Method and apparatus for detecting sleep disturbing events from signal indicative of peripheral arterial tension of individual

Abstract

The present invention relates to a method and an apparatus for detecting a sleep disturbing event from a signal indicative of peripheral arterial tension of an individual, the signal being affected by a small venous artery reflex wherein the sleep disturbing event is detected by: determining a vasoconstriction event from a change in the signal; deriving a reference amplitude and a baseline amplitude for the vasoconstriction event, the reference amplitude being different from the baseline amplitude; correlating the reference amplitude of the vasoconstriction event with the baseline amplitude of the vasoconstriction event, thereby obtaining a magnitude measurement of the vasoconstriction event; and thereby detecting a sleep disturbing event.

Description

Divisional Application Instructions

[0001] This application is a divisional application of Chinese patent application No. 2021800544085, filed on August 11, 2021 (PCT international application), which entered the national phase on March 2, 2023. The entire contents of all the aforementioned application documents are incorporated herein by reference. Technical Field

[0002] The present invention relates to a method and apparatus for detecting sleep disturbance events, and more specifically, to detecting sleep disturbance events from signals indicating peripheral arterial tension (PAT) of an individual. Background Technology

[0003] Pleometry, whether pneumatic or optical, is a measurement technique commonly used to monitor changes in blood volume in an individual's large and microvascular systems; more specifically, it is used to monitor changes in blood volume contained within arteries or arterioles. Changes in arterial blood volume are also affected by the contraction of the muscular walls of arteries or arterioles. Therefore, monitoring changes in arterial blood volume using pleometry empirically provides information about the relative changes in muscle tone or "tension" of the smooth muscle tissue of arterioles (also known as peripheral arterial tension, PAT).

[0004] Optical plethysmography, or photoplethysmography, measures changes in arterial blood volume by illuminating the volume under study with light from one or more light sources (e.g., LEDs) and detecting the collected light at a sensor corresponding to the light reflected or transmitted in the volume under study. This sensor may include, for example, a photodetector, such as a photodiode. The light source and sensor form a so-called plethysmographic probe and can be positioned, for example, on the opposite side of the volume under study—e.g., an individual's fingers, nostrils, ears, forehead, inside of the mouth, toes, wrists, ankles, etc.—allowing for the measurement of a transmission-mode photoplethysmogram (PPG), or positioned on the same side of the volume under study, allowing for the measurement of a reflection-mode PPG. Regardless of the measurement technique employed, both transmission-mode and reflection-mode PPG capture the periodic fluctuations in arterial blood volume within the volume under study, thus capturing changes in the individual's peripheral arterial tension.

[0005] Because arterial blood flow to the studied volume can be regulated by various other physiological phenomena such as respiration and heart rate, plethysmography can also be used to monitor respiratory and circulatory status, hypovolemia, and even to detect sleep disturbances that cause or are caused by sleep disorders. Sleep disorder diagnosis is one medical field that involves monitoring a patient's sleep over a period of time, such as one or more nights. Based on monitoring, different sleep-related or better sleep disturbances can be identified, such as apnea events, snoring, limb movements, or others. For example, reuptake of breath at the end of a sleep apnea event is usually consistent with the release of norepinephrine. Norepinephrine is released into the bloodstream and binds to adrenergic receptors in the arterioles of the studied volume, triggering an increase in arterial muscle tone, leading to a decrease in arterial diameter and a decrease in arterial blood volume in the studied volume. Therefore, monitoring peripheral arterial tone (PAT) by plethysmography, for example, can provide valuable information about the occurrence of these sleep disturbances, such as sleep apnea. This observation was widely reported in the last century and explained in detail in a scientific publication entitled “Effect of pain on autonomicnervous system indices derived from photoplethysmography in healthy volunteers”, published by Hamunen et al. on May 1, 2012 in the British Journal of Anesthesia, Volume 108, Issue 5, pp. 838-844. The authors described that the pulse plethysmography amplitude, or PPGA, recorded by photoplethysmography is due to pulsatile changes in tissue volume (mainly arterial blood) and that PPGA decreases during sympathetic activation or vasoconstriction.

[0006] However, due to gravity, a hydrostatic pressure gradient is automatically generated when certain parts of the body are below the level of the heart. This hydrostatic pressure gradient affects the venous system, an inherently low-pressure system, causing venous dilation, also known as venous pooling. Venous pooling can induce a reflexive contraction response in the arteries supplying the veins, resulting in additional physiological changes that mask the desired arterial vasomotor response studied by plethysmography. This reflex is commonly referred to as the venous arteriole reflex or VAR, and is described in detail in Part A of Bar et al.'s white paper, "An Illustrated Atlas of PATSignals in Sleep Medicine." To address this issue, US7374540B2 proposes a probe capable of completely covering the surface of the distal finger and providing a uniform pressure field extending to the fingertip. US7374540B2 proposes a probe with an intima and an adventitia; the intima applies a predetermined static pressure to the body part, while the adventitia ensures that the predetermined static pressure applied by the intima is substantially unaffected by volume changes in the body part. Therefore, the external counterpressure applied by the adventitia is claimed to reduce venous blood pooling and dilation within the measurement site, and allegedly reduces distal venous pooling. This is claimed to reduce the likelihood of inducing reflex vasoconstriction in venous arterioles, which would otherwise lead to the development of vasoconstriction of uncertain degree. Consequently, the prior art describes a complex and expensive probe for providing uniform pressure to reduce problems associated with vasoconstriction of uncertain degree. Summary of the Invention

[0007] The objective of embodiments of the present invention is to provide a solution capable of detecting sleep disturbance events that cause or are caused by sleep disorders, overcoming the shortcomings of conventional solutions, and more specifically, to provide a solution capable of accurately and robustly detecting sleep disturbance events, including sleep disorders, based on physiological information acquired through conventional off-the-shelf medical devices. Another objective of embodiments of the present invention is to provide a solution capable of accurately and robustly detecting sleep disturbance events, including sleep disorders, based on signals indicating an individual's peripheral tension acquired by conventional off-the-shelf medical devices (e.g., plethysmography probes that do not provide uniform pressure).

[0008] The scope of protection sought by the various embodiments of the present invention is given by the independent claims. Embodiments and features described in this specification that do not fall within the scope of the independent claims, if any, shall be construed as examples useful for understanding the various embodiments of the invention.

[0009] According to a first exemplary aspect of the invention, these objectives are achieved by a computer-implemented method for detecting sleep disturbance events as defined in claim 1. Specifically, the computer-implemented method is capable of detecting sleep disturbance events based on a signal indicative of peripheral arterial tension (PAT) in an individual affected by venous arteriole reflection (VAR). The signal can be obtained using conventional off-the-shelf medical devices, provided that the obtained signal provides information indicative of PAT and the effect on the signal caused by VAR. Examples of such medical devices include standard probes employing pneumatic or photoplethysmography (PPG) capable of obtaining VAR-affected PAT signals. Therefore, the method includes processing the signal obtained from the PPG probe to derive sleep disturbance events therefrom. More specifically, the method includes determining one or more vasoconstriction events based on changes in the signal. The method also includes deriving a reference amplitude and a baseline amplitude for the determined vasoconstriction events. The reference amplitude and the baseline amplitude are different from each other. The reference and baseline amplitudes correspond to the amplitudes of characteristic points in the signal of the vasoconstriction event, which allows for the characterization of the vasoconstriction event. The obtained reference amplitude and baseline amplitude are then correlated to obtain a measurement indicating the magnitude of the vasoconstriction event, i.e., a magnitude measurement characterizing the intensity or significance of the vasoconstriction event, and thus an amplitude measurement characterizing the intensity or significance of the observed physiological event. Once the magnitude of the detected physiological event is obtained, the method proceeds to detect sleep disturbance events from it. Correlating the reference amplitude with the baseline amplitude allows for a substantial reduction in the influence of slower changes (i.e., steady-state changes) observed in the signal. Steady-state changes are effects observed in the signal that change at a rate slower than the signal changes produced by the vasoconstriction event. As mentioned above, such steady-state changes can be caused, for example, by venous arteriole reflexes. In other words, the reference and baseline amplitudes are correlated such that when determining the magnitude of the vasoconstriction event, steady-state changes in the signal, and thus venous arteriole reflexes, are taken into account. Doing so ensures a correct assessment of the magnitude of the vasoconstriction event, and thereby ensures the correct detection and characterization of sleep disturbance events. As a result, false positives and false negatives in the detection of sleep disturbance events are essentially eliminated.

[0010] The reference and baseline amplitudes for the vasoconstriction event are preferably derived from a baseline-invariant version of the signal. The baseline-invariant signal can be calculated specifically for either the vasoconstriction event or the complete signal. That is, a partial baseline-invariant signal or a baseline-invariant signal of the complete signal can be calculated for the vasoconstriction event. The reference amplitude and the baseline value of the vasoconstriction event are then selected from the baseline-invariant signal. As mentioned above, the reference and baseline amplitudes correspond to the amplitudes of characteristic points in the signal of the vasoconstriction event, which allow for the characterization of the vasoconstriction event. Therefore, the reference and baseline amplitudes are preferably selected in a manner that allows for the calculation of a magnitude measurement of the vasoconstriction event.

[0011] The baseline-invariant signal is preferably calculated by dividing the obtained signal by a baseline. Segmentation can be performed relative to either the signal portion corresponding to the vasoconstriction event or the complete signal. In the first case, the baseline-invariant signal for the vasoconstriction event is calculated by dividing the signal portion corresponding to the vasoconstriction event by the baseline of the event. The baseline is the baseline level, which can be the baseline value or signal portion of the corresponding vasoconstriction event. In the second case, the baseline-invariant signal is obtained by dividing the complete signal by the baseline of the complete signal. Here, the baseline is the baseline signal. This results in the elimination of the venous arteriole reflex effect on arterial tension, at least for the vasoconstriction event, thereby ensuring the correct assessment of the magnitude of the vasoconstriction event and thus ensuring the correct detection and characterization of sleep disturbance events. The reference amplitude and the baseline amplitude are derived from the baseline-invariant signal. For example, the maximum amplitude observed in the baseline-invariant signal portion corresponding to the vasoconstriction event can be selected as the reference amplitude, while the average amplitude observed in the baseline-invariant signal portion corresponding to the vasoconstriction event can be selected as the baseline amplitude.

[0012] The selected reference amplitude and baseline amplitude are correlated with each other to obtain a measurement of the magnitude of the vasoconstriction event. Preferably, the amplitude is correlated with the baseline amplitude by calculating the absolute magnitude of the vasoconstriction event from the reference amplitude and the baseline amplitude. Since the reference and baseline amplitudes are selected from baseline-invariant versions of the signal, the magnitude of the vasoconstriction event can be derived, for example, as the difference between them. The magnitude measurement calculated in this way is an absolute value that allows quantification of the intensity of the vasoconstriction event on an absolute scale.

[0013] The baseline of a vasoconstriction event is preferably derived by calculating the envelope of the signal. Calculating the signal envelope allows for the extraction of venous arteriole reflections observed in the signal. In other words, the calculated signal envelope characterizes venous arteriole reflections throughout the entire signal. For example, the peak envelope, trough envelope, peak-to-trough average envelope, percentile-based envelope, or a smoothed version of the signal can be calculated and used as the signal envelope. Alternatively, instead of calculating the envelope of the entire signal, the amplitude of the vasoconstriction event can be calculated, which can be used as the baseline of the vasoconstriction event. For example, the peak amplitude, trough amplitude, peak-to-trough average amplitude, or percentile of the vasoconstriction event can be calculated and used as the baseline of the vasoconstriction event.

[0014] Alternatively, the reference and baseline amplitudes of the vasoconstriction event can be derived directly from the acquired signal. This can be done, for example, by calculating the peak amplitude, trough amplitude, peak-to-trough average amplitude, or percentile of the vasoconstriction event directly from the acquired signal. These amplitudes can be used as the baseline or reference amplitudes of the vasoconstriction event, provided that the baseline and reference amplitudes are chosen to be different from each other. For example, the peak amplitude of the peak of the vasoconstriction event can be used as the reference amplitude, and any other amplitude characterizing the vasoconstriction event, i.e., the trough amplitude, peak-to-trough average amplitude, or average amplitude, can be chosen as the baseline amplitude.

[0015] The selected reference amplitude and baseline amplitude are then correlated with each other to obtain a measurement of the magnitude of the vasoconstrictive event. Preferably, the reference amplitude is correlated with the baseline amplitude by calculating the relative magnitude of the vasoconstrictive event from the reference amplitude and baseline amplitude. The relative magnitude of the vasoconstrictive event can be derived, for example, as their relative difference or relative change, or any other substantially relative measurement of magnitude. The magnitude measurement calculated in this way is a relative value that allows quantification of the intensity of the vasoconstrictive event on a relative scale.

[0016] Detection of sleep disturbance events preferably includes identifying vasoconstrictive events characterized by a magnitude measurement above a predetermined value. In other words, vasoconstrictive events with a certain magnitude measurement are considered sleep disturbance events. The predetermined value can be determined based on measurements obtained, for example, during clinical trials. Preferably, the step of detecting sleep disturbance events includes using a classifier trained or developed for detection. Any conventional classifier can be trained, such as a neural network, decision tree, or support vector machine.

[0017] Preferably, the identification step also considers at least one of the duration of the vasoconstriction event, the duration of the amplitude decline phase and / or the duration of the amplitude rise phase, and the steepness of the amplitude decline phase and / or the steepness of the amplitude rise phase. The duration of the vasoconstriction event, the duration of the amplitude decline phase, and / or the duration of the amplitude rise phase are additional measures that can be used to improve the quantification of the intensity of the vasoconstriction event. Similarly, the steepness of the amplitude decline and / or the steepness of the amplitude rise are other additional measures that can also be used to improve the quantification of the intensity of the vasoconstriction event. Using any of these additional measurements in combination with magnitude measurements allows for the quantification of the intensity of the vasoconstriction event based on various signal characteristics, and thus improves the detection of sleep disturbance events and their better characterization.

[0018] The determination of a vasoconstriction event preferably involves identifying a portion of a signal characterized by a decrease in amplitude followed by an increase in amplitude. In other words, to identify a vasoconstriction event, the method searches for changes in the signal characterized by a decrease in amplitude followed by an increase in amplitude. The determination can be performed using any signal processing technique suitable for this purpose.

[0019] Preferably, the determination also considers at least one of the durations of the amplitude decline and rise cycles of the signal portion, the duration of the amplitude decline and / or rise cycles of the signal portion, and the steepness of the amplitude decline and / or rise cycles of the signal portion. Taking into account the durations of the amplitude decline and / or rise cycles, and the steepness of these respective cycles, allows for the quantification of amplitude based on various signal characteristics, and thus improves the determination of vasoconstriction events based on the obtained signal.

[0020] Pleoplegia is preferred for obtaining signals indicative of peripheral arterial tension in individuals affected by venous arteriole reflexes. Pleoplegia allows the acquisition of signals, i.e., pleopleural signals or pleopleural maps, indicating changes in blood volume, such as pulsatile blood volume, at selected anatomical locations in the patient (e.g., fingers, nostrils, ears, forehead, inside the mouth, toes, wrists, ankles, etc.). Pleopleural signals can be obtained using so-called pleopleural probes, which can employ pneumatic-based or optical-based pleoplegia. Because pleopleural signals empirically provide information about the relative changes in muscle tension or “tension” of the muscular tissue of the arterioles and about venous arteriole reflexes, pleopleural signals allow the derivation of signals indicative of changes in peripheral arterial tension affected by venous arteriole reflexes.

[0021] Preferably, the signal indicating peripheral arterial tension (PAT) originates from an optical plethysmography signal measured at the volume of the individual under study, acquired by optical plethysmography at two or more time points along the optical plethysmography signal. Thus, the change in arterial blood volume in the studied volume between two or more time points is derived by determining the logarithm of a function of light intensity or an approximation thereof, thereby assessing the individual's PAT. The logarithm of the light intensity function or an approximation thereof is referred to herein as the evaluation function. Preferably, this evaluation function corresponds to the logarithm of the ratio of light intensity, and the evaluation function depends on one or more of the following: optical path length; an estimate of oxygen saturation or a function of SpO2; and the change in arterial blood volume in the studied volume. Preferably, at least one time point corresponds to cardiac diastole during the individual's cardiac cycle, and / or at least one of the time points corresponds to cardiac systole during the individual's cardiac cycle.

[0022] According to a second exemplary aspect, an apparatus is disclosed configured to detect sleep disturbance events from signals indicating peripheral arterial tension in an individual affected by venous arteriole reflexes, the apparatus characterized by the features of claim 13. The apparatus includes at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to utilize the at least one processor to cause the apparatus to perform the steps of the first exemplary aspect. Such an apparatus can therefore provide one or more advantages mentioned with respect to the first exemplary aspect.

[0023] According to a third exemplary aspect, a system including the apparatus according to the second exemplary aspect is disclosed. Preferably, the system further includes a plethysmography probe. Preferably, the plethysmography probe is an optical plethysmography probe, which includes a light source configured to emit light and a sensor configured to collect propagating light by optical plethysmography, the propagating light corresponding to light transmitted or reflected as it propagates in the studied volume of an individual at two or more time points. The sensor is also configured to determine the light intensity of the light propagating at the two or more time points. Optionally, the probe is also provided with an accelerometer to detect the location of the studied volume. Optionally, the system may also include a wireless transmitter having a wireless communication interface, wherein the wireless transmitter is configured to wirelessly transmit the determined peripheral arterial tension for further processing by the apparatus. The wireless communication interface is preferably a low-power communication interface, such as a Bluetooth Low Energy (BLE) wireless interface. Such a system can therefore provide one or more advantages with respect to the first exemplary aspect.

[0024] According to the fourth exemplary aspect, the use of its logarithmic or functional approximation for evaluating a signal indicative of peripheral arterial tension (PAT) in an individual monitored by optical plethysmography is provided. The evaluation of the signal indicative of peripheral arterial tension includes: obtaining an optical plethysmography signal measured at the individual's study volume and light intensities acquired by optical plethysmography at two or more time points along the optical plethysmography signal; and determining the change in arterial blood volume at the study volume between the two or more time points by determining the logarithm of a function of the light intensity or an approximation thereof, thereby evaluating the individual's PAT. The use of the logarithmic or functional approximation allows obtaining a signal indicative of an individual's PAT based on light intensity measured by optical plethysmography.

[0025] According to a fifth exemplary aspect, a computer program product is disclosed, comprising computer-executable instructions for causing a computer to perform the method according to the first exemplary aspect.

[0026] According to a sixth exemplary aspect, a computer-readable storage medium is disclosed, comprising computer-executable instructions for causing a computer to perform the method according to the first exemplary aspect.

[0027] Such computer program products and computer-readable storage media may provide one or more advantages with respect to the first exemplary aspect. Attached Figure Description

[0028] Exemplary embodiments will now be described with reference to the accompanying drawings.

[0029] Figure 1A A simplified block diagram of a system for detecting sleep disturbance events according to an exemplary embodiment of the present invention is shown;

[0030] Figure 1B The steps for detecting sleep disturbance events according to an exemplary embodiment of the present invention are shown;

[0031] Figure 2 An example of a vasoconstriction event derived from a signal indicating peripheral arterial tension according to an exemplary embodiment of the present invention is shown;

[0032] Figure 3 A comparative embodiment is shown, illustrating a signal indicating preprocessed peripheral arterial tension according to an exemplary embodiment of the invention, and a signal in its original, unprocessed form.

[0033] Figure 4A A comparative embodiment is shown, illustrating a signal indicating preprocessed peripheral arterial tension according to an exemplary embodiment of the invention, and a signal in its original, unprocessed form.

[0034] Figure 4B Various amplitudes of vasoconstriction events used as baseline amplitudes are shown according to exemplary embodiments of the present invention;

[0035] Figure 5 Various feature points in a preprocessed signal according to an exemplary embodiment of the present invention are shown;

[0036] Figure 6 Examples are shown of how the end of a respiratory event corresponds to characteristic phenomena observed in various physiological signals; and

[0037] Figure 7 An exemplary embodiment of a suitable computing system for performing one or more steps in the embodiments of the present invention is shown. Detailed Implementation

[0038] This invention generally relates to methods and apparatus for evaluating signals indicating peripheral arterial tension (PAT) in an individual to detect sleep-related or better sleep disturbance events. More specifically, the evaluation of the signal is performed in such a manner as to ensure the fundamental invariance of any steady-state changes observed in the signal, which may be caused, for example, by physiological phenomena such as venous arteriole reflexes, thereby ensuring accurate and robust detection of sleep disturbance events.

[0039] In the context of this invention, the study volume of an individual is, for example, a volume defined in the study tissue of the individual, which is monitored by pneumatic-based or optical-based plethysmography. Optical plethysmography is a measurement technique in which emitted light is collected on a sensor by optical plethysmography. In other words, the study volume of an individual is, for example, a volume defined in the study tissue of the individual, for which an optical plethysmography signal is acquired. For example, the study volume is the volume of the individual's peripheral tissue. For example, the study volume is the volume defined in the individual's fingers, fingertips, distal ends of fingers and toes, nostrils, ears, forehead, inside of the mouth, toes, toe tips, wrists, and ankles. In the context of this invention, the study volume of an individual includes the individual's skin contained within the study volume, and also includes the blood volume present in the study volume.

[0040] In the context of this invention, arterial blood volume should be understood as the arterial blood volume within the studied volume. In the context of this invention, peripheral arterial tone, or PAT, is understood as the change in arterial tone within the studied arterial bed of an individual's studied volume. In other words, determining the change in pulsatile volume within the vascular bed of an individual's studied volume allows for the determination or assessment of information indicating the muscle tone or 'tension' of the smooth muscle tissue of the arterioles within the studied volume, and thus allows for the determination or assessment of peripheral arterial tone regulated by the sympathetic nervous system. Determining peripheral arterial tone is non-invasive and can be used, for example, to detect heart disease, erectile dysfunction, sleep apnea, obstructive sleep apnea, cardiovascular disease, etc.

[0041] In the context of this invention, an optical volumetric signal is a signal measured by optical volumetric methods. For example, an optical volumetric signal is an optical volumetric map. For example, an optical volumetric signal is a PPG signal. An optical volumetric signal is measured, for example, at the fingertip of an individual using an optical volumetric device comprising at least one light source and a sensor. In the context of this invention, light intensity corresponds to the intensity of light collected on the sensor of the optical volumetric device, wherein the light collected on the sensor corresponds to light generated by one or more light sources, which is transmitted through or reflected in the study volume of the individual.

[0042] In the context of this invention, the oxygen saturation estimate, or SpO2, or hemoglobin composition corresponds to the fraction of oxyhemoglobin associated with the total amount of hemoglobin in the arterial blood volume of the studied volume. For example, the oxygen saturation estimate, or SpO2, or hemoglobin composition corresponds to the ratio of the sum of the concentrations of oxyhemoglobin and deoxyhemoglobin in the arterial blood volume monitored in the studied volume. Alternatively, the oxygen saturation estimate, or SpO2, or hemoglobin composition corresponds to the ratio of the volume fraction of oxyhemoglobin to the sum of the volume fraction of deoxyhemoglobin in the arterial blood volume monitored in the studied volume.

[0043] In the context of this invention, deoxyhemoglobin is defined as a form of hemoglobin that does not have bound oxygen and does not have any other bound molecules (e.g., carbon monoxide, carbon dioxide, or iron). In the context of this invention, oxyhemoglobin is defined as a form of hemoglobin that has bound oxygen. In the context of this invention, light emitted by a light source of an optical volumetric plethysmography device comprises photons arriving at the sensor via a probabilistic path through one or more scattering events. The optical path is not straight and is generally assumed to follow a curved spatial probability distribution. The capacity studied along the curved optical path forms the capacity sampled or studied by optical volumetric plethysmography.

[0044] In the context of this invention, an individual's PAT is assessed by evaluating one or more changes in arterial blood volume within a studied volume between two or more time points. In the context of this invention, the change in arterial blood volume within a studied volume between two time points corresponds to the relative change between the arterial blood volume present at a first time point in the studied volume and the arterial blood volume present at a second time point different from the first time point.

[0045] In the context of this invention, a chromophore is a molecular unit that absorbs or scatters light in the capacity under study. Examples of chromophores in the context of this invention include melanin molecules, oxyhemoglobin, deoxyhemoglobin, etc. In the capacity under study, the attenuation of the light intensity of incident light emitted by a light source set by optical volumetric plethysmography follows the Beer-Lambert law, which can be formulated as in equation (1):

[0046] (1)

[0047] in:

[0048] —— This corresponds to the intensity of the incident light emitted by the light source of the optical volumetric recording device;

[0049] —— This includes an absorption coefficient and / or a scattering coefficient and / or an extinction coefficient for the chromophore;

[0050] —— Corresponding to the volume fraction or concentration of the chromophore in the studied volume;

[0051] —— Corresponding to the optical path length, which is the length of the path a photon travels along before reaching the sensor of the optical volumetric imaging device, the optical path length is a function of the wavelength of the incident light and the composition of the chromophores in the studied capacity, and the optical path length depends on the distance between the source emitting the photon and the sensor. According to the publication entitled “Monte Carlo Analysis of Optical Interactions in Reflectance and Transmittance Finger Photoplethysmography” by Chatterjee et al. published on February 15, 2019 in Sensors (Basel) 19(4):789: doi:10.3390 / s19040789, for a distance of a few millimeters between the source emitting the photon and the sensor, such as 3 mm or less, the optical path length can be approximated as constant;

[0052] —— This corresponds to the light intensity loss parameter that depends on scattering, and it depends on the wavelength of the incident light emitted by the light source.

[0053] Consider the following parameters:

[0054] —— This corresponds to the volume fraction or concentration of chromophores in the studied volume at the first moment along the optical volumetric signal;

[0055] —— This corresponds to the capacity fraction or concentration of chromophores in the capacity studied at the second time point along the optical volume recording signal;

[0056] —— The light intensity measured at the first time point by the sensor configured for optical volumetric recording; and

[0057] —— This corresponds to the light intensity measured by the sensor of the optical volumetric recording device at the second time point;

[0058] The Beer-Lambert law expressed in formula (1) can be evaluated at the first and second time points. When the ratio of the two expressions is taken, equation (2) is obtained:

[0059] (2)

[0060] Then, by taking the natural logarithm of both sides of equation (2), we obtain equation (3), as follows:

[0061] (3)

[0062] If we use Euler's number Another base used The logarithm of equation (3) then becomes:

[0063] (3')

[0064] It can be seen that equation (3') is equal to equation (3) up to the constant. .

[0065] When the difference renamed Then, equation (3) can be simplified to equation (4), thus obtaining:

[0066] (4)

[0067] As can be seen from equation (4), the logarithm of the light intensity fraction at the first time point and the second time point is linearly related to the difference in the capacity fraction or concentration of the chromophore between the first time point and the second time point.

[0068] Some chromophores remain attached to the epidermis of an individual between two time points along the optical volumetric signature. For example, melanin molecules remain fixed to the capacity under study between two time points along the optical volumetric signature. Therefore, the difference in capacity fraction or concentration of such chromophores (e.g., melanin molecules) between these two time points is zero. Consequently, the contribution of such chromophores to the right-hand side of equation (4) is also zero.

[0069] The primary chromophores whose volume fraction or concentration fluctuates between two time points along the optical volumetric recording signal are oxyhemoglobin and deoxyhemoglobin in arterial blood volume. In the context of this invention, the two main forms of hemoglobin, namely oxyhemoglobin and deoxyhemoglobin, exhibit significantly different absorption and scattering coefficients for most wavelengths of light.

[0070] The effect of all other chromophores, where none of them are oxyhemoglobin or deoxyhemoglobin, whose volume fraction or concentration fluctuates along the optical volumetric recording signal between two time points, can be written as the product of their combined extinction coefficients. 1 minus the sum of the volume fractions or concentrations of oxyhemoglobin and deoxyhemoglobin, where the sum of all volume fractions or concentrations equals 1.

[0071] Considering the above factors, equation (4) can then be rewritten as equation (5):

[0072]

[0073]

[0074]

[0075] (5)

[0076] The estimated value of oxygen saturation can be defined according to equation (6):

[0077] (6)

[0078] in:

[0079] —— This corresponds to the volume fraction or concentration of oxyhemoglobin contained in the arterial blood within the studied volume;

[0080] —— This corresponds to the volume fraction or concentration of deoxyhemoglobin contained in the arterial blood within the studied volume.

[0081] in addition, It is defined as the total volume fraction or total concentration of oxygenated and deoxygenated hemoglobin contained in the arterial blood of the studied volume, and is defined in equation (7) as follows:

[0082] (7)

[0083] It is assumed that under normal circumstances, the total volume fraction or concentration of oxyhemoglobin and deoxyhemoglobin within the arterial blood volume, or the total concentration of oxyhemoglobin and deoxyhemoglobin within the arterial blood volume, remains approximately constant throughout the measurement of the optical plethysmography signal. In reality, during individual monitoring via optical plethysmography, such as during sleep apnea, only the ratio of oxyhemoglobin to deoxyhemoglobin, i.e., only the oxygen saturation estimate, may change significantly.

[0084] From equations (6) and (7), we can obtain the following terms:

[0085]

[0086] (8)

[0087] By substituting expression (8) into equation (5), we can obtain the following expression:

[0088] (9)

[0089] Two predetermined calibration coefficients and , are two constants, which can be defined as follows:

[0090]

[0091] Taking into account two predetermined calibration coefficients and After rewriting equation (9), we obtain equation (10), where the left-hand side of equation (10) corresponds to the evaluation function, and where The corresponding compensation function is a function of the oxygen saturation estimate:

[0092] (10)

[0093] Equation (10) highlights the term "evaluation function" on the left-hand side, which shows a linear relationship:

[0094] —Optical path length ;

[0095] —Arterial blood volume One or more variations of which are the parameters of interest; and

[0096] --coefficient Its linearity depends on the oxygen saturation estimate.

[0097] Under the assumption of a constant volume fraction or concentration of the total oxygenated and deoxygenated hemoglobin in arterial blood, It is a linear representation of the fluctuation of arterial blood volume within the studied volume, and therefore corresponds to the measurement of peripheral arterial tension.

[0098] For a constant value of the oxygen saturation estimate, i.e. When the logarithm of the ratio of light intensities collected on the sensor is determined using optical plethysmography at two time points, the change in arterial blood volume in the studied volume between the two time points is thus assessed. Mathematically, this can be expressed as...

[0099] (11)

[0100] Because there is only a linear relationship between the measurable parameters, and With research Relative change rather than precise The value is related, therefore, as is done by determining the evaluation function, the value is determined. A constant factor of 1 is sufficient.

[0101] if If it is not a constant, then the evaluation function will also change accordingly. It changes with change. However, compensation methods can be used to compensate for it. The impact of changes. For example, the compensation function can be determined. In this case, equation (11) can be rewritten as:

[0102] (12)

[0103] result, It is a signal indicating the peripheral arterial tension of the individual being studied. Therefore, The signal is a signal obtained from the light intensity measured by optical volumetric plethysmography and the optical volumetric plethysmography signal by determining the logarithm of a function of light intensity or an approximation thereof, wherein the function of light intensity may optionally be divided by a function depending on SpO2, where the function of light intensity corresponds to a ratio of light intensity.

[0104] According to equation (3), the evaluation function corresponds to the natural logarithm of the function of light intensity. Alternatively, starting from equation (2), any other evaluation function defined as a function of light intensity can be used, such as a linear approximation of the logarithm of the function of light intensity, or a Taylor series approximation of the function of light intensity, or a linear approximation of other basis logarithms of the function of light intensity. Alternatively, the evaluation function can roughly correspond to the ratio of the pulsating waveform or AC component of the optical volumetric signal to the slowly changing baseline or DC component of the optical volumetric signal, resulting in equation (13):

[0105] (13)

[0106] The evaluation function therefore corresponds to the logarithm of the ratio of light intensity; and the evaluation function depends on one or more of the following:

[0107] —Optical path length;

[0108] —Estimated blood oxygen saturation or a function of SpO2;

[0109] —Study changes in arterial blood volume.

[0110] In addition, at least one time point corresponds to cardiac diastole in an individual's cardiac cycle, and / or at least one of the time points corresponds to cardiac contraction in an individual's cardiac cycle.

[0111] During cardiac systole, the volume of arterial blood in the individual's study volume is at its maximum, resulting in maximum absorption and scattering of light at any point in time within the cardiac cycle (i.e., the period between two heartbeats), because hemoglobin is one of the main absorbers and scatterers of photons in the study volume, thus resulting in the lowest measurable light intensity on the sensor of the optical plethysmography device. Conversely, during cardiac diastole, the volume of arterial blood in the individual's study volume is at its minimum, resulting in minimum absorption and scattering of light at any point in time within the cardiac cycle, and thus the highest measurable light intensity on the sensor of the optical plethysmography device. At least one first time point corresponds, for example, to cardiac diastole in a first cardiac cycle, and / or at least one second time point corresponds, for example, to cardiac systole in a second cardiac cycle different from the first cardiac cycle. Alternatively, at least one first time point corresponds, for example, to the systolic phase of a first cardiac cycle, and / or at least one second time point corresponds, for example, to the diastolic phase of a second cardiac cycle different from the first cardiac cycle. Alternatively, at least one first time point corresponds, for example, to the systolic or diastolic phase of a cardiac cycle, and at least one second time point corresponds to any point in time within the same or different cardiac cycles.

[0112] As mentioned above, light intensity can be obtained by means of optical volumetric recording as follows:

[0113] —Emitting light of a specific wavelength through a light source; and

[0114] —By collecting propagating light through optical volumetric plethysmography and sensors, the propagating light corresponds to the wavelengths of light transmitted or reflected when propagating within the studied capacity of an individual at two or more time points; and

[0115] — Determine the light intensity propagating on the sensor at two or more time points.

[0116] Optical plethysmography (OPP) utilizes a simple and non-invasive setup of probes or biosensors. OPP biosensors non-invasively measure pulsatile volume changes in the studied volume by collecting optical plethysmography signals, thereby evaluating PAT (pulsatile thrombotic effect). The light source is, for example, an LED or any other suitable light source, which can be miniaturized to fit the OPP biosensor. The wavelength is, for example, included in the red spectrum. Alternatively, the wavelength is included in the infrared spectrum. The physical distance between the light source and the sensor is, for example, a few millimeters, such as less than 3 mm.

[0117] The assessment of the signal indicating peripheral arterial tension (PAT) for detecting sleep disturbance events in individuals will now be described in detail with reference to the accompanying drawings.

[0118] Figure 1AA simplified block diagram of a system according to an exemplary embodiment of the present invention is shown. System 100 includes: device 102 configured to measure fluctuations in arterial blood volume of an individual at a studied volume using optical plethysmography; and device 104 configured to acquire light intensity measured by device 102 and process the acquired light intensity to thereby detect sleep disturbance events 14. Device 102 is placed, for example, on an individual's finger. Device 102 thus measures the light intensity of light propagating through the individual's finger over time. As described in detail above and shown, for example, in equation (10), the measured light intensity reflects fluctuations in the individual's arterial blood volume. Furthermore, as described in detail above and shown in equations (11) and (12), the measured light intensity empirically provides information about the individual's peripheral arterial tension. Device 102 thus outputs light intensity 12, measured over time, reflecting the individual's peripheral arterial tension. The measured light intensity is fed to device 104. Device 104 includes at least one processor and at least one memory, the at least one memory being configured to store algorithms of operating means stored in the at least one memory in the form of software or program instructions. In addition to the storage software, at least one memory may also store any data generated by the device and any other data necessary for its proper operation. However, the data may be stored in another memory external to the device. At least one processor may execute program instructions stored in at least one memory to control the operation of the device. In other words, in one embodiment, device 104 includes a computing system comprising hardware and software components for processing the acquired measured light intensity 12 and determining sleep disturbance events 14 therefrom.

[0119] Figure 1B The various steps performed by device 104 to detect sleep disturbance event 14 are illustrated. In the first step (i.e., step 111), the peripheral arterial tension signal (i.e., The signal is derived from the acquired light intensity and the measured optical volumetric signal, as detailed above and shown in equations (11) and (12). In summary, device 104 acquires the light intensity 12 acquired by device 102 over time, for example, at two or more time points using optical volumetric plethysmography. Then, based on the acquired light intensity 12, device 104 determines the change in arterial blood volume in the studied volume between the two or more time points. This is accomplished by determining the logarithm of a light intensity function or a functional approximation thereof, optionally divided by a function dependent on SpO2. The result is the individual as shown in equations (11) and (12). Signal.

[0120] In the next step, namely step 112, according to Signals identify vasoconstriction events. As mentioned above, vasoconstriction events are characterized by... Fluctuations in signal amplitude. More specifically, The vasoconstriction event in the signal is characterized by a decrease in amplitude followed by an increase in amplitude. The vasoconstriction event in the signal can be determined using any signal processing algorithm suitable for this purpose. In summary, in the steps... The signal is segmented into event segments characterized by a decrease in signal amplitude followed by an increase in signal amplitude. Each of these event segments reflects vasoconstriction, specifically the contraction of arteries within the studied volume. In other words, the event segment corresponds to... Vasoconstrictive events observed in the signal. These vasoconstrictive events may involve, for example, sleep disturbances such as breathing events like apnea or hypopnea, breathing-related arousal events or RERA events, periodic or non-periodic limb movements, bruxism events, or snoring.

[0121] Figure 2 An example of a signal indicating an individual's peripheral arterial tension (PAT) is shown, where the highlighted episodes of sudden changes in arterial tension are characterized by a decrease in amplitude followed by an increase. It can be seen that each detected event segment is characterized by a decrease in amplitude followed by an increase. The figure further shows that fluctuations in peripheral arterial tension in each event segment are somewhat correlated with fluctuations in the individual's oxygen saturation estimate and SpO2.

[0122] In the next step, step 120, the device characterizes each event segment, i.e., the vasoconstriction event, based on characterization features. These characterization features include, for example, the following: the size of the event segment, i.e., the difference between the minimum and maximum amplitude values ​​of the event segment; the duration of the event segment; the duration of the amplitude fall phase and / or the duration of the amplitude rise phase of the event segment; the steepness of the amplitude fall phase and / or the steepness of the amplitude rise phase of the event segment; the full width at half maximum (FWHM) of the event segment; and so on. However, it is necessary to calculate the characteristic features of each event segment so that the influence of the VAR event on the amplitude within the event segment is significantly reduced.

[0123] Therefore, the device calculates in step 121 A baseline-invariant version of a signal, i.e., a baseline-invariant signal. A baseline-invariant signal can be obtained by... The signal amplitude is derived by correlating it with a baseline level, for example, by... Signal divided by:

[0124] • Corresponding to The baseline signal of the amplitude of the signal, that is, its peak envelope or peak baseline;

[0125] • Corresponding to The baseline signal with a lower amplitude, i.e., its trough envelope or trough baseline;

[0126] • Corresponding to The baseline signal of the average upper and lower amplitudes, i.e., its peak-to-valley average envelope;

[0127] • Corresponding to The baseline signal of any percentile amplitude, i.e., the percentile envelope;

[0128] • Any of the smoothed versions mentioned above, =

[0129] • A smoothed version of the signal, for example, by calculating a window size longer than the duration of the vasoconstriction event. The moving or sliding average version of the signal, and corresponding to The window sliding step size of one or more samples of the signal.

[0130] When the baseline signal is any of the smoothed signals described above, it is important to ensure that the time variation of the smoothed signal, for example, characterized by a time constant, is slow enough that the resulting smoothed signal does not conform to the shape of the event segments to such an extent that the event segments, or those divided by the smoothed signal, are not... The signal results in a baseline-invariant signal with a significant loss of information relating to the morphology of the original event fragment. In other words, fluctuations in the baseline-invariant signal derived from peripheral arterial tension should be substantially preserved, while fluctuations caused by VAR episodes should be substantially reduced.

[0131] Figure 3 An example of a signal indicating peripheral arterial tension (PAT) in an individual affected by a VAR attack caused by venous blood pooling triggered by a finger-to-arm descent, where the optical volumetric PPG signal is measured below cardiac level at the arm, is shown, highlighting how the PAT channel amplitude is suppressed during VAR. More specifically, the two curves above show how the amplitude of the PPG signal 210 and its filtered and normalized version 220 are suppressed during a VAR attack. The third curve shows how the amplitude of the PAT channel 230, derived as shown in equation (11) or (12), decreases during a VAR attack. The fourth curve shows how dividing each sample of the PAT channel by the baseline value results in a baseline-invariant version of the PAT channel 240, for which the effects of VAR amplitude modulation are essentially eliminated. As can be seen from the figure, the amplitude changes caused by peripheral arterial tension in event segments 110_1 to 110_3 and 110_n in the baseline-invariant versions of the original PAT channels 220 and 240 are preserved, while the amplitude modulation caused by VAR events is essentially eliminated in the baseline-invariant version of PAT channel 240.

[0132] Figure 4A It shows that Each sample of signal 230 is divided by the extracted peak envelope 231, trough envelope, peak-to-trough average envelope, and... A detailed view of the effect of a smoothed version of the signal. Peak or trough envelopes correspond to... The signal contains local maxima and local minima, while the peak-to-valley average envelope corresponds to the average of the peak and trough envelopes. It can be seen that from the original... The baseline-invariant signal 232, generated by dividing the sampled signal 230 by the upper or lower amplitude of the signal, the average of the upper and lower amplitudes, or the amplitude of the smoothed version, essentially eliminates the amplitude drop caused by VAR while maintaining the integrity of the form and the relative amplitude changes of each event segment.

[0133] Once the baseline-invariant signal is calculated, the device continues to characterize the event segment. To do this, the device derives various characteristics characterizing the event segment from the baseline-invariant version of the signal. These characteristics include, for example, the following: the maximum and / or minimum amplitude of the event segment; the size of the event segment; the median and / or average amplitude of the event segment; the quartile amplitude of the event segment; the duration of the vasoconstriction event; the duration of the amplitude fall phase; the duration of the amplitude rise phase of the vasoconstriction event; the steepness of the amplitude fall phase; the steepness of the amplitude rise phase of the vasoconstriction event; the full width at half maximum (FWHM) of the event segment, etc.

[0134] To derive these features, the device calculates various feature points from the baseline-invariant signal portion corresponding to the event segment in step 122. The feature points are described based on amplitude and / or time. For example, a feature point could be a sample of an event segment with the largest amplitude, a sample of an event segment with the smallest amplitude, the first and last samples of an event segment, and so on.

[0135] The device then uses the information obtained from the feature points to derive various features characterizing the event segment. To this end, in step 123, the device correlates the obtained information to derive various features. For example, the size of the event segment can be calculated as the difference between the maximum and minimum amplitudes observed in the event segment. In other words, the magnitude of the amplitude drop in the event segment can be calculated as the difference between the amplitude of the first sample or the amplitude of the sample with the largest amplitude and the amplitude of the sample with the smallest amplitude. Typically, the first sample coincides with or is substantially coincident with the sample with the largest amplitude. Here, the first sample or the sample with the largest amplitude is used as a reference point, while the sample with the smallest amplitude is used as a baseline point, and their respective amplitudes are used as the reference amplitude and the baseline amplitude. The duration of the event segment can be calculated as the time difference between the first and last samples of the event segment. The steepness of the amplitude drop in the event segment can be calculated as the ratio of the amplitude difference between the first sample or the sample with the largest amplitude and the sample with the smallest amplitude to the time difference, and so on.

[0136] Figure 5 An example of an event segment 310 of a baseline-invariant signal 240 is shown, illustrating various feature points that allow for the calculation of various characteristics characterizing the event segment. In this example, the reference amplitude and the baseline amplitude correspond to signal samples 313 and 311, respectively, having the maximum and minimum amplitudes. The duration 322 of the event segment is identified by samples 313 and 312, corresponding to the first and last samples in the event segment. The amplitude drop portion and the amplitude rise portion of the event segment correspond to slopes 325 and 326 with corresponding durations 323 and 324. In this example, the steepness of the amplitude drop is simply illustrated as the absolute magnitude 321 of the amplitude drop. Similarly, the steepness of the amplitude rise period is shown here as the absolute magnitude between samples 312 and 311.

[0137] Alternatively, the characteristics of the event segment on an absolute scale can be derived by correlating the amplitude of the signal portion corresponding to the event segment with a baseline level. For example, this can be achieved by correlating the amplitude of the signal portion corresponding to the event segment with the baseline level. This is accomplished by dividing the signal portion by the baseline level of the event segment. For example, the baseline level of the event segment could be:

[0138] • The maximum amplitude of the event segment, i.e., its peak value;

[0139] • The minimum amplitude of an event segment, i.e., its trough value; or

[0140] • The average or median amplitude of the event segment;

[0141] • The quartile or any percentile magnitude of the event segment; or

[0142] • The magnitude of any other characteristic point of the event segment, such as the point of maximum upward or downward slope of the event segment, or

[0143] • A smoothed version of an event fragment.

[0144] As can be seen from the list, the baseline level of an event segment can be either a baseline value or a baseline signal. Figure 4B Examples of possible baseline values ​​for two event segments are shown, which can be used, for example, by... The signal portion is divided by the baseline level corresponding to the event segment to correlate the amplitude of the event segment with the baseline level. In the figure, each event segment is represented by a pair of dashed lines. The figures show the amplitudes representing the maximum amplitude, minimum amplitude, average amplitude, median amplitude, lower quartile amplitude, lqr amplitude, and the amplitudes at the maximum and minimum slopes of the event segment.

[0145] The corresponding event fragment Dividing the signal portion by the baseline amplitude of the event segment results in the baseline-invariant portion of the corresponding event segment. Therefore, the output in step 121 is the baseline-invariant signal portion of each event segment. Once the baseline-invariant signal portion of each event segment has been calculated, the device proceeds to execution steps 122 and 123 as described above to derive the characteristic features of each event. That is, in step 122, the device derives each feature point from each baseline-invariant signal portion corresponding to each event segment. Then, in step 123, the device uses the information obtained from the feature points of each event segment to derive various features characterizing each event segment in the same manner as described above. For example, the size of the event segment can be calculated as the difference between the maximum and minimum amplitudes. The magnitude of the amplitude drop of the event segment can be calculated as the difference between the amplitude of the first sample or the amplitude of the sample with the maximum amplitude and the amplitude of the sample with the minimum amplitude, and so on.

[0146] Alternatively, any size-dependent features can be obtained on a relative scale rather than an absolute scale, such as features calculated using one or more amplitudes corresponding to the signal portion of the event segment. In this case, step 121 is omitted, and the apparatus is derived from the original, unmodified... Various characteristic features are derived from signal 230, such as Figure 3 The third curve is shown. In this case, to correct for amplitude fluctuations caused by VAR events, the characteristics affected by VAR, such as magnitude and steepness, are calculated on a relative scale. For this purpose, refer to the above... Figure 5 In detail, characteristic amplitude points of the event segment are derived, such as peak amplitude, trough amplitude, peak-to-trough average amplitude, or percentiles. Unlike the above, here the size of the event segment is derived, for example, by calculating the relative difference between the reference and baseline amplitudes. Similarly, other amplitude-related features characterizing the event segment, such as steepness features, derived from one or more amplitudes corresponding to the signal portion of the event segment, can be derived on a relative scale.

[0147] Therefore, the output of step 120 is to characterize one or more features of the event segment by means of at least one magnitude measurement of the intensity of the event segment, and may include one or more additional features, such as the duration of the event segment, the duration of the amplitude fall phase of the event segment, the duration of the amplitude rise phase of the event segment, the steepness of the amplitude fall phase of the event segment, the steepness of the amplitude rise phase of the event segment, and the full width at half maximum (FWHM) of the event segment.

[0148] In the final step of the method, step 130, the method proceeds to detect sleep disturbance events 14 based on one or more features identified to characterize vasoconstriction events. Sleep disturbance events can be detected, for example, based at least on a size measurement of the vasoconstriction event. If the size measurement is above a predetermined threshold, the vasoconstriction event is considered a sleep disturbance event. In addition to the size measurement, other features characterizing the vasoconstriction event can be considered. These additional features can further characterize the vasoconstriction event based on its morphological features. For example, they can be associated with, for example, the shape of the vasoconstriction event. For example, at least one or a combination of the duration of the event segment, the duration of the amplitude fall phase of the event segment; the duration of the amplitude rise phase of the event segment, the steepness of the amplitude fall phase of the event segment, the steepness of the amplitude rise phase of the event segment, and the full width at half maximum (FWHM) of the event segment can be further considered. In this case, a parameterized cost function can be evaluated, which considers the considered features to assess whether the vasoconstriction event is a sleep disturbance event.

[0149] The step of detecting sleep disturbance events can be implemented using a classifier, which is developed to perform this step. Therefore, the classifier implements a parameterized cost function by designing and inputting a set of rules for detection. As mentioned above, these rules can be based on thresholds for one or more corresponding features or for corresponding combinations of features, the thresholds checking how well the event's features conform to criteria defined by the rules. The thresholds for the corresponding features can be derived, for example, based on measurements obtained during clinical trials. Based on these measurements, sleep disturbance events are first identified by manual or computer-aided scoring, and then the thresholds for the corresponding features are determined based on the identified sleep disturbance events. Furthermore, these features can be ranked according to their accuracy in detecting a particular sleep disturbance event, and one or more optimal features can then be selected for detecting the corresponding sleep disturbance event. Using only the optimal features for detecting sleep disturbance events allows for simplified classifier implementation without sacrificing detection accuracy. A classifier developed in this way can then be evaluated using the parameterized cost function that implements these rules to determine whether an event is a sleep disturbance event.

[0150] Device 104 can also provide indications of certain medical conditions and physiological states based on the concurrence of sleep disturbance events characterized by specific features. For example, this can be achieved by analyzing the concurrence of vasoconstrictive events with certain features, the duration of a minimum event and a minimum distance between the minimum and maximum sample values ​​(i.e., the duration of the amplitude decrease in the event), and / or features observed in other physiological signals, such as an increase in pulse rate or heart rate and / or a decrease in blood oxygen saturation and / or limb movement during the event. Limb movement can be voluntary or involuntary and can be picked up, for example, by an accelerometer. This simultaneous occurrence of sleep disturbance events and additional physiological features can further assist medical personnel in identifying certain medical conditions and physiological states in an individual.

[0151] Figure 6 An example of a respiratory event is shown, specifically a sleep apnea event. The figure illustrates the termination phase of a sleep apnea event and... Decrease in signal amplitude and / or increase in pulse rate (PR) and / or The decrease is consistent with that in the middle.

[0152] As described above, this solution can improve the accuracy of identifying various characterization features from physiological signals indicating peripheral arterial tension, and therefore can improve the accuracy of detecting sleep disturbance events from such physiological signals. Furthermore, as mentioned above, the method can provide concurrent indications of sleep disturbance events and features that may be observed in other physiological signals, offering additional information that can help healthcare professionals determine certain medical conditions and physiological states of an individual.

[0153] Furthermore, although the above observations and methods are described in relation to optical volumetric plethysmography, those skilled in the art will recognize that the same observations and methods can be readily applied to physiological signals obtained by pneumatic volumetric plethysmography.

[0154] Figure 7A suitable computing system 600 is shown that can implement an embodiment of the method for detecting sleep disturbance events according to the present invention. The computing system 600 can generally be configured as a suitable general-purpose computer and includes a bus 610, a processor 602, local memory 604, one or more optional input interfaces 614, one or more optional output interfaces 616, a communication interface 612, a storage element interface 606, and one or more storage elements 608. The bus 610 may include one or more wires that allow communication between components of the computing system 600. The processor 602 may include any type of conventional processor or microprocessor that interprets and executes programmed instructions. The local memory 604 may include random access memory, RAM, or another type of dynamic storage device that stores information and instructions for execution by the processor 602, and / or read-only memory, ROM, or another type of static storage device that stores static information and instructions for use by the processor 602. Thus, the processor can execute instructions stored in the local memory to perform the various steps of the method described above. Input interface 614 may include one or more conventional mechanisms that allow an operator or user to input information into computing device 600, such as keyboard 620, mouse 630, pen, voice recognition, and / or one or more PPG sensors. Output interface 616 may include one or more conventional mechanisms that output information to an operator or user, such as display 640. Communication interface 612 may include any transceiver-like mechanism, such as one or more Ethernet interfaces that enable computing system 600 to communicate with other devices and / or systems (e.g., with other computing devices 701, 702, 703). The processing of signals obtained from one or more PPG sensors can therefore be remotely processed by other computing devices. Communication interface 612 of computing system 600 can be connected to another such computing system via a local area network (LAN) or a wide area network (WAN), such as the Internet. Storage element interface 606 may include a storage interface, such as a Serial Advanced Technology Attachment (SATA) interface or a Small Computer System Interface (SCSI), for connecting bus 610 to one or more storage elements 608, such as one or more local disks, such as SATA disk drives, and controlling data reading from and / or writing to these storage elements 608. While the storage element 608 described above is a local disk, any other suitable computer-readable medium may be used, such as a removable disk, optical storage media such as a CD or DVD, a ROM disk, a solid-state drive, a flash memory card, etc. The computing system 600 may therefore correspond to circuitry for processing signals obtained from one or more PPG sensors to detect sleep disturbance events as described above with reference to FIG1.

[0155] As used in this application, the term "circuit" may refer to one or more or all of the following:

[0156] (a) Hardware circuit implementation only, such as implementation in analog and / or digital circuits only, and

[0157] (b) A combination of hardware circuitry and software, such as (if applicable):

[0158] (i) A combination of analog and / or digital hardware circuitry with software / firmware, and

[0159] (ii) Any part of a hardware processor having software (including digital signal processors), software, and memory, which work together to enable a device such as a mobile phone or server to perform various functions.

[0160] (c) Hardware circuitry and / or processors (e.g., microprocessors or a portion thereof) that require software (e.g., firmware) to operate, but which may not exist when software is not required to operate.

[0161] This definition of "circuit" applies to all uses of the term in this application, including in any claim. As another example, as used in this application, the term "circuit" also covers implementations of hardware circuitry or processors (or processors in general) or a portion thereof and their accompanying software and / or firmware. The term "circuit" also covers, for example, and if applicable to a particular claim element, baseband integrated circuits or processor integrated circuits for mobile devices or similar integrated circuits in servers, cellular network devices, or other computing or networking devices.

[0162] Although the invention has been described with reference to specific embodiments, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that various changes and modifications can be made to the invention without departing from its scope. Therefore, these embodiments are to be considered illustrative in all respects and not restrictive, and the scope of the invention is indicated by the appended claims rather than by the foregoing description; and thus all changes within the scope of the claims are intended to be included therein.

[0163] The reader of this patent application will also understand that the words “comprising” or “comprise” do not exclude other elements or steps, the words “a” or “an” do not exclude multiple, and a single element such as a computer system, processor, or other integrated unit may perform the functions of several means in the claims. Any reference numerals in the claims should not be construed as limiting the respective claims. The terms “first,” “second,” “third,” “a,” “b,” “c,” etc., are introduced to distinguish similar elements or steps and do not necessarily describe a sequence or chronological order. Similarly, the terms “top,” “bottom,” “above,” “below,” etc., are introduced for descriptive purposes and do not necessarily indicate relative positions. It should be understood that such terms are interchangeable where appropriate, and embodiments of the invention can operate in other orders or in directions different from those described above or shown.

Claims

1. A processing method for detecting sleep disturbance events from a signal indicative of peripheral arterial tone of an individual, the signal being affected by a venoarteriolar reflex, using one or more processors, the method comprising: - determining segments from the signal, wherein an event segment of the determined segments comprises a vasoconstrictive event; - deriving a reference amplitude and a baseline amplitude for the event segment with the vasoconstrictive event, the reference amplitude being different from the baseline amplitude; - correlating the reference amplitude of the event segment with the baseline amplitude of the event segment to obtain a size measure of the vasoconstrictive event; and - detecting a sleep disturbance event using the size measure of the vasoconstrictive event.

2. The treatment method of claim 1, wherein, The deriving comprises calculating a baseline invariant signal of the event segment from which the reference amplitude and the baseline amplitude of the event segment are selected, the reference amplitude and the baseline amplitude corresponding to amplitudes of the vasoconstrictive event, allowing the size measure of the vasoconstrictive event to be calculated.

3. The treatment method of claim 2, wherein, The calculating comprises dividing a signal portion corresponding to the vasoconstrictive event by a baseline of the vasoconstrictive event.

4. The treatment method according to claim 2 or 3, wherein, The correlating comprises calculating an absolute size of the vasoconstrictive event from the reference amplitude and the baseline amplitude.

5. The treatment method according to any one of claims 2 to 3, wherein, The baseline of the event segment is derived by calculating an envelope of the signal, such as a peak envelope, a trough envelope, a peak-trough average envelope, a percentile-based envelope, or a smoothed version of the signal, that characterizes the venoarteriolar reflex in the signal, or by calculating an amplitude of the vasoconstrictive event, such as a peak amplitude, a trough amplitude, a peak-trough average amplitude, or a percentile, that characterizes the venoarteriolar reflex of the vasoconstrictive event.

6. The treatment method of claim 1, wherein, The reference amplitude and the baseline amplitude of the event segment are derived by calculating a peak amplitude, a trough amplitude, a peak-trough average amplitude, or a percentile of the vasoconstrictive event, respectively.

7. The treatment method of claim 6, wherein, The correlating comprises calculating a relative size of the vasoconstrictive event from the reference amplitude and the baseline amplitude of the event segment.

8. The treatment method according to any one of claims 1 to 3, 6 and 7, wherein, The detecting comprises identifying a vasoconstrictive event characterized by a size measure above a predetermined value.

9. The treatment method of claim 8, wherein, The identifying further takes into account at least one of a duration of the vasoconstrictive event, a duration of an amplitude drop period and / or a duration of an amplitude rise period of the vasoconstrictive event, a steepness of the amplitude drop period and / or a steepness of the amplitude rise period of the vasoconstrictive event.

10. The treatment method according to any one of claims 1 to 3, 6 and 7, wherein, The determining comprises identifying a portion of the signal characterized by an amplitude drop followed by an amplitude increase.

11. The treatment method of claim 10, wherein, The determining further takes into account at least one of a duration of a period of the amplitude drop and a period of the amplitude increase, a duration of the period of the amplitude drop and / or a duration of the period of the amplitude increase, a steepness of the period of the amplitude drop and / or a steepness of the period of the amplitude increase of the signal portion.

12. The treatment method according to any one of claims 1 to 3, 6 and 7, wherein, The method further comprises obtaining a signal indicative of a change in pulsatile blood volume at a selected anatomical location of the patient by plethysmography, and deriving therefrom the signal indicative of a change in peripheral arterial tone.

13. A computer program product comprising computer-executable instructions for causing one or more processors to perform the processing method of any one of claims 1 to 3, 6 and 7.

14. A computer-readable storage medium comprising processor-executable instructions for causing one or more processors to perform the processing method of any one of claims 1 to 3, 6 and 7 when the processor-executable instructions are run on the one or more processors.

15. A system configured to detect sleep disturbance events from a signal indicative of peripheral arterial tone of an individual affected by venoarteriolar reflexes, the system comprising means configured to: — determining a segment from the signal, wherein the determined event segment in the segment comprises a vasoconstriction event; deriving a reference amplitude and a baseline amplitude for the event segment with the vasoconstriction event, the reference amplitude being different from the baseline amplitude; associating the reference amplitude of the event segment with the baseline amplitude of the event segment to obtain a size measure of the vasoconstriction event; and detecting a sleep disturbance event using the size measure of the vasoconstriction event.

16. The system of claim 15, further comprising a plethysmography tool for generating a signal indicative of peripheral arterial tone of the individual, and wherein, the plethysmography means comprises a pneumatic-based plethysmography means or an optical-based plethysmography means.

17. The system of claim 15, wherein, the deriving comprises calculating a baseline invariant signal of the event segment from which the reference amplitude and the baseline amplitude of the event segment are selected, the reference amplitude and the baseline amplitude corresponding to amplitudes of the vasoconstriction event, allowing the size measure of the vasoconstriction event to be calculated.

18. The system of claim 17, wherein, the calculating comprises dividing a signal portion corresponding to the vasoconstriction event by a baseline of the vasoconstriction event.

19. The system of claim 17 or 18, wherein, the associating comprises calculating an absolute size of the vasoconstriction event from the reference amplitude and the baseline amplitude.

20. The system of any one of claims 17-18, wherein, the baseline of the event segment is derived by calculating an envelope of the signal, such as a peak envelope, a trough envelope, a peak-trough average envelope, a percentile-based envelope, or a smoothed version of the signal, that characterizes the venoarteriolar reflex in the signal, or by calculating an amplitude of the vasoconstriction event, such as a peak amplitude, a trough amplitude, a peak-trough average amplitude, or a percentile, of the vasoconstriction event that characterizes the venoarteriolar reflex of the vasoconstriction event.

21. The system of claim 15 or claim 16, wherein, the reference amplitude and the baseline amplitude of the event segment are derived by calculating a peak amplitude, a trough amplitude, a peak-trough average amplitude, or a percentile, of the vasoconstriction event, respectively.

22. The system of claim 21, wherein, the associating comprises calculating a relative size of the vasoconstriction event from the reference amplitude and the baseline amplitude of the event segment.

23. The system of any one of claims 15 to 18, wherein, the detecting comprises identifying a vasoconstriction event characterized by a size measure above a predetermined value.

24. The system of claim 23, wherein, The identifying further takes into account at least one of a duration of the vasoconstriction event, a duration of a magnitude drop phase and / or a duration of a magnitude rise phase of the vasoconstriction event, a steepness of the magnitude drop phase and / or a steepness of the magnitude rise phase of the vasoconstriction event.

25. The system of any one of claims 15 to 18, wherein, The determining comprises identifying a portion of the signal characterized by a magnitude drop followed by a magnitude increase.

26. The system of claim 25, wherein, The determining further takes into account at least one of a duration of a period of the magnitude drop and a period of the magnitude increase, a duration of the period of the magnitude drop and / or a duration of the period of the magnitude increase, a steepness of the period of the magnitude drop and / or a steepness of the period of the magnitude increase of the signal portion.

27. The system of any one of claims 15 to 18, wherein, The tool is further configured to obtain, by plethysmography, a signal indicative of a change in pulsatile blood volume at a selected anatomical location of the patient, and to derive therefrom the signal indicative of a change in peripheral arterial tone.

28. A computing system for processing signals obtained from one or more PPG sensors and limb motion from an accelerometer, comprising: a bus; a processor; a local memory; one or more input interfaces; one or more output interfaces; a communication interface; a storage element interface; one or more storage elements; one or more PPG sensors; and an accelerometer; wherein the processor is configured by processor control instructions stored in the one or more storage elements, the processor control instructions configured to compare a concurrency of a limb motion signal picked up by the accelerometer with sleep disturbance events detected by a classifier trained to at least evaluate morphological features of events in the signals obtained from the one or more PPG sensors.

29. The computing system of claim 28, wherein, The classifier is configured to evaluate a parameterized cost function.

30. The computing system of claim 29, wherein, The classifier comprises a set of thresholds for a combination of features.

31. The computing system of claim 30, wherein, The features are ordered according to an accuracy in detecting certain sleep disturbance events.

32. The computing system of any one of claims 29-31, wherein, The processor control instructions configure the processor to generate an indication of certain medical conditions and physiological states based on a concurrency of sleep disturbance events characterized by a particular feature.

33. A computer readable storage medium comprising processor executable instructions for processing a signal obtained from one or more PPG sensors and limb motion from an accelerometer, wherein, The processor executable instructions comprise, when executed on one or more processors: comparing a concurrency of a limb motion signal picked up by the accelerometer with sleep disturbance events detected by a classifier trained to at least evaluate morphological features of events in the signals obtained from the one or more PPG sensors.

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