Patent Application
20240237941
The present invention relates to a system for monitoring health of a person and particularly to a system according to preamble of claim 1. The present invention relates also to a method for monitoring health of a person and particularly to a method according to preamble of claim 23. The present invention relates also to a computer program for monitoring health of a person and particularly to a computer program according to preamble of claim 37.
Vagus nerve is an important nerve in the human body and it plays a major role in the correct functioning of the parasympathetic nervous system. In the medical community, stimulation of the vagus nerve is believed to lead to various cardiovascular, cerebrovascular, metabolic and other physiological and mental health benefits.
In the prior art, vagus nerve has been stimulated in a non-invasive way for example by application of electricity to the frontal neck area of the human body. Devices to this purpose are commercially available, and they resemble an electrical razor apparatus in size and shape. Also invasive ways of vagus nerve stimulation exist. As an example, a device may be surgically implanted under the skin of a person's chest, and a wire may be threaded under the skin to connect the device to the vagus nerve. When activated, the device sends electrical signals along the vagus nerve to person's brainstem, activating the vagus nerve and the nervous system in general.
It is also known that breathing in a certain, in general, slow breathing rate may stimulate the vagus nerve favourably. As one way to stimulate the vagus nerve favourably, breathing with a certain slow breathing rate, during a so-called breathing exercise, offers many advantages. Breathing correctly by a person involves no invasive or non-invasive electrical stimulation of the nerve which can be difficult to arrange especially if the related arrangements require surgical procedures or dedicated electrical devises or both. However, monitoring the breathing with the prior art devices and systems is challenging as monitoring breathing requires its own set of dedicated devices. These devices may be based, for example, to the analysis of flow of breathing inhalations and exhalations and to the movement of breathing gasses in the respiratory tract, for example mouth or nose. Clearly, wearing or holding such metering devices in mouth or by the nose is relatively unpleasant and complex.
It is believed that an effective positive vagus nerve stimulus through breathing is achieved when breathing rate of a person follows the natural heart rate variation of the person. In the medical community, this kind of stimulation is sometimes called “cross-coherence” between breathing and heart rate variation. In general, when a person inhales, the instantaneous heart rate increases, and when a person exhales, the instantaneous heart rate decreases.
Normally at rest, an average heart rate is between 60 and 100 BPM (beats per minute). However, this is heavily dependent on the gender, age, fitness level and level of relaxation of the person. Heart rate variations and breathing are also personal traits and there is no one universal value for a correct breathing rate to achieve strong vagus nerve stimulus through cross-coherence. Instead, every person has his or her own best breathing rate that best matches the heart rate variability (“HRV”) and breathing. Usually, the breathing rate that may provide strongest vagus nerve stimulus is between 3 and 8 breaths in one minute. In general, a high variability in the heart rate may be an indication of a strong vagus nerve stimulation with positive health effects.
In the prior art, there are some notions of analysing the cross-coherence of breathing rate and heartbeat rate of a person and of giving information to the person to find the best breathing rate to reach a high heart rate variability that may indicate strong vagus nerve stimulation. This has involved the usage of Fourier transforms and complex signal processing techniques to indicate spectral characteristics of heart rate variation and breathing. To get reliable spectral data, a very long dataset comprising data of instantaneous heart rates and breathing spanning several minutes has been needed. This makes the practical implementation of the system very difficult as the person whose breathing and heartbeats are monitored needs to remain in complete rest, and at the same time, pay attention to the way he/she breathes and, possibly, alter the way he/she breathes for several minutes.
Preferably, vagus nerve stimulation through breathing is attempted frequently, somewhat like a repeated fitness routine or exercise, as the positive health effects may grow gradually stronger. With repeated breathing exercises, a person may achieve the health benefits faster during the exercise. Optimal breathing rate for vagus nerve stimulation may also decrease somewhat over time. Clearly, a comfortable and fast approach for determining the heart rate variation, and a related indication of the strength of the related vagus nerve stimulation through breathing is needed.
Thus, at least two prior art problems can be raised: Stimulating the vagus nerve requires complex devices and procedures and there is a clear need to overcome the above-mentioned problems in the prior art. If vagus nerve is to be stimulated with a correct breathing rate and through breathing in general, determining the breathing rate well suited for a person to increase heart rate variability that may stimulate the vagus nerve favourably with prior art methods and devices has been challenging.
An object of the present invention is to provide a system, a method and a computer program for monitoring health of a person during a health monitoring session so that the prior art disadvantages are solved or at least alleviated. The objects of the invention are achieved by a system according to the independent claim 1. The objects of the invention are further achieved by a method according to the independent claim 23. The objects of the invention are further achieved by a computer program according to the independent claim 37.
The preferred embodiments of the invention are disclosed in the dependent claims.
The present invention is based on an idea of providing a system for monitoring health of a person during a health monitoring session. The system comprises:
the system comprises
Advantage of the system is that a breathing frequency of the person that strongly stimulates the vagus nerve is determined quickly when compared to prior art systems, without complex arrangements in the respiratory tract. As an example, a timed and controlled breathing frequency sequence which is decreased down from a high breathing rate (for example 8 breaths/minute) a low one (for example 3 breaths/minute) at for example 0.5 breaths/minute intervals gives a broad and comprehensive dataset. It is straightforward to determine an optimal vagus nerve stimulation from this dataset.
In some embodiments, the data processing system is further configured, during a health monitoring session, to
In some embodiments, the data processing system is further configured, during a health monitoring session, to
In some embodiments, successive time windows in the time window sequence have different breathing rates timed by the breathing pacer.
Alternatively, successive time windows in the time window sequence have decreasing breathing rates timed by the breathing pacer.
Further alternatively, successive time windows in the time window sequence have increasing breathing rates timed by the breathing pacer.
In some embodiments, the data processing system is further configured, during a health monitoring session, to
Alternatively, the data processing system is further configured, during a health monitoring session, to
In some embodiments, the data processing system is further configured, during a health monitoring session, to
In some alternative embodiments, the data processing system is further configured, during a health monitoring session, to
In an embodiment, in the fitting, the data processing system is configured to: determine the cumulative difference E between the instantaneous heart rate data and the periodic biofeedback function within each of the time windows, the time windows comprising the timepoints, and perform the fitting based on a calculated minimum of the cumulative difference E within each of the time windows to determine the parameters of the periodic biofeedback function at each of the timepoints, the time windows comprising the timepoints.
Fitting a periodic function (here, the periodic biofeedback function) to a measurement data (here, to instantaneous heart rate data) based on a minimum of a cumulative difference is an effective way to achieve the fitting.
In an embodiment, the data processing system is configured perform the fitting within each of the time windows with a least squares method; or a modified least squares method; or a random search; or an exhaustive search; or any combination thereof. These fitting methods are effective methods for the fitting and they may be used in combination to avoid the convergence of the fitting method to a local minimum of the cumulative difference instead of a global minimum.
In an embodiment, in fitting, in determining the parameter indicating the amplitude A of the periodic biofeedback function at each of the timepoints, the data processing system is arranged to determine a minimum instantaneous heart rate and a maximum instantaneous heart rate of instantaneous heart rate data within each of the time windows, subtract the minimum instantaneous heart rate from the maximum instantaneous heart rate divide the value of subtraction by 2, and determine the parameter indicating the amplitude A of the periodic biofeedback function at each of the timepoints based on the value of the division. For a periodic, alternating function such as the periodic biofeedback function this is an effective way to determine an amplitude parameter of the function, or an initial guess for the amplitude parameter of the function.
In an embodiment, the parameters of the periodic biofeedback function comprise an angular frequency P, and in fitting, in determining the parameter indicating the angular frequency P of the periodic biofeedback function at each of the timepoints, the data processing system is arranged to determine a cycle time TD of the instantaneous heart rate data within each of the time windows, and determine the parameter indicating the angular frequency P of the periodic biofeedback function at each of the timepoints based on the cycle time TD. For a periodic, alternating function this is an effective way to determine an angular frequency parameter of the function, or an initial guess for the angular frequency parameter of the function.
In an embodiment, the parameters of the periodic biofeedback function comprise a mean M (or mean value M) of the periodic biofeedback function and in fitting, in determining the mean M of the periodic biofeedback function at each of the timepoints, the data processing system is arranged to determine a mean value of the instantaneous heart rate data within each of the time windows, and determine the mean M of the periodic biofeedback function at each of the timepoints based on the mean value. For a periodic, alternating function such as the periodic biofeedback function this is an effective way to determine the parameter indicating the mean of the function, or an initial guess for the parameter indicating the mean of the function.
In an embodiment, the periodic biofeedback function f(t) at each of the timepoints comprises a sinusoidal function sin(l such that f(t) is defined as f(t)=A sin(Pt−T)+M, in which A is the amplitude A of the instantaneous heart rate data within each of the time windows, P is the angular frequency P of the instantaneous heart rate data within each of the time windows, T is a time displacement, and M is a mean value M of the instantaneous heart rate data within each of the time windows. A sinusoidal function is able to represent the variation of the instantaneous heart rate well and is readily computed with digital means.
In an embodiment, the periodic biofeedback function f(t) at each of the timepoints comprises a skewed sinusoidal function sk sin( ) such that f(t) is defined as f(t)=A sk sin(Pt−T)+M=A sin[(Pt−T)+k*sin(Pt−T)], in which A is the amplitude A of the instantaneous heart rate data within each of the time windows, P is the angular frequency P of the instantaneous heart rate data within each of the time windows, T is a time displacement, k is a skew factor, * is a multiplication operator, and M is a mean value M of the instantaneous heart rate data within each of the time windows. A skewed sinusoidal function is also able to take into account the asymmetry of the inhale-exhale cycle of breathing and related heart rate variation as inhalation is often shorter than exhalation.
In an embodiment, the data processing system is configured to analyse the heart rate measurement data for each heartbeat measured with the heart rate sensor. This provides the most accurate set of data into which the periodic biofeedback function may be fitted.
In an embodiment, the breathing pacer is configured to provide feedback to the person within at least one of the time windows based on: the parameters of the periodic biofeedback function, or the cumulative difference E, or any combination thereof. A good fitting with a high amplitude parameter A, and small cumulative difference E is indicative of strong vagus nerve stimulation.
In an embodiment, the data processing system is configured to determine a resonance score from the amplitude A of the periodic biofeedback function and from the cumulative difference E at least within one of the time windows, and the breathing pacer is configured to provide feedback to the person within at least one of the time windows based on the resonance score. In another embodiment, the data processing system is configured to determine a resonance score from the amplitude A of the periodic biofeedback function and from the cumulative difference E within each of the time windows, and the breathing pacer is configured to provide feedback to the person by indicating to the person the breathing rate having the best resonance score. These are advantageous ways in delivering information on breathing rate which provides the strongest vagus nerve stimulation, the breathing rate determined, for example, in the ramp-down or ramp-up of the instructed breathing frequencies.
In an embodiment, the breathing pacer is configured to provide feedback to the person within at least one of the time windows visually, or audially, or haptically, or any combination thereof. These are advantageous ways in delivering information on how well the breathing stimulates the vagus nerve.
In an embodiment, the breathing pacer comprises third software means executable on a mobile computing device, and the third software means are functionally connectable with the data processing system, and the third software means comprises computer-executable instructions for providing feedback based on the parameters of the periodic biofeedback function, or the cumulative difference or any combination thereof. A mobile computing device like a smartphone or a tablet computer is an advantageous unit for implementing the functionality of the breathing pacer.
In an embodiment, the breathing pacer is arranged to time the breathing events to the person by indicating to the person a start of an inhalation of each breathing cycle; or an end of an inhalation of each breathing cycle; or a start of an exhalation of each breathing cycle; or an end of an exhalation of each breathing cycle; or any combination thereof. Many points in the breathing cycle are viable when instructing the person to breathe in an externally timed fashion.
In an embodiment, the data processing system comprises first software means executable on a mobile computing device, the first software means being functionally connectable with the heart rate sensor, and the first software means comprises computer-executable instructions to receive the heart rate measurement data, arrange the breathing pacer to time the breathing events, analyse the heart rate measurement data, and store the parameters and the cumulative difference. A mobile computing device like a smartphone or a tablet computer is an advantageous unit for implementing the functionality of data processing system.
In an embodiment, the data processing system comprises second software means executable on a network data server, the first software means and the second software means are configured to exchange data over a network connection, and the second software means comprise computer-executable instructions for performing at least one of the steps of receive the heart rate measurement data, arrange the breathing pacer to time the breathing events, analyse the heart rate measurement data, and store the parameters and the cumulative difference. A data server which is, for example, implemented in a “cloud” or a server cluster in a computer also an advantageous unit for analysing the heart rate measurements with a suitably fast network connection, for example a WLAN or a 3GPP connection.
In an embodiment, the second software means comprises computer-executable instructions for displaying the parameters of the periodic biofeedback function and the cumulative difference. A data server can provide output functionality of the analysis results, for example through a personal webpage or an applet of a mobile computing unit like smartphone.
In an embodiment, the breathing pacer is configured to time the breathing events to the person with the breathing rate sequence comprising breathing rates such that a successive breathing rate of the breathing rate sequence is lower than a previous breathing rate of the breathing rate sequence.
In an embodiment, the breathing pacer is configured to time the breathing events to the person with the breathing rate sequence comprising breathing rates such that a successive breathing rate of the breathing rate sequence is higher than a previous breathing rate of the breathing rate sequence.
In an embodiment, the breathing pacer is configured to time the breathing events to the person with the breathing rate sequence comprising breathing rates such that the breathing rate sequence is a predetermined breathing rate sequence.
Said three breathing rate sequences above are advantageous for iterating the best resonance score indicating the strongest vagus nerve stimulation for the person.
The present invention is also based on an idea of providing a method for monitoring health of a person during a health monitoring session. The method comprises:
In some embodiments, the method comprises storing the parameters of the periodic biofeedback function, and the cumulative difference E at each of the timepoints.
In some embodiments, the method comprises comparing the resonance scores of the time windows to each other and determine the breathing having the best resonance score.
In some embodiments, the method comprises providing successive time windows in the time window sequence have different breathing rates timed by the breathing pacer.
In some alternative embodiments, the method comprises providing successive time windows in the time window sequence have decreasing breathing rates timed by the breathing pacer.
In some further alternative embodiments, the method comprises providing successive time windows in the time window sequence have increasing breathing rates timed by the breathing pacer.
Advantage of the method is that a breathing frequency of the person that strongly stimulates the vagus nerve is determined quickly when compared to prior art systems, without complex arrangements in the respiratory tract. For example, a timed and controlled breathing frequency sequence which is ramped down from a high breathing rate (for example 8 breaths/minute) a low one (for example 3 breaths/minute) at for example 0.5 breaths/minute intervals gives a broad and comprehensive dataset. An optimal, personal breathing frequency that provides the strongest vagus nerve stimulation is straightforward to determine from this dataset.
In an embodiment, in the fitting, the method comprises determining the cumulative difference between the instantaneous heart rate data and the periodic biofeedback function within each of the time windows, the time windows comprising the timepoints, and performing the fitting based on a calculated minimum of the cumulative difference within each of the time windows to determine the parameters of the periodic biofeedback function at each of the timepoints, the time windows comprising the timepoints. Fitting a periodic function (here, the periodic biofeedback function) to a measurement data (here, to instantaneous heart rate data) based on a minimum of a cumulative difference is an effective way to achieve the fitting.
In an embodiment, the fitting is performed with a least squares method; or a modified least squares method; or a random search; or an exhaustive search; or any combination thereof. These fitting methods are effective methods for the fitting and they may be also used in combination to avoid the convergence of the fitting method to a local minimum of the cumulative difference instead of a global minimum.
In an embodiment, the periodic biofeedback function f(t) at each of the timepoints comprises a sinusoidal function sin( ) such that f(t) is defined as f(t)=A sin(Pt−T)+M, in which A is the amplitude A of the instantaneous heart rate data within each of the time windows, P is the angular frequency P of the instantaneous heart rate data within each of the time windows, T is a time displacement, and M is a mean value M of the instantaneous heart rate data within each of the time windows. A sinusoidal function is able to represent the variation of the instantaneous heart rate well and is readily computed with digital means.
In an embodiment, the periodic biofeedback function f(t) at each of the timepoints comprises a skewed sinusoidal function sk sin( ) such that f(t) is defined as f(t)=A sk sin(Pt−T)+M=A sin[(Pt−T)+k*sin(Pt−T)], in which A is the amplitude A of the instantaneous heart rate data within each of the time windows, P is the angular frequency P of the instantaneous heart rate data within each of the time windows, T is a time displacement, k is a skew factor, * is a multiplication operator, and M is a mean value M of the instantaneous heart rate data within each of the time windows. A skewed sinusoidal function is also able to take into account the asymmetry of the inhale-exhale cycle of breathing and related heart rate variation as inhalation is often shorter than exhalation.
In an embodiment, the method comprises providing feedback with the breathing pacer within at least one of the time windows, the feedback based on the parameters of the periodic biofeedback function, or the cumulative difference E, or any combination thereof. A good fitting indicated for example with a high amplitude and small cumulative difference is indicative of strong vagus nerve stimulation.
In an embodiment, the method comprises determining a resonance score from the amplitude of the periodic biofeedback function and from the cumulative difference within at least one of the time windows, and providing feedback to the person within at least one of the time windows based on the resonance score with the breathing pacer. In another embodiment, the method comprises determining a resonance score from the amplitude A of the periodic biofeedback function and from the cumulative difference E within each of the time windows, and providing feedback to the person by indicating to the person, with the breathing pacer, the breathing rate having the best resonance score. These are advantageous ways in delivering information on breathing rate which provides the strongest vagus nerve stimulation, the breathing rate determined for example in the ramp-down of the instructed breathing frequencies.
In an embodiment, the method comprises providing feedback: visually, or audially, or haptically or any combination thereof. These are advantageous ways in delivering information on how well the breathing stimulates the vagus nerve.
In an embodiment, in the method, in timing the breathing events to the person with the breathing pacer and with the breathing rate sequence comprising breathing rates, a successive breathing rate of the breathing rate sequence is lower than a previous breathing rate of the breathing rate sequence.
In an embodiment, in the method, in timing the breathing events to the person with the breathing pacer and with the breathing rate sequence comprising breathing rates, a successive breathing rate of the breathing rate sequence is higher than a previous breathing rate of the breathing rate sequence.
In an embodiment, in the method, in timing the breathing events to the person with the breathing pacer and with the breathing rate sequence comprising breathing rates, the breathing rate sequence is a predetermined breathing rate sequence.
Said three breathing rate sequences above are advantageous for iterating the best resonance score indicating the strongest vagus nerve stimulation for the person.
In an embodiment, the method is executed in a system according to the system aspect and its embodiments of the invention. The system defined above is an advantageous system to execute the method.
As an aspect of the present invention, a computer program is disclosed. The computer program comprises executable instructions which are configured to execute all the steps of a method according to the method and its embodiments as defined above in a computer, or a mobile computing device, or network data server, or any combination thereof. A computer program is an advantageous way to implement the method in various types of computer hardware.
The invention is based on the idea of determining and informing the person of the breathing rate from the instantaneous heart rate data that provides the best vagus nerve stimulation through breathing by altering a sequence of instructed or “timed” breathing rates for the person. Optimal vagus nerve stimulation through breathing may be achieved when the variation of the instantaneous heart rate becomes periodic, has a steady and high amplitude and alternates around a mean. By fitting a periodic biofeedback function, for example a time dependent sinusoidal function with an amplitude, angular frequency, phase and offset (mean) value, to the instantaneous heart rate data, the amplitude of the variation and the cumulative difference between the periodic biofeedback function and the instantaneous heart rate data become available very quickly when compared to prior art methods. In the optimal breathing frequency, the amplitude of the variation is maximal and the cumulative difference (that is, the error) between the fitted periodic biofeedback function and the measured data is minimal.
The invention has many advantages. The optimal breathing rate for vagus nerve stimulus through breathing can be determined without any invasive procedures or electrical stimulation. Breathing measurements in, from or by the respiratory tract are also not needed. Instead, mere heart rate measurements of individual heartbeats are sufficient. Heart rate measurements are very convenient with modern technologies. With the fitting of the periodic biofeedback function on instantaneous heart rate data, a very quick determination of the relevant parameters of the function is achieved as the data does not have to be subjected to complex spectral analyses like Fourier transforms that may require very long samples of heartbeat information to become reliable.
With the invention, information on the heart rate variation and consequently the indication on how well various breathing rates suit the vagus nerve stimulation may become available between five to ten seconds. This is considerably faster than with prior art systems or methods.
It is to be noted that the presented invention does not disclose a diagnostic or therapeutic invention. To be a diagnostic invention, there would need to be one or more “normal” ranges of heart rate variabilities, and then one or more “abnormal” ranges of heart rate variabilities, and, then according to the invention, a determination and diagnostics should be reached based on the measured heart rate variability, and the normal and abnormal ranges. However, the present invention is not aimed into such determination.
Similarly, to be a therapeutic invention, the invention (system or method) should directly have a therapeutic effect. However, it is the breathing of the person that may stimulate the vagus nerve to a varying degree, and the strength of the stimulus may be dependent on the breathing rate. However, the invention lets the user to monitor the effect of the breathing to the heart rate variability. High heart rate variability may have health benefits and it may be indicative of improved vagus nerve stimulation, achieved through “correct” breathing. Thus, the invention allows monitoring of the person, and it is up to the user to perform the breathing through the results of the monitoring. The breathing with a certain breathing rate may then have health related positive effects.
For the purposes of this text, “vagus nerve stimulation” means “vagus nerve stimulation through breathing” that may be guided and observed by the system, method and computer program, as the system, method or computer program disclosed in the present text do not, in any way, directly stimulate the body of the person.
The invention is described in detail by means of specific embodiments with reference to the enclosed drawings, in which
In the Figures and in this text, like numbers (for example 112) and like labels (for example 112m) relate to like elements.
For the purposes of this text, a health monitoring session, labelled s, is any period of time during which the person is interested in observing his/her physiological matters related for example to breathing, cardiac activity or state of the nervous system, and in particular the interest of the person may be in the optimal vagus nerve stimulation through breathing.
For the purposes of this text, the terms “breathing rate” and “breathing frequency” are used interchangeably, and they all relate to a time-dependent breathing frequency of a person, calculated for example from the inverse of timespan of start of two subsequent inhalations when a person breathes. Usually breathing frequency is expressed in breathes per one minute (60 seconds), and it is usually 3-8 breaths/minute for an adult person in rest, especially when the person attempts strong or optimal vagus nerve stimulation through breathing.
For the purposes of this text, “within the time window w” means a period of time that starts and ends in the time window w, but does not necessarily span the entire time window w. Letter w is the label for a time window, as are symbols w1 and w2.
For the purposes of this text, the “instantaneous heart rate” f1 may be defined as the inverse of the interval (in time) of two consecutive heartbeats TR-R, and usually this heart rate is expressed as a frequency in minutes. The interval may be measured, for example, as the interval between timepoint of two consecutive R waves in the so-called QRS complex of a beating human heart. Thus, stated exactly, f1=60/TR-R, implying that an instantaneous heart rate of 60 beats in one minute (“BPM”, beats per minute) means that the interval between two consecutive R waves of a beating heart is 1 s. Instantaneous heart rate varies from one heartbeat to the next, and this variation is called, in general, heart rate variability. Instantaneous heart rate usually varies periodically and around a mean. For example, mean of an instantaneous heart rate mean may be 70 beats in one minute, and the instantaneous heart rate may vary with an amplitude of 8 beats/minute around the mean such that minimum instantaneous heart rate is 62 beats/minute and maximum 78 beats/minute over a period of time when the person is at rest. Especially breathing, as is discussed in the present text, has the ability to alter the instantaneous heart rate.
The system 1 comprises a heart rate sensor 10 which is arranged to measure heart beats of the person 90 for providing heart rate measurement data 110. Heart of the person is indicated with symbol 91, and breathing information is associated with symbol of lungs 92. Several kinds of heart rate sensors exist, for example electrocardiographic sensors that measure the electrical activity of heart and comprise usually an elastic band or belt worn around the chest area of a person 90, and photoplethysmographic sensors that detect the heart rate based on variations of optical properties of tissue and which comprise for example a clip with a sensor worn in an earlobe or a finger of a person 90.
The system also comprises a breathing pacer 20 configured to indicate breathing information to the person 90. The breathing pacer 20 may be a device, or a utility arranged, for example, in a mobile computing device like a smartphone or a tablet computer that is configured to guide the person to adapt a breathing rate that optimizes the vagus nerve stimulation through breathing.
The breathing pacer 20 may comprise digital electronics, analogue electronics, memory or memories, power unit like a battery, ASICs, FPGAs, digital busses, microcontrollers and microprocessors to arrange its various operations. The breathing pacer 20 may also comprise one or more input devices like a keyboard, a microphone or a touch screen display, and output devices like a LED, a display, a loudspeaker or a haptic device like a linear resonant actuator.
Specifically, the breathing pacer 20 is configured to time the breathing events to the person 90 with a breathing rate sequence 125 comprising breathing rates 125a, 125b. This is illustrated in
For the purposes of this text, “timing breathing events” or “to time the breathing events” means that the breathing pacer 20 signals or is arranged to signal to the person the moment in time the person should breathe, for example, start the next inhalation.
For this purpose, the health monitoring session s comprises a time window sequence wn, the time window sequence wn comprising time windows labelled w1, w2, such that each of the breathing rates 125a,125b of the breathing rate sequence 125 is associated with associated time window w1, w2 of the time window sequence wn.
The system 1 comprises also a data processing system 30 which is configured (as operation A) to receive the heart rate measurement data 110 from the heart rate sensor 10 during the health monitoring session s. This data may be transmitted for example in digital or analogue voltage signals for example with electrical connections from the heart rate sensor 10 to the data processing system 30. The data processing system 30 which is also configured (as operation B) during the health monitoring session s to arrange the breathing pacer 20 to time the breathing events to the person 90 in each of the breathing rates 125a, 125b.
The data processing system 30 is also (as operation C) configured to analyse the heart rate measurement data 110.
In the analysis (operation C), the data processing system 30 is configured to, (as operation C1) calculate instantaneous heart rate data 112 comprising the instantaneous heart rate of each measured heartbeat. The instantaneous heart rate f1 112 may be defined as the inverse of the interval (in time) of two consecutive heartbeats, as defined already above. The calculating is performed over each of the time windows w1, w2 in the health monitoring session s, each of the time windows w1, w2 comprising a timepoint t1, t2. The instantaneous heart rate data 112 is calculated based on the heart rate measurement data 110 received from the heart rate sensor 10. As an example, the time window w1 might start at timepoint 1500s (measured from an arbitrary point in time) and end at timepoint 1540s, (making the time window 40s wide) and timepoint t1 can be the midmost timepoint at 1520s. In a practical implementation, the time windows w1, w2 may comprise discrete timepoint, for example 10, 20 or 40 discrete timepoint where the instantaneous heart rate data 112 is represented for example by digital computing means. Thus, the heath monitoring session s comprises many, possibly overlapping time windows w1, w2. The time windows w1, w2 may be consecutive in time, arranged in the time window sequence wn.
Next, referring to
In the analysis, the data processing system 30 (as operation C3) is also configured to fit the periodic biofeedback function 200 into the instantaneous heart rate data 112 within each of the time windows w1, w2 to determine the parameters 210 of the periodic biofeedback function 200 at each of the timepoints t1, t2, and to determine the cumulative difference E 220 at each of the timepoints t1, t2.
The data processing system 30 is also configured, as operation D, to store the parameters 210 of the periodic biofeedback function 200 at each of the timepoints t1, t2, and the cumulative difference E 220 at each of the timepoints t1, t2. Storing is arranged, for example, for determining the best breathing frequency for vagus nerve stimulation after the breathing pacer suggests and times different breathing frequencies 125a, 125b of the breathing rate sequence 125. In other words, storing may be arranged for determining the best breathing frequency for vagus nerve stimulation after the breathing rates 125a, 125b of the breathing rate sequence 125 are tried by the person 90.
The data processing system 30 may also be configured, in operation D, to store the breathing rates 125a, 125b associated with the time windows w1, w2 comprising the timepoints t1, t2.
The data processing system 30 may also be configured provide the time window sequence wn such that the time window sequence wn comprises time windows w1, w2.
The data processing system 30 may also be configured to provide the breathing rate sequence 125 comprising the breathing rates 125a,125b such that each of the breathing rates 125a, 125b of the breathing rate sequence 125 is associated with the associated time window w1, w2 of the time window sequence wn.
Thus, with the system 1, determining the optimal vagus nerve stimulation is achieved with no complex and inconvenient measurements for example in the respiratory tract, and also quickly, as long sample periods needed by a Fourier transform are not needed.
In an embodiment, referring to
After the fitting (the operation C3), the data processing system 30 may also be configured determine the cumulative difference E 220 at each of the timepoints t1, t2 from the calculated minimum of the cumulative difference E 220 within each of the time windows w1, w2.
After the fitting (the operation C3), the data processing system 30 may also be configured to set the value of the cumulative difference E 220 at each of the timepoints t1, t2 to the value of the calculated minimum of the cumulative difference E 220 within each of the time windows w1, w2. In other words, after the fitting, the cumulative difference E 220 may have a minimum value within each of the time windows w1, w2.
The concept of the cumulative difference E is illustrated further in
In an embodiment, to provide the cumulative difference E 220, the data processing system 30 may be arranged to calculate the cumulative difference E 220 as the sum of absolute values of the discrete differences (251a-251N) between values of the instantaneous heart rate data 112 and values of the periodic biofeedback function 200 at the discrete timepoints n(i) . . . n(i+N),
where the | | denotes the absolute value and t is a timepoint within the discrete datapoints n(j).
As another embodiment, the data processing system 30 may be arranged to calculate the cumulative difference E 220 as the sum of squared values of the discrete differences (251a-251N) between values of the instantaneous heart rate data 112 and values of the periodic biofeedback function 200, wherein t is a timepoint within the discrete datapoints t=n(j),
In fitting, in operation C3, the data processing system 30 may be arranged to vary the parameters 210 of the periodic biofeedback function 200, and for each set of parameters 210, determine the cumulative difference E 220. In fitting, in operation C3, the data processing system 30 may be also arranged determine the calculated minimum of the cumulative difference E 220, and the parameters 210 of periodic biofeedback function 200 used to reach the calculated minimum of the cumulative difference 220. To determine the calculated minimum of the cumulative difference E 220, the data processing system 30 may be arranged to vary of the parameters 210 of the periodic biofeedback function 200 for a number of iteration loops. The data processing system 30 is arranged to base the variation of the parameters to one or more search algorithms or fitting methods. The data processing system 30 is then configured to determine the parameters 210 resulting in a calculated minimum of the cumulative difference 220, as the parameters 210 resulting in a calculated minimum of the cumulative difference 220 are then the result of the fitting, providing the values of the parameters 210 of the periodic biofeedback function 200.
In an embodiment, the data processing system 30 is configured to perform the fitting (in operation C3) within each the time windows w1, w2 with a least squares method, or a modified least squares method, or a random search, or an exhaustive search, or any combination thereof. In other words, the data processing system 30 may be arranged to base the varying of the parameters 210 of the periodic biofeedback function 200 within each the time windows w1, w2 to determine the calculated minimum of the cumulative difference E 220 on various search algorithms or fitting methods like a least squares method, a modified least squares method, a random search method or an exhaustive search method. The data processing system 30 may be also arranged to use any combination of the search algorithms or fitting methods of the least squares method, the modified least squares method, the random search method or the exhaustive search method to determine the calculated minimum of the cumulative difference E 220.
With the random search method or fitting method, the data processing system 30 is configured to perform the fitting (operation C3) by varying the parameters 210 of the periodic biofeedback function 200 within each the time windows w1, w2 randomly such that the values of the parameters 210, within ranges of feasible values, are arranged to be randomly assigned during the iteration loops. The data processing system 30 is then arranged to choose the set of parameters 210 reaching the calculated minimum of the cumulative difference 220 as the parameters 210 of the periodic biofeedback function 200. The data processing system 30 may also be arranged to start the random assignment of the parameters from an initial guess of one or more of the parameters.
With the exhaustive search method or fitting method, the data processing system 30 is configured to perform the fitting (operation C3) by vary the parameters 210 of the periodic biofeedback function 200 within each the time windows w1, w2 such that every combination of the parameters 210, within ranges of feasible values in a discrete set of the feasible values, are arranged to be tried during the iteration loops. For example, it may be determined that the maximum amplitude A 201 of the instantaneous heart rate is 20 beats/minute, all values of A, from zero to 20 beats/minute are tried with a discrete interval of for example 0.2 beats/minute. In other words, values 0, 0.2, 0.4, . . . 20 are tried when the amplitude parameter A 201 is varied. The same holds of other parameters. The data processing system 30 is then arranged to choose the set of parameters 210 achieving the calculated minimum of the cumulative difference 220 as the parameters 210 of the periodic biofeedback function 200. The data processing system 30 may also be arranged to start the variation of the parameters 210 from an initial guess of one or more of the parameters.
With the least squares search method or fitting method, the data processing system 30 is arranged to perform the fitting (operation C3) by varying the parameters 210 of the periodic biofeedback function 200 within each the time windows w1, w2 based on the numerical Gauss-Newton algorithm to find the calculated minimum of the cumulative difference E 220. By representing the parameters 210 of the periodic biofeedback function with a vector β, and discrete difference between the periodic biofeedback function 201 and the instantaneous heart rate data 112 at discrete timepoint n(j) by a “residual” rj as function of the parameters β 201 (number of parameters being k), calculated minimum of the cumulative difference E 220 is found when sum of squares S(β) is minimized, where
With an initial guess for the parameters 210 of #°, the data processing system 30 is arranged to vary the parameters 210 based on an iteration rule that may be for example
where L is the Lth iteration loop, and J is the Jacobian matrix with elements,
T denotes the matrix transpose and superscript −1 the matrix inverse.
With the modified least squares search method or fitting method, the data processing system 30 is arranged to perform the fitting (operation C3) by varying the parameters 210 of the periodic biofeedback function 200 within each the time windows w1, w2 based on the numerical Levenberg-Marquardt algorithm to find the calculated minimum of the cumulative difference E 220. The Levenberg-Marquardt algorithm is similar to the Gauss-Newton algorithm, but with the Levenberg-Marquardt algorithm the iteration rule may be for example
where λ is a damping parameter and I is an identity matrix. The damping factor λ may be arranged by the data processing system 30 for the iteration loops to a value, based on the error of the iteration, determined by the difference of the results of two consecutive iteration loops, that configures the iteration to find the parameters 210 of the periodic biofeedback function 200 faster. If the error of the iteration increases, the damping factor may be increased. If the error of the iteration decreases, the damping factor may be decreased. Clearly, with λ=0, the Levenberg-Marquardt algorithm reduces to the Gauss-Newton algorithm.
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The angular frequency P 202 may also indicate the measured breathing rate fB 120 of the person 90 through relation fB=P/2Π. The breathing pacer 20 may be arranged to show the measured breathing rate 120, for example to the person 90.
The breathing pacer 20 may be arranged to show the measured breathing rate 120 and at least one (instructed) breathing rate 125a of the breathing rate sequence 125.
The breathing pacer 20 may be arranged to show information based on a difference between the measured breathing rate 120 and at least one (instructed) breathing rate 125a of the breathing rate sequence 125.
Still referring to
The amplitude A 201 of the instantaneous heart rate data 112 indicates or determines the heart rate variability of the person 90 in frequency domain (1/time unit) and is thus a frequency domain characterisation. A measure of the heart rate variability in time domain may also be calculated as (1/(M−A))−(1/(M+A))=2A/(M2−A2)≈2A/M2, where A is the amplitude A 201, and M the mean 205.
In an embodiment, and still referring to
In an embodiment, and referring next to
In an embodiment, the periodic biofeedback function 200 at each of the timepoints t1, t2 is an alternating trapezoidal pulse train function with an amplitude A, angular frequency P and mean value of M.
In an embodiment, that the data processing system 30 is configured to analyse (as operation C) the heart rate measurement data 110 for each heartbeat measured with the heart rate sensor 10.
In another embodiment, the data processing system 30 is configured to analyse, (as operation C), the heart rate measurement data 110 for every second heartbeat measured with the heart rate sensor 10.
In another embodiment, the data processing system 30 is configured to analyse, (as operation C), the heart rate measurement data 110 such that the frequency of analysis is based on the cumulative difference E 220 such that increasing cumulative difference E 220 makes the frequency of analysis higher, maximally every heartbeat measured with the heart rate sensor 10. Decreasing cumulative difference E 220 makes the frequency of analysis lower such that minimally the data processing system 30 is configured to analyse, as operation C, the heart rate measurement data 110 every fifth, every tenth of every fifteenth heartbeat measured with the heart rate sensor 10.
In an embodiment, referring to
Optimal vagus nerve stimulation is achieved when the behaviour of the instantaneous heart rate data 112 is periodic and alternates over time around a mean with a large amplitude A 201. As the instantaneous heart rate data 112 is arranged to be represented by the periodic biofeedback function 200 in the data processing system 30, the cumulative difference E 220 between the periodic biofeedback function 200 and the instantaneous heart rate data 112 may also determine the impact on the vagus nerve stimulation, as the amplitude A 201 may also be large when the periodic biofeedback function 200 does not follow the instantaneous heart rate data 112 well. Thus, as a measure of the ability of the breathing to stimulate the vagus nerve, the data processing system 30 may be configured to determine a resonance score 230. As discussed in relation to FIG. 3a, and also referring to
In an embodiment, the data processing system 30 is configured to determine a resonance score 230 from the amplitude A 201 of the periodic biofeedback function 200 and from the cumulative difference E 220 within each of the time windows w1, w2, and the breathing pacer 20 is configured to provide feedback to the person 90 by indicating to the person 90 the breathing rate 125a, 125b having the best resonance score 230. This is illustrated in
For the purposes of present text, the best resonance score 230 relates to the strongest ability of the associated breathing rate 125a, 125b to stimulate the vagus nerve.
Referring still to
The resonance score 230 may also be defined by a two-dimensional table of values for amplitude 201 and cumulative difference 220 E. Referring still to
Generalizing, any arrangement for the determination of the resonance score 230 that indicates success in the vagus nerve stimulation based on a large value of amplitude A 201 and low value of cumulative difference E 220, and wise versa, is possible.
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A “breathing cycle” means a respiratory cycle in the present text. One breathing cycle is one sequence of inhalation and exhalation. During the health monitoring session s, the person takes one or more breaths, for example 10, 30, 50 or 70 breaths, one breath corresponding to one breathing cycle.
In an embodiment, and referring to
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The first software means 33 may also comprise computer-executable instructions 36 to store, in operation D, the breathing rates 125a, 125b associated with the time windows w1, w2 comprising the timepoints t1, t2.
As the breathing pacer 20 and the data processing system 30 may be implemented in the same unit, for example a mobile computing device 45, the breathing pacer 20 and the data processing system 30 may be functionally connected through the digital data processing units like memories, information busses, microcontrollers and microprocessors of the mobile computing device 45.
Next referring to
The second software means 34 may also comprise computer-executable instructions 37 to store, in operation D, the breathing rates 125a, 125b associated with the time windows w1, w2 comprising the timepoints t1, t2.
In an embodiment, in the system 1, the second software means 34 comprises computer-executable instructions 37 for displaying information based on the parameters 210 of the periodic biofeedback function 200 and the cumulative difference E 220. This is advantageous for example if the health monitoring results are to be published for example over the internet or in an app of a smartphone sharing information over the internet.
In an embodiment, referring back to
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Storing (step 320D) may also comprise storing the breathing rates 125a, 125b associated with the time windows w1, w2 comprising the timepoints t1, t2.
The method 300 may also comprise the data processing system 30 providing the time window sequence wn such that the time window sequence wn comprises time windows w1, w2.
The method 300 may also comprise the data processing system 30 providing the breathing rate sequence 125 comprising the breathing rates 125a,125b such that each of the breathing rates 125a, 125b of the breathing rate sequence 125 is associated with associated time window w1, w2 of the time window sequence wn.
Various concepts of the method aspect of the invention are already defined in the system aspect of the invention related to system 1 above.
Referring next to
The method 300 may also comprise, after step 320C5, determining the cumulative difference E 220 at each the timepoints t1, t2 from the calculated minimum of the cumulative difference E 220 within each of the time windows w1, w2.
The method 300 may also comprise, after step 320C5, setting the value of the cumulative difference E 220 at each of the timepoints t1, t2 to the value of the calculated minimum of the cumulative difference E 220 within each of the time windows w1, w2. In other words, after the fitting, the cumulative difference E 220 may have a minimum value within each of the time windows w1,w2.
In an embodiment, in step 320C5, the method 300 comprises performing the fitting (as step 320C5) with a least squares method, a modified least squares method, a random search, an exhaustive search, or any combination thereof. Operation of these methods is discussed above in relation to system 1.
In an embodiment, and referring back to
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In an embodiment, as illustrated in
In an embodiment, as illustrated in
In an embodiment, as illustrated in
In an embodiment, the method 300 comprises determining the resonance score 230 based on the division of the amplitude A 201 of the periodic biofeedback function 200 by the average cumulative difference EAVE 220 within the time window w. The average cumulative difference EAVE 220 may be calculated by dividing the cumulative difference E by the number of discrete datapoints N used to determine the cumulative difference E as illustrated in relation to
In an embodiment, the method 300 comprises determining the resonance score 230 based on a tabulated and predetermined set of values for resonance score 230, the tabulated and predetermined set of values for resonance score 230 arranged based on the amplitude A 201 of the periodic biofeedback function 200 and cumulative difference E 220 within at least one of the time windows w1, w2.
In an embodiment, as illustrated in
In an embodiment, as illustrated in
In an embodiment, as illustrated in
In an embodiment, as illustrated in
As an aspect of the invention, the method 300 defined above may be executed in the system 1 as defined above.
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For the purposes of this text, the data processing system 30 may comprise digital electronics, analogue electronics, memory or memories, power unit like a battery, ASICs, FPGAs, digital busses, microcontrollers and microprocessors to arrange its various operations. The data processing system 30 may also comprise one or more input devices like a keyboard, a microphone or a touch screen display, and output devices like a LED, a display, a loudspeaker or a haptic device like a linear resonant actuator. The data processing system 30 may be arranged in a mobile computing device like a smartphone or tablet computer, for example through software and hardware means. Various communication standards and protocols like I2C, SPI, Ethernet, WLAN and Bluetooth may be employed for communications. If the heart rate measurement data from the heart rate sensor 10 is in analogue format, the data processing system 30 may also comprise analogue-to-digital converters to arrange conversion of an analogue signal into a digital signal for further processing.
The invention has been described above with reference to the examples shown in the figures. However, the invention is in no way restricted to the above examples but may vary within the scope of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20215426 | Apr 2021 | FI | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/FI2022/050236 | 4/11/2022 | WO |
