WO2023067609A1

WO2023067609A1 - System and method for monitoring variations in respiratory biomarkers by analysing tidal breathing waveforms

Info

Publication number: WO2023067609A1
Application number: PCT/IL2022/051118
Authority: WO
Inventors: Gregory Shuster; Nadav BACHAR; Noy DANINO; Wieland Voigt
Original assignee: Nanovation G.s. Ltd.
Priority date: 2021-10-22
Filing date: 2022-10-23
Publication date: 2023-04-27

Abstract

Systems and methods for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms are presented, comprising: receiving, obtained measurements from a wearable non-invasive measurement system for a period of time; periodically calculating a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements; and monitoring the plurality of sub-breath-cycle respiratory biomarkers of a subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject.

Description

SYSTEM AND METHOD FOR MONITORING VARIATIONS IN RESPIRATORY

BIOMARKERS BY ANALYSING TIDAL BREATHING WAVEFORMS

FIELD OF THE INVENTION

Embodiments of the invention relate generally to systems and methods for monitoring variations in respiratory biomarkers, in particular to personalized measuring and monitoring of breathing, lung function, and changes in respiratory patterns.

BACKGROUND OF THE INVENTION

Breathing is a unique biological process and biomarker because it usually happens autonomously (e.g., controlled by the nervous system) but can be instantly controlled and changed by will of thought. For example, one may choose to breathe faster or slower, deeper, or shallower, pause breathing, perform longer exhalations or shorter inhalations, etc. Such different breathing patterns may not be comfortable to execute but can indeed be performed on purpose. This is in contrast to most other biomarkers, which can be measured but cannot be directly and/or immediately controlled by will of thought, such as, for example, blood pressure, heart rate, blood oxygen saturation levels, and/or blood glucose.

Tidal Breathing is the natural process of inhalation and exhalation during restful breathing or during activity/exercise, when the subject is breathing normally and naturally, and there are no special breathing manoeuvres the subject is required to perform for a measurement test. Typically, analysis of tidal breathing waveforms allows extraction of various “tidal breathing parameters” and breathing patterns. The abundance of these parameters allows one to choose which are most relevant to analyse for a given situation or need.

Several technologies and methodologies have been previously utilized in the art to monitor and analyse tidal breathing for various purposes. Some non-exhaustive examples are discussed herein. Some prior art methods rely on a non-direct measurement of breath, for example based on body movements. Such solutions are limited by their susceptibility to movement artifacts (which in turn may yield unreliable results) and an inability to measure subjects except in controlled and stationary positions. Body-movement based devices are well known and widely used for monitoring respiration. Among them are, for example: video-assisted structured light plethysmography; transthoracic impedance (TTI) pneumography; respiratory inductance plethysmography, with piezo or strain sensor belts placed on the chest, abdomen and other locations; and ECG electrodes placed on the chest, abdomen and other locations. United States Patent Application 2019/0038179 discloses a piezoelectric sensor positioned to generate vibration signal data from a nasal bridge of a subject, which is also a non-direct measurement of breath and subject to the same movement artifacts. The main drawback of such non-direct methods is their sensitivity to any movement, which may result in the generation of movement artifacts that obscure the measurement and introduce a significant noise, reducing reliability, even in a subject who is sitting or lying down. Reliable measurement of a moving subject (e.g. during exercise) using such systems may become even more challenging, as one may need to remove the movement signals and clean the waveform signal to leave only those signals which are associated with the breathing motion. Since breathing and other movements may appear the same and/or overlap, the complexity of data analysis for such methods can present a significant challenge.

Direct respiratory sensors are sensors that detect a change due to inhaled and/or exhaled breath, usually from direct contact of the sensor with respiratory air flow. Direct monitoring of respiratory mass, flow, pressure, humidity, and/or temperature is typically utilized for various settings including capnography, nasal pressure cannulas, thermistors, pressure transducers, humidity sensors and more, used in hospitals, clinics, sleep labs and at home. Typically, these types of sensors are incorporated into a mask or nasal prongs with whiskers aimed towards the nostrils and mouth, which may improve sample delivery to and from the sensor. However, the time and flow resolution of these sensors is typically too low to resolve separate events within a single breath, and while the use of a closed mask may improve the signal-to-noise ratio and resolution, the extracted information may be unreliable due to back-pressures and other effects on the respiratory flow leaving the respiratory orifices. Moreover, incorporating these sensors within a mask or nasal prongs may result in a cumbersome system which may be intrusive and not seamless to subjects, which can make the subject aware that their breathing is being measured, which can accordingly make the subject focus on their breathing, affecting normal breathing patterns.

Another example of prior art approaches to monitoring breathing includes ventilator-derived respiratory parameters. In such solutions, a direct measurement and analysis of tidal breathing in a ventilated patient is made using a ventilating machine as a measurement module and source of sensors. Examples include a continuous positive airway pressure (CPAP) device, variable or bilevel positive airway pressure (VPAP/BPAP) device (e.g. the BiPAP® manufactured by Respironics Corporation), and other types of ventilator machine and means of breathing assistance devices. Ventilator-based solutions have been established not only as means to support the patient with adequate ventilation, but also to derive respiratory patterns in such a patient. Within this scenario, the measurement is performed by the ventilator device, using the flow and pressure sensors that are part of the ventilator. Because the patient is breathing in a closed circuit, this may allow calculation of the exact volumes, flows, times and other parameters of the subject’s tidal breathing. Whilst this may be valuable for various applications (e.g. including the decision and process of weaning a patient off of the ventilator), it does not provide a seamless and non-intrusive measurement of tidal breathing required for the uses mentioned above, because the coupling to the ventilator (e.g. via a mask) affects the patient’s breathing patterns, which may result in unnatural, forced breathing patterns, as opposed to tidal breathing patterns. Moreover, such systems are generally not portable and are highly invasive (e.g. requiring an intubated tube and/or sealed mask around the breathing orifices), thereby obscuring normal passive breathing patterns (e.g. tidal breathing) and inducing different breathing mechanics, thus limiting their application to very specific use scenarios. In addition, the long tubing of a ventilator may create a “signal diluting” effect on the respiratory flows and volumes generated by the patient, such that by the time the respiratory flow reaches the sensors at the end of the tube the flow values and times are different from their values back at the originating respiratory orifice.

Other prior art approaches include pneumotachograph (PNT), plethysmographs, spirometers, and similar techniques. Pneumotachographs are flow-resistive type devices in which gas flows through a tube containing a fixed laminar flow-resistive element. The resistive element can be either a fine- mesh screen (Lilly type) or a bundle of small capillaries (Fleisch type), both of which produce a pressure drop that is linearly proportional to flow so long as it is within a specified laminar flow range (higher flows give rise to turbulence and a nonlinear response). Accordingly, pneumotachographs that have linear flow ranges appropriate to the maximum expected flow for a given subject are typically selected. The flow characteristics may be altered by accumulation of humidity, secretions, varying gas viscosity, gas composition, and/or temperature. Pressure drops across the resistive element are typically measured with a sensitive differential pressure transducer, which typically exhibits a linear response over the appropriate pressure range, and which typically have sufficient frequency response and phase characteristics so as to capture any rapid transients contained in the flow signal. The pneumotachograph, as well as most other types of flow sensors, typically require calibration, and may require the subject to seal their lips around the mouthpiece while blocking their nostrils with a nose clip. As such, these approaches are invasive, and are not representative of normal, unconscious breathing typical of tidal breathing flows.

Another prior art approach that has been proposed and utilized is capnography-based analysis of lung function and airway obstruction level. However, capnography typically requires either a mask or nasal cannula(s) which are obtrusive and invasive, and are not seamless. Moreover, standard capnography devices are large, cumbersome, and expensive. Portable and wearable capnographs are emerging but typically have several drawbacks such as non-miniature size, low waveform resolution and high-power usage. Therefore, these methods are limited to specific use scenarios, while the extracted information is also limited by the settings of the measurement and the resolution of the sensor.

Other prior art methods which are intrusive to some extent and thus affect the breathing of the subject (and risk obscuring the actual condition) include, for example, both oscillometry and ultrasound techniques. Both may be used for tidal breathing analysis of poorly-cooperative subjects who are not able to do spirometry. Neither method is seamless, may affect the breathing mechanics, and lack the possibility for the subject to “forget they are being measured” and breathe in an unconscious and natural manner. Instead, the subject is required to hold a mouthpiece in their mouth while using their lips to enclose it and create a closed breathing circuit and in parallel have a nose clip to block the nasal orifices. Such method, while indeed measuring passive breathing, is still affecting the breathing mechanics, due to the awareness of the subject and the need to focus on holding the lips tight around the mouthpiece. Moreover, such methods are limited in the settings in which they can be applied and do not allow for objective measurement or seamless monitoring while a subject is performing daily activities, such as exercise and sleep. Therefore, these methods may not be applicable for non-cooperative or unconscious subjects (not to be confused with use herein of conscious/unconscious control over breathing), as they require correct test execution and supervision of a professional, whether by merely making sure the nose is well blocked and that the lips are sealed around the mouthpiece, or even more so when required to maintain a correct neck and head position and to press on the cheeks with both hands while executing the test.

Nebulizers and other respiratory apparatus have also been suggested in the art to provide the ability to assess the lung function of a patient through measurement of the tidal breathing parameters while the device is being used, including in the context of assessing the lung function of patients with Cystic Fibrosis. This approach requires the subject to use the medication inhalation device and typically analyses the breaths the subject takes to inhale a drug. The requirement of closed loop breathing with lips enclosing the mouthpiece of the nebulizer is a limiting factor of the technique, as described previously, but even more limiting is the fact that during the test the subject is not breathing normally but is instead trying to inhale a drug.

BRIEF SUMMARY

According to one or more embodiments, there is provided a method of measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, the method comprising: equipping a subject with a wearable non-invasive measurement system, the measurement system operable to obtain direct measurements of a respiratory airflow of the subject, the obtained measurements being indicative of a tidal breathing waveform comprising one or more sub-breath-cycle respiratory biomarkers; receiving, by at least one processor in operative communication with the wearable non-invasive measurement system, the obtained measurements from the system for a period of time; periodically calculating for the period of time, using the at least one processor, a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements, the plurality of sub-breath-cycle respiratory biomarkers including: an inhalation time parameter, “Tin”, of the subject; an exhalation time parameter, “Tex”, of the subject; an air holding time parameter between inhalation and exhalation, “Thold”, of the subject; and, a resting time parameter between exhalation and the next inhalation, “Trest”, of the subject; and, monitoring the plurality of sub-breath-cycle respiratory biomarkers of the subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject.

According to some embodiments, the method further comprises calculating and monitoring changes in an inhalation/exhalation time ratio, “Tin/Tex”, for indicating a magnitude of airway obstruction or restriction of the subject.

According to some embodiments, the method further comprises calculating a total time of a respiratory cycle, “Ttof ’, and therefrom calculating and monitoring changes in an exhalation/total time ratio, “Tex/Ttot”, for indicating a variation in a level of airway obstruction of the subject. According to some embodiments, the method further comprises calculating a total time of a respiratory cycle, “Ttot”, and therefrom calculating and monitoring changes in an inhalation/total time ratio, “Tin/Ttof ’, for indicating a variation in a level of airway restriction of the subject.

According to some embodiments, the method further comprises outputting a depiction of at least one of: the tidal breathing waveform of the subject; variation in a respiratory function of the subject; and, one or more of the calculated sub-breath-cycle respiratory biomarkers of the subject.

According to some embodiments, the wearable non-invasive measurement system is used to obtain measurements of the respiratory airflow of the subject during a methacholine challenge test, “MCT”, or during an assessment of chronic obstructive pulmonary disease (COPD).

According to some embodiments, the method further comprises generating an alert in the event that at least one of: one or more of the calculated sub-breath-cycle respiratory biomarkers; one or more time ratios derived therefrom; or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time, contravene a corresponding predefined policy.

According to some embodiments, the wearable non-invasive measurement system is configured to obtain measurements with a temporal resolution of at least one of: 0.01 seconds, 0.1 seconds, or 0.5 seconds.

According to some embodiments, the method further comprises identifying at least one of: a physiological condition; a mental condition; a hormonal condition; or physical stress, based on an analysis of one or more of: the sub-breath-cycle respiratory biomarkers; one or more time ratios; or, variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time.

According to some embodiments, the wearable non-invasive measurement system is further operable to heat or cool at least one of: the respiratory airflow of the subject; a pathway of the respiratory airflow to a sensor of the wearable non-invasive measurement system; the sensor; or a region proximate to the sensor.

According to some embodiments, the wearable non-invasive measurement system is configured to be unobtrusive such that the central nervous system of the subject assumes responsibility over breathing, thereby facilitating an analysis of natural tidal breathing waveforms of the subject and minimizing conscious intervention by the subject over respiratory function.

According to one or more embodiments, there is also provided a system for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, the system comprising: a wearable non-invasive measurement system configured to obtain direct measurements of a respiratory airflow of a subject, the obtained measurements being indicative of a tidal breathing waveform comprising one or more sub-breath-cycle respiratory biomarkers; and at least one processor configured to be in operative communication with the wearable non-invasive measurement system and configured to: receive the obtained measurements from the wearable non- invasive measurement system for a period of time; periodically calculate, for the period of time, a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements, the plurality of sub-breath-cycle respiratory biomarkers including: an inhalation time parameter, “Tin”, of the subject; an exhalation time parameter, “Tex”, of the subject; an air holding time parameter between inhalation and exhalation, “Thold”, of the subject; and, a resting time parameter between exhalation and the next inhalation, “Trest”, of the subject; and monitor the plurality of sub-breath-cycle respiratory biomarkers of the subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject.

According to some embodiments, the at least one processor is further configured to calculate and monitor changes in an inhalation/exhalation time ratio, “Tin/Tex”, indicative of a magnitude of airway obstruction or restriction of the subject.

According to some embodiments, the at least one processor is further configured to calculate a total time of a respiratory cycle, “Ttot”, and therefrom calculate an exhalation/total time ratio, “Tex/Ttot”, indicative of a variation in a level of airway obstruction of the subject.

According to some embodiments, the at least one processor is further configured to calculate a total time of a respiratory cycle, “Ttot”, and therefrom calculate an inhalation/total time ratio, “Tin/Ttof ’, indicative of a variation in a level of airway restriction of the subject.

According to some embodiments, the system further comprises an output device configured to output a depiction of at least one of: the tidal breathing waveform of the subject; variation in a respiratory function of the subject; and, one or more of the calculated sub-breath-cycle respiratory biomarkers of the subject.

According to some embodiments, the at least one processor is further configured to generate an alert in the event that at least one of: one or more of the calculated sub-breath-cycle respiratory biomarkers one or more time ratios derived therefrom; or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time, contravene a corresponding predefined policy. According to some embodiments, the wearable non-invasive measurement system is configured to obtain measurements with a temporal resolution of at least one of: 0.01 seconds, 0.1 seconds, or 0.5 seconds.

According to some embodiments, the at least one processor is further configured to identify at least one of: a physiological condition; a mental condition; a hormonal condition; or physical stress, based on an analysis of one or more of: the sub-breath-cycle respiratory biomarkers; one or more time ratios; or, variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time.

According to some embodiments, the wearable non-invasive measurement system further comprises a temperature varying element configured to heat or cool at least one of: the respiratory airflow of the subject; a pathway of the respiratory airflow to a sensor of the wearable non-invasive measurement system; the sensor; or a region proximate to the sensor.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting examples of embodiments of the disclosure are described below with reference to the figures attached hereto. Dimensions of features shown in the figures are chosen for convenience and clarity of presentation and are not necessarily shown to scale. The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may be understood by reference to the following detailed description when read with the accompanied drawings. Embodiments are illustrated without limitation in the figures, in which like reference numerals indicate corresponding, analogous, or similar elements, and in which:

Fig. 1 shows a respiratory waveform, as measured according to some embodiments of the invention;

Fig. 2 is a flowchart of a method for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, according to some embodiments of the invention;

Fig. 3 is a block diagram of a system for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, according to some embodiments of the invention; Fig. 4 is a block diagram of an exemplary computing device, which may be used with some embodiments of the present invention;

Fig. 5 shows a system for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, according to some embodiments of the invention;

Fig. 6 shows a respiratory waveform as measured according to some embodiments of the invention; Fig. 7A shows an example waveform of a subject as measured according to some embodiments of the invention;

Fig. 7B shows an example waveform of a subject as measured according to some embodiments of the invention;

Fig. 7C is a graph plotting two respiratory biomarkers, according to some embodiments of the invention;

Fig. 8 shows (left) an example change in the respiratory parameters of a COPD patient on the first and last day of their hospital stay, as measured by an embodiment of the invention, and (right) the clinical improvement of the same patient as estimated from a doctor’s questionnaire;

Fig. 9 shows (left) an example change in the respiratory parameters of a COPD patient on the first and last day of their hospital stay, as measured by an embodiment of the invention, and (right) the clinical improvement of the same patient as estimated from a doctor’s questionnaire;

Fig. 10A is a graph showing the respiratory rate of a COPD patient in the hospital, before and after administration of fast acting bronchodilators, as measured by embodiments of the invention;

Fig. 10B is a graph showing the Trest/Ttot ratio of the same COPD patient in the hospital, before and after administration of fast acting bronchodilators, as measured by embodiments of the invention;

Fig. 11 shows (top, middle) respiratory waveforms of healthy subjects and (bottom) a respiratory waveform of an exacerbated COPD patient;

Fig. 12A shows a graph of Tin/(Tex+Trest) for a hospitalized COPD patient over 5 days of hospitalization;

Fig. 12B shows a graph of Tin/Tex for the same hospitalized COPD patient over 5 days of hospitalization, as measured by embodiments of the invention;

Fig. 13 shows a graph of Tex/Ttot for a COPD patient as measured by embodiments of the invention from the first day of admission to the day of discharge;

Fig. 14 shows the results of an MCT study, according to some embodiments of the invention; and Fig. 15 shows a box plot comparison of change in the tidal breathing parameters between admission and discharge days, between two patients’ groups, as measured according to embodiments of the invention.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention can be practiced without these specific details. In other instances, well- known methods, procedures, and components, modules, units and/or circuits have not been described in detail so as not to obscure the invention.

As used herein, “tidal breathing” may refer to the normal and natural process of inhalation and exhalation during restful breathing or during activity/exercise, when the subject is not consciously modifying their breathing (e.g. for the purposes of spirometry manoeuvres and/or tests), and/or wherein the central nervous system assumes responsibility over breathing and is the sole breathmanaging authority rather than the conscious will of the subject.

As used herein, “seamless” may refer to when the subject does not feel any elements of the system as being intrusive into their respiratory orifices, and/or where no elements of a measurement system for measuring and monitoring variations in respiratory biomarkers over time obstruct or modify the airflow prior to the airflow’s exit from the respiratory orifices. Seamless, non-invasive measurement systems in accordance with some embodiments of the invention may allow completely natural tidal breathing and/or not affect the flows which are exiting the nostrils and mouth by being configured to be unobtrusive such that the central nervous system of the subject assumes responsibility over breathing, thereby facilitating an analysis of natural tidal breathing waveforms of the subject and minimizing conscious intervention by the subject over respiratory function.

The Inventors have identified that when the central nervous system is the breath-managing authority rather than the conscious will of the subject (e.g. when the subject is breathing naturally and freely or undergoing unconscious, tidal breathing), additional and new breathing patterns that were not seen during controlled or conscious breathing may emerge. The Inventors realized that these breathing patterns represent measurable respiratory biomarkers which can be monitored over a period of time to observe an indication of variation in a respiratory function or lung function of the subject. The Inventors have also realized that analysing a breath as a cycle of four sub-breathcycle respiratory biomarkers of the subject, namely: an inhalation time parameter, “Tin”; an exhalation time parameter, “Tex”; an air holding time parameter between inhalation and exhalation, “Thold”; and a resting time parameter between exhalation and the next inhalation, “Tresf ’, allows for calculation of the net inhalation and exhalation times without the hold and rest periods, which may allow for improved monitoring in variation of respiratory biomarkers and breathing patterns over time.

Embodiments of the invention provide systems and methods which may allow a subject to breathe in a manner that is completely natural, passive, and seamless, while at the same time providing precise breathing information not affected by measurement artifacts such as subject’s movements, but also with a high-enough accuracy and resolution to resolve and view each breath as a cycle of 4 steps (inhale, hold, exhale, rest) allowing capturing of the important information with relevant accuracy but without affecting the subject’s status and breathing. In some embodiments, the systems are reliable, inexpensive, and not affected by the subject’s correct or incorrect execution of a test, thereby removing the need for a professional’s supervision.

Fig. 1 depicts a respiratory waveform 100, as measured according to some embodiments of the invention. Respiratory waveform 100 may represent one or more breath cycles 110. According to some embodiments of the invention, a single breath cycle 110 may be analysed as a cycle of one or more sub-breath-cycle stages. These sub-breath- cycle stages may represent sub-breath-cycle respiratory biomarkers which may be used to measure and monitor variations in respiratory biomarkers over time.

Breath cycle 110 may include a first sub-breath-cycle 102, which may represent an inhalation time, for example the period of time taken for a subject to draw a breath in. Sub-breath-cycle 102 may have an associated time parameter (e.g. a sub-breath-cycle respiratory biomarker), which may be referred to as an inhalation time parameter, “Tin”.

Breath cycle 110 may include a second sub-breath-cycle 104, which may represent a hold time, for example the period of time taken between drawing a breath in and exhaling that breath. Sub-breathcycle 104 may have an associated time parameter (e.g. a sub-breath-cycle respiratory biomarker), which may be referred to as an air holding time parameter (e.g. between inhalation and exhalation), “Thold”. Breath cycle 110 may include a third sub-breath-cycle 106, which may represent an exhalation time, for example the period of time taken for a subject to let a breath out. Sub-breath-cycle 106 may have an associated time parameter (e.g. a sub-breath-cycle respiratory biomarker), which may be referred to as an exhalation time parameter, “Tex”.

Breath cycle 110 may include a fourth sub-breath-cycle 108, which may represent a rest time, for example the period of time taken between exhalation and the next inhalation (e.g. between letting a breath out and drawing the next breath in). Sub-breath-cycle 108 may have an associated time parameter (e.g. a sub-breath-cycle respiratory biomarker), which may be referred to as a resting time parameter between exhalation and the next inhalation, “Tres ’.

It will be understood that multiple different breath cycles 110 may have corresponding multiple different sub-breath-cycle stages/respiratory biomarkers, which may have correspondingly same, similar, or different durations, depending on the state of the subject and/or change in activity during the monitoring time.

It should be noted that in some cases, values of some of these sub-breath-cycle respiratory biomarkers may be zero. For example, Thold may be zero in many healthy subjects during most of their breathing, and Trest may be zero in physically or mentally stressed subjects, or subjects with an obstructive lung condition.

A total time of a respiratory cycle, e.g. of one or more breath cycles 110, may be referred to as “Ttot”. A respiratory rate, “RR” may be determined based on Ttot, for example in breaths per minute.

As used herein, “breathing patterns” may describe the values of the different respiratory biomarkers/parameters extracted from breathing at a given time, for example from a single breath or over multiple breaths over a period of time, as well as their statistical analysis results and their changes over time or as compared to other subjects. Breathing patterns parameters can be seen within a single breath (e.g. breath cycle length, inhalation and exhalation volume, the times of inhalation (Tin) and exhalation (Tex), etc.), or from a measurement of several breaths, or as an evolution of the measured parameters over time, from a plurality of separate time-limited measurements or from a continuous measurement.

The analysis of breathing patterns may be on the basis of a single breath, but a more reliable and complete picture may appear when analysis of these parameters is made on the basis of a plurality of breaths, with more than one breath taken into consideration for the analysis (such as a plurality of breath cycles 110, as shown in respiratory waveform 100 of Fig. 1). The analysis of a plurality of breaths may also allow an average value (e.g. mean value) to be taken and may allow a variability of the measured parameter (e.g. standard deviation) to be analysed.

Breathing patterns may be present on the level of whole breaths, with each breath taken as a single event, such as in the cases of Cheyne-Stokes breathing pattern, Biot respiration pattern, and many other examples. Alternatively, the breathing patterns can be analysed in a higher-resolution view, taking sub-breath-cycle respiratory biomarkers into account. Some embodiments utilise a mix of both approaches.

Tidal breathing parameters which may be extracted from a tidal breathing waveform by embodiments of the invention may be broken down into several categories, with each category including a plurality of possible parameters (non-exhaustive examples):

Some embodiments of the invention may measure pure time-parameters, which may be respiratory time parameters which do not relate to or rely upon the volume or magnitude of the flow which is breathed in and out. Instead, these parameters are measured only when looking at the time dimension, and only from a timing perspective of an event (e.g. start of breath in to end of breath in), rather than magnitude. Examples for respiratory time-parameters include: a. Respiratory Rate (RR); b. Time of breath cycle (Ttot); c. Inhalation Time (Tin); and d. Exhalation Time (Tex).

Pure time parameters may also include values derived from or obtained by mathematical operations involving the above parameters, for example the addition, subtraction, multiplication, or calculation of ratios between the above-mentioned parameters, such as the Tin/Tex, Tin/Ttot, or Tex/Ttot ratios. It should also be understood that derivatives, gradients/slopes, and other calculations related to the waveform itself, such as its shape and signal characteristics, may be considered.

It will be appreciated that within-subject and between-subject variability of the parameters above may be considered. Variability may be highly important as a measure of the flexibility and stress of the biological system, and may provide different important indications, depending on the subject’s state. As an example, for lung-related conditions, respiratory variability may indicate the flexibility and capacity of the subject’s respiratory function, and may provide an indication of whether the subject is close to their maximal breathing capacity and whether additional stressing of the subject might result in breathing insufficiency. Another example is respiratory variability in intubated ventilated patients, which is indicative of successful or less successful weaning of such patient from the ventilator. In other cases, variability of the respiratory function can provide indication of the sleep state or the mental stress/relaxation of the subject.

A further category of breathing parameters which may be measured by embodiments of the invention may include volume/flow/mass parameters, which may represent those parameters that account and quantify the flows, masses and volumes of the inhaled and exhaled breaths as a whole or per specific component of the breath (e.g. CO2, H2O, 02, Volatile Organic Compounds (VOCs), etc.). A few examples of flow/mass/volume parameters include: a. Tidal Volume; b. Inhalation volume; c. Exhalation volume; d. Peak tidal inspiratory flow (PTIF); and e. Peak tidal expiratory flow (PTEF), together with the within-subject and between-subject variability of such parameters (and others not mentioned here).

The humidity, temperature and/or heat capacity of the respiratory flow may also be measured by some embodiments of the invention.

A closed breathing system may be required in order to measure and quantify such flow/mass/volume parameters reliably, because the whole volume and the exact flow must pass through the sensor. If only part of the volume passes through the sensor (e.g. in an open, nonclosed, breathing circuit), while the other part does not, the ability to calculate the volume of the breath may be limited (this may be approximated through calibrations, but is complicated, and the reliability of the measurement may be lower, because different subjects with differently shaped/placed/directed noses and mouths will be directing a different and unpredictable portion of their breath towards the sensor, thus making it possible to do an individual calibration but challenging to have a good fit for the general population). Thus, systems according to some embodiments of the invention may effectively utilize less than 100% of the air flow from a subj ect’ s breathing, for example 90% or 80% or less, to obtain time parameters without obtaining flow parameters. Another category of breathing parameters which may be measured by some embodiments of the invention include mixed time-flow parameters, which are usually based on both dimensions of information (time and volume/mass/flow) and provide a plurality of possible parameters to be calculated based on these. A few examples of mixed parameters include: a. The ratio of time until peak tidal expiratory flow as a proportion of total expiratory time (tPTEF/Tex); b. The ratio of volume until peak expiratory flow to the total expiratory volume (VPEF/Ve); c. The ratio of inspiratory to expiratory flow at 50% tidal volume (IE50); and d. Time taken to reach PTIF and PTEF points (tPTIF and tPTEF), together with the within-subject and between-subject variability of the parameters above (and others not mentioned here).

Reference is made to Fig. 2, which is a flowchart of a method 200 for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms (such as respiratory waveform 100 shown in Fig. 1). Measuring and monitoring variations in respiratory biomarkers over time, in accordance with some embodiments of the invention, may include equipping a subject with a wearable non-invasive measurement system, the measurement system operable to obtain direct measurements of a respiratory airflow of the subject, the obtained measurements being indicative of a tidal breathing waveform comprising one or more sub-breathcycle respiratory biomarkers (Step 202).

The measurement system may be a measurement system as depicted in Fig. 3 herein. The measurement system may be non-invasive in the sense that it is configured to be unobtrusive such that the flow is not limited or affected by the measurement system and that the central nervous system of the subject assumes responsibility over breathing, thereby facilitating an analysis of natural tidal breathing waveforms of the subject and minimizing conscious intervention by the subject over respiratory function and/or minimizing the effects of the measurement system on the flows and breathing patterns. Further discussion of non-invasiveness is presented herein.

The measurement system may be wearable by a subject. For example the measurement system may be configured to rest or be held proximate to the subject’s nose and/or mouth by use of cords or other adjustable fixtures placed around the head or ears of the subject. A direct measurement of a respiratory airflow of the subject may include placing a respiratory sensor in an airflow of the subject (e.g. in proximity to a nose or a mouth of the subject), as compared to non-direct measurement of respiration such as monitoring chest movements or recording a sound of the airways.

Embodiments of the invention may include receiving, for example by at least one processor in operative communication with the wearable non-invasive measurement system, the obtained measurements from the system for a period of time (Step 204). The period of time may be a predefined period, e.g. five minutes. The period of time may include a continuous monitoring period, of unfixed duration, ending only when the wearable non-invasive system is removed from the subject’s face. The period of time may include continuous and/or discontinuous measurement sessions, inter-session comparison, historical analysis etc.

According to some embodiments, the at least one processor periodically calculates (e.g. for the said period of time) a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements (Step 206). The plurality of sub-breath-cycle respiratory biomarkers may include: an inhalation time parameter, “Tin”, of the subject (see, for example, inhalation time 102 in Fig. 1); an exhalation time parameter, “Tex”, of the subject (see, for example, exhalation time 106 in Fig. 1); an air holding time parameter between inhalation and exhalation, “Thold”, of the subject (see, for example, hold time 104 in Fig. 1); and, a resting time parameter between exhalation and the next inhalation, “Trest”, of the subject (see, for example, rest time 108 in Fig. 1). The calculations may be performed post-measurement. In some embodiments the calculations are performed during measurement.

Embodiments of the invention may include monitoring the plurality of sub-breath-cycle respiratory biomarkers of the subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject (Step 208). The variation may be with respect to previously measured sub-breath-cycle respiratory biomarkers of the subject, for example observing a difference from historic subject data, e.g. in contrast to comparing sub-breath-cycle respiratory biomarkers of the subject to “standard” or “text-book” values.

According to some embodiments, method 200 further comprises calculating an inhalation/exhalation time ratio, “Tin/Tex”, for indicating a magnitude of airway obstruction or restriction of the subject. The Tin and Tex values may have had the Thold and Trest values subtracted therefrom so that the ratio is more representative of the actual inhalation and exhalation phases.

Method 200 may further comprise calculating a total time of a respiratory cycle, “Ttot”. In some embodiments the method may further comprise calculating therefrom (and/or monitoring changes in) an exhalation/total time ratio, “Tex/Ttot”, for indicating a variation in a level of airway obstruction of the subject. The exhalation phase time parameter value Tex may have had a Trest and/or Thold value subtracted or otherwise separated therefrom. In some embodiments, an inhalation/total time ratio, “Tin/Ttot”, may be calculated and/or monitored for indicating a variation in a level of airway restriction of the subject (The Tin/Ttot ratio may correspond to a respiratory duty cycle). The inhalation phase time parameter value Tin may have had a Thold and/or Trest value subtracted or otherwise separated therefrom.

According to some embodiments, method 200 comprises outputting a depiction of at least one of: the tidal breathing waveform of the subject; variation in a respiratory function of the subject; and one or more of the calculated sub-breath-cycle respiratory biomarkers of the subject. The output may be on a display device, for example a screen in wired or wireless communication with the non- invasive wearable measurement system. In some embodiments, the non-invasive wearable measurement system is in communication with a software app (e.g. executed on a smartphone) which displays said outputs, for example a depiction of a respiratory waveform as shown in Fig. 1, or the calculated parameters.

In some embodiments, method 200 includes using the wearable non-invasive measurement system to obtain measurements of the respiratory airflow of the subject during a methacholine challenge test, “MCT” and/or during an assessment of chronic obstructive pulmonary disease (COPD).

Method 200 may include generating an alert in the event that at least one of: one or more of the calculated sub-breath-cycle respiratory biomarkers; one or more time ratios derived therefrom; or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time, contravene a corresponding predefined policy. For example, the at least one processor may determine that one or more sub-breath- cycle respiratory biomarkers are above (or, alternatively, below) a predefined threshold and may trigger a notification, warning, or alert to the user. In some embodiments, a trend analysis of changes over time may be used instead of or in addition to threshold violations in order to trigger a notification, warning, or alert to the user. Other parties may be alerted, such as a relative, neighbour, responsible doctor, and/or the emergency services (e.g. ambulance, first aid responder, etc.).

According to some embodiments, method 200 includes use cases where the wearable non-invasive measurement system is configured to obtain measurements with a temporal resolution of at least one of: 0.01 seconds, 0.1 seconds, or 0.5 seconds. A temporal resolution may relate to a sampling rate of the sensor, for example, sensor 310 may be configured to sample the respiratory air flow every 0.5 seconds or less. Such temporal resolutions may be “high enough” (e.g. of small enough duration) so as to resolve a single breath event into more than one stage, for example, the four stages discussed herein of Tin, Tex, Thold, and/or Trest (e.g. sub-breath- cycle stages 102, 104, 106, and/or 108 shown in Fig. 1).

According to some embodiments, method 200 includes identifying at least one of: a physiological condition; a mental condition; a hormonal condition; or physical stress of the subject based on an analysis of one or more of: the sub-breath-cycle respiratory biomarkers; one or more time ratios; and/or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time.

Method 200 may include heating or cooling the respiratory airflow of the subject, a pathway of the respiratory airflow to a sensor of the wearable non-invasive measurement system, the sensor, and/or a region proximate to the sensor. For example, the wearable non-invasive measurement system may be operable to heat or cool the respiratory airflow of the subject by way of a heating/cooling element (see, for example, heating/cooling element 340 of Fig. 3). Heating and/or cooling of the respiratory airflow, the pathway of the respiratory airflow (e.g. via one or more conduits/troughs/channels directing the airflow), the sensor(s) and/or a region proximate to (e.g. in the vicinity of) the sensor(s) may allow for better extraction of sub-breath-cycle respiratory biomarkers from the associated tidal breathing waveform. Heating and/or cooling of the respiratory airflow may change a water content level of the respiratory flow, which, when used in conjunction with sensors which undergo a change in resistance based on the water content of airflow, may allow for increased sensitivity to smaller, otherwise undetectable, flows. Heating or cooling of the sensor(s) and their vicinity may improve the resolution and response times of the sensor(s), in particular when the sensors are configured to measure humidity and/or temperature (particularly when the sensors are measuring condensed humidity). Slightly heating the sensor(s) above room temperature may allow faster removal of residual humidity upon cessation of the exhaled airflow, which may allow the sensor(s) to return to a baseline value faster and/or may reduce sensor delays due to residual humidity remaining after the exhalation has ended. Heating the sensor(s) or the vicinity proximate to the sensor(s) may increase a resolution of the sensor in resolving separate breath events, e.g. resolving between two or more sub-breath-cycle respiratory stages such as inhalation, hold, exhalation and/or rest.

For example, while the exhalation persists, more and more respiratory airflow arrives at the sensor(s), creating additional condensation, which reduces the resistance of the sensor, and this is the reason for the shape of the signal (shown, for example, in Fig. 6): a strong reduction in resistance upon beginning exhalation, and gradual decline in the rate of change of resistance while more and more flow is arriving. An equilibrium may be reached, where further addition of condensation does not significantly decrease the resistance further, which may remain at a constant low value (e.g. flat line) or reduce at a much smaller rate. Accordingly, during the exhalation, there is addition of more and more water to the surface of the sensor(s), which is measured by some embodiments of the invention. Once the exhalation stops, and no new flow arrives at the sensor, there is a need for the resistance to begin to change in a different direction (e.g. increasing) immediately and with minimum delay, so that the exhalation time Tex can be identified with high accuracy. However, the cooler the sensor is, the longer it may take for the condensed humidity to begin leaving the sensor surface at detectable amounts and for the resistance to start increasing, which may be indicative of the end of exhalation and the start of the rest time. Accordingly, heating the sensor may speed up this process and may lead to shorter delays in the response time of the sensor. Once inhalation begins, it creates a flow of room air in the vicinity of the sensor, which leads to a much faster change (e.g. increase) in resistance due to venting and removal of condensed humidity from the sensor surface, allowing the end of the rest phase and the beginning of the exhalation phase to be identified.

Accordingly, in some embodiments of the invention, one or more sensors may analyse the derivatives and slopes of the resistance.

A reduction in resistance may indicate that there is active exhalation. Even if the resistance becomes constant at some point, this may still indicate exhalation, because the sensor may be saturated and adding more humidity/condensation may not affect the resistance.

When heating the sensor, a slow increase in resistance (e.g. small rate of change) may indicate a "no flow" situation, and the increase may be attributed to natural desorption of humidity from the sensor. Heating the sensor may increase humidity desorption (e.g. by tilting an equilibrium of water absorption/desorption from the sensor surface towards desorption). Sensors used by embodiments of the invention may be more sensitive to changes in humidity than to changes in temperature, and so measurements (e.g. of resistance) indicative of a respiratory waveform as measured by some embodiments of the invention may show a near “instant” stop in resistance decrease trend (caused by the exhalation) and an “immediate” increase in resistance.

Without the heating, the same mechanisms of absorption/desorption exist, but the dynamics are slower, and more variable due to environmental factors such as ambient temperature and relative humidity. The morphology of the sensor surface may also affect the absorption/desorption. For example, if the sensor were not heated, the resistance may have continued to decrease or remain steady for some fractions of a second, and in some cases even for several seconds due to environmental or other factors. Heating in accordance with embodiments of the invention may reduce this time delay and may induce a change in the slope of resistance, from negative or zero slope to slightly positive slope.

When the resistance begins to increase at a faster rate, this may be indicative of the inhalation phase, since the inhalation may cause active ventilation of the sensor by drawing room air past the sensor, which may result in faster removal of condensed humidity. Heating in accordance with some embodiments of the invention may allow a differentiation between the four sub-cycle phases of breath. Without heating, it may be harder to distinguish between two or more sub-breath-cycle respiratory stages such as inhalation, hold, exhalation and/or rest.

Heating in accordance with embodiments of the invention may also prevent residual condensed humidity form obscuring the next inhalation following exhalation and/or rest.

As has been discussed, monitoring and analysis of breathing and/or breathing patterns may be important across various fields, and there may be cases in which there is a need to measure lung function and breathing patterns in a seamless and direct manner. Systems suitable to carry out such methods are presented herein.

Breathing may be considered a bi-directional communication channel of the body which is able to both provide information about the mental, neurological, hormonal, and physiological states of the subject, along with other conditions of a subject, as well as create an effect upon or influence said states by controlling breathing: it is well known that deep breaths may have a calming effect, for example. Therefore, measurement of breathing patterns allows collection of output information from the body, while controlled and intentional breathing rhythms or patterns may enable the provision of inputs to the body. For example, breathing can be used as a channel to read-out information from the body as well as an input channel for introducing changes to the body, allowing for bi-directional tapping into the body’s function. As such, measuring and monitoring tidal breathing (whether conscious or non-conscious) can be beneficially utilized for various purposes, some of which are discussed herein.

One advantage of measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms may be the ability to continuously or periodically assess the lung function, the mechanics of the subject’s breathing and/or dynamic changes in breathing of the subject on a breath-to-breath level and over time. Traditional approaches for testing lung function typically rely on techniques that require forced inhalation and exhalation manoeuvres (e.g. spirometry, peak flow meter, plethysmography, etc.). While conscious tidal breathing can provide some information on the lung function, additional information may be present while unconscious breathing is taking place, as subjects are not trying to control or normalize their breathing and may present natural breathing patterns. Nevertheless, irritation or stress may also affect the breathing pattern of the unconsciously breathing subject, and therefore using a measurement system and environment that do not stress the subject may be important. Also, the true variability of the operation of a subject’s breathing system (and therefore its overall condition, flexibility, and ability to bear more load) may be capable of being measured when subjects are breathing normally and unaware, since such breathing is controlled by their nervous system and not affected by the subject’s will or attempts to control their breathing.

Another advantage of measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms may be the ability to assess the subject’s physiological and/or mental condition. Whilst not necessarily directly related to the condition of the lungs and their function, such monitoring may allow changes in these conditions to be identified, for example in a single spot check of several seconds to several minutes, in periodic spot checks, over continuous long-term measurement, or over many episodes of long-term continuous measurements. Such unconscious breathing patterns may contain information that is less apparent (or not apparent at all) in measurements that interfere with a subject’s normal breathing or make the subject aware/conscious of their breathing process. This may be because when the subject is aware and focuses on the measurement of their breathing, the subject may control their breathing and could intentionally or unintentionally change their breathing during the measurement, obscuring the actual condition due to erroneous test results. Analysis of unconscious tidal breathing patterns and variability at various states (e.g. sleeping, resting, walking, exercising, working, before and after medication, etc.) may, for example, provide insights on levels of stress and relaxation related to (or not related to) the activity the subject is engaging in. Among the possibilities that are already known may be the differentiation of sleep stages and indication of sleep quality, or identification of underlying mental or physiological conditions or abnormalities and their changes over time, between different environments, and/or during different activities.

Another advantage of measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms may be the identification of periodic/sporadic breathing patterns. Such identification may be used to diagnose conditions based on breathing patterns which do not appear constantly but only periodically or during a specific activity. For example, monitoring of an office employee in accordance with some embodiments of the invention may determine that when this employee is reading emails or is focused on a highly-demanding task, the employee uptakes a different from normal breathing pattern which may not be optimal and/or which may create more stress, which may make it harder for the employee to focus or lead to unwanted physiological and mental effects (for example, the condition called “email apnea”, wherein a person reading emails begins to breathe as if they are in a “fight or flight” response). Typically, the subject may not be aware that they are engaging in such breathing, but these patterns may be measured and monitored by embodiments of the invention during the relevant time frames and the subject may be informed. Identification of such unwanted patterns may allow one to seek professional help and/or avoid such situations, which may result in, for example, better health and/or better work performance.

Another advantage of measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms may be identification of driver’s mental/physical condition, based on changes in breathing patterns over the time. For example, after several hours of driving the driver may begin to breathe differently due to tiredness, anxiety, anger, and/or hunger. The condition may not always be subjectively identified by the driver, or noticed with high importance, but measurement and monitoring of their breathing patterns may identify their condition. This may be of importance for monitoring those who drive regularly, such as long distance truck drivers, bus drivers, etc., and may help identify suitable times for breaks. It may also be important to precisely monitor breathing during execution of controlled and intentional breathing patterns, during execution of exercise, and/or during rehabilitation/physiotherapy activities, which may allow quantification and feedback to the subject, instructor, and/or healthcare professional. Embodiments of the invention may be used for these purposes. Typically, such measurements may be relevant in situations where specific breathing patterns are attempted to be executed in order to induce a change or effect in the subject. Such controlled breathing and breathing exercises may be powerful and important intervention tools, as they may enable active introduction of inputs and changes to the body, and may be considered tools for control/intervention of the body and its functions (e.g. mental, neurological, physiological, hormonal, etc., as well as specifically the improvement of lung function). Examples can be drawn from pulmonary rehabilitation, Yoga breathing techniques, or the Wim Hof breathing method which, among other things, may be capable of: inducing mental relaxation; reducing blood pressure; changing heart rate and/or heart rate variability; changing physiological states of the subject, such as alter their blood 02 levels, CO2 levels, and/or pH levels; and/or reduce the subject’s sensitivity to cold. As part of these practices of attempting to achieve specific breathing patterns, it may be beneficial to monitor the subject’s breathing and provide live (or offline) feedback on whether the breathing is or was correctly performed, since a precise execution may mean faster achieved effects, while incorrect execution might delay progress or even generate a counterproductive effect, worsen the condition, or endanger the subject. Thus, monitoring the intentional execution of such breathing techniques with increased resolution and insight may be beneficial and may allow for successfully reaching the goal of such exercises.

A system for monitoring breathing may detect and measure flow rates, volumes and durations of each inhalation and exhalation as well as determine Thold and Trest time durations (between inhalation and exhalation). Such a system, even if not configured to quantify the flows and volumes directly, may at least be able to do so after personal calibration or provide a relative-change flow and volume semi-quantification. However, this requirement is notable as it collides with the requirement for seamless and non-interfering measurement: on one hand the masks, mouthpieces and all other methods of coupling between flow or volume sensors to the respiratory system are intrusive, but on the other hand, when these coupling solutions are not used, there is a significant challenge to precisely quantify flows or volumes due to the uncertainty with respect to which part or portion of the flow reached the sensor and which missed it. For example, placing a flow sensor in front of the nostrils may not ensure that the maximum exhaled or inhaled flow reaches the sensor, and it might be that the sensor is only exposed to half of the maximal flow actually exiting the nostril.

The Inventors have discovered that a measurement system that does not quantify flows and volumes may still be beneficial and provide an advancement over the current state of the art as it may be able to seamlessly and precisely capture the pure time-parameters, with specific importance to precisely quantify not only Ttot, Tin and Tex but also Thold and Trest.

By utilizing the inventive approach of non-invasive measurement of tidal breathing and calculation of the 4 sub-breath-cycle parameters with sufficient resolution, patterns with clinical or other relevance that were previously unseen may be identified and distinguished, enabling characterization of various conditions as well as characterization of the operation of the lungs and the respiratory system.

Fig. 3, shows a block diagram of a system 300 for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, according to some embodiments of the invention.

System 300 may include a wearable non-invasive measurement system 301 configured to obtain direct measurements of a respiratory airflow of a subject. The obtained measurements may be indicative of a tidal breathing waveform comprising one or more sub-breath-cycle respiratory biomarkers. Measurements may be obtained by one or more sensors 310. At least one sensor 310 may be a direct respiratory sensor. Respiratory airflow of the subject may be guided from the respiratory orifices of the subject to the one or more sensors 310 by one or more channels, troughs, and/or half-pipes.

System 300 may include at least one processor 320. The at least one processor may be, or may include elements of, a computing device as described in Fig. 4. The at least one processor 320 may be configured to be in operative communication with the wearable non-invasive measurement system 301, for example via a wired connection or via a wireless connection. In some embodiments, the at least one processor 320 may be part of wearable non-invasive measurement system 301.

The at least one processor 320 may be configured to receive measurements obtained by wearable non-invasive measurement system 301 for a period of time. The period of time may be a predefined period of time, such as five minutes. In some embodiments, the period of time is of unfixed duration, for example to allow continuous monitoring by the system, and may cease upon removal of the measurement system by the subject and/or other users (e.g. supervising medical professional).

The at least one processor 320 may be configured to periodically calculate (e.g. for the period of time) a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements. The plurality of sub-breath- cycle respiratory biomarkers may include: an inhalation time parameter, “Tin”, of the subject; an exhalation time parameter, “Tex”, of the subject; an air holding time parameter between inhalation and exhalation, “Thold”, of the subject; and/or a resting time parameter between exhalation and the next inhalation, “Tresf ’, of the subject.

The at least one processor 320 may be configured to monitor the plurality of sub-breath-cycle respiratory biomarkers of the subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject.

According to some embodiments, the at least one processor 320 is further configured to calculate an inhalation/exhalation time ratio, “Tin/Tex”, which may be indicative of a magnitude of airway obstruction or restriction of the subject.

The at least one processor 320 may be further configured to calculate a total time of a respiratory cycle, “Ttot”, and therefrom calculate an exhalation/total time ratio, “Tex/Ttot”, which may be indicative of a variation in a level of airway obstruction of the subject.

In some embodiments, the at least one processor 320 is further configured to calculate a total time of a respiratory cycle, “Ttot”, and therefrom calculate an inhalation/total time ratio, “Tin/Ttot”, which may be indicative of a variation in a level of airway restriction of the subject.

According to some embodiments, system 300 further includes an output device 330. Output device 330 may be, for example, a screen connected to wearable non-invasive measurement system 301 and/or the at least one processor 320, e.g. a TV/computer monitor in wired communication with system 300, or a smartphone/tablet display in wireless communication with system 300. Output device 330 may be configured to output a depiction of at least one of: the tidal breathing waveform of the subject (such as respiratory waveform 100 shown in Fig. 1); variation in a respiratory function of the subject; and/or one or more of the calculated sub-breath-cycle respiratory biomarkers of the subject.

In some embodiments, the at least one processor 320 is further configured to generate an alert in the event that at least one of: one or more of the calculated sub-breath-cycle respiratory biomarkers; one or more time ratios derived therefrom; or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time, contravene a corresponding predefined policy. The policy may include, for example, one or more threshold values of the sub-breath-cycle respiratory biomarkers. The alert may include audio, visual, and/or haptic components. The alert may notify a third party, such as a medical professional responsible for a user/subject, and/or the emergency services (e.g. ambulance).

According to some embodiments, the wearable non-invasive measurement system is configured to obtain measurements with a temporal resolution of at least one of: 0.01 seconds, 0.1 seconds, or 0.5 seconds. For example, sensor 310 may be a sensor with a resolution of 0.5 seconds or finer (e.g. shorter duration/higher resolution).

In some embodiments, the at least one processor 320 is further configured to identify at least one of: a physiological condition; a mental condition; a hormonal condition; and/or physical stress of the subject, based on an analysis of one or more of: the sub-breath-cycle respiratory biomarkers; one or more time ratios; and/or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time.

In some embodiments, the wearable non-invasive measurement system further comprises a temperature varying element (e.g. heating/cooling element 340) configured to heat or cool at least one of: the respiratory airflow of the subject; a pathway of the respiratory airflow to a sensor of the wearable non-invasive measurement system; the sensor; or a region proximate to the sensor, as described above.

In some embodiments, sensor(s) 310 may be based on technology described in United States Patent No. 10,663,420, which is incorporated herein by reference. The building blocks of the nanomaterial based respiratory sensors therein are spherical metal nanoparticles encapsulated within an organic monolayer, which are deposited in a unique topography on a rigid or flexible substrate with electrodes (e.g. rigid and flexible printed circuit boards (PCBs) made from either FR4, polyimide or polyester or silicon thermal oxide wafers). The nanoparticle-based film may create electricaltransport pathways between the otherwise disconnected electrodes. Thus, the film’s electrical properties, such as resistance, conductance, capacitance, and impedance (and changes thereof) can be measured. In some embodiments, the film’s resistance may depend on the amount of condensed humidity, and may change rapidly in response to minor changes in the amount of condensed humidity added or removed from it, which may allow precise monitoring of the respiratory flow of a subject by means of measurement, visualization and mathematical analysis of these humidity content changes and the resulting resistance changes over time.

In some embodiments, sensor(s) 310 may be sensitive to other physical properties or chemical elements of the respiratory flow, such as temperature, air-humidity, air-flow, pressure, amount of carbon-dioxide, and/or amount of volatile-organic-compounds.

In some embodiments, sensor(s) 310 are respiratory sensors comprising resistors with two electrical connections and are wire-connected to an impedance-measurement circuit which is managed by the at least one processor 320, which may be an on-board microcontroller unit (MCU). During the process of obtaining a respiratory waveform measurement, the electrical resistance of the sensor(s) may be sampled periodically, with a constant or changing frequency. The more points sampled, the higher the spectral resolution achieved. A typical measurement frequency may be in the range 10-100 Hz. The changes in sensor’s resistance may be induced by the respiratory airflow. The measured resistance values may then be stored with a time stamp as data points. Plotting the resistance versus time may draw the respiratory waveform. The data points may be stored on the measurement system. In some embodiments, the measurements are sent in real-time or post-hoc via any applicable data transfer method (such as Bluetooth, Bluetooth low energy (BLE), WiFi, cellular, LoRa, NFC, USB, SD card etc.) to a receiver device (such as a mobile device) or directly to the cloud. The data analysis may be performed either on the hardware of the measuring device, on the receiver device, or in the cloud.

In some embodiments, the hardware of measurement system 301 is miniature and integrated within a housing for sensor(s) 310. In some embodiments, the hardware is larger and includes a screen or other output device 330 for showing real-time or post-hoc measurements, waveforms, and/or calculated results.

Measurement system 301 may incorporate a single sensor 310 or multiple sensors 310, each serving as a data-source/data-channel. In embodiments with multiple sensors, the sensors may be sampled (e.g. by one or more processors 320) either in parallel or consecutively. In addition, some sensors may be redundant, used as a backup for one or more other sensors, or may be used as different data-channels.

In some embodiments of the invention, the system incorporates a single respiratory sensor to collect the airflow directed to it either from the nose or from the mouth. In some embodiments of the invention, the system incorporates three respiratory sensors, with two sensors configured to collect signals of a respiratory flow from the subject’s nostrils, and one sensor configured to collect signals of a respiratory flow from the subject’s mouth. All three sensors may measure in parallel (or substantially in parallel, for example within a bounded time period of one second or less) and each sensor may measure at a frequency of 25 Hz, which may result in a total of 75 measurements per second for the three sensors.

In some embodiments, where multiple sensors are used, a pre-processing algorithm is periodically applied to analyse each of the sensor signals and identify which sensor of the multiple sensors to use as a predominant data-source sensor. The choice of which sensor to use as the predominant data-source sensor may be based on different possible decision trees and rules, which may include: amplitude of signal; noise of signal (e.g. signal to noise ratio, SNR); sensor baseline attributes; and/or number of artifacts in the signal. The respiratory parameters may then be calculated based on the waveform of only the predominant data-source sensor.

In some embodiments, an algorithm is applied to first identify if there is a respiratory signal from the nostrils, ignoring respiratory signals collected from sensors at the mouth. The algorithm may determine which of the two redundant nasal sensors to use as the predominant data-source. If a respiratory signal from the nostrils is not detected, then the algorithm may determine which of the sensors at the mouth to use as the predominant data-source. If no respiratory signals are detected from any of the sensors, then this may be identified as an apnea or no-breathing event, and an alert may be triggered.

In some embodiments, an algorithm is used to analyse each of the sensor signals independently, to yield different sets of respiratory parameters. Selection criteria may be applied to determine whether to choose one sensor as a data-source, or whether to apply averaging or other data merging means over two or more sensors.

Reference to “analysis” or an “algorithm” may refer to analysis of the resistance values over time, filtering and/or smoothing the data, calculation of moving averages, derivatives, and/or ratios etc., which may allow the identification of the start and end of each breath and/or sub-breath event separately. The start and end of each sub-breath phase within each breath (e.g. inhalation duration, exhalation duration, etc.) may also be identified.

In some embodiments, such analysis is done in two steps: (1) identification of each breath event through peak detection algorithms, allowing calculation of RR; and (2) identification of the start and end of each of the sub-breath phases within a breath, based on thresholds of the derivatives of the respiratory sensor’s resistance values over time (for example, positive derivative values above a given threshold may be assigned to inhalation; negative derivative values below a given threshold may be assigned to exhalation; small (e.g. within 5%) positive or negative derivative values outside the given thresholds may be assigned to Trest or Thold, depending on whether it is after inhale or after exhale). Once the start and end of each sub-breath-cycle has been identified, the algorithm may calculate the length of each phase, resulting in values for Ttot, Tin, Thold, Tex, Trest. Additional calculations may be made of averages, ratios, variability within subject over a single measurement, over several measurements over time, and/or between subjects.

Fig. 4 shows a block diagram of an exemplary computing device 400 which may be used with embodiments of the present invention. The at least one processor 320 of Fig. 3 may be (or may include elements of) computing device 400, for example. Computing device 400 may include a controller or computer processor 405 that may be, for example, a central processing unit processor (CPU), a chip or any suitable computing device, an operating system 415, a memory 420, a storage 430, input devices 435 and output devices 440 such as a computer display or monitor displaying for example a computer desktop system.

Operating system 415 may be or may include code to perform tasks involving coordination, scheduling, arbitration, or managing operation of computing device 400, for example, scheduling execution of programs.

Memory 420 may be or may include, for example, a random-access memory (RAM), a read-only memory (ROM), a flash memory, a volatile or non-volatile memory, or other suitable memory units or storage units. At least a portion of memory 420 may include data storage housed online on the cloud. Memory 420 may be or may include a plurality of different memory units. Memory 420 may store for example, instructions (e.g. code 425) to carry out methods as disclosed herein. Memory 420 may use a datastore, such as a database.

Executable code 425 may be any application, program, process, task, or script. Executable code 425 may be executed by controller 405 possibly under control of operating system 415. For example, executable code 425 may be, or may execute, one or more applications performing methods as disclosed herein, such as calculations on or between sub-breath-cycle respiratory biomarkers, in particular ratios between sub-breath-cycle respiratory biomarkers. In some embodiments, more than one computing device 400 or components of device 400 may be used. One or more processor(s) 405 may be configured to carry out embodiments of the present invention by for example executing software or code.

Storage 430 may be or may include, for example, a hard disk drive, a floppy disk drive, a compact disk (CD) drive, a universal serial bus (USB) device or other suitable removable and/or fixed storage unit. Data described herein (such as measurements obtained by wearable non-invasive measurement system 301 shown in Fig. 3) may be stored in a storage 430 and may be loaded from storage 430 into a memory 420 where it may be processed by controller 405. Storage 430 may include cloud storage. Storage 430 may include storing data in a database.

Input devices 435 may be or may include a mouse, a keyboard, a touch screen or pad or any suitable input device or combination of devices. Output devices 440 may include one or more displays, speakers and/or any other suitable output devices or combination of output devices. Any applicable input/output (I/O) devices may be connected to computing device 400, for example, a wired or wireless network interface card (NIC), a modem, printer, a universal serial bus (USB) device or external hard drive may be included in input devices 435 and/or output devices 440.

Embodiments of the invention may include one or more article(s) (e.g. memory 420 or storage 430) such as a computer or processor non-transitory readable medium, or a computer or processor non- transitory storage medium, such as for example a memory, a disk drive, or a USB flash memory encoding, including, or storing instructions, e.g. computer-executable instructions, which, when executed by a processor or controller (such as processor 320 of Fig. 3 or controller 405), carry out methods disclosed herein.

The systems and methods described herein may allow for measuring breathing and determining a subject’s condition (and, for example, changes in such condition), without interfering with the subject’s breathing or affecting the results.

One advantage of embodiments of the invention may be the ability to provide precise and valuable respiratory and lung function information without the need to: (i) use systems that precisely quantify the inhaled/exhaled flows or volumes, and thus require closed or semi-closed breathing systems or any coupling between the airways and measurement system (such as masks or sealing lips around a tube while breathing through the tube, which may create bias in the results and may therefore be less preferable), (ii) use systems that measure respiration indirectly which may not be precise and may introduce movement artifacts or (iii) use systems which are obtrusive to the patient such as nasal cannulas with nasal prongs entering the nostrils, affecting the subject and making them aware of their breathing resulting in less natural breathing.

Another advantage of embodiments of the invention may be the attainment of more accurate and meaningful results by measuring and quantifying Thold and Trest and disassociating them from Tin and Tex as part of the analysis, leading to more meaningful and relevant results compared with measurements of exhalation and inhalation times which include the hold or rest phases as part of the inhalation or exhalation phases.

The methods and systems according to embodiments of the invention disclosed herein may generate more relevant and insightful information than compared to current state of the art approaches to measuring and analysing breathing. Therefore, the invention may enable an improvement beyond the state of the art with respect to monitoring and analysis of breathing, allowing generation of deeper, more precise and more relevant information, at various situations and conditions, and leading to better conclusions and outcomes.

A system according to embodiments of the invention may combine the following characteristics:

1. Seamless, non-invasive, and non-interfering: embodiments of the invention may allow measurement of natural and free breathing, for example by not confining, restricting, or affecting the subject, the subject’s airways, or the subject’s breathing in any way. In some embodiments the subject may forget that they are being monitored or that that their breathing is being measured, which may allow the subject to focus on things other than their breathing and not think about the measurement (e.g. allowing the subject to perform unconscious/tidal breathing). When the subject is not focusing on their breathing, the respiratory function (characterised, for example, by a respiratory waveform such as respiratory waveform 100 of Fig. 1) may be presented in its most natural and true form, which may allow additional conditions and abnormalities to be distinguishable/identifiable than when the subject is aware of their breathing and attempting to control their breath. Moreover, additional condition-related insights may become apparent from the measured data, when the system is seamless enough to allow the subject to breathe unconsciously without awareness or focus on their breathing. This is due to the fact that when the central nervous system is the breath-managing authority rather than the conscious will of the subject, additional and new breathing patterns that were not seen during conscious breathing may emerge. A seamless, non-invasive, and non-interfering method and/or measuring system, in accordance with some embodiments of the invention, may avoid or obviate a requirement for the following features:

(a) use of invasive elements entering the nostrils or mouth or other areas of the subject’s body, such as a nasal canula, endotracheal tube, or tracheotomy tube;

(b) use of masks or any enclosure around the respiratory orifices which may obstruct the natural airflow of breathing or create a change in the pressure surrounding the respiratory orifices or a back-pressure to the flow exiting the orifices;

(c) use of respiratory measurement techniques that require a closed breathing system, e.g. masks (such as a nasal mask, a nasal/oral mask, a full face mask, a total face mask, a partial rebreathing mask, etc.), or breathing through a tube with lips sealed around the tube and nasal clip closing the nostrils or any other coupling with the airways;

(d) use of respiratory apparatuses such as nebulizers or inhalers or other devices intended to provide inhaled medication and to provide measurement while the subject is inhaling or exhaling through them;

(e) elevating or modulating the pressure in or near the breathing orifices or the airways themselves or creating any interference with the respiratory flow leading to flow or pressure modulations;

(f) use of mouthpieces, nose-clips, or other means of unnaturally directing the flow between the mouth and nose;

(g) requiring determined and fixed measurement positions, or imposing any restrictions on position and movement of the subject; and/or

(h) use of specific and/or forced breathing manoeuvres or protocols which required by the subject in order to execute the measurement.

2. Wearable and si

to use system Systems and/or methods in accordance with embodiments of the invention may be portable, wearable, and not be affected by subject’s movement (thus removing the need for a subject to control or restrict their movement during the measurement), thus allowing measurement at different and changing positions, and during different activities of the subject (such as sleeping, or exercising), anywhere and anytime. Systems and/or methods in accordance with embodiments of the invention may allow the subj ect to take any position and move as they please (e.g. lay, sit, stand, change positions), or perform any type of activity (e.g. move, walk, climb stairs, driving, while at work, while exercising, while sleeping, etc.) during the measurement, without the system/method interfering with the activity or having the activity interfere with the measurement. Embodiments of the invention may allow self-measurement by the subject, and as such may allow for a simple and straightforward measurement at any time and any place. Embodiments of the invention may include a simple operational workflow and may be operable without any required explanation to the subject, and without the need for the subject’s cooperation during use or any understanding by the subject of the measurement process/protocol. A simple execution/workflow, in accordance with embodiments of the invention, may allow for natural breathing without interfering/affecting the subject’s breathing, which may avoid obscuring the information expected to be gained from the measurement. Embodiments of the invention may be versatile and may not be restricted to time or location of the measurement, allowing the ability to diagnose or monitor various conditions which may be apparent, for example, only at certain times of the day (e.g. sleep apnea), during certain situations, or during specific activities. Because embodiments of the invention may allow the subject to perform measuring and monitoring of respiratory biomarkers on their own, at their preferred location and/or time, there may be an elimination/reduction in possible bias due to observer effect, location effect, and/or a bias introduced to the measurement due to stress on the subject created by the presence of a health care professional, medical environment, or other people near the measured subject.

3. Utilize reliable and direct respiratory sensors: In embodiments, a plurality of sensors is provided in a housing worn on a subject’s face. Embodiments of the invention may use one or more direct flow-sensing elements, which may provide a benefit over indirect respiratory sensing methods, such as sensors for monitoring the subject’s body movements. Body-movement derived parameters may be affected and/or obscured by movement artifacts, and may be misleading or incorrect even when no movement artifacts are present due to the possibility that not every time there is a breath- related flow there is also a significant movement of the external body parts (e.g. chest, abdomen, etc.). Direct flow measurement may allow direct identification of the inhaled and exhaled flows, rather than a proxy measurement of the body’s movement. This may allow execution of measurements that are not affected by movement of the subject, which may provide more precise and clean data (e.g. compared to data which includes movement artifacts), and may further allow extended measurement scenarios to non-resting or non-controlled conditions (such as during exercise, during daily living activities, sleeping, or otherwise). Even a subject who is laying or sitting may generate some level of movement beyond a breathing related motion, which may introduce irrelevant signals to the respiratory waveform. Thus, embodiments of the invention may utilize direct measurement of respiratory airflows using direct sensors, which may be preferable over the indirect approaches described herein due to the higher accuracy and reliability afforded by direct sensors.

4. Have sufficient sensor and algorithmic accuracy and resolution: Embodiments of the invention may use sensors, processors, and/or algorithms adapted to capture and resolve the breathing signal into multiple sub-breath- cycle biomarkers and quantify the time period of each of these stages with sufficient accuracy. Four exemplary sub-breath-cycle respiratory biomarkers are depicted in Fig. 1, wherein a single breath cycle 110 is comprised of an inhalation stage 102, a holding stage 104, an exhalation stage 106, and a rest stage 108. A minimal required time-accuracy and resolution of a system according to embodiments of the invention may be dependent on the type of information attempting to be obtained from the measurement, and may also vary based on the subject’s individual lung parameters, specific condition during the measurement, and breathing frequency. An accuracy and resolution of at least 20% of the total breath cycle may be sufficient for embodiments of the invention. As an example, for an adult human who is breathing at 12 breaths per minute (an average of 5 seconds per breathing cycle), an accuracy or resolution of 1.0 seconds may be sufficient to gain useful respiratory information. However, for the same human breathing at 30 breaths per minute (a 2 second breathing cycle), a higher resolution of 0.4 seconds may be required in order to gain meaningful measurements of the length of time of the sub-breath-cycle respiratory biomarkers. In a preferred embodiment, a resolution of 0.1 seconds is sufficient to cover most human conditions and to provide meaningful and valuable results, and in a more preferred embodiment a resolution of 0.01 seconds provides even more accurate and valuable results. For smaller than average lung capacities and higher breathing frequencies, especially for infants or animals with smaller lungs, embodiments of the invention may provide a resolution and accuracy below (e.g. finer than) 0.01 seconds.

5. Enable the measurement of multiple respiratory and sub-breath-cycle respiratory biomarkers: Embodiments of the invention may measure and/or monitor one or more of: respiratory rate (RR), total time of respiratory cycle (Ttot), inhale time (Tin), exhale time (Tex), time of air hold between inhale and exhale (Thold), and/or the time between the exhalation and the next inhalation (Trest). When using the above-described non-invasive method and system for measuring and monitoring variations in respiratory biomarkers over time, just measuring the time parameters (Respiratory Rate, cycle time Ttot, and the four sub-breath-cycle parameters Tin, Thold, Tex and Trest) may already be highly beneficial and allow important and valuable information to be obtained. Therefore, the analysis of these breath patterns resulting from pure time parameters, when collected with a system to measure breathing objectively, in a non-interfering and non-invasive manner, provides new and valuable abilities, insights and information.

Embodiments of the invention may identify and separate Thold and Trest from Tin and Tex. This may be important for two reasons: (1) in order to precisely measure the time of inhalation flow or the time of exhalation flow, it may be important to differentiate the times at which there was no flow (Thold and Trest) rather than include them as part of Tin, Tex, or both; and (2) Thold and Trest may contain valuable information about different conditions.

Since a subject’s Respiratory Rate (RR) affects the absolute values of Ttot, Tin, Thold, Tex and Trest, embodiments of the invention may calculate normalized values of these respiratory biomarkers by dividing each of these parameters by total breath time (Ttot), which may eliminate the effect of varying breath length within a subject or between subjects.

One example of the importance of Trest and Trest/Ttot is that these values and their variability act as biomarkers for physical stress or effort on the breathing system, and indicators of how much the respiratory system is stressed or relaxed, and how much further the respiratory system can be stressed. Accordingly, a healthy individual initiating and proceeding a physical stress exercise may not only elevate his respiratory rate, but also shorten his Trest and the normalized relative rest time - Trest/Ttot.

Fig. 5, shows a wearable non-invasive measurement system 500 for obtaining direct measurements of a respiratory airflow of a subject, according to some embodiments of the invention. System 500 includes respiratory sensors in a housing 510, and a processing and transmitting module located in a housing 520. The respiratory sensors may be sensitive to condensed humidity and may change its resistance based on an amount of water content present in the respiratory flow (e.g. based on the principles of respiratory sensors described in US20130171733A1, by the inventor(s) herein which is incorporated by reference).

The housing 510 is non-invasive, as can be seen from the inset portion of Fig. 5, with no part of the system entering the nostrils and/or mouth (e.g. the housing and the sensors are placed outside the nostrils and mouth and do not seal or block the airflow from and to the nostrils and mouth), and no requirement for the subject 501 to seal their lips around any part of housing 510. Housing 510 may be shaped to ensure that an effective portion of the respiratory flow (but not necessarily 100%) reaches the respiratory sensors so as to generate a reliable signal. The housing may include channels, troughs, and/or half-pipes to guide the respiratory flow to the respiratory sensors, without the need for a closed system or tubing which may affect the flow as described herein (e.g. back pressure), and without the need for nasal prongs entering the nostrils to direct part of the flow towards the sensors. Cords 515 seen in Fig. 5 may be wires placing the respiratory sensors in operative communication with one or more processors located in a second housing 520. Cords 515 may also be utilized to help position housing 510 containing the respiratory sensors in the vicinity of the respiratory orifices by looping over the ears of subject 501. It should be noted that cords 515 are not tubes for transporting respiratory flow. The shape of housing 510 and the flow-guiding solution is described in United States Patent Application Publication 2021/0145312 Al by the inventor(s) herein and owned by the present assignee, which is incorporated by reference. For example, housing 510 may be anatomically shaped to rest in front of a face of a subject in proximity to the respiratory orifices, and may include a concave/curved trough, channel, or half-pipe configured to be placed proximate the nostrils (e.g. below the nostrils) and to non-invasively funnel or otherwise guide a respiratory flow to one or more sensors based on fluid mechanic principles. When worn by a subject 501, system 500 may collect, analyse, and present (e.g. by transmitting to a display device, not shown in Fig. 5) a respiratory waveform 600 as shown in Fig. 6. In this embodiment, the resistance of the sensor (Y axis of Fig. 6, with X axis being time) increases during the inhalation phase, decreases during the exhalation phase, and is relatively constant during the hold time and rest time. The processor may analyse the generated waveform 600 and may identify time periods during which a respiratory flow is present and/or the time periods during which there is no respiratory flow (or where the flow is negligibly small). Such no-flow areas are not necessarily present in each breath, but when they are, they may be identified and taken into consideration. As depicted in Fig. 6, during the inhalation phase there is a strong increase in resistance (phase 610), during the exhalation there is a strong reduction in the resistance (phase 620). Phase 630 between the end of exhalation and the start of the next inhalation, where the resistance is not significantly changing, may correspond to the rest time, Trest. In the example shown in Fig. 6, the subject’s breathing is exhibiting a hold time, Thold, of zero, meaning there is no pause between the inhalation and the exhalation phases. The total respiratory cycle time, Ttot, may be calculated for each breath as the sum of the subsequent Tin, Thold, Tex, and Trest times. Further statistical and mathematical analysis by the processor may allow calculation of various additional parameters, for example Tin/Tex ratio and variability over time, as just two examples. The processor may calculate the total number of breath cycles per given time period, thereby obtaining the respiratory rate, RR. In some embodiments, the processor may first identify the total breath cycle as a peak in the waveform (e.g. using peak detection algorithms), isolate the different subsequent breath events, and analyse each breath to extract the sub-breath-cycle respiratory biomarkers, as described above.

Examples

Figs. 7A and 7B present an example waveform obtained by an embodiment of the invention (where the x-axis represents time in seconds, the y-axis represents resistance in arbitrary units, and Fig. 7B is a continuation of the waveform which starts in Fig. 7A), and Fig. 7C presents calculated values of respiratory rate (RR) and a ratio between the respiratory biomarkers Trest and Ttot plotted for three segments of a healthy subject’s breathing (e.g. the calculated results of Figs. 7A and 7B): 3 minutes at rest (712, 722), intensive exercise for 3 minutes (714, 724), and 2.5 minutes following completion of the exercise (716, 726). FIG. 7C shows a moving average over six breaths of RR, and Trest/Ttot over the course of these approximately 9 minutes. It can be seen that before the exercise the subject is breathing with RR=7-8 breaths per minute (722), and a Trest/Ttot value of 0.4 (712). When starting the exercise, the subject’s RR increases to above 30 breaths per minute (724), while Trest/Ttot is decreasing and becomes zero (714), because Trest is itself decreasing. After finishing the exercise, the RR (726) and Trest/Ttot (716) start to return towards the baseline values of breathing at rest.

It can be seen that the rise in RR and decline in Trest/Ttot upon initiation of exercise appeared in a different pattern and rate of change than from how they changed when returning to baseline after exercise ended, and these patterns of change and the difference between the rise and the return may be of additional informational value. For example, a less fit subject may have a faster climb in RR and reduction of Trest/Ttot upon initiation of exercise, but also a slower return to baseline after finishing the effort. Similar effects may appear in patients with cardiac or pulmonary conditions when these patients are put to exercise. A fit and healthy subject may retain lower RR and nonzero rest time during the first minutes of exercise, or even throughout all exercise time.

A method according to some embodiments the invention includes measuring and/or monitoring a ratio of an increase in a subject’s RR to a decrease of Trest/Ttot. The method may measure such ratio before, during, and/or after the exercise, and may compare such measurements to one another, to a baseline measurement of the subject, and/or to a predetermined value. In some embodiments, measuring RR and Trest/Ttot during the exercise includes determining a time for the subject to reach minimal rest time and the duration of that rest time, and/or determining a time to reach maximum RR and the maximum value RR, in order to estimate the respiratory effort and its propagation during exercise.

Determining Trest by embodiments of the invention may also be important due to the relation between Trest and the evaluation of respiratory stress in patients with lung, heart, or other conditions related to the cardio-pulmonary systems. For such patients, measurement of Trest/Ttot at rest may identify a level of stress caused by their condition. One group of patients for which this may be of relevance are those with Chronic Obstructive Pulmonary Disease (COPD). COPD is a lung disease characterized by a persistent airflow limitation that makes it difficult to breathe. Approximately 20% of smokers will develop COPD during their lifetime, with chronic bronchitis and emphysema among some of the underlying conditions. Whilst COPD may be considered chronic and irreversible, it is preventable.

Acute exacerbation of COPD (AECOPD) is a sustained worsening of the patient’s day-to-day condition. Bacteria, viruses, and pollutants causing airway inflammation may all be triggers of exacerbation. According to the World Health Organization (WHO), COPD is the third leading cause of death worldwide, causing more than three million deaths in 2019 and approximately 350,000 deaths in Europe alone.

Currently, there are no tools able to provide seamless, effortless and objective means to assess the change in the respiratory condition of non-ventilated patients with pulmonary disease, or to provide early detection of their deterioration or improvement, both in hospital and at home.

The left-hand side of Fig. 8 shows an example change in the respiratory parameters of a COPD patient on the first and last day of their hospital stay, as measured by an embodiment of the invention. It can be seen that the RR has been reduced and the Trest/Ttot has increased, both indicating an improvement between admission and release from hospital. The right-hand side of Fig. 8 shows the clinical improvement of the patient as estimated by a doctor’s questionnaire.

The left-hand side of Fig. 9 shows example changes in RR and Trest/Ttot of a patient during a hospitalization period, as measured by an embodiment of the invention. The patient exhibited an overall improvement while staying in the hospital. The patient did not show any significant reduction in RR between the day of admission and the day of release, but did show a significant improvement based on the Trest/Ttot parameter, which demonstrates that this sub-breath-cycle respiratory biomarkers may be of importance in the analysis of breathing patterns and estimation of overall breathing stress caused by clinical conditions. An additional indication of improvement may be seen in a rise of RR variability measured by the invention. The right-hand side of Fig. 9 shows the clinical improvement of that patient as estimated by a doctor’s questionnaire.

Figs. 10A and 10B show the RR and Trest/Ttot, respectively, of a COPD patient in the hospital, before and after administration of fast acting bronchodilators, as measured by embodiments of the invention. The part of Fig. 10A labelled 1011 shows the RR before treatment, and the part of Fig. 10A labelled 1012 shows the RR after treatment. The part of Fig. 10B labelled 1021 shows the Trest/Ttot before treatment and the part of Fig. 10B labelled 1022 shows the Trest/Ttot after treatment. It can be seen that once the effect of the drug has settled in, the subject started breathing with longer Trest/Ttot than before the treatment, showing the effects of treatment on the overall stress and respiratory condition.

Embodiments of the invention may provide real-time, direct, accurate, and objective measuring and monitoring inspiratory and expiratory durations and Thold and Trest in order to assess the condition and condition changes as an overall physical/physiological stress and effort of the respiratory system of healthy or sick subjects.

As discussed, Thold and Trest may be important sub-breath-cycle respiratory biomarkers to measure due to the need for more precise Tin/Tex ratio calculations, for example for medical purposes (also known as I/E ratio). The ratio between the time of inhalation to the time of exhalation is a well described indicator of airway obstruction or restriction, and of other conditions related to airway mechanics and lung function. According to the literature, in a healthy adult, the ratio is typically 1 :2, meaning that the exhalation is twice as long as the inhalation. For obstructive conditions, the ratio grows to 1:3, 1:4, or even 1 :5, with higher levels of obstruction leading to longer exhalation times. For restrictive conditions, the ratio changes towards elongation of the inhalation time and the ratio may move towards 1 : 1 or even 2: 1.

The Inventors have discovered that existing methods of measuring the Tin/Tex ratio may not be optimal or precise, and may not always provide valuable clinical information, even missing it in some cases. The Inventors have discovered that a healthy subject actually breathes closer to a Tin/Tex ratio of 1 : 1, but also presents a rest time of similar average length as the inhalation and the exhalation times. These values and their ratios are not constant, and may vary between different subjects and within the same subject over time, but embodiments of the invention have identified that, on average, healthy people may be regarded as breathing at a 1:1 :1 ratio (inhalation: exhalation: rest). The existing typical 1 :2 Tin/Tex ratio for healthy patients may arise as a result of not separating the exhalation time Tex from the rest time Trest, which may result in loss of important information.

For example, a patient with a severely exacerbated COPD condition may present an inhalation time of 1 second, exhalation time of 4 seconds and no Trest between the exhale and the inhale phases, indicating that the subject is breathing at a 1 :4 Tin/Tex ratio. A patient with a mild COPD condition may present an inhalation time of 1 second, exhalation time of 3 seconds and a Trest time of 1 second. Existing analysis approaches which do not separate Trest from Tex may determine that both patients are breathing at a 1:4 Tin/Tex ratio, and may therefore determine that both patients have a same or similar condition. However, embodiments of the invention distinguish Trest from Tex and may therefore reveal the truth that the second patient is actually breathing at a 1 :3 Tin/Tex ratio, with 1 second attributed to Trest. This indicates a much better patient condition both in terms of the Tin/Tex ratio (a Tin/Tex ratio of, e.g. 1:3 instead of 1:4, may indicate that the patient’s airway obstruction is less severe), and from a general health perspective of the respiratory system, since if a patient is presenting rest time between breaths, this means the patient does not have a very severe condition and their respiratory system is still flexible and not stretched to operation at the highest capacity possible. As such, the rest time Trest may be considered to indicate reserves of breathing capacity. Thus, if a patient is presenting rest time (which may be variable and not present in every breath), it may mean their respiratory system is flexible enough to change and adapt breathing if more stress is endured.

When a patient does not exhibit one or more rest times Trest, this may indicate that the patient is breathing near, at, or beyond their maximal capacity. Further stress to this patient may bring the patient to the point of insufficient breathing, and/or may require a higher respiratory rate to compensate, which may cause unwanted results such as hyperinflation.

An example can be found in Fig. 11, where the top and middle waveforms (1101 and 1102, respectively) are of healthy subjects and the bottom waveform (1103) is of an exacerbated COPD patient. Each waveform was measured by embodiments of the invention whilst the subjects were sitting at rest. As the calculations in the figures show, existing approaches of calculating Tin/Tex (e.g. Tin/(Tex + Trest)) may yield a ratio 0.45 (nearly 1 :2) for the healthy patient of waveform 901 and a similar ratio of 0.44 for the COPD patient of waveform 1103. For waveform 1102, existing approaches may calculate Tin/Tex as approximately 0.73 (e.g. 1:1.4). It is only upon separating Trest from Tex, as in embodiments of the invention, that there is a clear distinction between the healthy subjects having Tin/Tex ratio close to 1: 1 (0.89 and 1.06, top and middle respectively) and the AECOPD subject with clearly different Tin/Tex ratio of approximately 1:2 (0.44).

This result is contrary to the common notion that healthy patients exhibit a Tin/Tex ratio of 1 :2 and COPD patients exhibit a Tin/Tex ratio of 1:3 or more. Thus, embodiments of the invention allow for distinguishing between healthy breathing and obstructed breathing by resolving Tin, Tex and Trest in the waveform and separating Trest from Tex in the Tin/Tex ratio calculation.

Another example demonstrating why it may be important to remove Trest and Thold from Tex and Tin when calculating Tin/Tex ratios is shown in Figs. 12A and 12B.

Fig. 12A shows a graph of the Tin/Tex ratio of a hospitalized COPD patient over five days of hospitalization, as calculated according to existing methods. Fig. 12B shows a graph of the Tin/Tex ratio for the same patient calculated according to embodiments of the invention, e.g. after removing the Trest and Thold times (in this case Thold of the patient was always zero). A clinical condition improvement over the course of the hospital stay based on doctor’s questionnaires for this patient has been previously presented as part of Fig. 8. It can be seen that the calculation based on the existing approach in Fig. 12A does not show an improvement in the level of airway obstruction (e.g. as characterised by the ratio) over the course of the hospital stay, while in Fig. 12B, the Tin/Tex ratio calculated after removing Trest shows a trend of improvement, in line with the clinical evaluation of the doctor for this patient shown in Fig. 8. Accordingly, embodiments of the invention show improved measurement of sub-breath- cycle respiratory biomarkers compared to existing approaches, which may allow improved monitoring in line with actual observed doctor assessment of the patient, thereby allowing embodiments of the invention to aid in such assessment. Tex, or a normalized Tex/Ttot ratio, may be an important sub-breath-cycle respiratory biomarker indicative of obstructive respiratory conditions, which until now may have been affected and/or obscured by the inclusion of Trest as part of Tex by existing approaches, making the resulting value less sensitive to clinical changes (if at all). The Inventors have discovered that once these two subbreath-cycle respiratory biomarkers are separated, the Tex/Ttot ratio may provide a better indication of the clinical improvement of patients with such obstructive respiratory conditions. An example can be seen in Fig. 13, showing a graph of Tex/Ttot for a COPD patient as measured by embodiments of the invention from the first day of admission to the day of discharge. It can be seen that the Tex/Ttot has reduced over time, indicating a reduction in a level of airway obstruction between admission and release from hospital. A clinical improvement for this patient as assessed by a doctor’s questionnaire has been previously shown in the right-hand side of Fig. 8.

United States Patent Application Publication 2015/0099994 discloses a method of monitoring the lung function of a patient, the method comprising determining one or both of an inhalation time and a rest time for a patient that is using a respiratory apparatus, the one or both of an inhalation time and a rest time for the patient being determined from measurements obtained from the respiratory apparatus, wherein the inhalation time is the amount of time for which the patient inhales through the respiratory apparatus, and the rest time is the amount of time between the end of an exhalation and the start of the next inhalation; and analyzing the determined one or both of the inhalation time and rest time to determine an indication of the lung function of the patient.

In contrast to United States Patent Application Publication 2015/0099994, embodiments of the present invention may analyse at least four sub-breath-cycle respiratory biomarkers of Tin, Tex, Thold and Trest, and may separate the inhalation phase into Tin and Thold (e.g. subtracting Thold from a conventionally determined Tin) and may separate the exhalation phase into Tex and Trest (e.g. subtracting Trest from a conventionally determined Tex) for the purposes of analysis using these parameters, for example in analysing Tin/Ttot, Tin/Tex and/or Tex/Ttot ratios and changes thereof. Embodiments of the invention are also non-invasive (e.g. do not require the insertion of mouthpieces or use of a mask) and analyse tidal breathing waveforms (forced drug inhalation and/or use of a mask/mouthpiece affects the natural respiratory flow and therefore cannot be considered as completely natural tidal breathing).

Embodiments of the invention may be used to obtain measurements of the respiratory airflow of the subject during a methacholine challenge test, “MCT”, and/or during an assessment of chronic obstructive pulmonary disease (COPD). A confidential preliminary methacholine challenge test (MCT) study was performed to evaluate how tidal breathing parameters measured by a wearable device (SenseGuard™) according to embodiments of the invention compare to FEV1 as measured by spirometry during an MCT test and whether they can differ between MCT responders and nonresponders. Fig. 14 shows the results of the MCT study, according to some embodiments of the invention. In the study, thirty-five subjects suspected of asthma underwent MCT and were classified as responders or non-responders according to ATS guidelines. During MCT, they underwent tidal breathing measurements and parameters such as RR, inspiratory time (Tin), expiratory time (Tex), no flow time between breaths (Trest), total breath time (Ttot), and the corresponding ratios were calculated in the baseline, methacholine doses and recovery phases. The FEVi max. response change between responders (n=18) and non-responders (n=17) was significant (/?<0.001 ), with a reduction of 22% and 3%. Similarly, the difference of change in the biomarkers Tex/Ttot and Trst/Ttot as measured by embodiments of the invention between the groups was significant (/?<0.02 for both), with a significant increase (/?<0.00 l and /?<0.002, respectively) of 31% and a decrease of 51% for the responders, while no change for the non-responders. The remaining parameters did not change for both groups. Accordingly, tidal breathing parameters measured by embodiments of the invention can reliably detect changes in breathing pattern during MCT.

Clinical Trial

A confidential observational study that recruited subjects from the emergency department (ED) and internal medicine clinic at Halle (Saale) University Hospital, Germany was conducted using embodiments of the invention. COPD patients who were hospitalized with a primary or secondary diagnosis of AECOPD (GOLD 1-4/A-D with WHO performance status score < 3) were recruited for this study between March 2021 and July 2021.

After providing written informed consent, the ten recruited patients had their demographics, medical record and concomitant medications documented and had measurements with the wearable study device taken and clinical data were collected daily. The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was granted for the study from the Martin Luther University Halle-Wittenberg (Germany) Ethics Committee (CIV-19-07-029227, 2019-143) and is registered as a clinical trial on ClinicalTrials.gov (NCT05153122).

During the hospitalization period, patients were measured daily, using a wearable device according to some embodiments of the invention (SenseGuardTM, NanoVation-GS, Ltd., Haifa, Israel). The device contains sensors sensitive to exhaled humidity and allows direct and seamless measurement of the breathing at rest or during movement. Each measurement with the device was performed for 15 minutes during patients’ normal tidal breathing. Clinical data were collected daily and included medical evaluations that were performed routinely, a self-assessment questionnaire filled by each patient and an assessment questionnaire filled by the attending physician, which in addition provided a Borg Dyspnea score and a “Yes/No” decision score if discharging the patient based on the overall collected clinical data and evaluations could be possible. The collected clinical data and the given scores were used to assess the patients’ daily clinical condition and improvement during the hospitalization period and were compared with the data collected by the wearable device. At least one spirometry test was performed during each patient’s hospitalization. The mean %FEV1 predicted was 39% (±20%), indicating that the study sample was with a severe COPD (Gold 3-4). Clinical data was collected daily and included results from routine examinations, a self-assessment questionnaire filled by the patients, Borg Dyspnea score and an assessment questionnaire filled by the attending physician. Each questionnaire provided daily scores (higher score represents a worse condition). The self-assessment questionnaire included ratings (0-5) of four questions related to cough, phlegm, chest tightness and breathlessness (“self-assessment score”; scale 0-20). The physician-assessment questionnaire included ratings (1-5) of overall condition, cough, oxygen-use, wheezing, mobility, phlegm color and speaking velocity (“physician score”; scale 7-35) and dyspnea (“Borg dyspnea score”; scale 6- 20). The collected clinical data and the given scores were used to assess the patients’ daily clinical condition and to classify patients into two groups - those who improved significantly and those who did not. A > 5 score points change was considered, based on the collected data, between day of admission and day of discharge, in the Physician's assessment score (positive change represents improvement) to signify significant clinical improvement and to differentiate these patients from those who presented no significant clinical improvement.

Part of the trial was to establish if embodiments of the invention could improve the monitoring and analysis of breathing so as to aid in the physician’s assessment.

By measuring, with high sensitivity and fast response time, the changes in the (condensed) humidity in the vicinity of the sensors of the wearable device, that occur due to the inhaled and exhaled airflows, the embodiments of the invention can measure the respiratory waveforms with high-resolution, thus allowing, algorithmically, to identify the sub-breath phases and to accurately extract tidal breathing time parameters, including respiratory rate (RR), inspiratory time (Tin), inspiratory pause (Thold), expiratory time (Tex), expiratory pause (Trest), total breath time (Ttot; sum of inspiratory time, inspiratory pause, expiratory time and expiratory pause), and at least the corresponding ratios Tin/Tex, Tin/Ttot, Tex/Ttot and Trest/Ttot.

Every tidal breathing time parameter displays some within-subject variability. As data are acquired over 15 minutes of measurement, this method allows quantification of this variability.

Since this study began as a pilot, any findings with respect to various tidal breathing time parameters were unknown, hence power calculations were not carried out. Examination of variable distribution was done using Shapiro-Wilk test. Due to non-normal distributions (p < 0.05) of the tidal breathing time parameters, non-parametric analyses were employed. The median value (m) for each parameter over each measurement duration and its interquartile range (IQR) were calculated. To examine trends across days and between days, Kruskal -Wallis (KW) procedures were conducted (p < 0.05 indicates a significant change between days). The detection of a significant result was further probed by conducting Mann-Whitney (MW) tests between each consecutive measurement and day. When performing MW tests between consecutive days, p-value was corrected using post-hoc analyses with the Bonferroni method (p<0.01). Associations between the variables were computed using Spearman rank correlation. For normally distributed data, continuous variables were presented as mean and standard deviation (SD). Categorical variables were presented as absolute numbers and percentages.

All patients successfully performed their daily tidal breathing measurements, twice a day, with an average duration of 15.3 (±1.7) minutes, as required by the study protocol. From each measurement, the median and inter quartile range (IQR) of RR, inspiratory time (Tin), expiratory time (Tex), expiratory pause (Trest), total breath time (Ttot), and the ratios Tin/Tex, Tin/Ttot, Tex/Ttot and Trest/Ttot were calculated. The inspiratory pause was not depicted in the breathing waveform pattern of the study’s cohort. These measured parameters allowed an assessment of the patients’ daily respiratory condition.

Table 1 summarizes the changes (Amedian and AIQR) in the parameters for each patient, calculated as the difference between the admission and the discharge days, measured before the first treatment on each day. The data of patients P03 & P07 on day of discharge were not valid, thus the calculation was performed on the last day before day of discharge.

Fig. 15 shows a box plot comparison of change in the tidal breathing parameters between admission and discharge days, between the two patients’ groups, as measured according to embodiments of the invention. The differences in ATex/Ttot, ATrest/Ttot, ATin/Ttot, ATin/(Tex±Trest) were statistically significant (p=0.02, p=0.03, p=0.02, p=0.04 respectively). The measurement of Tin/(Tex+Trest) by embodiments of the invention may correspond to prior art methods which do not separate Tex and Trest, as performed by embodiments of the invention to arrive at the shown Tin/Tex ratio.

During the course of AECOPD, several pathophysiological changes in respiratory parameters are expected to occur, such as an increase in respiratory rate, exhalation flow limitation and subsequent dynamic hyperinflation.

A significant decrease in the Tex/Ttot and increase in the Trst/Ttot occurred in patients who presented with significant clinical improvement, while showing minor or no change in patients that were mildly improved or not improved at all, as can be seen in Table 1 and Fig. 15. This is in line with the notion that higher obstruction levels limit the exhalation airflow and elongate the expiratory phase. Observational data from nasal flow recordings suggest shortening or loss of expiratory pause in case of more severe obstruction in COPD patients.

Contrary to the traditional approach, using the SenseGuard™ in accordance with embodiments of the invention in this study allows to define and measure the exhalation times as such periods in the breath cycle during which there was actual expiratory flow, separating it from the expiratory pause (Trest) time during which there was no flow or non-significant flow. The results show that viewing them separately is highly beneficial for estimating changes in condition in COPD patients. Fig. 15 also shows that the Trest/Ttot ratio was strongly increased in patients who exhibited significant clinical improvement. The expiratory pause could be considered as a general indicator of respiratory stress (or the remaining respiratory capacity to further elevate the stress levels), with longer expiratory pauses indicating a better and less stressed condition. While in this cohort of COPD patients the elongation of the expiratory pause was mostly at the expense of shortening the expiratory phase, generally shortening of expiratory pause time is expected to be present also in restrictive conditions (due to longer inhalation times), and in other conditions or in healthy individuals. For example, a patient with cardiac insufficiency or a healthy individual undergoing physical stress, such as climbing the stairs, may have both elevated RR with less time to rest, and may be seen as shortening the expiratory pause time as the stress grows, while not changing their normalized inhalation or exhalation times or their ratio.

Taken together, embodiments of the invention may have value to dissect different levels of respiratory improvements during hospitalization. If confirmed by independent confirmatory studies, it might play a future role to allow for more objective monitoring of the clinical treatment response of AECOPD patients during hospitalization and to personalize treatment based on whether the patient can be considered a (early) responder or non-responder to treatment based on the pattern of changes in respiratory parameters detected.

Table 1

It should be recognized that embodiments of the invention may solve one or more of the objectives and/or challenges described in the background, and that embodiments of the invention need not meet every one of the above objectives and/or challenges to come within the scope of the present invention. While certain features of the invention have been particularly illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes in form and details as fall within the true spirit of the invention.

In the above description, an embodiment is an example or implementation of the inventions. The various appearances of "one embodiment,” "an embodiment" or "some embodiments" do not necessarily all refer to the same embodiments.

Although various features of the invention may be described in the context of a single embodiment, the features may also be provided separately or in any suitable combination. Conversely, although the invention may be described herein in the context of separate embodiments for clarity, the invention may also be implemented in a single embodiment.

Reference in the specification to "some embodiments", "an embodiment", "one embodiment" or "other embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments, of the inventions.

It is to be understood that the phraseology and terminology employed herein is not to be construed as limiting and are for descriptive purpose only.

The principles and uses of the teachings of the present invention may be better understood with reference to the accompanying description, figures, and examples.

It is to be understood that the details set forth herein do not construe a limitation to an application of the invention.

Furthermore, it is to be understood that the invention may be carried out or practiced in various ways and that the invention may be implemented in embodiments other than the ones outlined in the description above. It is to be understood that the terms “including”, “comprising”, “consisting” and grammatical variants thereof do not preclude the addition of one or more components, features, steps, or integers or groups thereof and that the terms are to be construed as specifying components, features, steps, or integers.

If the specification or claims refer to "an additional" element, that does not preclude there being more than one of the additional elements.

It is to be understood that where the claims or specification refer to "a" or "an" element, such reference is not to be construed that there is only one of that element.

It is to be understood that where the specification states that a component, feature, structure, or characteristic "may", "might", "may" or "could" be included, that a particular component, feature, structure, or characteristic is not required to be included.

Where applicable, although state diagrams, flow diagrams or both may be used to describe embodiments, the invention is not limited to those diagrams or to the corresponding descriptions. For example, flow need not move through each illustrated box or state, or in exactly the same order as illustrated and described.

Methods of the present invention may be implemented by performing or completing manually, automatically, or a combination thereof, selected steps or tasks.

The descriptions, examples, methods and materials presented in the claims and the specification are not to be construed as limiting but rather as illustrative only.

Meanings of technical and scientific terms used herein are to be commonly understood as by one of ordinary skill in the art to which the invention belongs, unless otherwise defined. The present invention may be implemented in the testing or practice with methods and materials equivalent or similar to those described herein.

While the invention has been described with respect to a limited number of embodiments, these should not be construed as limitations on the scope of the invention, but rather as exemplifications of some of the preferred embodiments. Other possible variations, modifications, and applications are also within the scope of the invention. Accordingly, the scope of the invention should not be limited by what has thus far been described, but by the appended claims and their legal equivalents. The description also includes the subject matter of the following clauses:

Clause 1 : A wearable system for seamless, non-invasive and non- interfering measurement of breathing and of respiratory waveforms, and analysis of breathing parameters and/or respiratory patterns, to assess changes in the parameters and/or respiratory patterns, comprising: at least one direct respiratory sensor configured to be placed in an airflow generated from the subject’s breathing and to generate a signal from the subject’s breathing, with the sensor having sufficient resolution and precision to identify and quantify sub-breath parameters; a housing for the sensor, shaped to be placed proximate the mouth or nose of the subject, comprising a channel to direct at least a part of the respiratory airflow of the subject over the sensor without blocking or affecting the subject’s breathing or impacting conscious or unconscious breathing patterns; and at least one processor operatively communicating with the sensor and adapted to receive a signal from the sensor and obtain sub-breath-cycle respiratory time parameters; and wherein the processor is adapted to perform calculations on the time parameters and between the parameters to obtain calculated values and analyse changes in the time parameters and calculated values over time.

Clause 2: The system according to clause 1, wherein the system is adapted to capture only a portion of the subject’s inhaled and exhaled breaths, and not to measure the full volume or precise flow rate of inhaled or exhaled breath of the subject.

Clause 3: The system according to any preceding clause, adapted to guide less than 90% of the flow of a subject’s breath in an area proximate the sensor to obtain the signal.

Clause 4: The system according to any preceding clause, wherein the processor is adapted to implement at least one algorithm to obtain sub-breath-cycle breathing time parameters including at least one of the time of air hold between inhale and exhale (Thold) and the time between the exhalation and the next inhalation (Trest), and wherein the processor is adapted to differentiate the inhale time (Tin) and the exhale time (Tex) and calculate them separately from each other and also differentiate them and calculate them separately from Thold and Trest.

Clause 5: The system according to any preceding clause, wherein the processor is adapted to implement at least one algorithm to obtain sub-breath-cycle breathing time parameters including at least time between the exhalation and the next inhalation (Trest) or Trest/Ttot (total time of respiratory cycle), and the changes in Trest or Trest/Tot over time.

Clause 6: The system according to any preceding clause, wherein the processor is adapted to implement at least one algorithm to measure Inhalation/Exhalation (Tin/Tex) time ratio, and in such algorithm the Tin and Tex are separated from the Thold and Trest.

Clause 7: The system according to any preceding clause, wherein the processor is adapted to calculate Tex and Tex/Ttot for evaluation of changes in airway obstruction, and for such calculations the Tex is separated from Tin, Thold and Trest.

Clause 8: The system according to any preceding clause, wherein the processor is adapted to calculate Tin and Tin/Ttot for evaluation of changes in airway restriction, and for such calculations the Tin is separated from the Tex, Thold and Trest.

Clause 9: The system according to any preceding clause, wherein the sensor, processor and algorithm are adapted to resolve breath events and sub-breath-cycle parts with a resolution of 0.5 seconds or finer.

Clause 10: The system according to any preceding clause, wherein the sensor, processor and algorithm are adapted to resolve breath events and sub-breath-cycle parts with a resolution of 0.1 seconds or finer.

Clause 11 : The system according to any preceding clause, wherein the sensor, processor and algorithm are adapted to resolve breath events and sub-breath-cycle parts with a resolution of about 0.01 seconds. Clause 12: The system according to any preceding clause, wherein the processor is adapted to obtain a respiratory waveform over a plurality of respiratory cycles.

Clause 13: The system according to any preceding clause, wherein the processor is adapted to obtain a respiratory waveform continuously from the subject.

Clause 14: The system according to any preceding clause, wherein the processor is adapted to identify physiological, mental or hormonal conditions from the respiratory waveform based on the analysis of the measured sub-breath- cycle respiratory time parameters and their changes over time.

Clause 15: The system according to any preceding clause, wherein the processor is adapted to identify physical stress and effort of the respiratory system from the respiratory waveform.

Clause 16: The system according to any preceding clause, wherein the physical stress is identified through measuring at least the Trest or Trest/Ttot and the changes in Trest or Trest/Tot over time.

Clause 17: The system according to any preceding clause, wherein the processor and algorithm are adapted to measure lung function and lung health condition from the respiratory waveform based on the analysis of the measured respiratory parameters and their changes over time.

Clause 18: The system according to any preceding clause, wherein the processor and algorithm are adapted to measure Inhalation/Exhalation (Tin/Tex) time ratio for evaluation of levels of the airway obstruction or restriction and for such calculation the Tin and Tex are separated from the Thold and Trest.

Clause 19: The system according to any preceding clause, wherein the system is used to measure Tex and Tex/Ttot for evaluation of changes in airway obstruction, and for such calculations the Tex is separated from the Tin, Thold and Trest. Clause 20: The system according to any preceding clause, wherein the system is used to measure Tin and Tin/Ttot for evaluation of changes in airway restriction, and for such calculations the Tin is separated from the Tex, Thold and Trest.

Clause 21: The system according to any preceding clause, wherein the system is used to diagnose, monitor or evaluate changes in subject’s lung condition, to draw conclusions on whether the subject is stable, improving or deteriorating over time.

Clause 22: The system according to any preceding clause, wherein the system is used to evaluate the effects of subject receiving therapy for respiratory condition, such as short-acting or long-acting medications, treatments or interventions for improving or maintaining breathing quality, lung function and respiratory condition.

Clause 23: The system according to any preceding clause, comprising a plurality of sensors received in a housing adapted to be worn near a patient’s airway.

Clause 24: The system according to any preceding clause comprising a heater positioned proximate the sensor adapted to heat the sensor when a measurement is obtained.

Clause 25: A method for seamless, non-invasive and non- interfering measurement of breathing parameters, respiratory waveforms, and/or respiratory patterns, to assess changes in the parameters, waveforms, and/or patterns, comprising: placing a wearable direct respiratory sensor in a housing proximate an airflow from a subject’s breathing airways without blocking or affecting the subject’s breathing or impacting conscious or unconscious breathing patterns, while enabling delivery of at least part of the respiratory airflow to the sensor to generate a signal from the subject’s breathing; receiving, by a processor operatively communicating with the sensor, respiratory parameters and sub-breath-cycle respiratory time parameters; and performing calculations on the time parameters and between the parameters, said calculations selected from the group consisting of add, subtract, multiply, divide the parameters among themselves, calculate ratios of the parameters, calculate variability of the parameters, ratios of their variabilities, and analyse the changes in these parameters over time.

Clause 26: The method according to clause 25, comprising capturing only a portion of the subject’s inhaled and exhaled breaths, and not measuring the full volume or precise flow rate of inhaled or exhaled breath of the subject.

Clause 27: The method according to any of clauses 25-26, where less than 90% of the full volume of a subject’s breath passes the sensor.

Clause 28: The method according to any of clauses 25-27, comprising measuring Tex and Tex/Ttot in a subject’s breathing to obtain a measurement, separating Tex from Tin, Thold and Trest, and thereby evaluating changes in airway obstruction.

Clause 29: The method according to any of clauses 25-28, wherein physical stress of a subject is identified through measuring at least the Trest or Trest/Ttot and the changes in Trest or Trest/Tot over time.

Clause 30: The method according to any of clauses 25-29, comprising measuring lung function and lung health condition from the respiratory waveform based on the analysis of the measured respiratory parameters and their changes over time.

Clause 31: The method according to any of clauses 25-30, comprising measuring Inhalation/Exhalation (Tin/Tex) time ratio for evaluation of levels of the airway obstruction or restriction and for such calculation separating the Tin and Tex from the Thold and Trest.

Clause 32: The method according to any of clauses 25-31, comprising measuring Tex and Tex/Ttot for evaluation of changes in airway obstruction, and for such calculations separating the Tex from the Tin, Thold and Trest. Clause 33: The method according to any of clauses 25-32, comprising measurement of Tin and Tin/Ttot for evaluation of changes in airway restriction, and for such calculations the Tin is separated from the Tex, Thold and Trest.

Clause 34: The method according to any of clauses 25-33, used to diagnose, monitor or evaluate changes in subject’s lung condition, to draw conclusions on whether the subject is stable, improving or deteriorating over time.

Clause 35: The method according to any of clauses 25-34, used to evaluate the effects of subject receiving therapy for respiratory condition, such as short-acting or long-acting medications, treatments or interventions for improving or maintaining breathing quality, lung function and respiratory condition.

Clause 36: The method according to any of clauses 25-35, comprising sampling the signal from said sensor at least every 0.5 seconds.

Clause 37: The method according to any of clauses 25-36, comprising sampling the signal from said sensor at least every 0.1 seconds.

Clause 38: The method according to any of clauses 25-37, comprising sampling the signal from said sensor at least every 0.01 seconds.

Clause 39: The method according to any of clauses 25-38, comprising heating the sensor when a measurement is obtained.

Clause 40: The method according to any of clauses 25-39, wherein signals from a plurality of sensors are used in combination or as separate data channels.

Claims

1. A method of measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, the method comprising: equipping a subject with a wearable non-invasive measurement system, the measurement system operable to obtain direct measurements of a respiratory airflow of the subject, the obtained measurements being indicative of a tidal breathing waveform comprising one or more sub-breathcycle respiratory biomarkers; receiving, by at least one processor in operative communication with the wearable non- invasive measurement system, the obtained measurements from the system for a period of time; periodically calculating for the period of time, using the at least one processor, a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements, the plurality of sub-breath-cycle respiratory biomarkers including: an inhalation time parameter, “Tin”, of the subject; an exhalation time parameter, “Tex”, of the subject; an air holding time parameter between inhalation and exhalation, “Thold”, of the subject; and, a resting time parameter between exhalation and the next inhalation, “Trest”, of the subject; and, monitoring the plurality of sub-breath-cycle respiratory biomarkers of the subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject.

2. The method according to claim 1, further comprising calculating and monitoring changes in an inhalation/exhalation time ratio, “Tin/Tex”, for indicating a magnitude of airway obstruction or restriction of the subject.

3. The method according to any of claims 1-2, further comprising calculating a total time of a respiratory cycle, “Ttof ’, and therefrom calculating and monitoring changes in an exhalation/total time ratio, “Tex/Ttot”, for indicating a variation in a level of airway obstruction of the subject.

4. The method according to any of claims 1-2, further comprising calculating a total time of a respiratory cycle, “Ttot”, and therefrom calculating and monitoring changes in an inhalation/total time ratio, “Tin/Ttof ’, for indicating a variation in a level of airway restriction of the subject.

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5. The method according to any of claims 1-4, further comprising outputting a depiction of at least one of: the tidal breathing waveform of the subject; variation in a respiratory function of the subject; and, one or more of the calculated sub-breath-cycle respiratory biomarkers of the subject.

6. The method according to any of claims 1-5, wherein the wearable non-invasive measurement system is used to obtain measurements of the respiratory airflow of the subject during a methacholine challenge test, “MCT”, or during an assessment of chronic obstructive pulmonary disease (COPD).

7. The method according to any of claims 1-6, further comprising generating an alert in the event that at least one of: one or more of the calculated sub-breath-cycle respiratory biomarkers; one or more time ratios derived therefrom; or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time, contravene a corresponding predefined policy.

8. The method according to any of claims 1-7, wherein the wearable non-invasive measurement system is configured to obtain measurements with a temporal resolution of at least one of: 0.01 seconds, 0.1 seconds, or 0.5 seconds.

9. The method according to any of claims 1-8, further comprising identifying at least one of: a physiological condition; a mental condition; a hormonal condition; or physical stress, based on an analysis of one or more of: the sub-breath-cycle respiratory biomarkers; one or more time ratios; or, variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time.

10. The method according to any of claims 1-9, wherein the wearable non-invasive measurement system is further operable to heat or cool at least one of: the respiratory airflow of the subject; a pathway of the respiratory airflow to a sensor of the wearable non-invasive measurement system; the sensor; or a region proximate to the sensor.

11. The method according to any of claims 1-10, wherein the wearable non-invasive measurement system is configured to be unobtrusive such that the central nervous system of the subject assumes

57 responsibility over breathing, thereby facilitating an analysis of natural tidal breathing waveforms of the subject and minimizing conscious intervention by the subject over respiratory function.

12. A system for measuring and monitoring variations in respiratory biomarkers over time by analysing tidal breathing waveforms, the system comprising: a wearable non-invasive measurement system configured to obtain direct measurements of a respiratory airflow of a subject, the obtained measurements being indicative of a tidal breathing waveform comprising one or more sub-breath-cycle respiratory biomarkers; and at least one processor configured to be in operative communication with the wearable non-invasive measurement system and configured to: receive the obtained measurements from the wearable non-invasive measurement system for a period of time; periodically calculate, for the period of time, a plurality of sub-breath-cycle respiratory biomarkers of the subject based on the obtained measurements, the plurality of subbreath-cycle respiratory biomarkers including: an inhalation time parameter, “Tin”, of the subject; an exhalation time parameter, “Tex”, of the subject; an air holding time parameter between inhalation and exhalation, “Thold”, of the subject; and, a resting time parameter between exhalation and the next inhalation, “Tresf ’, of the subject; and monitor the plurality of sub-breath-cycle respiratory biomarkers of the subject for the period of time to observe an indication of variation in a respiratory function or lung function of the subject.

13. The system according to claim 12, wherein the at least one processor is further configured to calculate and monitor changes in an inhalation/exhalation time ratio, “Tin/Tex”, indicative of a magnitude of airway obstruction or restriction of the subject.

14. The system according to any of claims 12 or 13, wherein the at least one processor is further configured to calculate a total time of a respiratory cycle, “Ttot”, and therefrom calculate an exhalation/total time ratio, “Tex/Ttof ’, indicative of a variation in a level of airway obstruction of the subject.

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15. The system according to any of claims 12 or 13, wherein the at least one processor is further configured to calculate a total time of a respiratory cycle, “Ttot”, and therefrom calculate an inhalation/total time ratio, “Tin/Ttof ’, indicative of a variation in a level of airway restriction of the subject.

16. The system according to any of claims 12-15, further comprising an output device configured to output a depiction of at least one of: the tidal breathing waveform of the subject; variation in a respiratory function of the subject; and, one or more of the calculated sub-breath-cycle respiratory biomarkers of the subject.

17. The system according to any of claims 12-16, wherein the at least one processor is further configured to generate an alert in the event that at least one of: one or more of the calculated subbreath-cycle respiratory biomarkers; one or more time ratios derived therefrom; or variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time contravene a corresponding predefined policy.

18. The system according to any of claims 12-17, wherein the wearable non-invasive measurement system is configured to obtain measurements with a temporal resolution of at least one of: 0.01 seconds, 0.1 seconds, or 0.5 seconds.

19. The system according to any of claims 12-18, wherein the at least one processor is further configured to identify at least one of: a physiological condition; a mental condition; a hormonal condition; or physical stress, based on an analysis of one or more of: the sub-breath-cycle respiratory biomarkers; one or more time ratios; or, variations in the sub-breath-cycle respiratory biomarkers and/or time ratios over time.

20. The system according to any of claims 12-19, wherein the wearable non-invasive measurement system further comprises a temperature varying element configured to heat or cool at least one of: the respiratory airflow of the subject; a pathway of the respiratory airflow to a sensor of the wearable non-invasive measurement system; the sensor; or a region proximate to the sensor.

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