Patent Application: US-48815004-A

Abstract:
the invention resides in a respiratory sonification monitoring method and system for monitoring respiration in a subject including the use of capnometric means for measuring carbon dioxide concentrations , flowmeter means for measuring gas flow and volume of gas ; means adapted to process into digital information , signal output from the capnometric means and the flowmeter means , sound synthesizer means adapted to convert the digital information into synthesised audio output , wherein , changes in respiratory flow during inhalation and exhalation , and changes in end tidal carbon dioxide concentrations and cumulative tidal volume of the subject can be represented as changes in synthesised sound heard through a loudspeaker , headphone or ear piece .

Description:
our respiratory sonification collects digital information about each of several respiratory parameters from a patient monitoring system or ventilator . it then maps the information about each respiratory parameter into a specific value of a specific auditory dimension that has been designated for that respiratory parameter . the onset of inhalation , and therefore of a new breath , and the duration of inhalation and the duration of exhalation are all determined from ventilator sensors such as the volume flowmeter . the onset of inhalation and the onset of exhalation define the periods over which component parts of the respiratory sonification should be played ( see arrows 1 and 2 on fig1 ). any movement of air in or out of the lungs is represented as a base sound stream . in our initial investigations we have used a pure tone as the base sound stream . however other waveforms or combinations of waveforms could be used as long as they do not sound so similar to natural respiration that they might be confused for it ( see points below ). the difference between movement into the lungs ( inhalation ) and out of the lungs ( exhalation ) is represented as a consistent difference in sound quality . in our initial investigations we have distinguished inhalation and exhalation as follows . inhalation is represented as the upper note of a musical third interval and exhalation is represented as the lower note ( see point 3 on fig1 ). however other ways of acoustically separating inhalation and exhalation could be used , such as other musical intervals . etco 2 is always measured at the end of a breath . therefore it is not possible to provide information ( auditory or visual ) about co2 concentration related to the exact breath on which it was measured . etco 2 is only available after the breath on which it was measured . therefore the etco 2 of a breath at time t - 1 is mapped to the sonification of the breath at time t . the relation between etco 2 , other variables , and the sonification produced is shown in fig1 . our respiratory sonification maps etco 2 into the relative pitch of the inhalation and exhalation sound ( see point 4 on fig1 ). in the example we have tested , etco 2 is represented as a frequency modulation of the minor third interval produced by the inhalation and exhalation . in other words , inhalation and exhalation are always in the same musical relationship or the same distance apart in pitch , but the relative pitch of the inhalation exhalation pair varies with etco 2 . finally , the respiration sonification needs to indicate tidal volume . tidal volume ( vt ) is a similar parameter to etco 2 — it is only known at the end of the breath . therefore , if vt is to be mapped to the sonification , like etco 2 it will be giving information about the breath immediately prior . we propose using instead volume flow ( vf ) or the reading of the flowmeter which can be fitted to the y - piece of the anaesthesia circuit ( see points 5 and 6 on fig1 ). vf gives immediate information about the rate at which gas is flowing to the patient &# 39 ; s lungs during the current breath , and so is available to be mapped to the current breath sonification in real time . vf integrated over time gives cumvt . we propose that cumvt be mapped into a mixture of sound intensity and sound quality ( timbre or brightness ). at present we have evaluated cumvt ( against vt — see example 1 ) both mapped only into sound intensity ( see example 1 ). fig1 shows that the inhalation and exhalation are depicted as being broader near their respective midpoints . this is not to be taken literally as a broadening of the sound spectrum with cumvt , but instead to indicate the presence of greater sound intensity and / or an increase in the harmonic complexity ( increased presence of the harmonics of the base frequency of the sound ). the significance of our respiratory sonification compared with prior examples lies in the following factors . our respiratory sonification is intended to work alongside existing commercial pulse oximetry systems rather than being part of a system for sonifying a broader range of physiological variables including cardiovascular variables . in previous art it has not been noted that because capnometry ( end tidal carbon dioxide level ) and tidal volume ( breath volume ) are not known until the end of the current breath , then they can only be sonified in the next breath . we therefore present a method for sonifying volume flow of the current breath , rather than sonifying tidal volume of the previous breath . this provides immediate acoustic information to the listeners about respiratory functioning on the current breath . within each inhalation and exhalation , volume flow is sonified so that it ranges from zero to the maximum seen for inhalation and exhalation rather than playing at a constant sound intensity and brightness . the distance the sound travels from its baseline therefore provides useful relative information rather than requiring the listener to judge overall tidal volume on the basis of an absolute and constant presentation of a specific sound intensity and brightness . because a sonification can never capture full array of clinical signs associated with respiration , we propose that using complex naturalistic sounds to represent respiration , such as a breathing or white noise sound , may be actively misleading . we therefore present as the base sound for respiratory sonificationan artificial sound that will not run the danger of being mistaken for the patient &# 39 ; s actual breathing . the distinction between inhalation and exhalation is presented as two pure notes played in sequence , with constant acoustic differentiation between them . our respiratory sonification operationalises this with the first note ( inhalation ) as the upper note of a musical third and the second note ( exhalation ) as the lower note of a musical third . variants are possible . therefore our sonification is not a method and interface for allowing the user to select which variables are sonified or how they are sonified , as in u . s . pat . no . 5 , 730 , 140 . a major distinction with u . s . pat . no . 5 , 730 , 140 is that the present method , unlike the former , is not based on generating or simulating a realistic bodily sound , but relies on the production of a synthesised audible tone . the use of synthetic tonal sound has two major advantages over the simulation of natural breathing sounds . first , it is much more free from masking by other sounds found in the operating theatre , such as suctioning . second , the tonal sound avoids the incorrect association caused by operators mistaking the sound as an audification ( a natural sound that has simply been amplified and transformed ) of the patient &# 39 ; s natural chest sounds . third , specifying the mapping of respiratory parameters to sound dimensions , rather than allowing the anaesthetist to select the mapping of physiological parameters to sound dimensions , supports other operating theatre staff members &# 39 ; awareness of the patient &# 39 ; s physiological state because non - anaesthetists will be able to learn to identify abnormal patient states from the combination of the respiratory and pulse oximetry sonifications . furthermore , u . s . pat . no . 5 , 730 , 140 does not provide information whereby etco 2 is represented by changes in relative pitch across the respiratory cycle such that the exhalation tone is set at a minor third interval below that of the inhalation tone and wherein high value etco 2 changes are represented by a change to a high pitch and low value etco 2 changes by a change to a low pitch . in addition , the calculation of cumvt is also not possible with u . s . pat . no . 5 , 730 , 140 . our respiration sonification uses two sensing systems : capnometry and the volume flowmeter . it takes digital output from these systems , adjusts the output to the ranges needed , maps the result to a dimension of sound accessible through a sound synthesizer , converts the information to sound using a sound synthesizer , and then provides audio output either through loudspeakers , headphones , or through an ear piece worn by the anaesthetist . there are many possible sound synthesis solutions for rendering our respiration sonification . our claim is for the design of the sonification itself rather than the means of implementing it . capnometry exists on most anaesthesia machines and most ventilators . a sensor in the breathing circuit provides a continuous signal for analysis of the concentration of co2 in expired gases and an end tidal co2 value is supplied by end expiration . our respiratory sonification system will use the capnography output from a serial port or through internal software communication , as it is expressed in mmhg ( millimeters mercury ) ( see box a of fig2 ). volume flow can be measured through a standard flowmeter that can be connected into the y - piece of the external breathing circuit ( see box b of fig2 ). the analogue signals from the flowmeter are converted into continuous digital outputs for inhaled or inspired volume flow ( vfinh ) and for exhaled or expired volume flow ( vfexh ) and are expressed as a flowrate of ml / minute ( milliliters per minute ) ( see box c of fig2 ). volume flow is integrated over time to yield cumulative vt , which is the value mapped to the sonification parameter as shall be discussed . the signal processing and mapping unit ( see box d of fig2 ) prepares the digital information received to be sent to the sound synthesizer . the unit takes the digitized output of the flowmeter , which provides information about the start of inhalation ( start of flow of gas from ventilator towards patient ) and the start of exhalation ( start of flow of gas from patient towards ventilator )., the unit also receives digitized information about etco 2 in mmhg at the end of each exhalation . the information is converted to a form that can be read by the sound synthesizer . specifically , the onset and offset of inhalation and exhalation are represented as tones . the absolute value of volume flow is represented as the sound intensity ( loudness ) and / or sound quality ( timbre or brightness ), the values being read from a mapping function . the etco 2 value from the last breath is mapped to the pitch of the inhalation tone , the actual pitch value being read from a further mapping function . the pitch of the exhalation tone is set to a musical third interval below the inhalation tone . the resulting information is sent to the sound synthesizer ( see box e of fig2 ). at the start of inhalation , the synthesizer plays a tone determined as described above . at the start of exhalation , the synthesizer plays a tone of the kind described above . the results are sent in real time to audio output ( see box f of fig2 ). as the absolute value of volume flow increases , sound intensity ( loudness ) and / or sound quality ( timbre or brightness ) increase , giving rise to a perception of rising and falling brightness of the sound within the inhalation and within the exhalation , and / or a rise and fall of loudness of the sound . the user interface ( see box g of fig2 ) allows the user to adjust the overall volume of the respiratory sonification . the mapping of carbon dioxide concentration in mmhg to hz is a non - linear function . the normal range for etco 2 is conventionally 38 – 42 mmhg . across this range , mmhg is mapped to hz in a way that produces very little change in hz for a change in mmhg , producing a flat or nearly flat region in the mapping function . when mmhg moves lower than 38 or greater than 42 , mmhg is mapping to hz in a way that produces significant change in hz for a change in mmhg , producing steep regions in the mapping function until extreme mmhg values are reached at which the function flattens and stays constant at extreme hz values . the result is a piece - wise defined function whose : general form is illustrated in fig3 . the mapping of cumulative vt to sound intensity ( loudness ) and / or sound quality ( timbre or brightness ) is performed according to the general schema shown in fig4 . as cumulative vt increases , sound intensity increases and / or the relative intensity of sound in the lower harmonics vs higher harmonics changes . the auditory perception of low cum vt is of a rounded sound of audible volume , whereas the perception of high cum vt is of a much sharper , brighter sound and / or somewhat louder volume . as the value of cum vt changes within the inhalation or exhalation , the above changes to sound loudness and / or quality occur . h1 at least one sonification of respiratory parameters as described above will support judgements about respiratory status as effectively as pulse oximetry supports judgements about cardiovascular status . h2 if respiratory sonification presents physiological information in a , way that is consistent with expert knowledge , then anaesthetists will perform better . h3 percentage correct responses for different physiological parameters will be sensitive to the changes in event rate . h4 percentage correct responses for different physiological parameters will be sensitive to the base rate probabilities of changes for different parameters . h5 when participants perform a cognitively loading primary task , they will be able to monitor patient physiological status more effectively when patient data is sonified than when it is visually supported . the invention will now be further described by way of reference only to the following non - limiting examples . it should be understood , however , that the examples following are illustrative only , and should not be taken in any way as a restriction on the generality of the invention described above . in comparing a preferred method of respiratory sonification with two older versions we made one hypothesis : “ at least one sonification of respiratory parameters will support judgements about respiratory status as effectively as pulse oximetry supports judgements about cardiovascular status .” all sonifications used a pure tone and mapped inhalation and exhalation to the upper and lower note of a musical third . respiration rate was represented by a direct temporal mapping of inhalation and exhalation . for the “ varying ” sonification , cumvt was represented by sound intensity ( but our proposed respiratory sonification also maps sound quality ( timbre or brightness ) into cumvt ). the integration of volume flow over time gave tidal volume . etco 2 was represented by a frequency modulation of the inhalation : exhalation minor third . three variants were tested : ( 1 ). the varying sonification worked as described above , mapping etco 2 from the previous breath to the pitch of inhalation on the current breath , and using current breath volume flow to ramp sound intensity up to the peak which indicated the vt for the present breath for inhalation or exhalation . ( 2 ). the even sonification was developed to test whether participants found it difficult to judge tidal volume by listening to the integration of volume flow of gas over time . the even sonification used etco 2 and tidal volume ( vt ) from the previous breath . etco 2 was mapped to the pitch of inhalation on the current breath and tidal volume was used to determine a constant , or even , sound intensity level for the inhalation and exhalation . the short sonification was developed to test whether participants found it difficult to extract information about respiration rate until the end of the breath . the short sonification triggered the respiratory sonification at the start of each inhalation , as for the varying and even sonifications but it compressed the duration of the sonification into approximately one - third of its normal duration . at the same time , it preserved the relative duration of longer and shorter breaths and the ratio of inhalation to exhalation . the goal of developing the short sonification was to see if performance was better when information about respiration rate could be extracted over a shorter period than the entire breath itself . our immediate focus was whether the mapping of physiological parameters to sound was intelligible . the study used a within - subjects design and participants were 23 tertiary - educated members of the general population with no medical or nursing training . participants listened to an anaesthesia scenario produced from an anaesthesia simulation created from the body ™ library , schil &# 39 ; s arbiter experimental environment providing the interface ( watson et al ., 1999 ; 2000a ; 2000b ). when probed at semi - regular intervals , participants reported whether each parameter was abnormally high , normal , or abnormally low , and whether it was rising , steady , or falling . for volume , they were not asked about volume flow but about cumulative tidal volume , which is the integration of volume flow over time . results for the percentage of correct responses are shown in fig5 . there is an apparent superiority of pulse oximetry measures ( hr and o2 ), but a deeper analysis of the results indicated to us that the a priori probability of signals strongly influenced the percentage of correct responses . seldom - experienced changes had a higher proportion of “ correct rejection ” responses , artificially inflating the probably of correct responses overall . we performed signal detection studies to separate discriminability from response bias , but the test with probably the greatest ecological validity is the following . in a conservative test of the intelligibility of sonified physiological parameters , we sought how many participants performed significantly better than chance given the number of answer alternatives , p ( chance ), and significantly better than would be expected if participants were responding randomly but with a response bias consistent with the base rate of non - normal or non - steady events , p ( correct / baserate ). in each case , the chance / baserate value had to lie in the lower 5 % tail of a subject &# 39 ; s distribution of responses for that item , in order for us to state that the participant had performed better than chance . as the results in table 1 show , the varying sonification supported best performance across kinds of judgement , across tests , and across physiological parameters , although there were minor points where another of the three sonifications was superior . number of participants for each sonification whose judgements were significantly better than chance . underlined figures in white squares indicate that the number of participants is significantly different from chance at p & lt ; 0 . 05 ; plain figures in white squares at 0 . 1 & gt ; p & gt ; 0 . 05 . figures in grey cells indicate that results were not significantly different from chance . the varying sonification supported better judgements of abnormality that the other two sonifications , with rr and co2 abnormalities apparently detected better than pulse oximetry measures hr and o2 . moreover , the varying sonification supported better across - the - board detection of directional changes , whereas the even sonification was not quite as good for rr and the short sonification was not quite as good for vt . it is important to note that the varying sonification led to markedly better performance . we used the varying sonification to test the relative performance of anaesthetists ( medically trained ) and postgraduate students in information technology who had no physiology training . the example used anaesthesia scenarios that led to a more even distribution of abnormal events across the five parameters , and also to a greater absolute number of abnormal events or directional changes to report . our hypotheses were : h2 if the varying sonification presents physiological information in a way that is consistent with expert knowledge , then anaesthetists will perform better than non anaesthetists . h3 percentage correct responses for physiological parameters will be sensitive to the different overall event rates in example 1 and example 2 . h4 percentage correct responses for different physiological parameters will be sensitive to the base rate probabilities of changes for different parameters . results of a between - within subjects anova indicated that h2 was supported : anaesthetists performed better than it postgraduates for judgements both of abnormality and of direction of change , f ( 1 , 19 )= 20 . 604 , p & lt ; 0 . 001 and f ( 1 , 19 )= 10 . 341 ; p & lt ; 0 . 01 , respectively . h3 was also supported . to compare relatively similar participant groups , the results of the general population in example 1 must be compared with those of the it postgraduates in example 2 . the number of abnormal events per trial was 2 . 2 in example 1 and 3 . 4 in example 2 and the number of directional changes per trial was 2 . 5 in example 1 and 4 . 1 in example 2 . percentage correct responding was lower in example 2 than example 1 : for abnormality judgements , f ( 1 , 31 = 23 . 8 , p & lt ; 0 . 0001 , and for directional judgements , f ( 1 , 31 )= 61 . 59 , p & lt ; 0 . 0001 . the lower accuracy of responding should not suggest that the sonifications are inherently unreliable . the event rate was sustained at a rate that would be seen only in the busiest parts of the most intense clinical situations . clearly , pulse oximetry is effective in clinical settings , so the respiratory sonification should be compared with it in the current context . h4 was also supported . in example 2 events were more evenly distributed across pulse oximetry ( hr and o2 ) and respiration ( rr , vt and co 2 ) to reduce the previous bias towards respiration . fig5 shows that the bias towards correct responding for hr and o2 is reduced . the respiratory sonification requires participants to distinguish three parameters whereas pulse oximetry requires them to distinguish only two parameters . therefore the respiratory sonification is performing especially well if it produces performance in a comparable range to pulse oximetry . table 2 repeats the highly conservative analysis of intelligibility performed in example 1 , but for the 11 anaesthetists and 10 it postgraduates tested in example 2 . results suggest that anaesthetists perform better than it postgraduates and that the respiratory sonification is doing as well if not better than pulse oximetry . discrimation of directional changes was less effective for hr and vt . number of participants from each group whose judgements were significantly better than chance . underlined figures in white squares indicate that the number of participants is significantly different from chance at p & lt ; 0 . 05 ; plain figures in white squares at 0 . 1 & gt ; p & gt ; 0 . 05 . figures in grey cells indicated that results were not significantly different from chance . total number of anaesthetists is 11 and of it postgraduates is 10 . overall , the results of example 2 obtained with no prior experience by anaesthetists or it postgraduates to respiratory sonification and only 15 minutes of initial familiarisation , are highly encouraging of the viability of respiratory sonification for clinical settings . in this example we examined participant &# 39 ; s ability to use the respiratory sonification when performing other tasks . monitoring was supported by sonification only , sonification plus a visual display , and a visual display only . the hypothesis was as follows : h5 when participants perform a cognitively - loading primary task , they will be able to monitor patient physiological status more effectively when patient data is sonified than when it is visually supported . to test this hypothesis we constructed a very conservative test . participants performed a primary task in which they were required to make true / false judgements about simple arithmetic expressions ( see fig6 ) with a new expression occurring every 10 seconds . this had some similar characteristics to drug dosage calculations that a clinician might need to perform while monitoring patient status . at the same time , they performed the secondary physiological monitoring task , as in previous examples . the physiological monitoring task was presented in one of three formats , varied within - subjects : sonification alone ( s condition ). no patient information appeared or could be called up on the computer screen . visualization alone ( v condition ). physiological readouts were not continually present . participants had to touch (“ query ”) the relevant part of the screen to see the current value for five seconds (“ withholding ” procedure ). sonification plus visualization ( sv condition ). again , the visualization condition used the withholding procedure . the 10 - second arrival rate of arithmetic problems gave participants in the v and sv conditions enough time to query all parameters before the next arithmetic expression appeared . although sonification is intended to provide information where visual information is unavailable or inconvenient , in this first test we set up the best possible conditions for the visual display to succeed . if s nonetheless leads to superior performance , then such superiority would have been observed under conditions in which it is possible to do the task visually to the same level of performance . if s leads to the same level of performance as for v , however , then we know that sonification does not support worse performance than a visual display does . the results will therefore provide a conservative baseline comparison between the three conditions . it is assumed thereafter that as the time between arithmetic problems decreases , the s and probably also the sv conditions would show increasingly effective monitoring performance . participants experienced plausible operating room scenarios . approximately every minute , participants were asked to make judgements about any recent abnormality and any direction of change within the last minute on one of the five parameters . questions were evenly distributed across parameters , so there was no in - built bias in questioning towards any parameter . primary ( arithmetic ) task performance was analysed using a between - within subjects anova . results are shown in fig7 a and b on the x axis of each graph . there was a significant effect of group , f ( 1 , 19 )= 9 . 54 , p & lt ; 0 . 01 , with anaesthetists performing better than it postgraduates . modality was significant , f ( 2 , 38 )= 10 . 05 , p & lt ; 0 . 001 , with responses most accurate in the s condition , followed by v and sv . a newman - keuls analysis showed that responding in the s condition was significantly more accurate than in either the v or the sv conditions . results for secondary task abnormality judgements are shown on the y axis of fig7 a . group was highly significant , f ( 1 , 19 )= 44 . 69 , p & lt ; 0 . 0001 , with anaesthetists much better than it postgraduates at reporting abnormalities . modality was significant , f ( 2 , 38 )= 5 . 69 , p & lt ; 0 . 01 , with performance worst with the s condition , but only because of it postgraduates &# 39 ; poor performance . in fact , modality interacted strongly with group , f ( 2 , 38 )= 6 . 49 , p & lt ; 0 . 01 . as fig7 a and 7 b show , anaesthetists judged abnormalities equally well across all three modalities whereas it postgraduates did particularly poorly with s alone . parameter was also significant , f ( 4 , 76 )= 13 . 83 , p & lt ; 0 . 0001 , with vt showing worst performance and hr showing best performance ( see fig8 a ). however the poorer performance with vt was much reduced for anesthetists compared with the it postgraduates , contributing to a group by parameter interaction , f ( 4 , 76 )= 4 . 08 , p & lt ; 0 . 01 . results for secondary task judgements of direction of change are shown on the y axis of fig7 b . group was significant , f ( 1 , 19 )= 41 . 06 , p & lt ; 0 . 0001 , with anaesthetists much better than it postgraduates . there were no main effects or interactions with modality , but parameter was significant , f ( 3 , 76 )= 6 . 06 , p & lt ; 0 . 001 , with vt showing worst performance ( see fig8 b ). there was also a significant drop in the frequency of querying in condition sv compared with v , but only for anaesthetists , and then only for hr and o2 , the most familiar pulse oximetry sonifications . with further experience , and with performance feedback , participants would probably develop greater self - confidence in their ability to extract information from sonification and reduce visual monitoring for respiratory parameters as well . whether this is the case is currently under investigation . overall , example 3 showed that sonification allowed anaesthetists to maintain monitoring accuracy while performing significantly better on an arithmetic task than when visual support was available . it postgraduates showed a tradeoff between the monitoring task and the arithmetic task . sonification led to worse monitoring performance , probably because of it postgraduates &# 39 ; lack of physiological training , but to better performance on the arithmetic task , probably because no further attention to the monitoring task would improve performance . vt appears to be slightly less effective than other sonified dimensions in examples 1 and 2 , where sonification with no redundant visual information is examined . however that fact that in example 3 judgements about vt have equivalent levels of accuracy across v , sv , and s conditions suggests that the less accurate performance with vt is endemic to vt itself rather than to any modality . given the scenarios used , vt shows many small fluctuations making it more challenging to monitor rather than a few large fluctuations , which would affect all modality conditions . in fact , anaesthetists abnormality judgements for vt appear to be somewhat better supported with sonification alone ( s condition ) than others . taken together , the results of examples 1 to 3 show that with a minimal level of familiarization , participants can monitor respiratory status with the varying respiratory sonification as well as they can monitor cardiovascular status with the pulse oximetry sonification . example 3 shows that when anaesthetists carry out a distracting task at the same time as monitoring , as is often the case in the operating room , sonification helps them time - share . however , instead of boosting monitoring performance while a distracting task is done , sonification allowed anaesthetists &# 39 ; monitoring performance to be sustained at high levels while performance at the distracting task become better . in ongoing research we are graduating the level of difficulty of the primary task and expect to see greater differences emerge across conditions for patient monitoring , increasingly favouring sonification . we are also adding brightness to the sonification for vt to determine its effects . results of our studies also suggest that researchers must be cautious about making claims about the superiority or otherwise of specific display conditions until the influence of a priori event probability and the size of changes has been taken into account . the relative accuracy of judgements for the five physiological parameters various across examples 1 , 2 and 3 because of these factors . in summary , sonification of patient physiology beyond traditional pulse oximetry appears a viable and useful adjunct when monitoring patient state . sonification may help anaesthetists maintain high levels of awareness of patient state and at the same time do other tasks more effectively than when relying upon visual monitoring of patient state . the varying respiration sonification appears to be effective in this role and it presents some advantages over related respiratory sonifications . fitch , w . t ., & amp ; 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