Device and Method for Assessing Respiratory Data in a Monitored Subject

Abstract

Disclosed is a method and device for assessing respiratory data in a monitored subject. The disclosed method comprises collecting respiratory data of the subject at different levels of exertion with a physiological monitoring system (15-19), the respiratory data at least relating to instantaneous lung volume and comprising the end expiratory lung volume (EELV) after expirations; collecting exertion level data of the subject at the different levels of exertion, the exertion level data at least relating to instantaneous oxygen demand and/or heart rate; establishing a parametric relation (14, 15) between the collected respiratory data and the collected exertion level data, the parametric relation being described by one or more parameters; and assessing the respiratory data of the subject in terms of the value of the one or more parameters. The method and device allow a reliable measuring of dynamic hyperinflation in subjects without requiring much attention on the part of the subject.

Claims

1. A method for assessing hyperinflation in a monitored subject, the method comprising: collecting respiratory data of the subject at different levels of exertion, the respiratory data at least relating to instantaneous lung volume, and comprising a plurality of end expiratory lung volumes (EELV) after expirations; collecting a plurality of exertion level data of the subject at the different levels of exertion, the exertion level data at least relating to instantaneous oxygen demand, such as heart rate and/or breathing frequency data; establishing a parametric relation between the collected plurality of respiratory data and the collected plurality of exertion level data, the parametric relation being described by one or more parameters; and assessing the presence of hyperinflation in the subject in terms of the value of the one or more parameters.

2. The method according to claim 1, where assessing the presence of hyperinflation in the subject in terms of the value of the one or more parameters comprises assessing dynamic hyperinflation in the monitored subject.

3. The method according to claim 1, wherein establishing a parametric relation between the collected plurality of respiratory data and the collected plurality of exertion level data comprises establishing a linear parametric relation between the collected respiratory data and the collected exertion level data.

4. The method according to claim 3, wherein establishing a linear parametric relation between the collected respiratory data and the collected exertion level data comprises establishing a gradient of the linear parametric relation.

5. The method of claim 1, wherein collecting respiratory data of the subject at different levels of exertion comprises collecting the respiratory data by respiratory plethysmography, including respiratory inductive plethysmography.

6. The method of claim 1, wherein collecting respiratory data of the subject at different levels of exertion comprises collecting the exertion level data that relate to oxygen demand as heart rate as measured by a heart rate measuring device.

7. The method of claim 1, wherein collecting a plurality of exertion level data of the subject at the different levels of exertion, the exertion level data at least relating to instantaneous oxygen demand, such as heart rate and/or breathing frequency data comprises collecting the exertion level data that relate to oxygen demand from the respiratory data.

8. The method according to claim 7, wherein collecting the exertion level data that relate to oxygen demand from the respiratory data comprises collecting the exertion level data that relate to oxygen demand as breathing frequency, obtained from the respiratory data.

9. The method according to claim 7, wherein collecting the exertion level data that relate to oxygen demand from the respiratory data comprises collecting the exertion level data that relate to oxygen demand as a Time of Inspiration (TI), obtained from the respiratory data.

10. The method of claim 7, wherein collecting the exertion level data that relate to oxygen demand from the respiratory data comprises collecting the exertion level data that relate to oxygen demand as a Time of Expiration (TE), obtained from the respiratory data.

11. The method of claim 1, further comprising collecting data related to the posture of the monitored subject.

12. The method according to claim 11, wherein collecting data related to the posture of the monitored subject comprises collecting instantaneous 3D shape data of the torso of the monitored subject.

13. A device for assessing hyperinflation in a monitored subject, the device comprising: respiration monitoring means for collecting respiratory data of the subject at different levels of exertion, the respiratory data at least relating to instantaneous lung volume and comprising a plurality of end expiratory lung volumes (EELV) after expirations; exertion level monitoring means for collecting exertion level data of the subject at the different levels of exertion, the exertion level data at least relating to instantaneous oxygen demand and comprising a plurality of exertion level data; and computing means for establishing a parametric relation between the collected plurality of respiratory data and the collected plurality of exertion level data, the parametric relation being described by one or more parameters; and assessing the presence of hyperinflation in the subject in terms of the value of the one or more parameters.

14. The device according to claim 13, where the computer means establishing a parametric relation between the collected plurality of respiratory data and the collected plurality of exertion level data, the parametric relation being described by one or more parameters; and assessing the presence of hyperinflation in the subject in terms of the value of the one or more parameters comprises a computer means for assessing dynamic hyperinflation in the monitored subject.

15. The device according to claim 13, wherein the respiration monitoring means comprises a respiration means for monitoring a linear parametric relation between the collected respiratory data and the collected exertion level data.

16. The device according to claim 15, wherein the respiration means for monitoring a linear parametric relation between the collected respiratory data and the collected exertion level data comprises a respiration means for monitoring a gradient of the linear parametric relation.

17. The device of claim 13, wherein the respiration monitoring means comprise respiratory plethysmographic sensors, including respiratory inductive plethysmographic sensors.

18. The device of claim 13, wherein the exertion level monitoring means comprises a heart rate measuring device.

19. The device of claim 13, wherein the exertion level monitoring means comprises the respiration monitoring means.

20. The device according to claim 19, wherein the exertion level monitoring means collects breathing frequency, obtained from the respiration monitoring means.

21. The device according to claim 19, wherein the exertion level monitoring means comprises an exertion level monitoring means for collecting a Time of Inspiration (TI), obtained from the respiratory data.

22. The device according to claim 19, where the exertion level monitoring means comprises an exertion level monitoring means for collecting a Time of Expiration (TE), obtained from the respiratory data.

23. The device according to claim 13, further comprising posture monitoring means for collecting data related to the posture of the monitored subject.

24. The device according to claim 23, wherein the posture monitoring means collects instantaneous 3D shape data of the torso of the monitored subject.

25. The device according to claim 13, wherein the computing means comprises: a processor; a computer-readable memory operatively coupled to the processor; wherein the computer-readable memory is adapted to receive the respiratory and/or exertion level data of the subject at different levels of exertion; and wherein the processor is configured to establish the parametric relation between the collected respiratory data and the collected exertion level data, and assess the respiratory data of the subject.

26. The device according to claim 13, wherein the device is portable by the monitored subject.

27. The device according to claim 13, further comprising a wearable item that carries the respiration monitoring means and/or the exertion level monitoring means.

28. The device according to claim 27, wherein the wearable item comprises a garment, a shirt, and/or one or more bands.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will now be described in more detail by reference to the following detailed description of a preferred embodiment of the present invention and the accompanying figures in which:

[0044] FIG. 1 schematically illustrates the definition of relevant lung volumes to be used in embodiments of the invention;

[0045] FIG. 2 schematically illustrates lung volume response to exercise for a healthy person and a person suffering from dynamic hyperinflation;

[0046] FIGS. 3A and 3B schematically illustrate embodiments of an ambulatory device in accordance with the invention;

[0047] FIGS. 4A and 4B schematically illustrate a graph of lung volume versus time and relevant parameters used in embodiments of the method of the invention;

[0048] FIG. 5 schematically represents a flow chart of a method in accordance with an embodiment of the invention; and

[0049] FIGS. 6A and 6B schematically illustrate a parametric relationship that is indicative of the presence or absence of dynamic hyperinflation, as used in an embodiment of the invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

[0050] With reference to FIG. 1, relevant lung volumes to be used in embodiments of the invention are schematically shown. Total lung capacity (TLC) corresponds to the total volume of air the lungs can contain. Vital capacity (VC) is the volume of air breathed out from a maximal inspiration to a maximal expiration (or the inverse) and the residual volume (RV) is the volume of air remaining in the lungs after a maximal expiration effort. The functional residual capacity (FRC) is the volume of air remaining in the lungs after a tidal expiration at rest. Volume names often used during exercise are end inspiratory lung volume (EILV) and end expiratory lung volume (EELV), and the difference of these volumes defines tidal volume (TV). The inspiratory reserve volume (IRV) is the maximal volume that can be inhaled from the end-inspiratory level and the expiratory reserve volume (ERV) is the maximal volume that can be exhaled from the end-expiratory level. The sum of TV and IRV results in the inspiratory capacity (IC) which is the inspiratory volume from a regular expiration up to maximal inspiration, and generally varies in proportion with the EELV.

[0051] FIG. 2(A) illustrates a normal subject's response (lung volume versus time) to an increased respiratory demand, such as occurs during exertion. The principal response is use IRV and ERV to increase TV, while a secondary response is to increase breathing frequency, in particular at higher levels of respiratory demand. Because normal subjects have substantial IRV and ERV, TV is easily increased. Healthy subjects using their ERV then demonstrate a decreasing EELV—as shown in FIG. 2(A), but the change in EELV may be small. When EELV decreases, IC increases.

[0052] FIG. 2(B) on the other hand schematically illustrates a patient suffering from COPD. The patient's ERV is difficult to exploit due to expiratory flow limitations and incomplete expiration and ERV is not used to increase TV when needed, e.g. during increased activity. Due to repeated incomplete expiration the EELV raises and the patient gets hyperinflated using the IRV without significantly increasing TV. As a result IC decreases and ventilation can only be increased by faster breathing, further worsening hyperinflation and breathing becomes so restricted that the patient has to stop activity. This phenomenon is known as “dynamic hyperinflation”. Dynamic hyperinflation is dynamic since lung volumes generally return to their original values after exertion is brought to lower levels again.

[0053] Particularly during exercise, COPD patients may experience discomfort such as dyspnea and breathlessness. Furthermore, dynamic hyperinflation can cause even more problems like alveolar overdistention resulting in hypoxemia, hypotension, or alveolar rupture. Being able to track and manage dynamic hyperinflation in COPD patients at an early stage is therefore important.

[0054] The invention in one embodiment offers a method for assessing dynamic hyperinflation in a monitored subject. The invented method is based on the discovery that the presence or absence of dynamic hyperinflation and an indication of its degree (volume and/or speed of induction) can be reliably determined by establishing a parametric relation between collected respiratory data and collected exertion level data, the parametric relation being described by one or more parameters, and assessing the degree of dynamic hyperinflation in terms of the value of the one or more parameters.

[0055] In a particularly useful embodiment, two parameters turn out to yield a particularly reliable and sensitive prediction or detection of the presence of dynamic hyperinflation. The parameters comprise end expiratory lung volumes (EELV) after expirations and the breathing frequency, obtained by the time difference between instants of ends of expiration. Breathing frequency is indicative of the level of exertion, and is easily obtained from respiratory data.

[0056] FIG. 4A illustrates exemplary respiratory data. The graph represents the tidal lung volumes 10 (in liter) of a series of breaths versus time 12 (in seconds). Each breath has a rising inspiratory portion and a falling expiratory portion. One inspiration-expiration cycle takes a certain amount of time 11, which may differ from cycle to cycle. Time intervals 11 are usually defined in seconds. The inverse of a time interval 11 for a cycle defines breathing frequency in 1/sec for said cycle. An average breathing frequency of the preceding n cycles may also be used. To each breathing cycle moreover is associated an EELV 13 (the minima between cycles). For each cycle therefore, a unique combination of values of EELV 13 and prior breathing frequency 26 may be calculated from the respiratory data taken during exertion. This results in a collection of data points (26, 13), as shown in FIGS. 6A and 6B. Data points shown on the left in the graphs are indicative of relatively low levels of exertion (low breathing frequencies), while data points shown on the right in the graphs are indicative of relatively high levels of exertion (high breathing frequencies). Instead of breathing frequency, heart rate (at each EELV) can also be used, also in combination with breathing frequency.

[0057] Other embodiments of the method of the invention use parts of respiration cycles such as the Time of inspiration TI and the Time of Expiration TE. FIG. 4B defines the TE and TI for respiratory cycles. The graph represents the tidal lung volumes 10 (in liter) of a series of breaths versus time 12 (in seconds). Each breath has a rising inspiratory portion and a falling expiratory portion. One inspiration takes a certain amount of time 44, which may differ from inspiration to inspiration. Time intervals 44 correspond to the TI and are usually defined in seconds. The TI is established by measuring the time difference 44 between instants of ends of expiration and subsequent instants of end of inspiration. The inverse of the TI defines some kind of inspiration frequency in 1/sec which may be used for data analysis, as described above. One expiration takes a certain amount of time 45, which may differ from expiration to expiration. Time intervals 45 correspond to the TE and are usually defined in seconds. The TE is established by measuring the time difference 45 between instants of ends of inspiration and subsequent instants of end of expiration. The inverse of the TE defines some kind of expiration frequency in 1/sec which may be used for data analysis, as described above.

[0058] It turns out that the collected data is very sensitive to the presence or absence of dynamic hyperinflation. FIG. 6A shows a graph obtained on a patient having COPD and associated dynamic hyperinflation, while FIG. 6B is indicative of a healthy person. As shown, the parametric relation between the EELV data 13 and the collected breathing frequencies 26 may be fitted with a linear function 14. Other functions may also be used if appropriate. A particularly sensitive parameter comprises the gradient (or slope) 15 of the linear parametric relation, depicted by line 14. Data obtained at different levels of exertion on a patient having COPD show a negative slope 15 (FIG. 6A), while data obtained at different levels of exertion on a healthy person show a positive slope 15. It should be noted that the slope for a healthy person may also be about zero, but a significantly negative slope turns out to be indicative of (incipient) dynamic hyperinflation.

[0059] The present invention may be used in any patient monitoring system as long as respiratory data is available from which at least EELV and breathing frequency can be determined. It is possible to use the method of the invention in a hospital, clinic, or laboratory environment and use data from respiratory sensors available in such environments. Suitable sensors include spirometric measuring systems and body plethysmography arrangements for instance. These however are less portable and may limit or even prevent patient motion. In a preferred embodiment of the invention therefore, the method is practiced in a patient's day-to-day environment while the patient is performing day-to-day activities, or while the patient performs some exercise, such as when cycling for instance. In such embodiments, respiratory sensors are preferably portable and light weight, and are arranged on or incorporated in a wearable item, such as a shirt, jacket, bands, patches, and the like.

[0060] An exemplary embodiment of a shirt provided with monitoring sensors is shown in FIGS. 3A and 3B. The subject of FIG. 3B is provided with two bands (15, 16) that are configured to measure respiratory lung volumes. One band 15 is arranged around the rib cage and produces first signals indicative of instantaneous lung volume. A second band 16 is arranged around the abdomen and produces second signals indicative of instantaneous lung volume. Both signals may be used as such to produce the graphs of FIGS. 6A and 6B, or they may be combined in some way to produce the graphs of FIGS. 6A and 6B, for instance by taking a (weighted) sum of the data produced.

[0061] The size sensors 19 incorporated in the bands (15, 16) may be based on technologies known in the art, including magnetometers; strain gauges using magnetic, mechanical or optical means; optical techniques including interferometry; electrical impedance; surface electrical or magnetic activity; body plethysmography, ultrasonic and doppler measurements of body wall motions or body diameters; and so forth. Preferred size sensors are based on respiratory inductive plethysmography (RIP). RIP responds to anatomic size changes by measuring the self-inductance of one or more conductive elements (metallic or non-metallic) arranged in the bands (15, 16) on the body portion to be measured. RIP sensor self-inductance varies with size in response to an underlying body part size change. The changing self-inductance is sensed by a variable frequency oscillator/demodulator module, the output of which is responsive to oscillator frequencies and ultimately to sensor size.

[0062] The data that originate from the sensor(s) is transmitted via suitable wiring 17 (see FIG. 3A) to a portable data unit or PDU 18, that is conveniently carried in a small pocket on the shirt. The bands (15, 16) incorporate a size sensor 19 that is sensitive to respiration and may also comprise other sensors (not shown), such as posture sensors, accelerometers, ECG sensors, temperature sensors, and so forth. The PDU 18 stores data and accepts input from the wearer of the shirt. The PDU 18 may also be incorporated in the shirt itself and further retrieves and (wirelessly) transmits sensor data to storage and analysis systems. The PDU 18 may be provided with a processing device for processing sensor data, and/or processed and/or raw data may also be transmitted to a remote computer system 20. As shown in FIG. 3A, a suitable data analysis system 20 comprises a workstation computer 21 with processor to which is connected a monitor 22 for viewing sensor data. Raw or (partly) processed sensor data (10, 11, 12, 13, 26, 43, 44, 45) is transferred to system 20, and stored in computer readable memory for further processing. The processor of computer system 20, or in other embodiments a processor of the PDA, or a processor of any other device, such as a smartphone, is configured to establish a parametric relation between the collected respiratory data and the collected exertion level data, and assess the respiratory data of the subject.

[0063] An exemplary flow chart of a programmed method according to an embodiment of the invention is illustrated in FIG. 5. After beginning at step 31, a next step 32 measures lung volumes 10 over a certain time period. This step 32 is performed while decreasing and increasing the workload (or the level of exertion) a number of times in step 33 to obtain a well defined and representative sample of respiratory data for data analysis. The minimum and maximum level of exertion (or breathing frequency 26) required to achieve a representable data sample depends on conditions such as the health of the person involved, the sensors used, and so on. One skilled in the art will readily be able to obtain a representative sample without undue burden. In a next step 34, the processor determines EELV timestamps, defined as the times where the person's exhalation stops and an inhalation starts. The EELV timestamps generally correspond to the instants in time where the lung volume 10 reaches a local minimum. A next step 35 evaluates breathing frequency for each EELV timestamp. This breathing frequency 26 for an EELV timestamp is defined as the inverse of the time expired since the previous EELV timestamp. The average breathing frequency of several preceding breaths can also be used as the breathing frequency for one EELV. The same step 35 also evaluates the EELV 13 (the volume of air present in the lungs at the end of exhalation) for each timestamp. A next step 36 creates a two-dimensional dataset from the computed EELV 13 and corresponding breathing frequency 26 data, the latter being the independent variable in the dataset. In a final step 37, a linear fit is carried out of the collected dataset (13, 26) which produces a gradient or slope 15 of the linear relationship 14. The value of the slope 15 turns out to be highly representative for the occurrence of dynamic hyperinflation. The algorithm ends at step 38.

[0064] The invention described herein is not to be limited in scope by the disclosed preferred embodiment, the latter being intended as illustration only of several aspects of the invention. Various modifications of the invention may be made and will become apparent to one skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims.