WO2023234840A1

WO2023234840A1 - A method, software product, and system for determining respiration quality

Info

Publication number: WO2023234840A1
Application number: PCT/SE2023/050544
Authority: WO
Inventors: Anders TJERNVIK
Original assignee: Linkura Ab
Priority date: 2022-06-02
Filing date: 2023-06-01
Publication date: 2023-12-07

Abstract

The present disclosure relates to a computer implemented method for determining respiration quality. The method (300) comprises obtaining (310) time series data (100) relating to a time period and being indicative of a beating heart; determining (320) interbeat intervals, IBIs, (102) between consecutive heartbeats (101) based on said obtained time series data (100); labelling (330) each IBI that is a first IBI in a series (103,104) comprising at least three subsequent IBIs, wherein said subsequent IBIs all progressively increases or all progressively decreases; and wherein each labelled IBI is identified as being first in a series (103) of progressively increasing IBIs or identified as being first in a series (104) of progressively decreasing IBIs; and determining (350) a parameter relating to heart rate variability respiratory coherence based on any labelled IBIs.

Description

A method, software product, and system for determining respiration quality

TECHNICAL FIELD

The present disclosure relates to heart rate monitoring, heart rate variability, heart rate variability respiratory coherence, respiration quality, and biofeedback.

The present disclosure relates to monitoring in the context of heart rate, respiration, heart rate variability, stress, and recovery.

The present disclosure relates to exercise in the context of breathing exercises (breathwork training), meditation, and biofeedback.

BACKGROUND

Heart rate and heart rate variability is of particular interest in the field of stress. The constant increase of cost for sick leaves related to stress in our modern society increases the need to find new methods for treating and preventing stress and burnouts.

Heart rate variability, HRV, has been shown in multiple studies to be a reliable indicator of physical and emotional health since it is heavily modulated by the balance between the sympathetic and parasympathetic nervous system. A high HRV during rest has shown to be a potential indicator for high self-regulatory strength indicating good tolerance upon exposure to acute stress. A low HRV is a possible indicator for a low self-regulatory strength and has been associated with several psychopathological states such as anxiety.

There exists training exercises to increase ones HRV that involve practicing slow deep breathing. For efficient HRV training it may be advantageous for a person doing the training to be informed of his/her current HRV quality, such as receiving real time biofeedback.

There is a demand for improved methods and tools for monitoring and evaluating heart rate variability. SUMMARY

Inspiration and expiration are the main drivers of heart rate variability, HRV. Since normal respiration is typically highly non-stationary there is a need for methods in the time-domain that correctly measure the influence of respiration on HRV. The respiratory part of HRV, also known as respiratory sinus arrhythmia, or respiratory coherence, has been shown to be a strong indicator of qualitative respiration and wellbeing, therefore there is a need to derive methods that accurately measure this component, for example, to track normal respiration over time, or to detect or treat respiratory illness. Another important field of the invention is real-time measurement of respiratory quality, also known as biofeedback. Evidence indicates that providing feedback on respiratory variation in HRV to an individual leads to better learning on how to breathe with high respiration quality. Real-time biofeedback breathing exercises can provide breathing with high respiration quality and coherence that has shown strong effects on recovery level. In all these situations, a time domain algorithm for detecting breaths is greatly needed.

One object of the invention is to provide a method for determining a parameter relating to heart rate variability respiratory coherence.

One object of the invention is to provide methods and tools to improve heart rate variability training.

This has in accordance with the present disclosure been achieved by means a computer implemented method for determining respiration quality. The method comprises

- obtaining time series data relating to a time period and being indicative of a beating heart;

- determining interbeat intervals, I Bls, between consecutive heartbeats based on said obtained time series data;

- labelling each IBI that is a first IBI in a series comprising at least three subsequent I Bls, wherein said subsequent I Bls all progressively increases or all progressively decreases, and wherein each labelled IBI is identified as being first in a series of progressively increasing I Bls or identified as being first in a series of progressively decreasing I Bls; and

- determining a parameter relating to heart rate variability respiratory coherence based on any labelled I Bls. This has the advantage of allowing trends in heart rate variability to be monitored.

In some embodiments, labelling I Bls comprises labelling each IBI also being the first IBI in a series, wherein the duration of the series is above a minimum series duration threshold value.

This has the advantage of allowing of monitoring trends heart rate variability based on the duration of said heart rate variability trends.

In some embodiments, determining of the parameter relating to heart rate variability respiratory coherence comprises comparing the number of labelled I Bls in the time period corresponding to the time series data and an occurrence threshold value, wherein said occurrence threshold value is based on said time period.

This has the advantage of allowing of monitoring trends heart rate variability based on the number of heartbeats in said heart rate variability trends.

In some embodiments, said obtained time series data comprises an electrocardiogram, and determining I Bls comprises determining R-R intervals based on said electrocardiogram.

This has the advantage of allowing high-resolution interbeat intervals to be obtained.

In some embodiments, the method further comprises identifying IBI trend alternations, wherein an IBI trend alternation is identified upon at least two labelled I Bls occurring within an alternation time window, wherein at least one IBI is identified as being first in a series of progressively increasing I Bls and at least one IBI is identified as being first in a series of progressively decreasing I Bls, and wherein determining the parameter relating to heart rate variability respiratory coherence is further based on any identified IBI trend alternations

This has the advantage of allowing monitoring of both increasing and decreasing heart rate variability trends occurring in a time window.

In some embodiments, said alternation time window is equal to a breathing cycle duration, wherein identifying IBI trend alternations comprises determining the breathing cycle duration based on said time series data. This has the advantage of allowing monitoring of both increasing and decreasing heart rate variability trends occurring within one exhalation and one inhalation.

In some embodiments, the determined parameter is based on one labelled IBI and/or one identified IBI trend alternation.

This has the advantage of allowing monitoring of single occurrences of heart rate variability trends.

In some embodiments, the determined parameter is based on all labelled I Bls and/or identified IBI trend alternations in the duration of said time series data.

This has the advantage of allowing monitoring heart rate variability trends over time.

In some embodiments, the method further comprises transmitting a signal indicative of the determined parameter relating to heart rate variability respiratory coherence and/or presenting information indicative of the determined parameter relating to heart rate variability respiratory coherence.

This has the advantage of allowing a user to receive biofeedback on their activities. This further has the advantage of providing the determined parameter relating to heart rate variability respiratory coherence and allowing a user to adapt their activities thereafter

In some embodiments, obtaining time series data further comprises obtaining breathing data, and

- labelling I Bls is further based on said obtained breathing data, and/or

- determining the parameter relating to heart rate variability respiratory coherence is further based on said obtained breathing data.

This has the advantage of allowing verification of the relationship between breathing and heart rate variability trends based on said time series data. This further has the advantage of allowing additional information to be provided to a user.

The present disclosure further relates to a system for determining respiration quality, the system comprises a heart rate sensor and a heart rate analyser; the heart rate analyser comprises control circuitry comprising a computer, wherein the control circuitry is arranged to control said heart rate sensor; the heart rate sensor is arranged to measure time series data indicative of a beating heart; the computer is arranged to

- obtain time series data from said the heart rate sensor,

- determine interbeat intervals, I Bl, between consecutive heartbeats in said time series data,

- label each IBI being a first IBI in a series comprising at least three subsequent I Bls in which said subsequent I Bls all progressively increases or all progressively decreases, wherein each labelled IBI is identified as being first in a series of progressively increasing or identified as being first in a series of progressively decreasing I Bls, and

- determine a parameter relating to heart rate variability respiratory coherence based on any labelled I Bls.

In some embodiments, the system further comprises a respiration sensor arranged to measure breathing data, wherein the control circuitry is arranged to control said respiration sensor, and wherein the computer is further arranged to

- obtain measured breathing data from said the respiration sensor, and

- determine the parameter relating to heart rate variability respiratory coherence further based on said measured breathing data.

In some embodiments, the system further comprises an accelerometer arranged to measure and provide acceleration data, wherein the control circuitry is arranged to control said accelerometer, and wherein the computer is further arranged to

- obtain measured acceleration data from said accelerometer, and

- determine the parameter relating to heart rate variability respiratory coherence further based on said measured acceleration data.

This has the advantage of allowing comparisons between activity based on acceleration data and heart rate variability trends based on said time series data. This further has the advantage of allowing additional information to be provided to a user.

In some embodiments, the system further comprises presentation means arranged to provide information for a user, wherein the control circuitry is arranged to control said presentation means, and wherein the computer is further arranged to

- present information indicative of said determined parameter relating to heart rate variability respiratory coherence utilizing said presentation means.

This has the advantage of allowing presenting biofeedback based on the determined parameter indicative of respiration quality to a user. This further has the advantage of presenting biofeedback to the user in real-time while utilizing the system.

The present disclosure further relates to a computer program product comprising a non- transitory computer-readable storage medium having thereon a computer program comprising program instructions. The computer program being loadable into a processor and configured to cause the processor to perform the method for determining respiration quality.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. la depicts time series data of a beating heart as an electrocardiogram.

Fig. lb depicts interbeat intervals plotted over time.

Fig. 2 depicts schematically an example system for determining a parameter relating to heart rate variability respiratory coherence

Fig. 3 depicts schematically an example method for determining a parameter relating to heart rate variability respiratory coherence

Fig. 4 depicts schematically a data processing unit comprising a computer program product.

DETAILED DESCRIPTION

Throughout the figures, same reference numerals refer to same parts, concepts, and/or elements. Consequently, what will be said regarding a reference numeral in one figure applies equally well to the same reference numeral in other figures unless not explicitly stated otherwise. Fig. la shows time series data 100 of a beating heart as an electrocardiogram. The example time series data 100 depicts four heartbeats 101. The time between consecutive heartbeats are referred to as interbeat intervals 102. Heart rate variance relates to changes in interbeat interval 102. The interbeat intervals 102 also define an amount of time equal to the duration of said interbeat interval 102.

For the first heartbeat 101 in the illustrated electrocardiogram, the characteristic peaks R of the heartbeats are seen as the dominant peaks. Typically, the interbeat intervals for an electrocardiogram are determined by the R-R intervals, which corresponds to the time between the peaks in the electrocardiogram.

Fig. lb depicts the duration of interbeat intervals 102 plotted over time, hereafter interbeat interval 102 relates to the duration of said interbeat interval 102. The interbeat intervals, I Bls, 102 are calculated based on heartbeats in example time series data 100. The example time series data 100 was measured on a person exhaling three times and inhaling two times. Upon exhalation the IBI 102 increases, and upon inhalation the IBI 102 decreases. The example interbeat intervals 102 plot is intended to represent time series data 100 showing desirable heart rate variability respiratory coherence. A substantially constant IBI 102 over a plurality of heartbeat cycles (not shown) may represent an undesirable heart rate variability respiratory coherence indicative of low respiration quality.

During exhalation the IBI plot shows a series of IBIs 103 in which I Bls progressively increase. During inhalation the IBI plot shows a series of IBIs 104 in which IBIs progressively decrease. Note that in the example interbeat intervals 102 plot in fig. lb, every IBI is part of a series of IBIs that either progressively increase or progressively decrease, such a level of heart rate variability respiratory coherence is not to be expected in real measurement data even from a healthy individual.

Fig. lb further shows an estimated breathing cycle duration 105. Interbeat intervals 102 plots, and thereby time series data 100 of a beating heart, may allow for breathing cycle durations 105 to be determined without directly measuring the breathing of the person from which the time series data 100 was measured. Fig. 2 depicts schematically a system for determining a parameter relating to heart rate variability respiratory coherence being indicative of respiration quality. The system comprises a heart rate sensor 230 and a heart rate analyser 210. The heart rate sensor 230 is arranged to measure time series data indicative of a beating heart. The heart rate analyser 210 comprises control circuitry 211 comprising a computer 212. The control circuitry 211 is in communication with the heart rate sensor 230.

The computer 212 is arranged to

- obtain time series data from said the heart rate sensor 230,

- determine the parameter relating to heart rate variability respiratory coherence based on any labelled I Bls.

It is to be understood that each labelled IBI and the corresponding subsequent I Bls are an identified series of I Bl s that increase or decrease a minimum number of times, thereby indicating a significant trend in interbeat intervals. Typically, the computer 212 calculates and stores information relating to the identified series of I Bls, and need not alter or label the I Bls as such.

The term interbeat intervals relates to an amount of time between to adjacent heartbeats. For example, the time between R-peaks in an electrocardiogram of two adjacent heartbeats is an interbeat interval. It is to be understood that an interbeat interval relates to the duration of a heartbeat, and may be measured between any two corresponding events during heartbeats.

The term "heart rate variability" relates to changes in interbeat intervals. During rest, a relatively high heart rate variability is typically a positive health indicator, and a relatively low heart rate variability is typically a negative health indicator. Typically, heart rate variability is measured based on a plurality of interbeat intervals for heartbeats measured over a period of time. The term "heart rate variability respiratory coherence" relates to the impact on the breathing cycle on heart rate variability, and is also known as respiratory sinus arrhythmia. During rest the major source of heart rate variability is the breathing cycle, wherein interbeat intervals decrease during inhalation and increase during exhalation. The absence of changes or small changes in interbeat intervals during the breathing cycle may be a negative health indicator. Correspondingly, several consecutive interbeat intervals decreasing during inhalation and increasing during exhalation may be a positive health indicator and a state desirable to maintain during heart rate variability breathing exercises. Note that typically, upon observing several consecutive interbeat intervals decreasing or several consecutive interbeat intervals increasing in resting person, then it may be assumed that the underlying cause of heart rate variability is the breathing cycle, allowing heart rate variability respiratory coherence to be determined without direct measurement of the person's respiration. Typically, when a person exhibits said respiratory coherence then a high heart rate variability is also present.

The expressions "all I Bls progressively decrease" and "all I Bls progressively increase" relates to a series of consecutive I Bls with the I Bls after the first IBI all being larger than the previous I Bl, or with the I Bls after the first IBI all being smaller than the previous IBI. A series of I Bls wherein all I Bls progressively decrease or increase is an example of a heart rate variability trend. For example, a person may inhale such that the I Bls progressively decrease during inhalation, and exhale such that I Bls progressively increase during exhalation.

The term "labelled IBI" relates to the first IBI in a series that fulfilled the criteria for being labelled. The labelled IBI relates to identifying an IBI that fulfilled the criteria for being labelled. It is to be understood that a step of labelling I Bls may be seen as identifying trends in the I Bls, and that labelling does not need to include physically labelling the heartbeats and/or I Bls as such. For some examples, labelling I Bls comprises determining information relating to the labelled IBI and the corresponding series of I Bls. For example, labelled IBI information may describe the labelled IBI as being first in an increasing series of I Bls consisting of five IBIs.

The term "IBI trend alternation" relates to a change in heart rate variability trend within some time window. An example IBI trend alternation occurs in a measurement of one breathing cycle comprising a first series of IBIs progressively decreasing as a person inhales, followed by a second series of IBIs progressively increasing as a person exhales. The term "parameter relating to heart rate variability respiratory coherence" relates to a value or set of values relating to heart rate variability respiratory coherence is indicative of respiration quality. For example, the parameter may describe a current occurrence of a labelled IBI and/or an IBI trend alternation. The parameter may describe a trend-annotated IBI plot based on all I Bls and/or an IBI trend alternations that occurred within a predetermined time period. It is to be understood that the parameter relating to heart rate variability respiratory coherence determined based on the labelled I Bls may take many forms and may be presented to a user in many ways, such as the percentage of heartbeats that are part of a series belonging to a labelled IBI, the number of labelled I Bls per minute, or a binary value for the respiratory quality being above a threshold value during the last breathing cycle.

The term respiration quality relates to the detection of coherent breaths in a heart rate variability signal. Respiratory coherence has been shown to be a strong indicator of qualitative respiration and wellbeing. Typically, the determined parameter relating to heart rate variability respiratory coherence is and/or defines a level of respiration quality.

In some examples, the heart rate sensor 230 comprises electrocardiogram sensors, and/or an optical sensor.

In some examples, said obtained time series data relates to a time period. Typically, the time period is the duration of a measurement corresponding to the time series data.

In some examples, said obtained time series data comprises an electrocardiogram, and the computer 212 is arranged to determine I Bls comprises determining R-R intervals based on said electrocardiogram.

In some examples, determining the parameter relating to heart rate variability respiratory coherence comprises dividing the number of I Bls being part of a labelled IBI with the total number of I Bl s, thereby obtaining value indicative of the respiration quality. In some of these examples, the I Bls are labelled if at least three subsequent I Bls that all progressively increases or all progressively decreases.

In some examples, the system 200 is arranged to measure and obtain time series data, and label each IBI in time series data, wherein time series data is indicative of at least a 24-hour heartbeat measurement. In some examples, the computer 212 is arranged to determine the parameter relating to heart rate variability respiratory coherence based on comparing the number of labelled I Bls in the time period corresponding to the time series data and an occurrence threshold value, wherein said threshold value is based on said time period. In some examples, said occurrence threshold value is proportional to the duration of said time period. For example, the parameter is assigned a positive value if the number of labelled I Bls per minute is at least five.

In some examples, the computer 212 is arranged to label I Bls comprises labelling each IBI being the first IBI in a series comprising at least four, at least five or at least six subsequent I Bl s. In some of these examples, the number of required subsequent I Bl s in the series is based on a determined heart rate. For example, during an elevated heart rate the determined parameter may be more representative if the require number of subsequent I Bls in the series is increased.

If an IBI is first in a series comprising six subsequent progressively increasing or decreasing I Bls, and only three subsequent IBI are required to be a labelled IBI, then typically the additional I Bls are included in the I Bls corresponding to the labelled IBI. Furthermore, the I Bls corresponding to a labelled IBI are typically omitted from themselves being labelled I Bls, such that long series of progressively increasing or decreasing I Bls do not result in multiple labelled IBIs.

In some examples, the computer 212 is arranged to label IBIs comprises labelling each IBI also being the first IBI in a series, wherein the duration of the series is above a minimum series duration threshold value. In some of these examples, the minimum series duration threshold value is based on a heart rate in corresponding time series data. In some examples, the minimum series duration threshold value is based on the number of heartbeats in the corresponding time series data.

In some examples, the computer 212 is arranged to

- identify IBI trend alternations, wherein an IBI trend alternation is identified as at least two labelled IBIs within an alternation time window, wherein at least one IBI is identified as being first in a series of progressively increasing IBIs and at least one IBI is identified as being first in a series of progressively decreasing IBIs, and - determine the parameter relating to heart rate variability respiratory coherence is further based on any identified IBI trend alternations.

In some of these examples, the alternation time window is in the range of 100 to 500 milliseconds, in the range of 2 to 4 seconds, and/or in the range of 8 to 20 seconds.

In fig. lb, under the assumption that the alternation time window is larger than the estimated breathing cycle duration 105, a plurality of IBI trend alternations may be identified as the alternation time window can, in several ways, fit a series of IBIs 103 in which I Bls progressively increase and a series of IBIs 104 in which IBIs progressively decrease.

In some of these examples, the computer 212 is arranged to

- determining a breathing cycle duration based on said time series data, and to determine said alternation time window based on the duration of the breathing cycle duration.

In some examples, determining the parameter is based on any labelled IBIs and/or IBI trend alternations occurring in a parameter time window. In some of these examples, said parameter time window is a predetermined duration. In some of these examples, said parameter time window duration is in the range of 1 to 2 seconds, in the range of 4 to 8 seconds, in the range of 16 to 32 seconds, in the range of 1 to 2 minutes, in the range of 5 to 20 minutes, in the range of 1 to 4 hours, and/or in the range of 8 to 24 hours.

In some examples, the system 200 further comprises a respiration sensor 250 arranged to measure breathing data, wherein the control circuitry 211 is arranged to control said respiration sensor 250, and wherein the computer 212 is further arranged to

- obtain measured breathing data from said the respiration sensor 250, and

The addition of the respiration sensor 250 and the measured breathing data it provides may allow verification that the series of IBIs in which IBIs progressively increase or decrease, that were assumed to be caused by the breathing cycle, actually correspond with the measured breathing data. It is to be understood that the respiration sensor 250 and the heart rate sensor 230 typically obtain measurement data from the same subject. It is to be understood that the respiration sensor 250 and the heart rate sensor 230 is arranged to be used on a human or a non-human animal.

In some examples, the system 200 further comprises an accelerometer 260 arranged to measure and provide acceleration data, wherein the control circuitry 211 is arranged to control said accelerometer 260, and wherein the computer 212 is further arranged to

- obtain measured acceleration data from said accelerometer 260, and

- determine the parameter relating to heart rate variability respiratory coherence also based on said measured acceleration data.

In some of these examples, said accelerometer 260 is arranged at said heart rate sensor 230, said respiration sensor 250, and/or said heart rate analyser 210. For example, the measured acceleration data may be indicative of physical activity of the measured subject. In some examples, the computer 212 is arranged to omit time series data and/or suspend determining the parameter for a period of time corresponding to an elevated physical activity based on the measured acceleration data.

In some examples, the computer 212 is further arranged to

- obtain measured acceleration data from said accelerometer 260, and

- label I Bls further based on said measured acceleration data.

In some examples, the system 200 further comprises presentation means 270 arranged to provide information indicative of respiration quality for a user, wherein the control circuitry 211 is arranged to control said presentation means 270, and wherein the computer 212 is further arranged to

- present information indicative of said determined parameter relating to heart rate variability respiratory coherence utilizing said presentation means 270.

In some of these examples, the computer 212 is arranged to control the presentation means 270 to present the determined parameter upon the determined parameter relating to heart rate variability respiratory coherence fulfilling at least one criteria. The presentation means 270 may comprise, but is not limited to, display screens, audio speakers, light sources, vibrators, or any other mechanical actuators arranged to be seen, felt and/or heard by a user. It is to be understood that the determined parameter, and/or respiratory quality derived thereof, may be communicated in a multitude of different ways by the presentation means 270. In a first example, the determined parameter is indicative of an identified IBI trend alternation that may be communicated as a sound from a speaker. In a second example the determined parameter is indicative of all labelled IBIs and identified IBI trend alternations occurring during the last 10 minutes which may be communicated as an IBI plot over time with highlighted IBI trend alternations shown on a display screen.

In some example, the presentation means 270 comprise a speaker arranged to generate at least two sounds, wherein the computer 212 is arranged control the speaker to generate a first sound upon the determined parameter being indicative of a series of IBIs 103 in which IBIs progressively increases, and generate a second sound upon the determined parameter being indicative of a series of IBIs 104 in which IBIs progressively decreases. In some corresponding examples, instead of the speaker, the presentation means 270 comprise a light source arrange to generate light of at least two colours, and the computer 212 is arrange to control the light source to generate a first or a second colour based on the determined parameter. For example, the system may generate a first colour if the parameter corresponds to a respiration quality above a desirable level, and the second colour if the parameter corresponds to a respiration quality below the desirable level.

In some examples, the determined parameter is based on one labelled IBI and/or one identified IBI trend alternation. For example, the parameter a Boolean indicating the occurrence of a labelled IBI and/or an identified IBI trend alternation, such as assuming a positive state for a duration corresponding to the series corresponding to the labelled IBI. In some of these examples, system 200 is arranged to generate a light and/or a sound for a predetermined notification duration upon a positive state of the determined parameter that is indicative of the occurrence of a labelled IBI and/or an identified IBI trend alternation. In some of these examples, the predetermined notification duration is in the range of 100 to 500 milliseconds, in the range of 2 to 4 seconds, and/or in the range of 8 to 20 seconds. For example, the system 200 may be arranged to measure time series data of a beating heart and only present the determined parameter upon labelling an IBI and/or identifying an IBI trend alternation.

In some examples, the determined parameter is based on all labelled I Bls and/or identified IBI trend alternations in the duration of said time series data. In some of these examples, the determined parameter is based on all labelled I Bls and/or identified IBI trend alternations in a predetermined duration. In some of these examples, the predetermined duration is in the range of 1 to 2 seconds, in the range of 4 to 8 seconds, in the range of 16 to 32 seconds, and/or in the range of 1 to 2 minutes.

For example, the system 200 may be arranged to measure time series data of a beating heart and present the determined parameter indicative of all labelled I Bl s and/or identified IBI trend alternations occurring during the last 5 minutes.

In some examples, the determined parameter comprises a percentage value. In some of these examples, the percentage value is indicative of

- a fraction and/or a frequency of labelled I Bls, and/or IBI trend alternations;

- a ratio between a new fraction and a previous fraction of labelled I Bls and/or IBI trend alternations.

In some examples, the determined parameter comprises at least one of

- a value indicative of the respiration quality of a breath being above a threshold value, such as a Boolean value that is positive if the respiration quality is above the threshold value;

- a value indicative of the fraction of breaths with respiration quality being above the threshold value;

- a value indicative of the fraction of the total number of I Bl s during a time period that correspond to a labelled IBI;

- a value indicative of breathing patterns in determined breathing cycle durations;

- a value indicative of an occurrence of sighing and/or a frequency of sighing;

- a value indicative of a relationship between the number of increasing labelled I Bls and the number of decreasing labelled IBIs;

- a value indicative of a relationship between the number heartbeats corresponding to increasing labelled IBIs and decreasing labelled IBIs; - a standard deviation for the fraction of the total number of I Bls during a time period that correspond to a labelled I Bl; and/or

- a standard deviation for any of the above for at least 12 hours of time series data.

In some examples, the determined parameter comprises an integer number representing the labelled I Bls, such as the number of occurrences of increasing and/or decreasing labelled I Bls.

In some examples, the determined parameter comprises a ratio between occurrences of the labelled I Bls matching criteria and not matching criteria, such the fraction of breaths during which at least one labelled IBI is identified.

In some examples, the determined parameter comprises a standard deviation representing the labelled I Bls, such as the standard deviation of the number of labelled I Bl s per 10 minutes during a 12-hour measurement.

In some examples, the determined parameter comprises a plot of the labelled I Bls, such as a plot showing time vs the number of I Bls corresponding to the labelled IBI with decreasing labelled I Bls as negative number.

In some examples, the determined parameter comprises a respiration quality value, such as a Boolean value indicating the labelled I Bls satisfying criteria, or a percentage value of how the labelled I Bls match a target state for the labelled I Bls.

In some examples, determining a parameter relating to heart rate variability respiratory coherence based on any labelled I Bl s comprises counting a number of occurances of labelled I Bls matching criteria and/or utilizing basic arithmetic operations to calculate a frequency and/or probability of said occurances.

In some examples, the system 200 further comprises communication means (not shown) arranged to transmit a signal indicative of the determined parameter relating to heart rate variability respiratory coherence, wherein the control circuitry 211 is arranged to control said communication means.

Fig. 3 depicts schematically a method for determining respiration quality, the method 300 comprises

- obtaining 310 time series data relating to a time period and indicative of a beating heart, - determining 320 interbeat intervals, I Bls, between consecutive heartbeats based on said obtained time series data,

- labelling 330 each IBI that is a first IBI in a series comprising at least three subsequent I Bls, wherein said subsequent I Bls all progressively increases or all progressively decreases, and wherein each labelled IBI is identified as being first in a series of progressively increasing I Bls or identified as being first in a series of progressively decreasing I Bls, and

- determining 350 a parameter relating to heart rate variability respiratory coherence based on any labelled I Bls.

The example method 300 in fig. 3 relates to the example system 200 in fig. 2. Features described for the method 300 may also apply to the system 200. Features described for the system 200 may also apply to the method 300. For example, described examples of the computer 212 being arranged to perform actions may be considered to also describe corresponding examples of the method.

In some examples, determining 350 the parameter relating to heart rate variability respiratory coherence is based on the number of labelled I Bls being above a threshold value, wherein said threshold value is based on the time period corresponding to the time series data.

In some examples, determining 350 of the parameter relating to heart rate variability respiratory coherence comprises comparing the number of labelled I Bls in the time period corresponding to the time series data and an occurrence threshold value, wherein said occurrence threshold value is based on said time period.

In some examples, labelling 330 I Bls comprises labelling each IBI being the first IBI in a series comprising at least four, at least five or at least six subsequent I Bl s. In some of these examples, the number of subsequent I Bl s in the series is based on a determined heart rate. For example, during an elevated heart rate the determined parameter may be more representative if the require number of subsequent IBIs in the series is increased.

In some examples, labelling 330 IBIs comprises labelling each IBI also being the first IBI in a series, wherein the duration of the series is above a minimum series duration threshold value. In some examples, said obtained time series data comprises an electrocardiogram, and wherein determining 320 I Bls comprises determining R-R intervals based on said electrocardiogram.

In some examples, the method 300 comprises identifying 340 IBI trend alternations, wherein an IBI trend alternation is identified as at least two labelled I Bls within an alternation time window, wherein at least one IBI is identified as being first in a series of progressively increasing I Bls and at least one IBI is identified as being first in a series of progressively decreasing I Bls, and wherein determining 350 the parameter relating to heart rate variability respiratory coherence is further based on any identified IBI trend alternations.

The term IBI trend alternation relates to at least one series of progressively increasing I Bls and at least one series of progressively decreasing I Bls occurring within an alternation time window. Typically, the alternation time window corresponds to one breathing cycle, such that IBI trend alternation is detected upon showing both increasing and decreasing IBI trends during one breathing cycle.

In some of these examples, identifying 340 IBI trend alternations comprises determining a breathing cycle duration based on said time series data, and wherein said alternation time window is determined based on the duration of the breathing cycle duration. In some of these examples, identifying 340 IBI trend alternations comprises obtaining the breathing cycle duration.

In some examples, the method 300 comprising transmitting 360 a signal indicative of the determined parameter relating to heart rate variability respiratory coherence and/or presenting information indicative of the determined parameter relating to heart rate variability respiratory coherence.

In some examples, the determined parameter is based on one labelled IBI and/or one identified IBI trend alternation. For example, the parameter a Boolean indicating the occurrence of a labelled IBI and/or an identified IBI trend alternation, such as assuming a positive state for a duration corresponding to the series corresponding to the labelled IBI. In some of these examples, presenting the determined parameter comprises generating a light and/or a sound for a predetermined notification duration upon a positive state of the determined parameter that is indicative of the occurrence of a labelled IBI and/or an identified IBI trend alternation. In some of these examples, the predetermined notification duration is in the range of 100 to 500 milliseconds, in the range of 2 to 4 seconds, and/or in the range of 8 to 20 seconds.

In some examples, obtaining 310 time series data further comprises obtaining breathing data, and determining 350 the parameter relating to heart rate variability respiratory coherence is further based on said obtained breathing data.

In some examples, obtaining 310 time series data further comprises obtaining breathing data, and labelling 330 I Bls is further based on said obtained breathing data.

In some examples, obtaining 310 time series data further comprises obtain acceleration data, and

- labelling 330 each IBI based on said measured acceleration data, and/or

- determining 350 the parameter relating to heart rate variability respiratory coherence further based on said measured acceleration data.

Fig. 4 depicts schematically a data processing unit comprising a computer program product for determining respiration quality. Fig. 4 depicts a data processing unit 410 comprising a computer program product comprising a non-transitory computer-readable storage medium 412. The non-transitory computer-readable storage medium 412 having thereon a computer program comprising program instructions. The computer program is loadable into a data processing unit 410 and is configured to cause a processor 411 to carry out the method for determining respiration quality accordance with the description of fig. 3.

Claims

1. A computer implemented method for determining respiration quality, the method (300) comprises

- obtaining (310) time series data (100) relating to a time period and being indicative of a beating heart;

- determining (320) interbeat intervals, I Bls, (102) between consecutive heartbeats (101) based on said obtained time series data (100);

- labelling (330) each IBI that is a first IBI in a series (103,104) comprising at least three subsequent IBIs, wherein said subsequent IBIs all progressively increases or all progressively decreases, and wherein each labelled IBI is identified as being first in a series (103) of progressively increasing IBIs or identified as being first in a series (104) of progressively decreasing IBIs; and

- determining (350) a parameter relating to heart rate variability respiratory coherence based on any labelled IBIs.

2. The method according to claim 1, wherein labelling (330) IBIs comprises labelling each IBI being the first IBI in a series (103,104) comprising at least four, at least five or at least six subsequent IBIs.

3. The method according to claim 1 or 2, wherein labelling (330) IBIs comprises labelling each IBI also being the first IBI in a series (103,104), wherein the duration of the series (103,104) is above a minimum series duration threshold value.

4. The method according to any preceding claim, wherein determining (350) of the parameter relating to heart rate variability respiratory coherence comprises comparing the number of labelled IBIs (102) in the time period corresponding to the time series data (100) and an occurrence threshold value, wherein said occurrence threshold value is based on said time period.

5. The method according to any preceding claim, wherein said obtained time series data (100) comprises an electrocardiogram, and wherein determining (320) IBIs comprises determining R-R intervals based on said electrocardiogram.

6. The method according to any preceding claim, further comprising identifying (340) IBI trend alternations, wherein an IBI trend alternation is identified upon at least two labelled I Bls occurring within an alternation time window, wherein at least one IBI is identified as being first in a series (103) of progressively increasing I Bls and at least one IBI is identified as being first in a series (104) of progressively decreasing I Bls, and wherein determining (350) the parameter relating to heart rate variability respiratory coherence is further based on any identified IBI trend alternations.

7. The method according to claim 6, wherein said alternation time window is equal to a breathing cycle duration, wherein identifying (340) IBI trend alternations comprises determining the breathing cycle duration based on said time series data (100).

8. The method according to claim 6 or 7, wherein the determined parameter is based on one labelled IBI and/or one identified IBI trend alternation.

9. The method according to claim 6 or 7, wherein the determined parameter is based on all labelled I Bls and/or identified IBI trend alternations in the duration of said time series data (100).

10. The method according to any preceding claim, further comprising transmitting (360) a signal indicative of the determined parameter relating to heart rate variability respiratory coherence and/or presenting information indicative of the determined parameter relating to heart rate variability respiratory coherence.

11. The method according to any preceding claim, wherein obtaining (310) time series data (100) further comprises obtaining breathing data, and wherein labelling (330) I Bls is further based on said obtained breathing data, and/or wherein determining (350) the parameter relating to heart rate variability respiratory coherence is further based on said obtained breathing data.

12. A computer program product comprising a non-transitory computer-readable storage medium (412) having thereon a computer program comprising program instructions, the computer program being loadable into a processor (411) and configured to cause the processor (411) to perform the method (300) for determining respiration quality according to any one of the preceding claims. A system for determining respiration quality, the system (200) comprises a heart rate sensor (230) and a heart rate analyser (210); the heart rate analyser (210) comprises control circuitry (211) comprising a computer (212), wherein the control circuitry (211) is arranged to control said heart rate sensor (230); the heart rate sensor (230) is arranged to measure time series data (100) indicative of a beating heart; the computer (212) is arranged to

- obtain time series data from said the heart rate sensor (230),

- determine interbeat intervals, IBI, (102) between consecutive heartbeats (101) in said time series data,

- label each IBI being a first IBI in a series (103,104) comprising at least three subsequent I Bls in which said subsequent I Bls all progressively increases or all progressively decreases, wherein each labelled IBI is identified as being first in a series (103) of progressively increasing or identified as being first in a series (104) of progressively decreasing I Bls, and

- determine the parameter relating to heart rate variability respiratory coherence based on any labelled I Bls. The system according to claim 13, wherein the system (200) further comprises a respiration sensor (250) arranged to measure breathing data, wherein the control circuitry (211) is arranged to control said respiration sensor (250), and wherein the computer (212) is further arranged to

- obtain measured breathing data from said the respiration sensor (250), and

- determine the parameter relating to heart rate variability respiratory coherence further based on said measured breathing data. The system according to any of claim 13 to 14, wherein the system (200) further comprises an accelerometer (260) arranged to measure and provide acceleration data, wherein the control circuitry (211) is arranged to control said accelerometer (260), and wherein the computer (212) is further arranged to

- obtain measured acceleration data from said accelerometer (260), and

- determine the parameter relating to heart rate variability respiratory coherence further based on said measured acceleration data. The system according to any of claim 13 to 15, wherein the system (200) further comprises presentation means (270) arranged to provide information for a user, wherein the control circuitry (211) is arranged to control said presentation means (270), and wherein the computer (212) is further arranged to - present information indicative of said determined parameter relating to heart rate variability respiratory coherence utilizing said presentation means (270).