WO2013120134A1

WO2013120134A1 - Respiratory monitoring

Info

Publication number: WO2013120134A1
Application number: PCT/AU2013/000125
Authority: WO
Inventors: Kartik Iyer
Original assignee: VISECOR Pty Ltd
Current assignee: VISECOR Pty Ltd
Priority date: 2012-02-17
Filing date: 2013-02-14
Publication date: 2013-08-22
Anticipated expiration: 2014-08-17

Description

RESPIRATORY MONITORING Background of the Invention

[0001] The present invention relates to a method and apparatus for respiratory monitoring and in particular to a method and apparatus for determining a breathing indicator indicative of respiratory function, such as breathing effort.

Description of the Prior Art

[0002] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0003] Respiratory monitoring is used in a variety of circumstances to assess the effectiveness of a subject's respiratory function. Such monitoring can be achieved by monitoring air flow into and out of a subject. However, such arrangements typically require the use of a mask, or sensor provided in the subject's airway, which can be both uncomfortable and inconvenient.

[0004] Alternative techniques have been proposed in which movement of the subject's chest and/or abdomen, are examined. However, such techniques have focussed on examining the amplitude of movement of the chest and/or abdomen, which has proven to be unreliable in identifying specific issues associated with respiratory function.

Summary of the Present Invention

[0005] In a first broad form the present invention seeks to provide an apparatus for respiratory monitoring, the apparatus including an electronic processing device that, for at least one breathing cycle:

a) determines first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors; b) determines a plurality of phase values indicative of the relative phase of the first and second movement signals; and, c) determines a breathing indicator indicative of respiratory function using the plurality of phase values.

[0006] Typically the electronic processing device determines the first and second movement signals by sampling signals recorded by respective chest and abdomen movement sensors.

[0007] Typically the electronic processing device:

a) determines first and second analytic signals from the first and second movement signals; and,

b) determines the phase values from the first and second analytic signals.

[0008] Typically the electronic processing device determines the analytic signals by applying a frequency transform to the first and second movement signals.

[0009] Typically the electronic processing device uses first, second and third transforms on the first and second signals to generate first and second analytic signals.

[0010] Typically the first transform is a Fourier Transform, the second transform is derived from a Hilbert transform and the third transform is an inverse Fourier transform.

(0011 ) Typically the electronic processing device determines a number of first and second phase values from the first and second analytic signals, respectively.

[0012] Typically the number of first and second phase values define a breathing loop.

[0013] Typically the electronic processing device normalizes the number of first and second phase values.

[0014] Typically the electronic processing device averages the phase values across a number of time periods.

[0015] Typically the electronic processing device averages the phase values using a sliding window.

[0016] Typically the electronic processing device modifies the number of first and second phase values using a polynomial function. - J -

[0017] Typically the electronic processing device determines a breathing indicator value representative of an effort of breathing.

[0018] Typically the electronic processing device determines a breathing indicator value at least partially based on an area of a breathing loop defined by a number of first and second phase values.

[0019] Typically the electronic processing device determines the breathing indicator value by:

a) determining characteristic vectors using the number of first and second phase values; and,

b) determining the breathing signal by determining a combined sum of the magnitude and angle of the characteristic vectors.

[0020] Typically the electronic processing device determines a breathing indicator value at least partially based on at least one of a gradient and angle of a breathing loop defined by a number of first and second phase values.

[0021 J Typically the apparatus includes first and second sensors for positioning on the chest and abdomen of a subject.

[0022) Typically the sensors include respiratory inductance sensors.

[0023] Typically the apparatus includes a filter for filtering movement signals acquired by the first and second sensors.

[0024] Typically the apparatus includes an analogue to digital converter for sampling movement signals to generate sampled first and second movement signal values.

[0025] Typically the apparatus includes a buffer for storing sampled first and second movement signal values.

[0026] Typically the apparatus includes a recording unit for receiving signals from first and second sensors and transferring data indicative of first and second movement signals to a processing unit for at least partially processing the first and second signals. [0027| Typically the apparatus includes a computer system coupled to the processing unit, the processing unit providing at least, one of the breathing indicator and recorded data to the computer system.

[0028] Typically the electronic processing device:

a) determines a breathing indicator;

b) compares the breathing indicator to at least one reference; and,

c) generates an indicator indicative of the results of the comparison.

[0029] Typically the at least one reference includes at least one of:

a) a breathing indicator previously determined for the subject; and,

b) a breathing indicator determined for one or more individuals in a sample population.

[0030] Typically the at least one reference is determined for one or more individuals having a breathing or respiratory disorder and wherein the results of the comparison are indicative of the presence, absence, degree or progression of a breathing or respiratory disorder.

[00311 Typically the electronic process device compares a plurality of breathing indicators to a plurality of references.

[0032] In a second broad form the present invention seeks to provide a method for respiratory monitoring, the method including, in an electronic processing device, and for at least one breathing cycle:

a) determining first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors; b) determining a plurality of phase values indicative of the relative phase of the first and second movement signals; and,

c) determining a breathing indicator indicative of respiratory function using the plurality of phase values.

[0033] In a third broad form the present invention seeks to provide apparatus for determining a breathing abnormality, the apparatus including an electronic processing device that, for at least one breathing cycle: a) determines first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors;

b) determines a plurality of phase values indicative of the relative phase of the first and second movement signals;

c) determines a breathing indicator indicative of respiratory function using the plurality of phase values; and,

d) determines the presence, absence or degree of a breathing abnormality in accordance with the breathing indicator.

[0034) In a fourth broad form the present invention seeks to provide a method for determining a breathing abnormality, the method including, in an electronic processing device:

a) determining first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors;

b) determining a plurality of phase values indicative of the relative phase of the first and second movement signals;

c) determining a breathing indicator indicative of respiratory function using the plurality of phase values; and,

d) determining the presence, absence or degree of a breathing abnormality in accordance with the breathing indicator.

Brief Description of the Drawings

[0035) An example of the present invention will now be described with reference to the accompanying drawings, in which; -

[0036) Figure 1 is a schematic diagram of an example of an apparatus for respiratory monitoring;

[0037] Figure 2 is a flowchart of an example of a process for respiratory monitoring;

[0038] Figure 3 is a schematic diagram of an example of the functionality of an apparatus for respiratory monitoring;

]0039] Figures 4A and 4B are a flowchart of a second example of a process for respiratory monitoring;

[0040] Figure 5 is a schematic diagram of an example of a breathing loop and defined eigenvectors used in calculating an effort of breathing index; (0041 ) Figures 6A and 6B arc graphs of a first example of movement signals recorded for the abdomen and chest respectively;

[0042] Figures 6C and 6D are graphs of examples of breathing loops derived from the movement signals of Figures 6A and 6B, using an amplitude and phase analysis respectively;

[0043] Figures 7 A and 7B are graphs of a second example of movement signals recorded for the abdomen and chest respectively;

[0044] Figures 7C and 7D are graphs of examples of breathing loops derived from the movement signals of Figures 7A and 7B, using an amplitude and phase analysis respectively;

[0045] Figures 8A and 8B are graphs of a third example of movement signals recorded for the abdomen and chest respectively;

[0046] Figures 8C and 8D are graphs of examples of breathing loops derived from the movement signals of Figures 8 A and 8B, using an amplitude and phase analysis respectively;

[0047| Figure 9 is a schematic diagram of a second example of an apparatus for respiratory monitoring;

[0048] Figure 10 is a schematic diagram of a third example of an apparatus for respiratory monitoring;

[0049] Figures 1 1 A and 1 I B are circuit diagrams of an example of first and second sensors, a recording unit, and a processing unit;

[0050] Figures 12A and 12B are graphs of examples of a fourth example of movement signals recorded for the abdomen and chest respectively;

[0051 ] Figures 12C and 12D are graphs of examples of a breathing loop and effort of breathing index (EBI) from the movement signals of Figures 12A and 12B using phase analysis;

[0052| Figures 12E and 12F are graphs of examples of a fifth example of movement signals recorded for the abdomen and chest respectively;

[0053] Figures 12G and 12H are graphs of examples of a breathing loop and effort of breathing index (EBl) from the movement signals of Figures 1 2E and 12F using phase analysis;

[0054] Figures 121 and 12J are graphs of examples of a sixth example of movement signals recorded for the abdomen and chest respectively; |0055] Figures 12 and 12L are graphs of examples of a breathing loop and effort of breathing index (EBI) from the movement signals of Figures 121 and 1 2J using phase analysis; and,

[0056] Figure 13 is a flowchart of a further example of a process for respiratory monitoring. Detailed Description of the Preferred Embodiments

[0057J An example of an apparatus for respiratory monitoring will now be described with reference to Figure 1 .

[0058] In this example, the apparatus 100 includes a processing system 120, which in use is coupled to first and second movement sensors 1 1 1 , 1 12, which are provided on the chest and abdomen of a subject.

[0059] The nature of the sensors will vary depending on the preferred implementation, and can include any sensors capable of detecting movement of the subject's chest and abdomen, and generating a corresponding signal that is indicative of the degree of movement. For example, the sensors 1 1 1 , 1 12 can include inductance sensors, elastomeric sensors, pressure sensors, current or voltage sensors, impedance or resistance sensors, accelerometers, or the like, although any suitable sensor arrangement can also be used.

[0060] The processing system 1 20 is capable of receiving first and second movement signals from the first and second sensors 1 1 1 , 1 12, and using these to determine a breathing indicator indicative of respiratory function.

[0061] In one example, the processing system includes an electronic processing device 121 such as a microprocessor 1 21 , as well as a memory 122, input/output (I/O) device 1 23 , such as a keyboard and display, and one or more interfaces 124, such as a Universal Serial Bus (USB) port, Ethernet port, wireless transmitter/receiver, or the like, for coupling to the sensors 1 1 1 , 1 12, interconnected via a bus 125. In use, the electronic processing device 1 21 receives movement signals via the interface 124, optionally storing these in the memory 1 22. The electronic processing device 1 2 1 then processes these in accordance with instructions stored in the memory 122, for example in the form of software instructions and/or in accordance with input commands provided by a user via the I/O device 1 23. An indication of the breathing indicator can then be provided as an output, for example via the I/O device 123, or via the interface 124 to a remote processing device.

[0062] However, this is for the purpose of example only, and it will be appreciated that the electronic processing device can include any form of electronic processing device that can receive and process signals from the movement sensors 1 1 1 , 1 12. Accordingly, the electronic processing device can include any one or more of a microprocessor, microchip processor, logic gate configuration, firmware optional ly associated with implementing logic such as an FPGA (Field Programmable Gate Array), a RISC (Reduced Instruction Set Computer) based processor, or the like, In one particular example, the electronic processing device 121 is part of a processing system such as a 32-bit or 64-bit Intel Architecture based processing system, a suitably configured computer system tablet, or smartphone, or any other electronic device, that executes software applications stored on non-volatile (e.g. , hard disk) storage, although this is not essential and any system or arrangement capable of receiving and processing movement signals can be used. Furthermore, whilst reference is made to a single electronic processing device, it will be appreciated that processing could be distributed across multiple electronic processing devices, and reference to a single device is not therefore intended to be limiting.

[0063] An example process for determining a breathing indicator is shown in Figure 2.

[0064] In this example, the process includes having the electronic processing device 121 receive first and second movement signals from the first and second sensors 1 1 1 , 1 12 at step 200. The electronic processing device 121 then determines a number of phase values indicative of the relative phase of the first and second movement signals at step 210. At step 220, the electronic processing device 121 uses the phase values to determine a breathing indicator, which can then be presented to the user, for example using the I/O device 123.

[0065] The breathing indicator can be of any suitable form and can include a graphical and/or numerical representation. In one example, the breathing indicator includes a representation of a breathing loop indicative of first and second phase values derived from the first and second movement signals respectively, and which in turn illustrate the relative phase of the first and second signals. Alternatively, the breathing indicator can be a numerical value indicative of an area of a breathing loop defined by a number of first and second phase values, or alternatively a slope and/or gradient and/or angle defined by the relationship between the first and second phase values or the phase difference between the first and second movement signals. Example indicators and specific example processes for their determination will be described in more detail below.

[0066] Accordingly, the above described apparatus uses the relative phase of the first and second movement signals to determine a breathing indicator that is indicative of respiratory function, and in one particular example, the effort of breathing. The use of the relative phase of the movement signals provides a more accurate indication of respiratory function, and in particular the effort involved in breathing, than traditional amplitude analysis. Accordingly, a breathing indicator determined using a phase analysis can provide a more useful and/or clinically relevant assessment of respiratory function than can be achieved using traditional amplitude analysis.

[0067] This in turn allows the arrangement to be used in a wide range of respiratory monitoring applications, such as monitoring breathing during sleep, under anaesthetic, in intensive care or during general admission in a hospital. For example, the apparatus can be used for diagnosing or monitoring sleep apnea and/or controlling associated treatment devices, such as VPAP (Variable Positive Airway Pressure) devices. The arrangement can also be used as for detecting breathing abnormalities, such as asthma, or as an alarm unit for detecting the onset of breathing difficulties associated with sudden infant death syndrome.

[0068) Sporting applications include respiratory monitoring in athletes for performance improvement, such as during pre-season training, injury recovery and rehabilitation, post- performance recovery, detection of breathing issues, a feedback mechanism to improve breathing technique, and the like. In this respect, the apparatus may include a wearable device, and this will be discussed further below, which may be provided in, or integrated with, sporting equipment such as clothing, Skins ^r compression clothing, swimsuits, or similar.

[0069] Home users may also utilise the above described apparatus in monitoring mild respiratory disorders, such as mild sleep apnea, snoring or the like. The apparatus could also be used in respect of sporting applications, for example, in order to improve fitness, or the like. [0070] Veterinary applications may include applications related to animal diagnosis, recovery from illness and monitoring during anaesthetic, such as discussed in respect of clinical settings described above, and additionally for performance animals, including in performance improvement, injury rehabilitation, and other applications discussed above in respect of athletes.

[0071 ] A number of further features will now be described with reference to Figure 3, which shows an example of the functionality that can be implemented in the electronic processing device.

[0072] In this example, the functionality includes an ADC (Analogue to Digital Converter) 300, for sampling analogue movement signals to thereby generate sampled first and second movement signal values, as well a filter 301 for filtering the first and second movement signals. This can be performed to remove unwanted artefacts, such as noise interference from remote equipment, noise generated by the sensors, as well as to prevent aliasing due to the sampling rate of the ADC, or the like. It will be appreciated that filtering and digitising can be performed in any order, so that filtering can be performed on either or both of the analogue or digitised movement signals.

[0073] The sampled first and second signal values are then typically stored in a buffer 302, for temporary storage before processing. In particular, this can be performed to enable movement signal values to be stored until a sufficient number of movement signal values have been acquired to allow a breathing indicator to be determined.

[0074| After storage in the buffer 302, signal processing 303 is performed. In this regard, as the acquired movement signals are difficult to analyse directly, it is typical to determine first and second analytic signals from the first and second movement signals, and then determine the phase values from the first and second analytic signals. Analytic signals are signals derived from the measured movement signals, which can be more easily interpreted, and typically include a complex representation of the first and second movement signals.

[0075J Analytic signals can be determined using appropriate techniques, such as applying a frequency transform method to the first and second movement signals. In one particular example, the electronic processing device 121 uses first, second and third transforms on the first and second movement signals to thereby generate respective first and second analytic signals. In a specific example, as will be described in more detail below, the first transform is a Discrete Fourier Transform (DFT), the second transform is derived from the Hilbert Transform, and the third transform is an inverse Discrete Fourier transform.

[0076] The electronic processing device 121 then determines a number of first and second phase values from the first and second analytic signals. The phase values can be derived using any appropriate technique, but this is typically achieved by calculating the phase angle using the real and imaginary parts of the analytic signals.

[0077] As part of the determination of the phase values, the electronic processing device 121 typically phase wraps and normalizes the first and second phase values, as well as averaging the phase values across a number of time periods, for example using a sliding window. The electronic processing device 121 may also modify the number of first and second phase values using a polynomial function. These processes are used to make subsequent stages of the analysis more straightforward, as well as to limit the effect of spurious measurements.

[0078] Once derived, the first and second phase values can be used to determine a breathing loop, which in one example involves generating a graphical plot of the first and second phase values, as will be described in more detail below.

[0079] Additionally and/or alternatively the electronic processing device 121 can determine a breathing indicator value indicative of an area of a breathing loop defined by the number of first and second phase values. In one example, the breathing indicator value is proportional to the area of the breathing loop, although any value indicative of the area of the breathing loop can be used.

[0080] In another example, the breathing indicator is indicative of a slope, gradient or angle defined by the breathing loop. Thus, a best fit line can be determined for the breathing loop, for example using least squares regression and/or estimation technique, allowing this to be used to determine a gradient or angle (for example relative to an axis or predefined line such as a line representing in phase breathing), which is in turn indicative of the relative phase between the first and second phase values. Deviation from the line can then also be used to ascertain variability of the phase values and hence of the subject's breathing patterns. Alternatively, the gradient could be determined by taking gradient values at fixed points on the breathing loop and then comparing and/or averaging these to determine an overall breathing loop gradient and/or variability.

[0081 ] Whilst the breathing indicator value can be determined using any suitable technique, in one example this is achieved by determining characteristic vectors, such as eigenvectors, using the number of first and second phase values, and then determining the breathing signal by determining a combined sum of the magnitude and angle of the characteristic vectors.

[0082] A representation of the breathing loop, and/or the numerical area value of the breathing loop can then be provided as an output 304, for example by presenting the representation using the I/O device 123.

[0083] A specific example of the process for determining a breathing indicator will now be described in more detail with reference to Figures 4A and 4B.

[0084] In this example, at step 400 first and second sensors 1 1 1 , 1 12 are attached to the subject allowing movement of the chest and abdomen of the subject to be measured. As mentioned above, any suitable form of sensors 1 1 1 , 1 12 could be used.

[0085] In one example, the sensor measures the tension in an elastic belt extending round the chest or abdomen of the subject. However, such tension based measuring systems suffer from a number of drawbacks and in general an inductance sensor is used that includes a conductive loop of wire attached to the subject. An alternating current is passed through the wire to generate a magnetic field normal to the loop. As the chest or abdomen expands and contracts, the area of the loop changes, which in turn generates an opposing current within the loop directly proportional to the change in the area. This opposing current can be measured and used as an indication of the movement of the subject^' s chest or abdomen. Example commercial inductance sensors include Philips Respironics zRIP inductive respiratory effort sensors.

[0086] At step 405, the first and second movement signals from the sensors 1 1 1 , 1 12 are filtered and digitally sampled, with corresponding sampled first and second movement signal values being stored in a buffer at step 410. To avoid aliasing and to ensure adequate resolution of the data, the signal values are typically sampled at a rate of 10Hz, so that a movement signal value is stored every 0.1 seconds, with up or down sampling being performed if required. However, it will be appreciated that this is for the purpose of example only and different sampling rates could be used depending on the preferred implementation and intended usage, and in further example the sampling rate could be controlled by a user using appropriate input commands or the like.

[0087] In general, at least 2-3s of data is required for processing, so at step 415 the electronic processing device 12 1 determines if sufficient data has been collected and i f not returns to step 410, allowing more data to be collected and stored.

[0088] At step 420, the electronic processing device 121 derives analytic signals, for each of the chest and abdomen signals, which in one specific example is achieved using a frequency domain method derived using the Hilbert transform. Other methods include, but are not limited to, the use of dual quadrature FIR (Finite Impulse Response) filters to generate the real and imaginary parts of the analytic signal in the time-domain, or alternatively a single FIR filter that approximates the time-domain Hilbert transform to generate the imaginary part of the analytic signal with the real part of the analytic signal being obtained directly from the original movement signal.

[0089] The frequency domain method performs a Discrete Fourier Transform (DFT), which in one embodiment could be done using a Fast Fourier transform (FFT) algorithm, to derive a frequency domain representation of the signal. The negative frequencies of this representation are then set to zero using a transform derived from the Hilbert transform, before an inverse DFT is performed, which in one embodiment could be done using an inverse FFT. The resulting vector is an approximation of the analytic signal, having real and imaginary parts, in the time domain.

[0090] Accordingly, the electronic processing device 121 extracts the N sampled signal values for each of the chest and abdomen signals from the buffer and processes these as respective chest and abdomen vectors. The electronic processing device 121 then applies a DFT followed by a transform to remove negative frequencies, to which an inverse DFT is applied to obtain respective analytic signal vectors in the "time" domain, for the chest and abdomen, respectively. [0091 ] In particular, for a sequence of movement signal values defining vectors, XAbdomen and xchcst, ' 1 the time domain, the DFT results in the corresponding frequency domain representation of the signals, XAbdomcn(i) and Xchest ). where i is in the range [ 1 :N] and where N is the number of elements in the vector. The frequency domain method proceeds to derive the analytic signal, with a method derived using the Hilbert transform, by creating vectors HAbdomen and Hci,_esi whose elements H(i) where i=[ l :N] have values:

• 1 for i = l , (N/2)+l

• 2 for i = 2, 3, ... , (N/2)

• 0 for i = (N/2)+2, ... , N

[0092] The vectors h_Abdomen and hchest are multiplied by their respective frequency domain representations, XAbdomcn(i) and Xchcst(i) to produce a frequency domain representation of the analytic signal, i.e.

Xchcsi ^x H_Chcst- An inverse FFT of Z_Abdomen and Zchest is then performed returning an analytic signal vector in the time domain and having real and imaginary parts:

^Abdomen^— Rs(ZAbdomen)^''^" j lrn(Z_Abdomen)

Zchest = Re(Zchcst) + j Im(Zchcs!)

where: z_Abdomc:n is a complex analytic signal vector for the abdomen signal; Re(zAbdomcn) is the real component of z_Abdomcn;

Im(zAbdomcn) is the imaginary component of ZAbdomcni

zchest is a complex analytic signal vector for the chest signal; Re(zchest) is the real component of zchesti

Im(zchest) is the imaginary component of zchest-

(0093) The derivation of analytic signals is described in more detail in "Computing the Discrete-Time 'Analytic' Signal via FFT' by S. Lawrence Marple Jr, 1058-6393/98.

[0094) At step 425 the electronic processing device 121 determines phase values from the real and complex parts of each of the chest and abdomen analytic signal vectors. This is achieved using an atan2(p,q) function, i.e. the arctangent of q/p in the range (-π, π), which corresponds to the angle in radians between the positive p-axis of a plane and the point given by the coordinates (p, q) on it. The angle is positive for counter-clockwise angles (upper half- plane, q > 0), and negative for clockwise angles (lower half-plane, q < 0):

Thus, phase vectors (aAbdomen and achcst) including a number of phase values, are determined for each of the first and second movement signals using the equation:

"Abdomen = atan2( I m(ZAbdomcn) , Re(z_Abdomen)), achest = atan2(Im(zchcst),Re(zci,est)),

where: aAbdomen is the phase vector of the abdomen signal;

achcst is the phase vector of the abdomen signal

[0095] At step 430, the electronic processing device 121 phase wraps and normalises the resulting phase vectors aAbdomen and achest, making the resulting values easier for subsequent processing, plotting and interpretation.

[0096] Phase wrapping is performed using a modulus 2π operation, so that phase values are constrained between 0 and 2π:

^IA- . = mod(a_AMamen ,2/r)

«cv,.* = mod(«₍ ._A„, ,2 r)

[0097| Normalisation is performed so that each wrapped phase value is in the range 0 to 1 :

2π

where: m_Abdumen is the normalised phase vector for the abdomen

mchcst is ^tne normalised phase vector for the chest signal [0098] Following this, the electronic processing device 121 processes the data to smooth the data and thereby mitigate the effect of spurious measurements. To achieve this, at step 435 the processor applies a moving average to the phase values to average values falling within overlapping sliding windows of B samples where B«N.

[0099] At step 440, the electronic processing device 121 then uses a smoothing algorithm, such as a least squares method, in which coefficients of a polynomial p(t) of degree n are derived that fit the phase values, m_Abdomcn and mc est- The result p is a row vector of length n+1 containing the polynomial coefficients, pi , p₂, . .. p_n+i , and it will be appreciated that a respective, smoothed vector is derived for the abdomen and chest signals respectively:

,,,,α, =

+ /V "~' + - + P + P„₊l

dan,, = Pi< " + Pi' '"' + - + P + Pn.y

where: the values of t corresponds to the time values of the pre-smoothed

phase signals, m_Abdomcn and m_Ci,_Cst-

[0100] Additional processing may also be performed at step 445, such as re-processing using a 'smoothing' filter for further de-noising.

[0101 ] The resulting first and second phase values, defined by the first and second phase vectors (d_Abdomen and dehest) lor the first and second movement signals, can be plotted against each other to define a respiratory loop, an example of which is shown in Figure 5. The nature of the loop, and in particular, the shape and area of the loop, can provide useful information regarding the subject's respiratory function, as will be described in more detail below.

[0102] Additionally the breathing loop, and in one example, the area of the breathing loop, is indicative of a breathing effort, allowing an effort of breathing index (EBI) to be optionally derived at step 450.

[0103] In one example, the EBI is derived using the characteristic eigenvector equation and a common reference point, EBIp, where in 0-to- l phase space, the midpoint (0.5, 0.5) coordinates are used to determine changes from this point in phase space and hence to determine corresponding eigenvalues in accordance with the formulae:

det( 4 - A/) = 0 , where:

0.5 0.5

so that det 0

d_au„

[0104] From this the eigenvectors for a given set of eigenvalues can be determined, as shown by the arrows within the breathing loop in Figure 5. The eigenvectors can then be used to determine an approximate breathing loop area, and from this for a given respiratory loop, i.e. one breath, the total effort or energy of breath can be characterized in terms of work (force).

[0105] At step 455, the EBIp is calculated based on the combined sum of the magnitude and angle of the eigenvectors in a given buffer of N. The formula for EBI_P is as follows: (0^■ Eig_Chesl ( )| · Angle (

where: Eig_Abdomen⁼ magnitude of abdomen eigenvector components,

Eigches_t - magnitude of chest vector eigenvector components, Angle(Eig(i)) is the Eigenvector angle

[0106] Alternatively, the EBI can be determined based on the geometrical area of the loop using the formula, EBI-r:

[0107] In each case, the value of N is typically selected to correspond to a single respiratory cycle, and this can be achieved by performing pre-processing to determine the number of first and second movement signal values that correspond to a single breathing loop. The EBI may also be scaled prior to output, so that it can be provided as a value between " 1 " and either "0" or depending on the preferred implementation or on the requirements of the user. It will be appreciated that this can be used to ensure that the user is presented with a value that can be immediately understood, so for example, " 1 " can indicate breathing is in phase, whilst "0" or "- can be used to represent out of phase breathing, or the like.

[0108] At step 460, a representation of the breathing indicator, and in particular either a plot of the breathing loop and/or a EBI value can then be output to a user, for example via the I/O device 123. This can be for a single respiratory cycle, or could include a sequence of one or more breathing loops and/or EBl values, thereby showing progression of respiratory function over time. This can be useful for example when monitoring a subject over time, for example during a sleep period, or whilst a subject is under anaesthetic.

[0109| Examples of first and second movement signals are shown in Figures 6A and 6B, 7 A and 7B, 8A and 8B, with resulting derived breathing loops being shown in Figures 6D, 7D and 8D respectively. Corresponding breathing loops determined using typical signal amplitude measuring techniques (as opposed to phase measuring techniques) are shown in Figures 6C, 7C, and 8C.

[01 10] In the example of Figures 6A to 6D, the movement of the chest and abdomen are substantially in phase, as evidenced by the upward left to right sloping of the breathing loop in Figure 6D. Furthermore, the small area of the breathing loop indicates that breathing is not requiring significant effort, thereby corresponding to normal respiratory function at rest. Whilst the phase based breathing loop of Figure 6D is tightly confined, the breathing loop of Figure 6C is less so, making it difficult to correctly analyse results.

[011 1 ] In the example of Figures 7A to 7D, movement of the chest and abdomen are initially in phase, before moving out of phase, and then returning to in phase, suggestive of obstructive breathing, in which an obstruction occurs that subsequently clears. Furthermore, the breathing loop has a larger area indicating that breathing is requiring significant effort. This is suggestive of an individual struggling to breath, for example in the case of an asthmatic or individual having sleep apnea.

[0112] In the example of Figures 8A to 8D, there is again little correlation between the movement of the chest and abdomen. However, in this instance at least some signals are 180° out of phase, at the start of the breathing cycle as shown by the downward slope, left to right, which is indicative of initial paradoxic breathing, a condition in which a part of the lung deflates during inspiration and inflates during expiration.

[0113] Thus, the phase derived breathing loops of Figures 6D, 7D, 8D provide useful information to a user, and can help distinguish more easily between different conditions, in contrast to the amplitude derived breathing loops of Figures 6C, 7C, 8C. [0114] It will be appreciated from the above that a range of different apparatus configurations can be used, and that the examples of Figures 1 and 3 are for the purpose of illustration only. A further example arrangement will now be described with reference to Figure 9.

[0115] In this example, the apparatus includes a recording unit 910, a processing unit 920 and a processing system 930. In use, the recording unit 910 is coupled to the sensors 1 1 1 , 1 12 and is for recording signals from the sensors and then transferring data indicative of first and second movement signals to the processing unit 920. The processing unit 920 then at least partially processes the first and second movement signals, providing either the breathing indicator and/or other recorded data to the processing system 930, allowing the data to be processed further and/or stored for later review, for example to perform a longitudinal analysis of the respiratory function of a subject.

[01 16] It will be appreciated that in this example, the functionality, of the electronic processing device 121 is therefore effectively split across a number of units, which can have certain benefits, depending on the configuration used.

[0117] In the current arrangement, the recording unit 910 includes an ADC 91 1 , a filter 912 and transmitter 913, with an optional memory 914 also being provided. The processing unit includes a processor 92 1 , a memory 922, a receiver 923 and an external interface 924, coupled together via a bus 925. The processing system 930 includes a processor 931 , a memory 932, a I/O device 933 and an external interface 934, coupled together via a bus 935.

[0118] In use, the recording unit 910 is typically custom hardware used to digitise and filter received movement signals, and then transmit digitised movement signals to the processing unit 920, which generally requires less bandwidth than transmitting raw analogue data. Despite this, processing and power requirements for digitising, filtering and transmitting the digitised signals are minimal, allowing the recording unit 910 to be provided as part of a wearable device, with data being transmitted wirelessly to the processing unit 920. This allows the apparatus to be used in a wide range of situations, without inconveniencing the subject, for example allowing the recording device to be used during physical activity or the like. Furthermore, sampled movement signal values can be stored in internal memor 914 for subsequent retrieval in the event that transmission to the processing unit 920 is unavailable.

[01 19] The processing unit 920 can then perform processing of the digitised movement signals, for example to perform specific calculations including deriving the phase values, as well as optionally determining the EBI and/or breathing loops. To achieve this, in one example the processor 921 is a programmable module formed from programmable hardware, the operation of which is controlled using instructions, in the form of firmware stored in the memory 922, that specifies the configuration of the programmable module. This allows processing to be performed using custom hardware configured to specifically perform defined tasks, thereby reducing the computational burden on the processing system 930.

[0120] Finally, the processing system 930 can be a standard computer system, which can be used for controlling the processing unit 920, and hence the recording unit 910, thereby controlling data acquisition and providing a means for a user to review and optionally further process the results, such as the breathing indicator. This can be used for example to review changes in breathing indicator for an individual obtained at different times, to thereby perform a longitudinal analysis, or to compare the breathing indicators to reference indicators obtained from a sample population of individuals. This in turn can assist in interpreting results, as well as monitoring the effectiveness of treatment, as will be described in more detail below.

[0121] Thus, in one particular example, the recording and processing units 910, 920 are custom hardware, whilst the processing system 930 is a standard computer system. This can assist in providing making the recording unit a wearable device, whilst reducing the overall computational and power requirements of the processing unit, reducing the cost of manufacture of the equipment. However, this is not essential, and alternatively the functionality of the processing unit 920 and processing system 930 could be implemented as part of a combined unit, such as a suitably programmed computer system, smartphone, or the like.

[0122] Wider variations on the abovementioned arrangement are possible, including remote and/or cloud processing. For example, the processing system 930 may include a smartphone, or other computer system, which receives the digitised movement signals from the recording unit, transfers the digitised movement signals to a remote server and/or cloud-based application for performing specific calculations, including deriving the phase values and/or determining the EBI and/or breathing loops. The smartphone then receives the results from the remote server or cloud application, and displays, and possibly further processes, the results. In this respect, the digitised movement signals may be transferred remotely using any suitable method, such as using the Internet, USB, Ethernet, wireless, Bluetooth, mobile network, or the like.

[0123] A further example arrangement of an apparatus for respiratory monitoring will now be described with reference to Figure 10. Features similar to those of the examples described above have been assigned correspondingly similar reference numerals.

[0124] In this example, the apparatus includes a recording unit 1010, a processing unit 1020 and a processing system 1030, similar to the example of Figure 9 described above. In this respect, the recording unit 1 010 is for recording signals from the sensors and transferring data indicative of first and second movement signals to the processing unit 1020. The processing unit 1020 then at least partially processes the first and second movement signals, providing either the breathing indicator and/or other recorded data to the processing system 1030, allowing the data to be processed further and/or stored for later review.

[0125] In the current arrangement, the recording unit 1010 includes two amplifiers 101 1 , 1012, two filters 1013, 1014 and two buffers 1015, 1016 electrically coupled to the sensors 1 1 1 , 1 12. Mowever, this is not essential and the recording unit 1010 may instead include one or more than two amplifiers 101 1 , 1012, filters 1013, 1014 and/or buffers 1015, 1016.

[0126] Additionally, in some circumstances it may not be desirable to include each of the amplifiers 101 1 , 1012, the filters 1013, 1014 and the buffers 1015, 1016, and in this respect including every element is not essential, for example, a recording unit may not include an buffers 1015. 1016.

[0127] Although the amplifiers 101 1 , 1012, the filters 1013, 1014 and the buffers 1015, 1016 are shown in Figure 10 in a particular order, this configuration is not essential and these components may be included in any suitable order, typically depending on the nature of the signals from the sensors 1 1 1 , 1 12. [0128] In addition, the amplifiers 101 1 , 1012 may also include any suitable component, including an amplifier, an operational amplifier, an instrumentation amplifier, a differential amplifier, or the like. Furthermore, the filters 1013, 1014 may include any suitable filter, such as any one or more of a low-pass filter, a band-pass filter, a high-pass filter, an active filter, a passive filter, a FIR filters, and an Infinite Impulse Response (IIR) filter. The buffers 101 5 may also include any suitable component for providing electrical impedance transformation, including an inverting buffer, a non-inverting buffer, a unity gain amplifier, or the like.

[0129] The processing unit 1020 receives the data indicative of first and second movement signals from the recording unit 1010, and performs similar functions to the processing unit 920 of the previous example, which will not therefore be discussed further here.

[0130] In this example, the recording unit 1010 and processing unit 1020 are typically provided as custom hardware, and therefore the data transmitted between the recording unit 1010 and processing unit 1020 need not necessarily include digitized movement signals. For example, the recording unit 1010 may transfer analogue data indicative of the first and second movement signals to the external interface 1024 of the processing unit 1020. In this respect, the processing unit 1020 includes an in-built or peripheral ADC for converting the analogue data to digital data for further processing.

[01311 Hence the recording unit 1010 and processing unit 1020 may be provided in a portable and/or wearable device, which allows the apparatus to be used in a wide range of situations. This not only reduces the computational burden on the processing system, but also can reduce the complexity and/or specialisation of the processing system such that in some examples, a generic processing system may be used to further analyse the processed digitized data, without the need for specialised software. However, this is not essential.

[0132) The processed digital data is transferred to the processing system 1030 in any suitable manner, for example, Universal Serial Bus (USB), Ethernet, wireless, Bluetooth, Zigbee, radio frequency, mobile network, or the like.

[0133] The processing system 1030 includes a processor 1031 , a memory 1032, an I/O device 1033, and an external interface 1034, coupled together via a bus 1035. In this respect, the processing system 1030 performs similar functions to the processing system 930 of the previous example, which will not therefore be discussed further here. Alternatively, the processing system 1030 may by used simply for receiving and displaying the processed digital data, and optionally further processing.

[0134] An example circuit diagram of an apparatus for respiratory monitoring will now be described with reference to Figures 11A and 1 IB. In this respect, the apparatus includes a recording unit 1110 and processing unit 1120 similar to those described above in respect of Figures 9 and 10.

[0135] In Figure 11A, signals from the sensors 1101, 1102 are transferred to respective differential amplifiers 1111.1, 1111.2, which amplify the differential signals that are output from the sensors 1101, 1102. In this example, the differential amplifiers 1111.1, 1111.2 have a gain of about 250, however any suitable gain may be chosen, depending upon the nature of the signals from the sensors 1101 , 1102. In addition, a differential amplifier 1111.1, 1111.2 is not essential and the type of amplifier included, if any, will typically depend upon the nature of the signals output from the sensors 1101, 1102. For example, in some arrangements the signals from the sensors 1101, 1102 are not differential signals and hence an inverting or non-inverting amplifier may be used.

[0136] The differential amplifiers 1111.1, 1111.2 are electrically connected to respective low pass filters 1112.1, 1112.2. In this respect, amplified data indicative of the signals from the sensors 1101, 1102 is output from the differential amplifiers 1111.1, 1111.2 and input into- the low pass filters 1112.1, 1112.2, which substantially filters out high frequency noise, and thus outputs filtered data indicative of the signals from the sensors 1101, 1102. In this example, the low pass filters 1112.1, 1112.2 include a cut-off frequency of 100Hz, however this is not essential, and any suitable cut-off frequency may be chosen, depending upon the nature of the signals from the sensors 1101 , 1102.

[0137| The filtered data is then transferred to respective buffers 1113.1, 1113.2, which in this example are unity gain buffers. In this respect, the buffers 1113.1, 1113.2 provide electrical impedance transformation between the recording unit 1110 and the processing unit 1120, thus reducing power consumption of the recording unit 1110, and ensuring a greater immunity to electromagnetic interference. [0138] Hence, the recording unit 1 1 10 outputs analogue data indicative of the signals from the sensors 1 101 , 1 102, which is then transferred to the processing unit 1 120, and in particular an external interface 1 124 of the processing unit 1 120, for further processing.

[0139] In this example, the processing unit 1 120 includes a microprocessor chip 1 1 21 , a second external interface 1 123, power management and supervision components 1 125, clock components 1 126, debugging components 1 127, and indicator components 1 128. Additionally, the microprocessor chip 1 121 includes an ADC which converts the analogue data to digitized data indicative of the signals from the sensors 1 101 , 1 102 for further processing.

[0140] However, these features are not essential, and the arrangement of the processing unit 1 120 will typically depend upon the type of microprocessor chip 1 1 21 , and its respective requirements for power management and supervision components 1 125, clock components 1 126, and the like. For example, commercial systems may not require debugging components 1 127.

[0141 ] The indicator components 1 128 may include one or more indicators, and in this example includes four light emitting diodes (LEDs). The indicator components 1 128 may be used to provide an indication of any suitable function, such as battery function, data transmission, data processing progress, an error, or the like. However, this is not essential and any suitable type of indicator components 1 128 may be used, including a display, touchscreen, LCD screen, or the like, and in some examples an indicator components 1 128 may not be required.

[0142] In addition, the microprocessor chip 1 121 also includes in-built memory onto which instructions, such as firmware, can be stored and utilised to control operation of the processing unit 1 120. In this respect, the processing unit 1 120 can perform processing of the digitized data, for example to perform specific calculations including deriving the phase values, as well as optionally determining the EBI and/or breathing loops.

[0143] The processed digitized data is then transferred to the second external interface 1 123 for transferring to a processing system. The second external interface 1 123 shown in Figure 1 1 B includes a USB interface, however this is not essential, and may instead include an Ethernet interface, a wireless transmitter, mobile network transmitter, or the like.

[0144] The processing system receives the processed digitized data and outputs a representation indicative of the breathing data to a display. However, this is not essential and the processing system may perform any functions similar to the processing systems of the previous examples, which will not therefore be discussed further here.

[0145] Examples of the representations are shown in Figures 12A to 12J, including first and second movement signals shown in Figures 12A and 1 2B, 12E and 12F, 121 and 12J, with resulting derived breathing loops and EBIs being shown in Figures 12C and 12D, 12G and 12H, and 12 and 12L, respectively.

[0146] For the purpose of these examples, the EBIs are normalised to have a value of between " 1 " and "0", with " 1 " representing in phase breathing and "0" representing breathing that is 180° out of phase.

[0147] In the example of Figures 12A to 12D, the movement of the chest and abdomen are substantially in phase, as evidenced by the upward left to right sloping of the breathing loop in Figure 12C. Furthermore, the small area of the breathing loop indicates that breathing is not requiring significant effort, thereby corresponding to an EBI of about " 1 " in Figure 1 2D, and hence a normal respiratory function.

[0148] In the example of Figures 12E to 12H, movement of the chest and abdomen arc initially out of phase, before moving in phase, and then returning to out of phase, suggestive of mildly abnormal breathing. Furthermore, the breathing loop in Figure 1 2G has a larger area indicating that breathing is requiring significant effort, and this is reflected in the EBI fluctuating from "0.2" to " 1 " in Figure 12H. This is suggestive of an individual struggling to breathe, for example in the case of an asthmatic or individual having sleep apnea.

[0149] In the example of Figures 121 to 12J, there is again little correlation between the movement of the chest and abdomen. However, in this instance at least some signals are 180° out of phase as shown by the downward slope, left to right, in the breathing loop of Figure 12 , which is indicative of paradoxic breathing, a condition in which a part of the lung deflates during inspiration and inflates during expiration. This is also reflected in the EBI which remains substantially around "0.2" in Figure 12L.

[0150] Thus, the phase derived breathing loops of Figures 12C, I 2G, 12 and the EBI of Figures 12D, 12H, 12L provide useful information to a user, and can help distinguish more easily between different conditions.

[0151 ) A further example of the process for respiratory monitoring will now be described in more detail with reference to Figure 13.

[0152] In this example, a breathing loop and/or breathing indicator such as the EBI value may be used in determining an indication of the presence, absence, degree or progression of a breathing or respiratory disorder. Additionally or alternatively, the breathing indicator may be used to monitor the impact of training or treatment on breathing, for example, after sporting training, treatment for sleep apnea or asthma or the like.

[0153] In this example, the process includes having an electronic processing device, such as processor 121 , determine one or more breathing indicators at step 1300 using any suitable method or process, as outlined above.

[0154] At step 1 3 10, the breathing indicators are compared to one or more references. The references may be derived from a baseline for an individual subject, a sample or reference population, or the like, as will be described in more detail below. The nature of the reference will vary depending on the preferred implementation, and this could therefore include a reference breathing loop, indicative of a typical breathing loop for an individual having a condition. Alternatively, the reference could in the form of a threshold value or range, such one or more breathing indicator values, that are indicative of an individual having a condition.

[0155] The manner in which the comparison is performed can therefore depend on the threshold. In one example, a single breathing indicator, such as an instantaneous EBI value, may be compared to a single reference value. However this is not essential, and alternatively a plurality of breathing indicators are compared to a plurality of references, for example to compare a temporal change in EBI values, and/or a value derived from a plurality of breathing indicators, such as a difference or an average, is compared to the reference. Alternatively, a measured breathing loop could be displayed together with one or more reference breathing loops, allowing an operator to perform a manual visual comparison, with the operator then typically providing an indication of the result of the comparison.

(0156) At step 1320, the electronic processing device generates an indicator indicative of the results of the comparison. In one example, the indicator is indicative of the presence, absence, degree or progression of the respiratory disorder. However, this is not essential and instead the indicator may be indicative of breathing progression, for example, after sporting training, to gain an indication of breathing improvement, during or after rehabilitation, or the like, or simply whether measured EBI or phase difference values are above or below a reference in the form of a threshold value.

(0157) In one example, the method outlined above may be used to perform longitudinal analysis on a subject. For example, periodic monitoring of a sportspersons respiration may be used to determine one or more breathing indicators, which are compared to references indicative of the subject's baseline measurements. The results of these comparisons may then be used to generate one or more indicators indicative of improvement, decline, or stabilisation/stagnation in the subject's breathing, in response to training, injury, or the like.

[0158] In clinical applications, sleep apnea is typically characterised by periods of obstructive breathing during sleep. In this respect, if a subject is undergoes respiratory monitoring, a breathing indicator, for example, the EBI values, may be used to determine the presence, absence, degree and/or progression of sleep apnea in the subject. For example, periods of obstructive breathing greater than a predefined duration, typically 3 to 5 seconds, and/or periods of obstructive breathing which occur with greater than a predefined frequency, may indicate the presence of sleep apena. Hence, the EBI values may be compared to a reference EBI value that is indicative of a lower EBI limit for normal breathing, and if EBI values arc below the reference for greater than the predefined duration, or more frequently than the predefined frequency, an indicator of the presence of sleep apena may be determined.

[0159] In order to determine references indicative of breathing or respiratory conditions/diseases, such as sleep apnea, typical EBI values and/or breathing loops of a sample population of subjects with the condition/disease may, for example, be used. Additionally or alternatively, typical EBI values and/or breathing loops of subjects with a degree of the condition/disease, for example mild sleep apnea or severe sleep apnea, may be used to determine the reference. In this respect, the reference may be derived from a sample population with a particular condition/disease, or a sample population with a particular degree of a condition/disease, or the like. Comparison to the reference can then be used to indicate a presence, absence or degree of a condition in the subject.

[0160] The indicator may also be displayed, transferred or output to the user in any suitable manner. In one example, the indicator may be output to the user via an alarm, which is particularly important in clinical settings, such as intensive care. In this respect, the indicator may be indicative of the presence of a serious breathing disorder or obstruction in which the subject requires immediate medical attention. However, this is not essential, and the indicator may be displayed via a display, or output via another stimulus such as a light emitting diode (LED), light, or the like. In addition, it is not essential that the indicator be output to the user.

[01611 The EBI value and/or breathing loop may also be used in determining an indicator of the presence of an error within the apparatus. For example, the EBI value and/or breathing loop may be used to determine a sensor error. In this regard, if first and second movement signals include amplitudes substantially around zero, and the breathing loop derived using phase measuring techniques is also substantially zero, this would indicate a sensor error is present. However if the amplitude is zero, and the breathing loop is substantially non-zero, this would indicate there is no sensor error, and that an error exists elsewhere in the apparatus.

[0162] In a further example, additional sensors may be integrated with, or used in conjunction with, the abovementioned apparatus for respiratory monitoring. For example, in sporting applications it may be useful to also determine an indication of heart rate, limb movement, distance travelled, time, or the like. In this respect, the apparatus may include separate third and fourth sensors, such as piezoelectric sensors, accelerometers, or the like capable of sensing a signal that is indicative of the heart rate, or any other signal of interest, which is then processed by the electronic processing device of the apparatus. [0163] Alternatively, in some arrangements the first and second movement sensors may also be capable of sensing a signal that is indicative of heart rate, limb movement, distance travelled, time, or the like.

[0164] In clinical applications, the additional sensors may include oxygen saturation sensors, such as infa-red sensors, volumetric sensors, such as plethysmographs, heart rate monitors, and the like. For example, oxygen saturation may provide an additional indication of the presence, absence or degree of sleep apena, therefore, when used as part of, or in conjunction with, the apparatus for respiratory monitoring, may provide increased accuracy in sleep apnea detection.

[0165] Plethysmographs detect volumetric changes in organs, such as lungs, and therefore are capable of sensing signals that are indicative of the degree of movement in the chest and abdomen and the heart rate of a subject. Thus, the electronic processing device may receive these signals and use them to determine an enhanced breathing indicator indicative of respiratory function and/or an enhanced indication of the presence, absence or degree of a respiratory or breathing disorder.

[0166] Accordingly the above described method and apparatus can be used for respiratory monitoring, and in particular for deriving a breathing indicator based on the phase difference between movement of a subject's chest and abdomen, which in turn provides an effective mechanism for monitoring a subject's respiratory function.

[0167] The term subject will be understood to apply to any biological entity whose breathing can be monitored, including but not limited to patients, as well as other individuals. It will also be appreciated that the term subject can include a non-human subject such as an animal, including but not limited to, primates, livestock, performance animals, such as race horses, camelids or the like, pets, such as dogs or cats, or any other animal including a lung based respiratory system.

[0168] Throughout this specification and claims which follow, unless the context requires otherwise, the word "comprise", and variations such. as "comprises^" or '"comprising", will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers. [0169J Persons skilled in the art will appreciate that numerous variations and modifications will become apparent. All such variations and modifications which become apparent to persons skilled in the art, should be considered to fall within the spirit and scope that the invention broadly appearing before described.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1 ) Apparatus for respiratory monitoring, the apparatus including an electronic processing device that, for at least one breathing cycle:

a) determines first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors;

b) determines a plurality of phase values indicative of the relative phase of the first and second movement signals; and,

c) determines a breathing indicator indicative of respiratory function using the plurality of phase values.

2) Apparatus according to claim 1 , wherein the electronic processing device determines the first and second movement signals by sampling signals recorded by respective chest and abdomen movement sensors.

3) Apparatus according to claim 1 or claim 2, wherein the electronic processing device: a) determines first and second analytic signals from the first and second movement signals; and,

b) determines the phase values from the first and second analytic signals.

4) Apparatus according to claim 3, wherein the electronic processing device determines the analytic signals by applying a frequency transform to the first and second movement signals.

5) Apparatus according to claim 4, wherein the electronic processing device uses first, second and third transforms on the first and second signals to generate first and second analytic signals.

6) Apparatus according to claim 5, wherein the first transform is a Fourier Transform, the second transform is derived from a Hilbert transform and the third transform is an inverse Fourier transform.

7) Apparatus according to any one of the claims 3 to 6, wherein the electronic processing device determines a number of first and second phase values from the first and second analytic signals, respectively.

8) Apparatus according to claim 7, wherein the number of first and second phase values define a breathing loop.

9) Apparatus according to claim 7 or claim 8. wherein the electronic processing device normalizes the number of first and second phase values. 10) Apparatus according to any one of the claims 7 to 9, wherein the electronic processing device averages the phase values across a number of time periods.

1 1 ) Apparatus according to claim 10, wherein the electronic processing device averages the phase values using a sliding window.

12) Apparatus according to any one of the claims 7 to 1 1 , wherein the electronic processing device modifies the number of first and second phase values using a polynomial function.

13) Apparatus according to any one of the claims 1 to 12, wherein the electronic processing device determines a breathing indicator value indicative of an effort of breathing.

14) Apparatus according to claim 13, wherein the electronic processing device determines a breathing indicator value at least partially based on an area of a breathing loop defined by a number of first and second phase values.

1 5 ) Apparatus according to claim 13 or claim 14, wherein the electronic processing device determines the breathing indicator value by:

16) Apparatus according to claim 13, wherein the electronic processing device determines a breathing indicator value at least partially based on at least one of a gradient and angle of a breathing loop defined by a number of first and second phase values.

17) Apparatus according to any one of the claims 1 to 16, wherein the apparatus includes first and second sensors for positioning on the chest and abdomen of a subject.

1 8) Apparatus according to claim 17, wherein the sensors include respiratory inductance sensors.

1 9) Apparatus according to any one of the claims 1 to 1 8, wherein the apparatus includes a filter for filtering movement signals acquired by the first and second sensors.

20) Apparatus according to any one of the claims 1 to 19, wherein the apparatus includes an analogue to digital converter for sampling movement signals to generate sampled first and second movement signal values.

21 ) Apparatus according to any one of the claims 1 to 20, wherein the apparatus includes a buffer for storing sampled first and second movement signal values. 22) Apparatus according to any one of the claims 1 to 21 , wherein the apparatus includes a recording unit for receiving signals from first and second sensors and transferring data indicative of first and second movement signals to a processing unit for at least partially processing the first and second signals.

23) Apparatus according to claim 22, wherein the apparatus includes a computer system coupled to the processing unit, the processing unit providing at least one of the breathing indicator and recorded data to the computer system.

24) Apparatus according to any one of the claims 1 to 22, wherein the electronic processing device:

a) determines a breathing indicator;

b) compares the breathing indicator to at least one reference; and,

c) generates an indicator indicative of the results of the comparison.

25) Apparatus according to claim 24, wherein the at least one reference includes at least one of:

a) a breathing indicator previously determined for the subject; and,

26) Apparatus according to claim 25, wherein the at least one reference is determined for one or more individuals having a breathing or respiratory disorder and wherein the results of the comparison are indicative of the presence, absence, degree or progression of a breathing or respiratory disorder.

27) Apparatus according to claim 25, wherein the electronic process device compares a plurality of breathing indicators to a plurality of references.

28) A method for respiratory monitoring, the method including, in an electronic processing device, and for at least one breathing cycle:

a) determining first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors:

b) determining a plurality of phase values indicative of the relative phase of the first and second movement signals; and,

29) Apparatus for determining a breathing abnormality, the apparatus including an electronic processing device that, for at least one breathing cycle: a) determines first and second movement signals indicative of chest and abdomen movements acquired by respective chest and abdomen movement sensors;

30) A method for determining a breathing abnormality, the method including, in an electronic processing device: