The Sheffield respiration analysis systemA.J. Wilson, B.Sc., Ph.D., and C.I. Franks, B.Sc, Ph.D., C.Eng., M.I.E.E.

Indexing terms: Biomedical engineering, Instrumentation and measuring science, Computer applications

Abstract: The paper describes a computerised system for analysing the respiration and the instantaneousheart rate from long-term recordings of respiration and ECG. The data are analysed as they are replayed at upto 64 times the real time. The respiratory signal is obtained using thoracic wall movement sensors or im-pendence pneumography. The factor used to quantitate the respiration is the breath-to-breath interval, whichleads directly to the identification of respiratory pauses (or apnoea). The data are analysed in nonoverlappingtime epochs of 1.7 min duration (real time). The system presents the results in two forms: firstly, a 'trendplot' which is a summary of the results for the 1.7 minute epochs plotted against time, and, secondly, anumerical summary which allows comparisons to be made between subjects. The system is principally designedfor use in investigations into the sudden infant death syndrome (cot death), but alteration of some of the'clinical' values contained in the system may make it a suitable tool for other research.

1 Introduction

During the past decade a considerable amount of investigationhas been carried out into sudden and unexplained death ininfancy — called sudden infant death syndrome (SIDS). Someof the post-mortem studies that have been carried out haveproduced findings from SIDS cases that are consistent withchronic hypoxia [1, 2] . In view of these findings and thefindings from studies carried out into infants who have re-covered from an episode of collapse (called 'near-miss' SIDSinfants [3]) it has been suggested that a fundamental defectmay exist in the control mechanism of the cardiorespiratorysystem in those infants who died of SIDS.

One method by which the cardiorespiratory system may bestudied is by performing long-term monitoring of the respirationand the ECG. Two different approaches have been used toobtain these data: the first is to perform the study within aspecial sleep laboratory in a hospital where the infant iscontinuously observed; and the second is to monitor the infantwithin its own home, and to use a tape recorder to store thedata for later analysis. In performing studies related to SIDSthis latter approach has several philosophical advantages, inthat the majority of SIDS cases occur at home, during theovernight sleep period and unobserved. However, such anapproach presents many technical and organisational dif-ficulties as the environment is less controlled, the equipmentused must be portable and robust and there is no control overthe 'quality' of the data obtained. In addition, the techniquesused must be simple, safe for an observed infant and acceptableto the parents. Clearly, such requirements preclude the use ofmany of the techniques that are acceptable in a sleep laboratory[4].

Studies carried out within the home have centred onmonitoring the ECG using two or three chest electrodes andmonitoring respiration by monitoring thoracic wall movementusing impedance pneumography, the air cell [5] and a radarsystem [6]. The recordings are commonly made on OxfordMedical Systems Medilog recorders which are miniature 4-channel analogue tape recorders [7—9].

The vast amount of data which is generated as a resultof 12- or 24-hour recordings necessitates that computerisedtechniques be used for their analysis. The analysis systemdescribed here has been developed for analysing 24-hourrecordings of the respiration and the ECG made in the home.Although the primary use of the system has been in connectionwith SIDS research, the alteration of some of the 'clinical'

Paper 2246A, first received 20th September and in revised form 11thOctober 1982The authors are with the Department of Medical Physics & ClinicalEngineering, Royal Hallamshire Hospital, Glossop Road, SheffieldSlO 2JF, England

702 0143-702X/82I090702 + 05 $01.50/0

definitions used within the system may make it an appropriatetool for other types of research.

The analysis performed in our system is based on describingthe heart rate and breath-to-breath interval distributions fornonoverlapping time epochs of approximately 1.7 min durationthroughout the recording. The use of the breath-to-breathintervals leads directly to the detection of respiratory pausesor apnoea. The use of the interval (rather than a rate) providesa much more convenient way of describing respiration.

The analysis system processes the data as they are replayedat up to 64 times real time, without the need for an inter-mediate storage medium (e.g. digital magnetic tape). A24-hour recording made on the Medilog recorders which isreplayed at 60 times real time is, therefore, analysed in 24min.

2 Analysis overview

The analysis system is designed to run on a Data GeneralEclipse S200 computer, with 32 K words of random-accessmemory available for the users' programmes. The computerinstallation has a 10Mbyte disc drive (5 Mbyte removable;5 Mbyte fixed), an eight-channel analogue-to-digital convertor,a Centronics 101 dot-matrix printer fitted with a graphicsconversion system (Millhouses Design Ltd.) and a Tektronix4010 visual diaplay unit.

The following signals are digitised and input to the com-puter:

(i) heart rate(ii) respiratory waveform

(iii) loss of tape carrier (Medilog tapes)(iv) high-frequency content of respiratory signal.

Input signal (iii) from the above list is only required when thedata are recorded on the Medilog tape recorders. In these, therespiratory waveform is recorded as a square wave where themark/space ratio is modulated by the amplitude of the anal-ogue signal being recorded. If the amplitude of the signalbeing recorded is too large then the square wave is no longerrecorded onto the tape. On replay, this condition produces0 V output. Clearly, any system analysing these data must beable to recognise this condition, and the 'loss of tape carrier'signal provides this information.

One of the problems in analysing the respiratory waveformis to recognise periods when the respiratory sensor is respondingto patient movement rather than respiration. One character-istic by which patient movement may be identified in therespiratory waveform is that the amplitude changes it pro-duces contain much larger high-frequency components thanthose amplitude changes associated with normal respirations.To obtain information about the high-frequency content ofthe respiratory waveform it is passed through a highpass filter,

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tapereplayunit

clock

EGG

respiration

loss of

tapecarrier

heartratemeter

lowpass

filter

highpassfilter and. peakdetector

mo nos table

/

analogueto digitalconverter

/

computer

dot-matrixprinter

VDU

Fig. 1 Block diagram showing various parts of respiration analysissystem

and the output from the filter is then passed through a peakdetector. The output from the peak detector is digitised andinput to the computer (signal (iv) from the above list). Fordata replayed at 60 times real time the characteristics of thefilter are: cut-off frequency = 180 Hz, damping ratio f = 0.7;and the characteristic of the peak detector are: time constant= 33 ms.

A crystal-controlled clock recorded on the analogue tape isused to control the sampling so that precise time measure-ments can be made. The sampling rate is 20 Hz for datareplayed at the speed at which they were recorded, and isappropriately scaled for high-speed replay.

The data, once input to the computer, are analysed innonoverlapping epochs corresponding to approximately 1.7 minof real time (i.e. time at recording). The epoch length wasselected to be sufficiently long to allow statistically significantresults to be obtained, while being sufficiently short to allow'pattern' changes to become apparent. To ensure efficientprocessing of the data it is necessary that the number of datapoints in an epoch is an integer power of 2. 1.7 min gives2048 data points. For each 1.7 min epoch a set of numericalresults characterising the respiration and the heart rate duringthat epoch are written to a disc file. On completion of theanalysis of a tape these reuslts are presented in two forms.The first type of output is a 'trend plot' in which a summaryof the results from each epoch are plotted against time. Thisallows changes which occured during the recording to becomeapparent. The second type of output is a numerical summaryin which the value of each of the parameters analysed ispresented as a single number which attempts to describe thewhole recording. A block diagram showing the overall analysissystem is given in Fig. 1.

The various parts of the analysis performed by the computerwill be described in the following four Sections.

2.1 Respiratory waveformThe basic measurement used to characterise the respiration isthe time interval between breaths (TIBB). To determine thisit is necessary to detect breaths from the respiratory wave-form. This is acomplished by detecting peaks and troughs from

the respiratory waveform (Fig. 2) using the algorithms describedby Wilson etal. [10, 11].

These algorithms detect peaks and troughs from the respir-atory waveform by comparing the amplitude of a point onthe digitised waveform with the amplitude of the precedingpoint and amplitude of the following point. The algorithmsincorporate tests for both peak-to-trough and trough-to-peakamplitudes in which a 'threshold' level must be exceededbefore a breath can be detected. This threshold level is deter-mined from the amplitude of breaths detected from the pre-ceding 60s (real time) of the respiratory waveform. As will beseen in the following Section, a maximum amplitude thresholdlevel is set on the peak-to-trough and trough-to-peak ampli-tudes above which the signal is classed as artefact. If the respir-atory waveform is being monitored by impedance pneumo-graphy, then a change in impedance occurs immediatelyfollowing the systolic phase of the heart, and this is a resultof the large change in lung blood volume. This type of artefactis called 'heart bump'. To prevent these bumps from beingdetected as breaths, the peak-to-trough and trough-to-peakamplitudes for the same cyclical change in the amplitude ofrespiratory waveform are compared, and must be similarbefore a breath is detected.

To determine the time interval between breaths it is necessaryto identify a position on each breath which is not only ap-plicable to all breaths but is insensitive to the shape of thebreath. The measurement of the time interval between breathsis then made by measuring the time interval between thisposition on successive breaths. The peaks and troughs of therespiratory waveform are unsuitable for this purpose as noise(slight irregularities in the waveform) may affect their position.Therefore, the position used is the half-amplitude point on theinspiratory phase of the breath (Fig. 3). Once a peak andtrough in the respiratory waveform have been detected, the

Fig. 2 'Peaks' and 'troughs' of respiratory waveform

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tests for movement artefact are applied; these tests are des-cribed in the following Section. If movement artefact isdetected then that section of the respiratory waveform is notused in the determination of the time interval between breaths.At the end of each 1.7 min epoch the nonsequential histogram

respiratory waveform

A/VJWdetected breaths

Fig. 3 Position on respiratory waveform used in determining breath-to-breath intervals

is constructed from the TIBB values which occurred duringthat epoch. From this, the median, lower quartile and upperquartile values of the distribution are calculated. The medianand interquartile range of the distribution are used in placeof the mean and standard deviation because their values areless affected by the presence of extreme values in the distri-bution.

The median, upper quartile, lower quartile and the numberof breaths which occurred during that epoch are stored on adisc file.

2.2 Movement artefactPatient movement appears as an artefact within the respiratorywaveform. Three techniques are used for its identification:

(i) loss of tape carrier(ii) large-amplitude signal(iii) high-frequency content of the respiratory waveform.

Once the trough, peak and trough of a cyclical variation in theamplitude of the respiratory waveform have been detected bythe computer it is necessary to determine whether that cyclicalvariation is caused by patient movement or by respiration.If 'loss of tape carrier' occurs between the position of the twotroughs then that cyclical variation is attributed to patientmovement. If the trough-to-peak or peak-to-trough amplitudeexceeds three times the mean amplitude of breaths detectedfrom the preceding 1 min (real time) of respiratory data thenthat cyclical variation is attributed to patient movement.Finally, if the amplitude of the signal giving information aboutthe high-frequency content of the respiratory waveformexceeds a threshold level at any time between the positions ofthe two troughs then that cyclical variation is attributed topatient movement. The threshold level used for this is set to beequivalent to the output from the highpass-filter/peak-followersystem for the mean amplitude of breaths detected from thepreceding 60s of respiratory waveform and for a respiratoryrate of 120breaths/min. 120breaths/min has been selectedbecause sustained respiratory rates higher than this are veryunlikely to occur while the rapid amplitude changes in therespiratory waveform which occur as a result of patientmovement do produce outputs from the highpass-filter/peak-follower system above this level.

At the end of the epoch, the total time for which patientmovement was detected during that epoch is calculated as apercentage of the epoch time. This value is stored on the discfile.

2.3 Instantaneous heart rateTechniques available for obtaining the instantaneous heartrate from the ECG are well established (e.g. Reference 12),

and so this part of the analysis is performed by special-purposehardware. The instantaneous heart rate is input to the com-puter through the analogue-to-digital convertor. The blockdiagram of the system used to determine the heart rate is

input

Fig. 4A Block diagram of system used to determine instantaneousheart rate

1V

(i) OV

-IV

ECGtrigger

B instantaneousratemeter c

analogueswitch

(to

D

outputcomputer)

(ii)

(iii)Vn

IV

(iv)

OV

Fig. 4B Waveforms at various points in the system

Signal (iv) is the signal which is digitised and input to the computer

shown in Fig. 4A. Fig. 4B (iv) is a pulse train where the rising(or positive going) edge of each pulse corresponds to a QRScomplex in the ECG, and the amplitude of each pulse isproportional to the instantaneous heart rate for the precedingheart beat. It is this signal which is digitised and input to thecomputer. The ratemeter used is that described by Smallwood[13], and uses a clock and counter to determine the timeinterval between two events and a multiplying digital-to-analogue convertor configured as a divider to determine theinstantaneous rate. So that tape speed fluctuations do notaffect the heart rate value obtained, the clock frequency forthe ratemeter is derived from the crystal-controlled clocksignal recorded on the original analogue tape. The pulse trainshown in Fig. 4B (iv) is digitised and input to the computer.The amplitude of each pulse is detected and converted into theinstantaneous heart rate value. At the end of each epoch thenonsequential histogram of the instantaneous heart rate valueswhich occurred during that epoch is constructed. From this,the median, lower quartile and upper quartile values of thedistribution are calculated. The number of heart beats whichoccurred during that epoch and the median, lower and upperquartile values of the distribution are stored in the disc file.

During the analysis of each epoch, any episode of bradycardiaand tachycardia is detected. Bradycardia is defined in thesystem as a single value of instantaneous heart rate less than90beats/min. In addition to this, bradycardic episodes of morethan five beats duration are also detected. At the end of eachepoch the total number of bradycardic beats, the numberof bradycardic episodes of more than five beats duration andthe total number of bradycardic beats contained in thoseepisodes are recorded on the disc file. Similarly, the number oftachycardic beats (> 200 beats/min) and the number oftachycardic episodes of more than ten beats duration and thetotal number of heart beats contained in those episodes arealso stored on the disc file.

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2.4 ApnoeaApnoea is defined in the system as a value of time intervalbetween breaths which exceeds 5 s, if accompanied by brady-cardia, or a value of 10 s if not accompanied by bradycardia.The full division of apnoeic episodes is shown in Fig. 5. It

TIBB<5s

5-10S

10-15s 15-20s >20s

Fig. 5 Various classifications used to quantitate respiratory pauses inrespiratory waveform

should be noted that pauses between 5 and 10 without brady-cardia are not calssified as apnoea. For each division of apnoea(e.g. 10—15s without bradycardia) the number of occurrencesand the total time in that division in a particular epoch arestored in the disc file at the end of that epoch. The system willnot detect apnoeic pauses longer than 50 s.

If the mean peak-to-peak amplitude of the respiratorywaveform (measured over 50 s) has fallen to the level at whichbreaths can no longer be adequately detected then this isrecorded as part of the results of the analysis for that epoch.This situation can arise if the respiration sensor has becomedetached from the patient or if a very long apnoeic episodehas occurred. By outputting this information to the disc filewith the results for each epoch it is possible to refer back tothe chart paper printout of the original data produced duringthe analysis to determine which of these possibilities actuallyoccurred.

3 Output from system

On completion of the processing of a tape there is a recordof the results from each epoch analysed in a disc file. Using theinformation contained in the disc file the results are printedand plotted out. The complete output from the analysisfalls into two distinct parts:

(i) the 'trend plot'(ii) the numerical summary.

Each part of the output will now be described in turn. Itshould be noticed that as all the information required toproduce these is contained in a disc file it is possible to obtainduplicates or to perform further processing of the results.

3.1 'Trend plot'An annotated trend plot is given in Fig. 6. The trend plot isessentially a plot of a summary of the results for each epochagainst time. Each vertical column of entries represents theresults of the analysis for one epoch. The results for thetime interval between breaths and the instantaneous heartrate are presented in the same way — a cross to indicate themedian value and a bar to indicate the interquartile range.The amount of patient movement is presented as a bar, theheight of which gives the percentage of the epoch time forwhich patient movement was detected. For the purposeof the trend plot, apnoea is divided into two classifications:those pauses longer than 5 s with bradycardia and those pauseslonger than 10 s without bradycardia. In each classification theheight of the bar gives the total duration of apnoea in thatclassification. A period of low-amplitude respiratory waveform

is indicated by an asterisk at the very top of the trend plotentry for that epoch.

3.2 Numerical summaryThe numerical summary (Fig. 7) may be considered to be intwo parts. The first part is a listing of the apnoeic attackslonger than 20 s that were detected during the analysis of therecording; the listing also indicates whether they were ac-companied by bradycardia.

The second part of the numerical summary consists ofattaching single numerical values for the whole recording tothe parameters analysed. The arithmetic mean values of themedian and interquartile ranges of the breath-to-breath intervalresults and the instantaneous heart rate results are calculatedover all the epochs analysed. In addition, the total number ofapnoeic attacks in each classification which occurred duringthe recording are counted and their mean duration calculated.The results for bradycardia and tachycardia are processed in asimilar way.

4 Conclusions

The computerised system described has proved, through theanalysis of over 400 tapes, to be reliable. However, the com-puter can never mimic the sophisticated pattern recognitionsystem of the eye and brain. Long apnoeic episodes, move-ment artefact and sensor faults are much more easily recognisedby the eye than by the automated system. The machine willprovide information that is impossible to obtain by eye —the time interval between breaths and the instantaneous heartrate values — and hereby lies its strength. The storage of theresults for each epoch means that it is possible to performstatistical analysis and further processing of the data shouldthis be required.

The effectiveness of the breath detection system has beenassessed by comparing the results with a visual analysis [11],and has been shown to be 99% successful at detecting breathsand 85% successful at identifying movement artefact. However,there is a lack of an absolute 'standard' against which suchalgorithms can be evaluated.

SHEFFIELD PREP HEGLTH RUTHORITT - OEPRRTMENT OF MEOICOL PHfSICS

rs

9-

i5 o-i

% 9-

h

How Ampli'

Instantaneous Heart Rate (B.P.M.l

iplitude Respiratory Signal

,1 ,'i

Bradycardic episode of more Tachycardic episode of more than 10 beatsthan 5 beats I

Apnoea with Bradycardia

Apnoea without Bradycardia

Time Interval Between Breaths (TIBB)

Patient Movement f t segment time)

HRS16 17

COOE MO: 51 OS

Fig. 6 'Trend plot' output

This form of presentation of the results allows changes in the 'pattern'of the respiration and the instantaneous heart rate to be easily seen

IEEPROC, Vol. 129, Pt. A, No. 9, DECEMBER 1982 705

SHEFFIELD AREA HEALTH AUTHORITY - DEPARTMENT OF MEDICAL PHYSICSRESPIKATION ANALYSIS SYSTEM• •• SUMMARY OF RESULTS •••

CODE NUMBER 6021

APNOEA OF MORE THAN 20 SECONDS OCCURRED AT THE FOLLOWING TIMES AFTER THE START OF THE RECORDING

1 APNOEIC EPISODE OF 26 SECONDS DURATION OCCURRED AT 23 6 WITH BRADYCARDIA

(MEAN DURATION 7S )

iMEAN DURATION 10S

(MEAN DURATION OS )

(MEAN DURATION O

(MEAN DURATION OS I

TIBB RESULTS MEAN MEDIAN 1H6MS MEAN IOR 334MS

THE NUMBER fiF APNOEIC EPISODES BETWEEN 5 AND 10S WITH BRADYCARDIA 2

THE NUMBER OF APNOEIC EPISODES BETWEEN H"> AND 15S WITHOUT BRADYCARDIA 1

THE NUMBER OF APNOEIC EPISODES BETWEEN 10 AND I5S WITH BRAD*CARDIA 0

THE NUMBER OF APNOEIC EPISODES BETWEEN 15 AND iOS WITHOUT BRADYCARDIA 0

THE NUMBER riF APNOEIC FPISODES BETWEEN IS AND 20S WITH BRADYCARDIA O

THE TOTAL NUMBER OF BRADYCAROIC BEATS 80«

THE NUMBER fiF ERADYLBRDK EPISODES OF MORE THAN 5 BEATS O (MEAN DURATION OEEATS)

THE TOTAL NUMBER OF TACHVCARDK BEATS 13772

THE NUMBER UF IAI.HVCARDIC I ML 1 DENTS OF MORE THAN 10BEATS 177 (MEAN DURATION 17BEAT

INSTANTANEOUS HFART RATE RESULTS MEAN MEDIAN 154B P M MEAN I OR 1 IB P M

THE LOWEST VALUE OF I NS.TANTANEOUS HEART RATE 24B P M

Fig. 7 Numerical summary of results

These factors allow statistical comparisons to be made between groups of infants

The system described in this paper has been in use for thepast 3 years. During this period the advances in microprocessortechnology and the availability of software support for suchdevices has now made multiprocessor systems an attractiveproposition. We are currently working on a multiprocessorimplementation of the system described in this paper, in whichone processor will detect breaths from the respiratory wave-form and perform the preprocessing of the heart rate data, anda second processor will perform the statistical analysis. Suchan approach will greatly extend the complexity of processingthat can be performed to characterise the data and permitgreater shortage of intermediate results.

5 References

1 NAEYE, R.L.: 'Hypoxia and the sudden infant death syndrome',Science, 1974,186, pp. 837-838

2 NAEYE, R.L.: 'Brain stem and adrenalin abnormalities in the suddeninfant death syndrome', Am. J. Clin. Path., 1976, 66, pp. 526-530

3 BERGMANN, A.B., BECKWITH, J.B., and RAY, C.G.: 'Proceedingsof the second international conference on causes of sudden deathin infants' (University of Washington Press, Seattle, Washington,1970), p. 248

4 HOFFMANN, E., HAVENS, B., GEIDEL, S., HOPPENBROUWERS,T., and HODGEMAN, J.E.: 'Long term monitoring of multiplephysiological parameters in newborn young infants', Acta. Paed.Scand., 1977, Suppl. 266

5 WRIGHT, B.M.: 'An abdominal respiration sensor', /. Physiol.,June 1977, pp. 11-12

6 FRANKS, C.I., BROWN, B.H., and JOHNSTON, D.M. 'Contactlessrespiration monitoring of infants', Med. & Biol. Eng., 1976, 14,pp. 306-312

7 SOUTHALL, D.P., RICHARDS, J.M., JOHNSTON, P.G.B.,de SWIET, M., and SHINEBOURNE, E.A.: '24 hour tape recordingsof the E.C.G. and respiration in the newborn infant with findingsrelated to sudden death and unexplained brain damage in infancy',Arch. Dis. Child., 1980, 55, pp. 7-16

8 KELLY, D.H., SHANNON, D.C., and O'CONNELL, K.: 'Care ofinfants with near-miss sudden infant death syndrome', Pediatrics,1978, 66, pp. 511-514

9 FRANKS, C.I., WATSON, J.B.G., BROWN, B.H., and FOSTER, E.F.:'Respiratory patterns and risk of sudden death in infancy', Arch. Dis.Child., 1980, 55, pp. 595-599

10 WILSON, A.J., FRANKS, C.I., and FREESTON, I.L.: 'Methods offiltering the heart-beat artefact from the breathing waveformobtained by impedance pneumography', Med. & Biol. Eng. &Comput., 1982, 20, pp. 293-298

11 WILSON, A.J., FRANKS, C.I., and FREESTON, I.L.: 'Algorithmsfor the detection of breaths from respiratory waveform recordingsof infants', ibid., 1982, 20, pp. 286-292

12 BRYDON, J.: 'Automatic monitoring of cardiac arrythmias' inHILL, D.W., and WATSON, B.W (Eds.): 'IEE Medical ElectronicsMonographs 18-22' (Peter Peregrinus, 1976), pp. 18-22

13 SMALLWOOD, R.H.: 'An instantaneous ratemeter for low fre-quency signals', Electron. Eng, 1976, 50, p. 27

A.J. Wilson was born on 4th September1953, and obtained a B.Sc. in electronicengineering from the University of Sussexin 1976 and a Ph.D. from the Universityof Sheffield in 1982. Currently, Dr.Wilson is a physicist in the Department ofMedical Physics & Clinical Engineering atthe Royal Hallamshire Hospital, Sheffield.His main research interests include non-myasive physiological monitoring tech-niques and the processing and analysis

of physiological signals.

Dr. C.I. Franks obtained a B.Sc. inphysics at Southampton University in1966 and a Ph.D. at Sheffield MedicalSchool in 1977 on noninvasive respir-atory monitoring and analysis of long-term recordings. He is Head of theComputing Section at the SheffieldDepartment of Medical Physics & ClinicalEngineering, with interests in the appli-cation of computers and microprocessorsin a number of areas, including respiration,

blood flow and biomechanics.

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