FIELD OF THE INVENTION

[0001] The invention relates to the field of monitoring breathing pattern or respiratory effort. More particularly, the present invention relates to a device and method for assessing breathing pattern through ECG.

BACKGROUND OF THE INVENTION

[0002] Monitoring respiration pattern or respiratory effort is of importance in a variety of clinical applications. Examples of such applications are detection of sleep apnea and respiratory-gating imaging.

[0003] Sleep apnea is a sleep disorder characterized by pauses in breathing or shallow breaths during sleep. Current techniques for detection and diagnosis of sleep apnea rely upon polysomnography. A polysomnogram simultaneously record multiple physiologic signals from a sleeping patient.

[0004] Respiratory-gating is a system that tracks a patient's respiratory cycle during imaging. Imaging techniques may include computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography (SPECT), but are not limited thereto. These imaging techniques may improve the image quality and sometimes reduce patient's exposure to harmful radiation. Respiratory-gating may also be used in maximizing radiation dose to a tumor and limiting normal tissue exposure during radiation therapy (aka radiotherapy or radiation oncology) for tumor control in cancer treatment.

[0005] A conventional method for measuring respiratory effort is known as esophageal manometry. Esophageal manometry method comprises the measurement of esophageal pressure. The patient swallows a pressure catheter which then resides in the esophagus throughout the monitoring time. In clinical practice however, esophageal pressure is bothersome to most patients and is therefore not used routinely.

[0006] Another method used in polysomnography comprises measuring an airway flow. The airflow is correlated to the respiratory effort in unassisted breathing.

Measuring airway flow is usually done using a mask or nasal flow sensor. The mask or nasal flow sensor may cause patient discomfort, and move during the sleep study which in turn results in noisy and unreliable measurements.

[0007] Yet another method for measuring respiratory effort comprises measuring of changes in chest and/or abdominal volume, also known as plethysmography. This is commonly done by fastening two elastic belts around patient's chest and abdomen. Those belts not only cause patient discomfort, but also usually move during the sleep study which in turn results in noisy and unreliable measurements

[0008] Other methods, which are used for example in respiratory-gating radiotherapy, comprises tracking movement of a lightweight chest/abdomen marker using an infrared camera, or measuring an impedance variation between electrodes put on patient's chest. The impedance varies by the change of air volume inside lungs. However, measuring impedance change needs additional hardware which will add to the cost of the system and may not be compatible with the system in use.

[0009] It has been recognized however that many applications which need measuring the respiratory effort or breathing parameters also need measuring one or more leads of electrocardiogram (ECG).

[0010] The electrocardiogram (ECG) provides a convenient measurement of cardiac electrical activity. Each cardiac cycle in the ECG is characterized by successive waveforms, known as P wave, QRS complex and T wave. These waveforms represent the depolarization and repolarization activities in the cells of atrium and ventricle of the heart. The QRS complex represents the depolarization phenomenon of the ventricles preceding the mechanical contraction. As such, the QRS complex conveys useful information about the contraction.

[0011] It has been proposed to derive breathing pattern from ECG measurements. The underlying idea is that mechanical factors of respiration, in particular chest movements due to respiration, result in change of the relative positions of the electrodes and heart. These changes may be reflected on ECG signals. Such measurement of breathing pattern from ECG measurements is termed ECG-derived respiration or EDR.

[0012] However, current techniques to derive respiratory pattern from ECG measurements requires additional hardware to already running electrocardiogram

monitoring/measuring devices.

[0013] There is a need for an improved method and system for assessing a breathing pattern, in particular a respiratory effort, from ECG signal. [0014] There is a need for a method and system for assessing a breathing pattern, in particular a respiratory effort, from ECG signal that can be performed by existing ECG monitoring device, yet without requiring additional hardware. SUMMARY OF THE INVENTION

[0015] The present disclosure discloses a method for assessing a breathing pattern, the method comprising: acquiring a single electrocardiogram (ECG) signal, measuring at least two amplitudes of a QRS complex in the single ECG signal, and deriving, from the at least two amplitudes of the QRS complex, a value representative of the breathing pattern for the QRS complex.

[0016] In one aspect of the disclosure, the method comprises determining a minimum amplitude and a maximum amplitude of the QRS complex in the single ECG signal, and calculating a difference between the maximum amplitude and the minimum amplitude to derive the value representative of the breathing pattern for the QRS complex.

[0017] In yet another aspect of the disclosure, the method comprises determining a sum of the at least two amplitudes of the QRS complex in the single ECG signal to derive the value representative of the breathing pattern for the QRS complex.

[0018] The method preferably comprises sampling the single ECG signal in the QRS complex to obtain ECG samples, and summing the ECG samples contained within a time window.

[0019] The method in one aspect of the disclosure comprises the step of filtering a baseline wander.

[0020] According to yet another aspect of the disclosure, the method further comprises obtaining a series of values representative of the breathing pattern for respective QRS complexes in the single ECG signal, and interpolating a signal representative of the breathing pattern for the single ECG signal.

[0021] In a further aspect of the disclosure, the method comprises the step of filtering the signal representative of the breathing pattern for the ECG signal within a predetermined respiration rate.

[0022] A computer program product comprising a non-transitory computer- usable medium having control logic stored therein for causing a transceiver to execute a method for assessing a breathing pattern according to the present disclosure is also disclosed.

[0023] The present disclosure also teaches a device for assessing a breathing pattern comprising a computer program product comprising a non-transitory computer-usable medium having control logic stored therein for causing a transceiver to execute a method for assessing a breathing pattern according to the present disclosure.

[0024] The present disclosure further teaches a device for assessing a breathing pattern comprising an acquisition element adapted to acquire a single ECG signal, and a processing element adapted to measuring at least two amplitudes of a QRS complex in the single ECG signal, and deriving, from the at least two amplitudes of the QRS complex, a value representative of the breathing pattern for the QRS complex .

[0024] The present disclosure relies on the fact that because of the respiration, the relative position of the electrodes and heart changes, and therefore, the heart electrical axis changes. The heart's electrical axis refers to the general direction of the heart's depolarization wavefront (or mean electrical vector) in the frontal plane.

[0026] The changes in the heart electrical axis results in a change of the difference between the minimum and maximum amplitudes in the QRS complex - referred to as the QRS swing, or in changes of areas of the QRS complex. Therefore, the variation in the heart electrical axis is correlated with the variation in the QRS swing or in the QRS areas.

[0027] It will be understood that only one ECG signal or lead is needed for assessing the breathing pattern. This is particularly advantageous, as many ECG

monitoring/measuring devices are adapted or configured to record only one ECG signal. The method and computer program product of the present disclosure provide improved flexibility.

[0028] These and other aspects of the invention will be apparent from and illustrated with reference to the embodiment(s) described herein after.

BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Fig. 1 shows device 1 for assessing respiratory effort according to one aspect of the disclosure,

Fig. 2 shows a flowchart of a method in one aspect of the disclosure as proposed by the teachings disclosed herein,

Fig. 3 shows an example of measure performed on a QRS complex in the method of fig. 2 according to one aspect of the disclosure,

Fig. 4A shows an example of an ECG signal used for assessing respiratory effort, Fig. 4B and 4C show signals derived from the ECG signal of Fig. 4A according to the teachings disclosed therein,

Fig. 5 shows a flowchart of a method in yet another aspect of the disclosure as proposed by the teachings disclosed, Fig. 6 shows an example of measure performed on a QRS complex in the method of fig. 5 in one aspect of the disclosure according to the teachings disclosed herein,

Fig. 7 shows an example of filter according to the teachings disclosed herein, Fig. 8A shows an EDR signal before filtering with a bandpass filter, and Fig. 8B shows the EDR signal after filtering by the bandpass filter according to the teachings disclosed therein.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The invention will now be described on the basis of the drawings. It will be understood that the embodiments and aspects of the invention described herein are only examples and do not limit the protective scope of the claims in any way. The invention is defined by the claims and their equivalents. It will be understood that features of one aspect or embodiment of the invention can be combined with a feature or features of a different aspect or aspects and/or embodiments of the invention.

[0031] Fig. l shows a device 1 for assessing a breathing pattern according to one aspect of the disclosure. In this aspect of the disclosure, the breathing pattern is a respiratory effort, but it is not a limiting example.

[0032] The device 1 comprises an acquisition element 10 adapted to acquire at an ECG signal 100 and a processing element 20 adapted to process the ECG signal 100 and derive therefrom a signal 200 representative of respiratory effort. The signal representative of respiratory effort is derived from the ECG signal and is hereafter called EDR signal 200.

[0033] The acquisition element 10 of the device 1 of fig. 1 is adapted to receive the ECG signal 100 from an electrocardiogram (ECG) monitoring/measuring device 2 such as a Holier device or Mobile Cardiac Outpatient Telemetry (MCOT) device.

Electrocardiogram (ECG) monitoring devices are commonly known in the art and will not be described in details in the present disclosure. The ECG signal 100 is passed to the acquisition element 10 of device 1, via communication link 3.

[0034] It should be understood that although the device 1 of fig. 1 is shown as a stand alone device linked to the ECG monitoring device 2, this is not limiting the invention and the device 1 may also be part of the ECG monitoring device 2. Alternately, the acquisition element 10 of the device 1 could therefore be adapted to receive an ECG signal measured from the an ECG monitoring part of the device 1. It should be apparent that may configurations are possible, without departing from the scope of the invention. [0035] The device 1 also comprises an output element 30 for outputting the derived signal representative of the respiratory effort 200. It will be appreciated that the device 1 may include other components such as a memory 31, a bus 32 and a communication interface 33, as well as other components (not shown) that aid in receiving, transmitting, and/or processing data. Moreover, it will be appreciated that other configurations are possible.

[0036] The memory 31 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by the processing element 12, a read only memory (ROM) or another type of static storage device that stores static information and instructions for the processing element 12, and/or some other type of magnetic or optical recording medium and its corresponding drive for storing information and/or instructions.

[0037] The bus 32 may permit communication among the components of the device 1.

[0038] Communication interface 33 may include any transceiver- like mechanism that enables the device 1 to communicate with other devices and/or systems. For example, the communication interface may include mechanisms for communicating with the ECG monitoring device 2.

[0039] As will be described in detail below in reference with figures 2-8, the device 1 may perform processing associated with assessing a respiratory effort of a patient being monitored. The device 1 may perform these and other functions in response to processing element 20 executing software instructions contained in a computer-readable medium, such as a memory.

[0040] A computer-readable medium may be defined as one or more memory devices and/or carrier waves. The software instructions may be read into memory 31 from another computer-readable medium or from another device via the communication interface 32. The software instructions contained in memory 31 may cause processing element 20 of the device 1 to perform processes that will be described later in reference with figures 2-6. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes consistent with the principles of the invention. Thus, systems and methods consistent with the principles of the invention are not limited to any specific combination of hardware circuitry and software.

[0041] The processing element 20 is adapted to process the ECG signal 100, to derive the EDR signal 200 representative of a respiratory effort. The processing element 20 may include any type of processor or microprocessor that interprets and executes

instructions. In other implementations, processing element 20 may be implemented as or include an application specific integrated circuit (ASIC), field programmable gate array (FPGA), or the like.

[0042] The EDR signal 200 is outputted by output element 30. Output element

30 may include a device that outputs information to an operator, such as a display, a speaker, etc. The signal 200 may be displayed on a display of the device 1, or may be sent to another device for further processing. The EDR signal 200 may be displayed alone or with other physiological parameters, such as the ECG signal. The EDR signal 200 may be sent to a storing element.

[0043] Fig. 2 shows a flowchart of a method for assessing a breathing pattern in one aspect of the disclosure. The breathing pattern may be a respiratory effort, bit this is not limiting the present invention.

[0044] In a first step SI, an electrocardiogram (ECG) signal 100 is acquired. The acquisition of the ECG signal 100 may comprise the measuring of the ECG signal 100 by the ECG monitoring device 2, followed by the transmission of the ECG signal 100 to the device 1. Alternately, the acquisition of the ECG signal 100 may be performed by the device 1 itself as part of the ECG monitoring device 2. The acquisition of the ECG signal is well known in the art and will not be described in details in the present disclosure. The ECG signal 100 is illustrated on fig. 4A

[0045] In a second step S2, the ECG signal 100 is processed and analysed. The analysis may comprise the extraction of relevant information for the assessment of respiratory effort. Relevant information for the assessment of respiratory effort may include - but is not limited thereto - a location of the QRS complexes 110 and associated R peaks 111, and a morphology of the QRS complexes 110.

[0046] The ECG signal 100 with the location of QRS complexes 110 is illustrated on fig. 4A, and an example of a QRS complex 110 in the EGC signal 100 is depicted on fig. 3. The QRS complex 110 of fig. 3 comprises a first positive deflection, which is the R peak 111 , as is well-known in the art.

[0047] It should be understood that the QRS complex 110 as shown on fig.3 is an example of QRS complex, and that there are many other morphologies of QRS complexes. Many combinations of deflections, as well as isolated deflections are generally referred to as QRS complexes. [0048] The morphology of the QRS complexes 110 is part of relevant information for assessing respiratory effort. It is indeed desirable to derive the EDR signal 200 from normal QRS complexes - or normal beats - only. The analysis of the ECG signal 100 may locate abnormal beats. Abnormal beats are excluded from subsequent EDR signal calculations. An example of abnormal beat is a premature ventricular complex (PVC).

[0049] The analysis of the ECG signal 100 for finding QRS location and QRS morphology is well-known to the man skilled in the art and will not be described in details in the present disclosure.

[0050] The step of analysis of the ECG signal 100 in the first aspect of the disclosure is followed by a step of S3 of filtering and removal of the baseline wander. The filtering aims at removing all noise incurring during ECG signals acquisition, such as power line noise and wandering baseline.

[0051] In a fourth step S4, a respiration value 125 is derived for each respective normal QRS complex. The ECG-derived respiration value is hereafter called EDR value 125. An EDR value 125 may be derived for each normal QRS complex 110, leading to a series of EDR values 125, as illustrated on fig. 4B. The series of EDR values forms a raw EDR signal 150.

[0052] In this aspect of the disclosure, the EDR value for the QRS complex 110 is obtained by computing an area under the QRS complex 110. The techniques for computing the area is well-known, and comprises sampling, within a given time window or fixed time interval T, the ECG signal into a series of ECG signal samples. Amplitude of each ECG signal sample may be measured. The EDR value may be derived by summing the amplitudes of all the ECG signal samples of the series.

[0053] As shown on Fig. 3, the time window T is preferably centered around the R peak 111 of the QRS complex 110. The time window T may be chosen to be of the order of 80 milliseconds but this is not a limiting example. A typical normal QRS complex duration range may be of the order from 40-120 ms and the time window may be chosen depending on the QRS complex duration range of the patient.

[0054] Referring back to fig. 2, a step S5 of discarding outliers and bad values follows the step of deriving an EDR value for each normal QRS complex 110 of the ECG signal 100. The man skilled in the art will understand that the raw EDR signal 150 is a non- uniformly sampled signal in time, whereby there is one EDR value 125 for each QRS complex 110, preferably at the same instant than the R peak 111 of the QRS complex 110. [0055] The EDR values 125 of the raw EDR signal 150 are interpolated to derive a smooth EDR signal 200, at step S6. The smooth EDR signal 200 is illustrated on fig. AC.

[0056] The interpolation may be done at a chosen sample rate, leading to a smooth and uniformly sampled signal. The interpolation sample rate may be, for example, 8 samples per second. This is merely an example and it is not limiting the invention.

[0057] A step S6 of filtering the EDR signal 200 follows the interpolation of the raw EDR signal 150. The EDR signal may be filtered by a bandpass filter adapted to filter the EDR signal 200 within a meaningful respiration rate. An example of meaningful respiration rate may be of the order of 5 to 25 breath per minute, corresponding to a frequency in the range of 0,08 to 0,42 Hz.. This is an example only and is not intended to limit the invention.

[0058] A possible bandpass filter 300 is shown on fig.7. The filter 300 of fig. 7 is a Chebyshev type I filter, however any bandpass filter adapted to filter frequencies within the meaningful respiration rate may be contemplated.

[0059] Fig. 8A shows the EDR signal 200 before filtering with the bandpass filter 300, and Fig. 8B shows the EDR signal 200 after filtering by the bandpass filter 300. As can be seen, the step of filtering S6 removes notches in the EDR signal 200, leading to a smooth EDR signal 200.

[0060] Fig. 5 shows a flowchart of an alternative method for assessing the respiratory effort in another aspect of the disclosure. The alternative aspect of the method of Fig. 5 differs from the method of Fig. 2 in that the step S3 of removing the base line wander and the step S4 of deriving the EDR value by measuring the area of the QRS complex are replaced by a step S24 of deriving the EDR value.

[0061] After recording and processing of the ECG signal 100 at steps S I and S2, the respective EDR value 225 is derived for each respective normal QRS complexes 1 10. In the method of fig.5, the EDR value 225 is obtained by measuring a difference between a maximum amplitude and a minimum amplitude of the QRS complex 1 10. The difference between the maximum amplitude and the minimum amplitude is hereafter called the QRS swing. Fig. 6 shows an example of a QRS complex 1 10 with the maximum amplitude 160 and the minimum amplitude 170.

[0062] In the example of fig. 6, the maximum amplitude 160 is located at the R wave and the minimum amplitude 170 is located at the S wave. This is not limiting the invention, as other morphologies of QRS complexes are contemplated, where the minimum and maximum amplitudes are located elsewhere.

[0063] The man skilled in the art will recognize that the determination of the QRS swing is not sensitive to the baseline wander artefact. Accordingly, there is no need for removal of the base line wander in the method of fig.5. This is advantageous, as the removal of the base line wander is a technical challenge, which is costly in terms of resources such as CPU.

[0064] Referring back to fig. 5, the method for assessing respiratory effort comprises the step S5 of discarding outliers and bad values, followed by step S6 of interpolation of the raw EDER signal to obtain a smooth, uniformly sampled EDR signal 200. A last filtering step S7 follows, in order to filter the EDR signal 200 within a meaningful respiration rate. These steps are similar to the steps S5-S7 of the method described with reference to fig. 2.

[0065] The present invention therefore allows the assessment of a breathing pattern, in particular the respiratory effort. The EDR signal 200 according to the present teachings can be used to measure respiratory effort in any application which needs monitoring respiration. Examples are polysomnography, where EDR signal can be used as a surrogate of plethysmography belts. Alternately, EDR signal may also be used in parallel with (and in addition to) the plethysmography belts.

[0066] In respiratory-gating imaging, EDR signal can be utilized to improve image quality. Yet another application could be respiratory-gating radiotherapy, where EDR signal can be used to maximize radiation dose to the tumor while limiting normal tissue exposure.

[0067] It should be apparent from the disclosure that only one signal of ECG is enough to assess the EDR signal. Of course, this is not limiting the invention, and the method according to the present teachings can use signals obtained on different leads if there are more leads available. An EDR signal can be derived from all the leads.

[0068] The disclosure relates to a computer program product. The yet another computer program product comprises instructions to enable a processor to carry out the method for assessing a breathing pattern, in particular a respiratory effort, according to the invention.

[0069] It will be understood that the technique of the disclosure can be implemented on any device having a record of at least one channel of ECG without the need of any hardware change. The device may be an ECG device such as an already-running Holter or MCOT device. The present teachings may also be implemented on a stand alone device stand alone device such as a computer having an entry and a memory for storing the at least one channel of ECG.

[0070] While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example, and not limitation. It will be apparent to persons skilled in the relevant arts that various changes in form and detail can be made therein without departing from the scope of the invention. In addition to using hardware (e.g., within or coupled to a central processing unit ("CPU"), micro processor, micro controller, digital signal processor, processor core, system on chip ("SOC") or any other device), implementations may also be embodied in software (e.g. computer readable code, program code, and/or instructions disposed in any form, such as source, object or machine language) disposed for example in a non-transistory computer useable (e.g. readable) medium configured to store the software. Such software can enable, for example, the function, fabrication, modeling, simulation, description and/or testing of the apparatus and methods described herein. For example, this can be accomplished through the use of general program languages (e.g., C, C++), hardware description languages (HDL) including Verilog HDL, VHDL, and so on, or other available programs. Such software can be disposed in any known non-transitory computer useable medium such as semiconductor, magnetic disc, or optical disc (e.g., CD-ROM, DVD-ROM, etc.). The software can also be disposed as a computer data signal embodied in a non-transitory computer useable (e.g. readable) transmission medium (e.g., carrier wave or any other medium including digital, optical, analogue-based medium). Embodiments of the present invention may include methods of providing the apparatus described herein by providing software describing the apparatus and subsequently transmitting the software as a computer data signal over a communication network including the internet and intranets.

[0071] It is understood that the apparatus and method described herein may be included in a semiconductor intellectual property core, such as a micro processor core (e.g., embodied in HDL) and transformed to hardware in the production of integrated circuits. Additionally, the apparatus and methods described herein may be embodied as a combination of hardware and software. Thus, the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.