NonREM sleep
4. Frequency Analysis of the Rate and Depth of Breathing Autocorrelation, cross-correlation and spectral (Fourier) analysis1 are techniques extensively used in the physical sciences, and were recently introduced into thefieldof neurophysiology, for the detection of periodicities in data, the isolation of the frequencies present, and for seeking correlations between different periodic phenomena (see Blackman & Tukey, 1959; Bendat & Piersol, 1966). These techniques require either continuous (analogue) data, or data sampled at equi-spaced timeintervals. At first sight, the respiratory trace is itself a continuous signal and could therefore be analysed directly. The near-sinusoidal shape of the individual respiratory cycles, however, would result in a frequency spectrum completely dominated by a single peak at the respiratory frequency of the infant, and the amplitude of this peak would register the average VT over the period analysed (trivial information indeed!). Any long-term fluctuations in rate would be submerged, and oscillations in VT would be averaged out. The method selected to overcome this problem was to treat VT a n d / a s continuous variables, and then to sample them at equi-spaced time-intervals. This procedure is justified when analysing changes over periods longer than the individual cycle. This type of analysis was performed using a computer program which created a continuous histogram of VT o r / o n the ordinates, with the durations of successive respiratory cycles on the abscissae. These histograms were then sampled at equi-spaced time-intervals (Jones, Crowell, Nakagawa & Kapuniai, 1971), the final sampling interval being 0.5 second. Autocovariances and frequency spectra for both VT and / were calculated, using standard computer programs (Bendat & Piersol, 1966). These showed the presence of cyclical changes in both the rate and depth of breathing, particularly in the frequency range below 0.20Hz, i.e., with a period greater than 5 seconds. Comparison of the frequency spectra between different infants, and indeed between successive sets of data in the same infant, however, showed marked instabilities in the verylow-frequency portion of the spectrum. Such instability is commonly found in many types of data and is due to slow or long-term fluctuations in the data being analysed. The standard technique used to deal with this problem in continuous data is to pass the signal through a band-pass filter, which passes only the desired range of frequencies. A digital computer may be programmed as a "digital filter" to perform the same operation on time-sampled data. The problem, however, is to choose the appropriate frequency range. This was done by dividing the time-sampled data into two equal parts and calculating the cross-correlations and a crossspectrum between them, in order to detect those frequencies common to both parts of the data. By use of this method it was found that the consistent frequencies present, in both VT and/, lay between about 0.04Hz and 0.20Hz (i.e., a period of 25 to 5 seconds). A computer program was therefore written to pass all frequencies present in this range, and to suppress all higher and lower frequencies in the data (Blackman & Tukey, 1959). The filtered data showed the presence of oscillations in both
+ 10
o -10
+ 10 0 -10 30
100
Seconds
For abbreviations, see figure I
V-x and / in both sleep states (fig. 5). These oscillations show variation not only in amplitude, but also in the peak-to-peak interval between individual oscillations. In order to estimate mean amplitude and frequency over the duration of the data, frequency spectra were calculated for both VT and/. A typical result is shown in fig. 6. The following may be noted: (i) the amplitudes of the oscillations in VT and / a r e higher in REM than in Non-REM sleep (this has been found in all infants so far studied); (ii) the frequency ranges of the oscillations are similar for VT and / in both sleep states (main peak about 0.08 Hz in this infant); (iii) the frequency peaks are fairly broad, FIG. 6. Frequency spectra for (a) VT and (b) f in REM and Non-REM sleep, plotted on a semilogarithmic scale (infant is the same as in fig. 5)
100.0 r-
0.0
Frequency (Hz)
< Autocorrelation (or autocovariance) functions are calculated by estimating the correlation between a time-sories and a repKca thereof, the latter being displaced one lime-unit (or lag) at a time In relation to the original; this demonstrates the interval over which the periodic wave starta"repcating^' itself. The Fourier transform of these functions provides information on how variation in the data Is distributed between different frequencies. Cruss-correlations add cross-spectra are similarly calculated between two different time-aeries.
Ordinates are presented In terms o f " power", I.e., the distribution of variance according to frequency For abbreviations, see figures I and 4
10 Br. Med. Bull. 1975
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn FIG. 7. Cross-correlations between filtered VT and f during (a) Non-REM sleep and (b) periodic breathing
FIG. 8. Analysis of the rhythm of breathing in an infant during Non-REM sleep, showing onset of periodic breathing
40
SO
140
Seconds
+5
+10
Computer-drawn graph shows, from the top down, instantaneous V, VT and f In an Infant during Non-REM sleep (left), with a sudden transition to periodic breathing (right). The bottom trace shows the running correlation (R) between VT and f over a moving Interval of five seconds; the stippled areas show the positive correlations, the solid black areas the negative ones
Lag (seconds) Note the negative deflection at zero lag during Non-REM sleep (VT and f out of phase) and the predominantly positive deflection ( W and f i n phase) In periodic breathing. " L a g " Is the time over which the cross-correlations i r e calculatea(see footnote I, p. 10)
V: instantaneous pulmonary ventilation; for other abbreviations, see figures I and 4
For abbreviations, see figures I and 4
running correlation shows that Vr and / have a tendency to shift their phase relationship in a semi-periodic manner; this is possibly a "beat" phenomenon.
indicating that the oscillations do not have a single, sharply denned frequency, but instead vary around a mean value; and (iv) the frequency spectrum for/in a number of infants showed a second peak at approximately double the fundamental frequency (during Non-REM sleep in this infant). The relationships between periodicities in VT and / were examined by means of cross-correlations between these two time-series (fig. 7). In most infants, the cross-correlation showed a negative correlation between FT and/in both REM and Non-REM sleep, i.e., a peak in VT coincided with a trough in/. As periodic breathing is uncommon in term newborn infants, relatively few traces have as yet been analysed. These showed similar frequency spectra for VT and / ; cross-correlation, however, showed the oscillations in VT and / to be in phase with each other (fig. 7). 5.
6. Discussion There are a number of well-documented respiratory reflexes in the newborn infant involving stretch or other receptors in the respiratory passages, with a response-time sufficiently rapid to implicate purely neural pathways in the response. On the other hand, respiratory responses to alterations in the composition of inspired air are usually delayed by the time taken for changes in alveolar gas and hence pulmonary blood to reach the chemoreceptors via the blood stream. The low-frequency oscillations in VT and/described in this paper, with periods varying between limits of about 5 and 25 seconds, are compatible with the behaviour of feedback control systems involving chemoreceptor activity, although proof of this is at present lacking. The "transport-lag" of the feedback loop, i.e., the circulation time, may well be itself a timedependent quantity which alters with oscillations in heart rate (Tarlo, Valimaki & Rautaharju, 1971) or changes in blood flow during the respiratory cycle. This could account for some of the variation in cycle length of the oscillations in VT and /. Lenard (1970) hasreviewedsome of the differences found in the newborn in different sleep states. These include a lowamplitude electroencephalogram (EEG) in REM sleep, while during Non-REM sleep the amplitude is higher and may include bursts of "trace alternant"; these consist of bursts of high-amplitude activity occurring at an average interval of 7-9 seconds in infants 2-5 days old (Prechtl et al. 1968). With Prechtl's permission, one of his traces has been measured, and shows a significant cross-correlation between the EEG amplitude and the oscillations in respiratory rate. There arc also changes in reflex activity in different sleep states in the newborn infant (reviewed by Prechtl, 1972).
Changes in Breathing Patterns over Short Time-Intervals
The technique of frequency or spectral analysis is applicable only to data whose statistical properties do not change with time. If there are sudden changes in the pattern of breathing (as infig.2), each portion would have to be analysed separately; analysis of the whole trace would define only the average properties of the data and hence conceal any moment-tomoment changes in, for example, frequency or phase. (It was for this reason that only steady-state data were analysed in this way.) This problem is illustrated by fig. 8, which shows changes in V, VT and / in an infant in Non-REM sleep whose breathing suddenly became periodic. It will be seen that the predominantly negative running correlation between Vr and/during Non-REM sleep changed in a few seconds into a predominantly positive correlation with the onset of periodic breathing. Another point to note is that, in both parts of the trace, the 11
Vol. 31 No. 1
ANALYSIS OF THE RHYTHM OF INFANTILE BREATHING M. K. S. Hathorn Bolton & Herman (1974), however, have shown that the ventilatory response to 100 % oxygen is present in both REM and Non-REM sleep in the newborn infant. Prechtl & Lenard (1967) suggested that the irregularities in respiratory rate and heart rate in REM sleep may be the result of random noise in the central nervous system, consequent upon a change in the pattern of afferent inputs in this sleep state. The present findings indicate that at least part of the variability in ventilation in REM sleep is due to a higher amplitude of the oscillations in Vr and/already present in Non-REM sleep. This could be caused by alterations in damping or effective gain in respiratory control systems, or by alterations in the phase relationships between VT and / in the two sleep states; whatever the explanation, these findings point to the importance of supra-medullary neural influences on the operation of respiratory control mechanisms. The negative correlations between Vr and fin individual respiratory cycles in man (e.g. Priban, 1963) and in the cat (von Euler, Herrero & Wexler, 1970) are often cited as evidence of neural control mechanisms operating within the time-period of the single respiratory cycle, keeping V at a relatively stable level. Although statistically significant in most cases, the correlation coefficients between VT and/are in fact low in many infants, indicating that most of the variation found in VT and in/cannot be accounted for by such a reciprocal relationship between them. One possibility to be considered is that there are different control systems for VT and / ; these two systems would normally be more or less "locked " out of phase with each other, resulting in an over-all negative correlation between the rate and depth of breathing, with a consequent stability in ventilation. Under certain circumstances, however, their phase difference may change, resulting in sudden alterations in the pattern of breathing (fig. 2, 8). Most of the explanations of periodic or Cheyne-Stokes type of breathing implicate fairly gross changes in functional residual capacity, circulating blood volume, circulation time, or changes in respiratory centre sensitivity to COj (see Guyton, Crowell & Moore, 1956; Dowell, Buckley, Cohen, Whalen & Sieker, 1971). In the newborn infant, however, changes in and
out of periodic breathing occur far too rapidly for the above factors to be operating. Chernick & Avery (1966) have shown that increased sensitivity to CO2 does not appear to be a factor in periodic breathing of the premature infant. Here again, sudden shifts in phase between VT and/could provide an explanation for a sudden onset and offset of periodic breathing. As suggested by Chernick, Heldrich & Avery (1964), rapid changes in the pattern of breathing in the newborn may be a feature of immaturity of the developing nervous system. It would be of great interest to perform similar analyses of breathing patterns in premature infants and indeed in the fetus. 7. Conclusions Attempts have been made in recent years to apply the concepts of control engineering to biological systems, and some progress has been made with mathematical models of nerve membranes, muscle control systems, temperature regulation and other homoeostatic mechanisms (Jones, 1969). Attempts have also been made to construct models of the respiratory system (e.g. Grodins, Gray, Schroeder, Norins & Jones, 1954; Fincham & Beishon, 1973). As the latter authors point out, these models apply only to the simpler parts of such systems, and we have little understanding of the central control mechanisms. Analysis of the rhythm of infantile breathing may provide more information on these central respiratory mechanisms and at the same time provide a base-line for further analysis of the infant's responses to respiratory stimuli. Any model of the respiratory system in the newborn infant, to have genuine predictive value, in addition would have to make provision for different sleep states, and for the level of maturation of the central nervous system. While the latter may appear to be of little importance at the moment, we have to recognize that, while the HeringBreuer reflex, for example, is similar in both the newborn and adult rabbit, it is very dissimilar in the newborn and adult human. The human being may need to reorganize his respiratory control in order to be able to solve the problems of vocalization.
REFERENCES
Aserinsky, E. & Kleitman, N. (1953) Science (N. Y.) 118,273-274 Bcndat, J. S. & Piersol, A. G. (1966) Measurement and analysis of random data. Wiley, New York Blackman, R. B. & Tukey, J. W. (1959) The measurement of power spectra: from the point of view of communications engineering. Dover Publications, New York Bolton, D. P. G. & Herman, S. (1974)/. Physiol. (Lond.) 240,67-77 Chernick, V. & Avery, M. E. (1966) / . Appl. Physiol. 21, 434-440 Chernick, V., Heldrich, F. & Avcry, M. E. (1964) / . Pediatr. 64, 330-340 Cox, D. R. & Lewis, P. A. W. (1966) The statistical analysis of series of events. Methuen, London Cross, K. W. (1949) / . Physiol. (Lond.) 109, 459-474 Cross, K. W. (1954) Cold Spring Harbor Symp. Quant. Biol. 19, 126-130 Deming, J. & Washbura, A. H. (1935) Am. J. Dis. Child. 49,108124 Dowell, A. R., Buckley, E., Cohen, R., Whalen, R. E. & Sieker, H. O. (1971) Arch. Intern. Med. 127, 712-726 Euler, C. von, Herrero, F. & Wexler, I. (1970) Respir. Physiol. 10, 93-108 Fincham, B. & Beishon, J. (1973) Systems behaviour. Module 7: The human respiratory system. The Open University Press, Milton Keynes Grodins, F. S., Gray, J. S., Schroeder, K. R., Norins, A. L. & Jones, R. W. (1954) / . Appl. Physiol. 7, 283-308
Guyton, A. C, Crowell, J. W. & Moore, J. W. (1956) Am. J. Physiol. 187, 395-398 Hathorn, M. K. S. (1974) / . Physiol. (Lond.) 243, 101-113 Jones, R. H., Crowell, D. H. & Kapuniai, L. E. (1970) Biometrics, 26, 269-280 Jones, R. H., Crowell, D. H., Nakagawa, J. K. & Kapuniai, L. E. (1971) IEEE Trans. Bio-Med. Eng. BME-18, 360-365 Jones, R. W. (1969) In: Schwan, H. P., ed. Biological engineering, pp. 87-203. McGraw-Hill, New York Lenard, H. G. (1970) Ada Paediatr. Scand. 59, 572-581 Lenfant, C. (1967) /. Appl. Physiol. 22,675-684 Prechtl, H. F. R. (1972) In: Qemente, C. D., Purpura, D. P. & Mayer, F. E., ed. Sleep and the maturing nervous system, pp. 287-301. Academic Press, New York & London Prechtl, H. F. R., Akiyama, Y., Zinkin, P. & Grant, D. K. (1968) In: Keith, R. M. & Bax, M., ed. Studies in infancy, pp. 1-21 (Clinics in Developmental Medicine, no. 27). Heinemann Medical, London Prechtl, H. [F.R.] & Beintema, D. (1964) The neurological examination of the full-term newborn infant (Little Club Clinics in Developmental Medicine, no. 12). Heinemann Medical, London Prechtl, H. F. R. & Lenard, H. G. (1967) Brain lies. 5, 477493 Priban, L P. (1963) /. Physiol. {Lond.) 166,425-W4 Tarlo, P. A., Valunaki, I. & Rautaharju, P. M. (1971) / . Appl. Physiol. 31, 70-75
12
Br. Med. Bull. 1975
