Method and apparatus for detecting sleep disorder events from a signal indicative of peripheral arterial tone in an individual - Patent Application 20070122997

Abstract

The present disclosure relates to a method and apparatus for detecting sleep disorder events from a signal indicative of peripheral arterial tone in an individual affected by the venous arteriolar reflex, wherein the sleep disorder events are detected by determining vasoconstriction events from changes in the signal, deriving reference and baseline amplitude values ​​for the vasoconstriction events, where the reference amplitude values ​​are different from the baseline amplitude values, correlating the reference amplitude values ​​for the vasoconstriction events with the baseline amplitude values ​​for the vasoconstriction events to obtain magnitude measures of the vasoconstriction events, and detecting the sleep disorder events therefrom.

Description

[Technical Field]

[0001] The present disclosure relates to methods and apparatus for detecting sleep disorder events, and more particularly to methods and apparatus for detecting sleep disorder events from signals indicative of peripheral arterial tone (PAT) of an individual. [Background technology]

[0002] Plethysmography, whether pneumatically or optically based, is a measurement technique used to monitor changes in blood volume in an individual's macrovasculature and microvasculature, more specifically, changes in blood volume contained in arteries or arterioles. Changes in arterial blood volume are further influenced by contraction of the muscular walls of the arteries or arterioles. Thus, monitoring changes in arterial blood volume by plethysmography empirically provides information regarding relative changes in muscle tone or "tone" (also called peripheral arterial tone or PAT) of the smooth muscle tissue of the arterioles.

[0003] Optical plethysmography, or photoplethysmography, measures changes in arterial blood volume by illuminating a test volume with light from one or more light sources, e.g., LEDs, and detecting collected light corresponding to the light reflected or transmitted by the test volume on a sensor. The sensor may include or correspond to a photodetector, e.g., a photodiode. The light source and sensor form a so-called plethysmography probe, which may be positioned on opposite sides of the test volume, e.g., an individual's finger, nostril, ear, forehead, inside the mouth, toe, wrist, or ankle, to measure a transmission-mode photoplethysmogram (PPG), or on the same side of the test volume to measure a reflection-mode PPG. Regardless of the measurement technique used, both transmission-mode and reflection-mode PPG capture cyclic fluctuations in arterial blood volume within the test volume, thereby capturing changes in an individual's peripheral arterial tone.

[0004] Because arterial blood flow to the test 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 disorder events that contribute to or are caused by sleep disorders. Sleep disorder diagnosis is a medical field that involves monitoring a patient's sleep over a period of time, e.g., one or more nights. Based on the monitoring, different sleep-related events, or even sleep disorder events, such as apneic events, snoring, or limb movements can be identified. For example, the resumption of breathing at the end of a sleep apneic event typically occurs simultaneously with the release of norepinephrine. Norepinephrine is released into the bloodstream and binds to adrenergic receptors in the arterioles within the test volume. This increases arteriolar tone, narrowing their diameter and reducing arterial blood volume within the test volume. For this reason, monitoring peripheral arterial tone (PAT) by photoplethysmography, for example, can provide important information regarding the occurrence of sleep disorder events, such as sleep apnea. This observation has been widely reported in the last century and is described in detail in a scientific publication by Hamunen et al. entitled "Effect of pain on autonomic nervous system indices derived from photoplethysmography in healthy volunteities," published in the British Journal of Anesthesia, Vol. 108, No. 5, pp. 838-844, May 1, 2012. The authors report that photoplethysmographic pulse plethysmographic amplitude (PPGA) is due to pulsatile changes in tissue volume (mainly arterial blood) and that PPGA decreases during sympathetic activation or vasoconstriction.

[0005] However, due to the influence of gravity, when a body part is positioned below the level of the heart, a hydrostatic pressure gradient automatically occurs. This hydrostatic pressure gradient affects the venous system, which is a natural low-pressure system, causing venous dilation, also known as venous pooling. Venous pooling can induce a reflex contraction in the arteries supplying the veins, thereby adding additional physiological changes that can obscure the desired arterial vasomotor response examined by plethysmography. This reflex, commonly referred to as the venous arteriolar reflex (VAR), is described in detail in Part A of the white paper by Bar et al., entitled "An Illustrated Atlas of PAT Signals in Sleep Medicine." To solve this problem, US Patent No. 7,374,540 B2 proposed a plethysmography probe that can completely cover the surface of the distal tip of the finger and provide a uniform pressure field all the way to the finger's tip. This patent proposed a probe with an inner membrane that applies a predetermined static pressure to the internal body part and an outer membrane that ensures the predetermined static pressure applied by the inner membrane is substantially unaffected by volume changes in the internal body part. In this manner, external counter pressure applied from the outer membrane is said to reduce venous blood pooling and dilation within the measurement site, reducing distal venous pooling. This is said to reduce the likelihood of inducing reflex vasoconstriction of venous arterioles, which would otherwise cause an indeterminate degree of vasoconstriction. Accordingly, the prior art describes complex and expensive probes for providing equal pressure probes to reduce the problems associated with an indeterminate degree of vasoconstriction. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] U.S. Patent No. 7,374,540 [Non-patent literature]

[0007] [Non-Patent Document 1] Hamunen, "Effect of pain on autonomic nervous system indices derived from photoplethysmography in healthy volunteers," British Journal of Anesthesia, Vol. 108, No. 5, pp. 838-844, May 1, 2012 [Non-patent document 2] Bar,“An Illustrated Atlas of PAT Signals in Sleep Medicine”,PART A Summary of the Invention

[0008] An object of embodiments of the present disclosure is to provide a solution capable of detecting sleep disorder events that cause or are caused by sleep disorders, overcoming the shortcomings of conventional solutions, and more specifically, to provide a solution capable of detecting sleep disorder events, including sleep disorders, in an accurate and robust manner based on physiological information obtained from conventional commercially available medical devices. Another object of embodiments of the present disclosure is to provide a solution capable of detecting sleep disorder events, including sleep disorders, in an accurate and robust manner based on signals indicative of an individual's peripheral tension obtained from conventional commercially available medical devices, such as plethysmography probes that do not provide uniform pressure.

[0009] The scope of protection sought for the various embodiments of the invention is defined by the independent claims. Embodiments and features described herein that do not fall within the scope of the independent claims should be interpreted as examples to facilitate understanding of the various embodiments of the invention.

[0010] These objects are achieved, according to a first exemplary aspect of the present disclosure, by a computer-implemented method for detecting sleep disorder events as defined in claim 1. In particular, the computer-implemented method can detect sleep disorder events from a signal indicative of an individual's peripheral arterial tone (PAT), which is affected by the venous arteriolar reflex (VAR). This signal can be acquired via conventional, commercially available medical equipment, as long as the acquired signal provides information indicative of PAT and the influence on the signal caused by the venous arteriolar reflex (VAR). Examples of such medical equipment include standard probes using pneumatic plethysmography or photoplethysmography (PPG), probes capable of acquiring PAT signals affected by VAR. Accordingly, the method includes processing the signal acquired from the PPG probe to derive sleep disorder events therefrom. More specifically, the method includes determining one or more vasoconstriction events from changes in the signal. The method further includes deriving a reference amplitude value and a baseline amplitude value for the determined vasoconstriction events. The reference amplitude value and the baseline amplitude value are different from each other. The reference amplitude value and the baseline amplitude value correspond to amplitude values ​​of characteristic points in the signal of the vasoconstriction event, which allows for characterization of the vasoconstriction event. The obtained reference amplitude value and baseline amplitude value are then correlated to obtain a measure indicating the magnitude of the vasoconstriction event, i.e., a magnitude measure that characterizes the intensity or significance of the vasoconstriction event and, therefore, the intensity or significance of the observed physiological event. Once the magnitude of the detected physiological event is obtained, the method proceeds to detect a sleep disorder event therefrom. By correlating the reference amplitude value with the baseline amplitude value, the effects of slower changes (i.e., steady-state changes) observed in the signal can be significantly reduced. Steady-state changes are effects observed in the signal that change at a slower rate than changes in the signal due to the vasoconstriction event. As described above, such steady-state changes can be caused, for example, by venous-arteriolar reflexes.In other words, the reference amplitude value and the baseline amplitude value are related to each other so that the steady-state changes in the signal are taken into account when determining the magnitude of the vasoconstriction event, thereby taking into account the venous-arteriolar reflex. This ensures accurate assessment of the magnitude of the vasoconstriction event, thereby ensuring accurate detection and characterization of sleep disorder events. As a result, false positive and false negative detection of sleep disorder events are substantially eliminated.

[0011] The reference and baseline amplitude values ​​of the vasoconstriction events are preferably derived from a baseline-invariant version of the signal. This baseline-invariant signal may be calculated specifically for the vasoconstriction event or the complete signal. That is, a partial baseline-invariant signal of the vasoconstriction event or a baseline-invariant signal of the complete signal may be calculated. The reference and baseline amplitude values ​​of the vasoconstriction events are then selected from the baseline-invariant signal. As described above, the reference and baseline amplitude values ​​correspond to amplitude values ​​of characteristic points in the vasoconstriction event signal that allow characterization of the vasoconstriction event. Thus, the selection of the reference and baseline amplitude values ​​is preferably made to allow calculation of a magnitude measure of the vasoconstriction event.

[0012] The baseline invariant signal is preferably calculated by dividing the acquired signal by the baseline. This division can be performed on the portion of the signal corresponding to the vasoconstriction event or on the entire signal. In the first case, the baseline invariant signal of a vasoconstriction event is calculated by dividing the portion of the signal corresponding to the vasoconstriction event by the baseline of that event. The baseline is a baseline level, which can be a baseline value or a signal portion corresponding to the vasoconstriction event. In the second case, the baseline invariant signal is obtained by dividing the entire signal by the baseline of the entire signal. Here, the baseline is the baseline signal. This eliminates the influence of the venous-arteriolar reflex on arterial tone, at least with respect to the vasoconstriction event, ensuring accurate assessment of the magnitude of the vasoconstriction event and therefore accurate detection and characterization of sleep disorder events. Then, a reference amplitude value and a baseline amplitude value are derived from the baseline invariant signal. For example, the maximum amplitude value observed in the portion of the baseline invariant signal corresponding to the vasoconstriction event can be selected as the reference amplitude value, and the average amplitude value observed in the portion of the baseline invariant signal corresponding to the vasoconstriction event can be selected as the baseline amplitude value.

[0013] The reference amplitude value and the baseline amplitude value selected in this manner are correlated to each other to obtain a magnitude measure of the vasoconstriction event. Preferably, the reference amplitude value is correlated to the baseline amplitude value by calculating the absolute magnitude of the vasoconstriction event from the reference amplitude value and the baseline amplitude value. If the reference amplitude value and the baseline amplitude value are selected from a baseline-invariant version of the signal, the magnitude of the vasoconstriction event can be derived, for example, as the difference between them. The magnitude measure calculated in this manner is an absolute value that allows the intensity of the vasoconstriction event to be quantified on an absolute scale.

[0014] The baseline for the vasoconstriction event is preferably derived by calculating the signal envelope. By calculating the signal envelope, the venous arteriolar reflection observed in the signal can be extracted. In other words, the calculated signal envelope characterizes the venous arteriolar reflection across the complete signal. For example, the peak envelope, trough envelope, peak-to-trough average envelope, percentile-based envelope, or smoothed version of the signal can be calculated and used as the signal envelope. Alternatively, instead of calculating the complete signal envelope, an amplitude value of the vasoconstriction event can be calculated, which can be used as the baseline for the vasoconstriction event. For example, the peak amplitude, trough amplitude, peak-to-trough average amplitude, or percentile value of the vasoconstriction event can be calculated and used as the baseline for the vasoconstriction event.

[0015] Alternatively, reference and baseline amplitude values ​​of vasoconstriction events can be derived directly from the acquired signals. This can be achieved, for example, by calculating the peak amplitude, through amplitude, average peak-to-trough amplitude, or percentile value of the vasoconstriction events directly from the acquired signals. These amplitude values ​​can be used as the baseline or reference amplitude values ​​of the vasoconstriction events, as long as the baseline and reference amplitude values ​​are selected to be different from each other. For example, the peak amplitude of the vasoconstriction event can be selected as the reference amplitude value, and any other amplitude value that characterizes the vasoconstriction event, i.e., the through amplitude, average peak-to-trough amplitude, or average amplitude, can be selected as the baseline amplitude value.

[0016] The reference and baseline amplitude values ​​thus selected are then correlated to obtain a magnitude measure of the vasoconstriction event. Preferably, the reference amplitude value is correlated to the baseline amplitude value by calculating the relative magnitude of the vasoconstriction event from the reference and baseline amplitude values ​​of the vasoconstriction event. The relative magnitude of the vasoconstriction event may be derived, for example, as a relative difference or relative change therebetween, or any other substantially relative magnitude measure. The magnitude measure thus calculated is a relative value that allows the intensity of the vasoconstriction event to be quantified on a relative scale.

[0017] Detecting a sleep disorder event preferably includes identifying a vasoconstriction event characterized by a magnitude measure higher than a predetermined value. In other words, a vasoconstriction event having a specific magnitude measure is considered a sleep disorder event. The predetermined value may be determined based on measurements obtained during, for example, a clinical trial. Preferably, detecting a sleep disorder event includes using a trained or developed classifier for detection. Any conventional classifier, such as a neural network, a decision tree, or a support vector machine, may be trained.

[0018] Preferably, the discrimination step further considers at least one of the following: the duration of the vasoconstriction event; the duration of the amplitude drop period and / or the duration of the amplitude rise period of the vasoconstriction event; and the steepness of the amplitude drop period and / or the steepness of the amplitude rise period of the vasoconstriction event. The duration of the vasoconstriction event, the duration of the amplitude drop period, and / or the duration of the amplitude rise period are additional measures that can be used to improve the quantification of the intensity of the vasoconstriction event. Similarly, the steepness of the amplitude drop 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. By combining any of these additional measures with the magnitude measure, the intensity of the vasoconstriction event can be quantified based on various signal characteristics, thereby improving the detection and better characterization of sleep disorder events.

[0019] The step of determining a vasoconstriction event preferably includes identifying a portion of the signal characterized by a drop in amplitude followed by a rise in amplitude. In other words, to identify a vasoconstriction event, the method looks for a change in the signal characterized by a drop in amplitude followed by a rise in amplitude. This determination step may be performed by any signal processing technique suitable for the purpose.

[0020] Preferably, the determining step further considers at least one of the duration of the amplitude drop and amplitude rise periods of the signal portion, the duration of the amplitude drop and / or amplitude rise periods of the signal portion, the steepness of the amplitude drop and / or amplitude rise periods of the signal portion. By considering the duration of the amplitude drop and / or rise periods and the steepness of each period, the amplitude can be quantified based on various signal characteristics, thus improving the determination of vasoconstriction events from the acquired signals.

[0021] A signal indicative of an individual's peripheral arterial tone, which is affected by the venous arteriolar reflex, is preferably obtained by plethysmography. Plethysmography allows for the acquisition of a signal, i.e., a plethysmographic signal or plethysmogram, indicative of changes in blood volume, such as changes in pulsatile blood volume, at selected anatomical locations on a patient, such as the fingers, nostrils, ears, forehead, inside the mouth, toes, wrists, ankles, etc. The plethysmographic signal may be obtained via a so-called plethysmographic probe, which may employ pneumatic or optical-based plethysmography. Because the plethysmographic signal empirically provides information regarding relative changes in muscle tone or "tone" of arteriolar musculature and information regarding the venous arteriolar reflex, the plethysmographic signal allows for the derivation therefrom of a signal indicative of changes in peripheral arterial tone, which is affected by the venous arteriolar reflex.

[0022] Preferably, the signal indicative of peripheral arterial tone is derived from a photoplethysmographic signal measured in the individual's test volume and light intensity acquired by photoplethysmography at two or more time points along the photoplethysmographic signal. From there, the change in arterial blood volume within the test volume between the two or more time points is derived by determining the logarithm of a function of light intensity or a functional approximation thereof, thereby assessing the individual's peripheral arterial tone (PAT). Herein, the logarithm or functional approximation of the function of light intensity is referred to as an evaluation function. Preferably, this evaluation function corresponds to the logarithm of the ratio of light intensities, and the evaluation function depends on one or more of the optical path length, an estimated oxygen saturation value or a function of SpO2, and the change in arterial blood volume within the test volume. Preferably, at least one of the time points corresponds to a diastolic phase in the individual's cardiac cycle, and / or at least one of the time points corresponds to a systolic phase in the individual's cardiac cycle.

[0023] According to a second exemplary aspect, there is disclosed an apparatus configured to detect sleep disorder events from a signal indicative of peripheral arterial tone of an individual affected by the venous arteriolar reflex, characterized by the features of claim 13. The apparatus comprises at least one processor and at least one memory containing computer program code configured to cause the apparatus to perform the steps according to the first exemplary aspect by the at least one processor. Thus, such an apparatus can provide one or more of the advantages mentioned with respect to the first exemplary aspect.

[0024] According to a third exemplary aspect, a system is disclosed that includes the device according to the second exemplary aspect. Preferably, the system further includes a plethysmography probe. Preferably, the plethysmography probe is a photoplethysmography probe that includes a light source configured to emit light and a sensor configured to photoplethysmographically collect propagated light corresponding to the light transmitted or reflected as it propagates through an examination volume of an individual at two or more time points. The sensor is further configured to determine the light intensity of the propagated light at the two or more time points. Optionally, the probe further includes an accelerometer for detecting the position of the examination volume. Optionally, the system further includes a wireless communication interface and a wireless transmitter configured to wirelessly transmit the determined peripheral arterial tone for further processing by the device. The wireless communication interface is preferably a low-power communication interface, such as a Bluetooth Low Energy (BLE) wireless interface. Thus, such a system can provide one or more advantages over the first exemplary aspect.

[0025] According to a fourth exemplary aspect, there is provided the use of a logarithm or a functional approximation thereof for evaluating a signal indicative of peripheral arterial tone (PAT) of an individual monitored by photoplethysmography. Evaluating the signal indicative of peripheral arterial tone includes obtaining a photoplethysmographic signal measured in a test volume of the individual and a light intensity obtained by photoplethysmography at two or more time points along the photoplethysmographic signal, and determining the change in arterial blood volume within the test volume between the two or more time points by determining the logarithm of a function of the light intensity or a functional approximation thereof, so as to evaluate the individual's PAT. The use of the logarithm or functional approximation enables obtaining a signal indicative of the individual's PAT based on the light intensity measured by photoplethysmography.

[0026] According to a fifth exemplary aspect, a computer program product is disclosed that includes computer-executable instructions for causing a computer to perform a method according to the first exemplary aspect.

[0027] According to a sixth exemplary aspect, a computer-readable storage medium is disclosed that includes computer-executable instructions for causing a computer to perform a method according to the first exemplary aspect.

[0028] Such computer program products and computer-readable storage media may provide one or more advantages related to the first exemplary aspect. Some exemplary embodiments will now be described with reference to the accompanying drawings. [Brief explanation of the drawings]

[0029] [Figure 1A] FIG. 1A illustrates a simplified block scheme of a system for detecting sleep disorder events according to an exemplary embodiment of the present disclosure. [Figure 1B] FIG. 1B illustrates a process for detecting a sleep disorder event according to an exemplary embodiment of the present disclosure. [Figure 2]FIG. 2 illustrates an example of a vasoconstriction event derived from a signal indicative of peripheral arterial tone according to an exemplary embodiment of the present disclosure. [Figure 3] FIG. 3 shows a comparison between a signal indicative of peripheral arterial tone that has been preprocessed in accordance with an exemplary embodiment of the present disclosure and the signal in its original, unprocessed form. [Figure 4A] FIG. 4A shows a comparison between a signal indicative of peripheral arterial tone that has been preprocessed in accordance with an exemplary embodiment of the present disclosure and the signal in its original, unprocessed form. [Figure 4B] FIG. 4B illustrates various amplitude values ​​of vasoconstriction events as baseline amplitude values, according to an exemplary embodiment of the present disclosure. [Figure 5] FIG. 5 illustrates various feature points in a preprocessed signal according to an exemplary embodiment of the present disclosure. [Figure 6] FIG. 6 shows an example of how the end of a respiratory event corresponds to characteristic phenomena observed in various physiological signals. [Figure 7] FIG. 7 illustrates an exemplary embodiment of a computing system suitable for performing one or more steps in embodiments of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0030] The present disclosure generally relates to a method and apparatus for detecting sleep-related events, and more particularly, sleep disorder events, by evaluating a signal indicative of, inter alia, an individual's peripheral arterial tone (PAT). More specifically, the evaluation of the signal is performed in a manner that ensures substantial invariance to any steady-state changes observed in the signal, which may be caused by physiological phenomena such as, for example, the venous arteriolar reflex, thereby ensuring accurate and robust detection of sleep disorder events.

[0031] In the context of the present disclosure, an individual's test volume is a volume defined in the individual's test tissue that is monitored, for example, by pneumatic or optical-based plethysmography. Photoplethysmography is a measurement technique in which emitted light is collected by a sensor using photoplethysmography. In other words, an individual's test volume is a volume defined in the individual's test tissue from which, for example, a photoplethysmography signal is acquired. For example, the test volume is a volume of an individual's peripheral tissue. For example, the test volume is a volume defined by an individual's fingers, fingertips, distal finger tips, nostrils, ears, forehead, inside of the mouth, toes, toe tips, wrists, and ankles. In the context of the present disclosure, an individual's test volume includes the individual's skin that is included in the test volume and further includes the volume of blood within the test volume.

[0032] In the context of the present disclosure, arterial blood volume should be understood as the arterial blood volume within a test volume. In the context of the present disclosure, peripheral arterial tone or PAT should be understood as the change in arterial tone of a test arterial bed within an individual's test volume. In other words, by determining the change in pulsatile blood volume within the vascular bed of an individual's test volume, information indicative of the muscle tone or "tone" of the smooth muscle tissue of the arterioles within the test volume can be determined or evaluated. This allows the determination or evaluation of peripheral arterial tone, which is regulated by the sympathetic nervous system. Determining peripheral arterial tone is non-invasive and can be used to detect, for example, heart disease, erectile dysfunction, sleep apnea, obstructive sleep apnea, cardiovascular disease, etc.

[0033] In the context of the present disclosure, a photoplethysmography signal is a signal measured by photoplethysmography. For example, the photoplethysmography signal is a photoplethysmogram. For example, the photoplethysmography signal is a PPG. The photoplethysmography signal is measured, for example, at an individual's fingertip by a photoplethysmography device comprising at least one light source and a sensor. In the context of the present disclosure, light intensity corresponds to the intensity of light collected by a sensor of the photoplethysmography device, which light collected by the sensor corresponds to light generated by one or more light sources that is transmitted through or reflected by an examination volume of the individual.

[0034] In the context of the present disclosure, an estimate of oxygen saturation, SpO2, or hemoglobin composition corresponds to the percentage of oxygenated hemoglobin relative to the total amount of hemoglobin in the arterial blood volume within the test volume. For example, an estimate of oxygen saturation, SpO2, or hemoglobin composition corresponds to the ratio of the concentration of oxygenated hemoglobin to the sum of the concentrations of oxygenated and deoxygenated hemoglobin in the arterial blood volume being monitored in the test volume. Alternatively, an estimate of oxygen saturation, SpO2, or hemoglobin composition corresponds to the ratio of the volume fraction of oxygenated hemoglobin to the sum of the volume fractions of oxygenated and deoxygenated hemoglobin in the arterial blood volume being monitored in the test volume.

[0035] In the context of the present disclosure, deoxygenated hemoglobin is defined as a form of hemoglobin that does not contain bound oxygen and does not contain other bound molecules, such as carbon monoxide, carbon dioxide, or iron. In the context of the present disclosure, oxygenated hemoglobin is defined as a form of hemoglobin that has bound oxygen. In the context of the present disclosure, light emitted by a light source in an optical plethysmography device comprises photons that reach the sensor through a stochastic path of one or more scattering events. This optical path is often assumed to be non-linear, but rather set according to a curved spatial probability distribution. The volume examined along this curved optical path forms the volume sampled or examined by optical plethysmography.

[0036] In the context of the present disclosure, one or more changes in arterial blood volume within a test volume between two or more time points are assessed to thereby assess the individual's PAT. In the context of the present disclosure, the change in arterial blood volume within a test volume between two time points corresponds to the relative change between the arterial blood volume present within the test volume at a first time point and the arterial blood volume present within the test volume at a second time point different from the first time point.

[0037] In the context of this disclosure, a chromophore is a molecular entity that absorbs or scatters light in the examination volume. For example, in the context of this disclosure, chromophores include melanin molecules, oxygenated hemoglobin, deoxygenated hemoglobin, etc. In the examination volume, the attenuation of the light intensity of incident light emitted from the light source of the photoplethysmography device follows the Beer-Lambert Law, which can be formulated as follows:

number

[0038] The following parameters are given: -V i,dcorresponds to either the volume fraction or concentration of the chromophore in the interrogation volume at a first time point along the photoplethysmography signal. -V i,s corresponds to either the volume fraction or concentration of the chromophore in the interrogation volume at a second time point along the photoplethysmography signal. -I h corresponds to the light intensity measured by the sensor of the photoplethysmography device at the first time point. -I l corresponds to the light intensity measured by the sensor of the photoplethysmography device at the second time point. The Beer-Lambert law, formulated in equation (1), can be evaluated at a first time point and a second time point. Taking the ratio of both equations yields equation (2).

number

[0039] Next, by taking the natural logarithm of both sides of equation (2), the following equation (3) is obtained.

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[0040] If we use a logarithm with a base b other than Euler's number e, then equation (3) becomes:

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[0041] From equation (4), it can be seen that the logarithm of the ratio of light intensity at the first time point to the second time point is linearly related to the difference in either the volume fraction or concentration of the chromophore between the first time point and the second time point.

[0042] Between two time points along the photoplethysmography signal, some chromophores remain attached to the individual's epidermis. For example, between two time points along the photoplethysmography signal, melanin molecules remain fixed in the examination volume. Therefore, the difference in either the volume fraction or concentration of chromophores, such as melanin molecules, between the two time points is null. Therefore, the contribution of such chromophores to the right-hand side of equation (4) is also null. The primary chromophores whose volume fraction or concentration varies between two points in time along the photoplethysmography signal are oxygenated and deoxygenated hemoglobin in the arterial blood volume. In the context of this disclosure, the two major forms of hemoglobin, i.e., oxygenated and deoxygenated hemoglobin, exhibit significantly different absorption and scattering coefficients for most wavelengths of light.

[0043] The effect of all other chromophores, neither oxygenated nor deoxygenated hemoglobin, whose volume fraction or concentration varies between two time points along the photoplethysmographic signal, is expressed as the extinction coefficient ε of their combination. other and the sum of the volume fractions or concentrations of oxygenated and deoxygenated hemoglobin minus one, where the sum of all volume fractions or all concentrations equals one.

[0044] Considering the above, equation (4) can be rewritten as equation (5) as follows:

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[0045] It is known that an estimate of oxygen saturation can be defined based on equation (6).

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[0046] Furthermore, V blood is defined as the total volume fraction or concentration of oxygenated and deoxygenated hemoglobin in the arterial blood of the test volume, as defined in equation (7) as follows:

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[0047] It is typically assumed that the volume fraction or sum of the concentrations of oxygenated and deoxygenated hemoglobin in the arterial blood volume or the sum of the concentrations of oxygenated and deoxygenated hemoglobin in the arterial blood volume remains approximately constant during measurement of the photoplethysmographic signal. In reality, only the ratio of oxygenated to deoxygenated hemoglobin, and thus the oxygen saturation estimate, may change significantly in the course of monitoring an individual with photoplethysmography, for example, during sleep apnea.

[0048] From equations (6) and (7), we obtain the following:

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[0049] Substituting equation (8) into equation (5) gives the following:

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number

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[0050] Equation (10) emphasizes the term on the left side, called the evaluation function, which is - optical path length d, - the parameter of interest, arterial blood volume ΔV blood one or more changes in, and -It shows a linear relationship with three terms (Q1SpO2+Q2) that are linearly dependent on the estimated oxygen saturation value.

[0051] Assuming that the total volume fraction or concentration of oxygenated and deoxygenated hemoglobin in arterial blood is constant, ΔV blood is a linear proxy for arterial blood volume variation within the test volume and therefore corresponds to a measure of peripheral arterial tone.

[0052] If the oxygen saturation estimate is constant, i.e., SpO2 = const, the change in arterial blood volume of the test volume between two time points is assessed by determining the logarithm of the ratio of the light intensities collected by the sensor when measured by photoplethysmography at the two time points. Mathematically, this can be expressed as:

number

[0053] Measurable parameters and ΔV blood The only linear relationship between ΔV blood In relation to the examination of the relative change in blood Since it is not related to the value of ΔV, it is performed by determining the evaluation function, up to a certain coefficient blood It is sufficient to determine

[0054] If SpO2 is not constant, the evaluation function will change as SpO2 changes. However, a compensation method can be used to compensate for the effect of SpO2 changes. For example, the compensation function f λ1 For a compensation method that can determine, equation (11) can be rewritten as:

number

[0055] As a result, PAT channel is a signal indicative of the peripheral arterial tone of the test volume of an individual. channel The signal can be obtained from the light intensity measured by photoplethysmography and the photoplethysmography signal by determining the logarithm of a function of light intensity, or an approximation of that function, which can be arbitrarily divided by a function that depends on SpO2, and the function of light intensity corresponds to the ratio of the light intensities.

[0056] According to equation (3), the evaluation function corresponds to the natural logarithm of a function of light intensity. Alternatively, starting from equation (2), any other evaluation function defined as a function of light intensity can be used, for example a linear approximation of the logarithm of a function of light intensity, a Taylor series approximation of a function of light intensity, or a linear approximation of another base logarithm of a function of light intensity. Alternatively, the evaluation function corresponds approximately to the ratio of the pulsatile waveform or AC component of the photoplethysmography signal to the slowly varying baseline or DC component of the photoplethysmography signal, which results in equation (13).

number

[0057] Therefore, the evaluation function corresponds to the logarithm of the ratio of light intensities, and the evaluation function is -optical path length, - Oxygen saturation estimate or function of SpO2, and -Depends on one or more of the following: changes in arterial blood volume within the test volume.

[0058] Furthermore, at least one of the time points corresponds to a diastolic phase in the individual's cardiac cycle, and / or at least one of the time points corresponds to a systolic phase in the individual's cardiac cycle.

[0059] During systole, the arterial blood volume within the individual's test volume is at its highest, resulting in the highest absorption and scattering of light at any given time during the cardiac cycle, i.e., the period between two heartbeats. Because hemoglobin is one of the primary absorbers and scatterers of photons within the test volume, the light intensity measured by the sensor of the photoplethysmography device is at its lowest. Meanwhile, during diastole, the arterial blood volume within the individual's test volume is at its lowest, resulting in the lowest absorption and scattering of light at any given time during the cardiac cycle. Therefore, the light intensity measured by the sensor of the photoplethysmography device is at its highest. The at least one first time point may, for example, correspond to a diastole in a first cardiac cycle, and / or the at least one second time point may, for example, correspond to a systole in a second cardiac cycle different from the first cardiac cycle. Alternatively, the at least one first time point may, for example, correspond to a systole in a first cardiac cycle, and / or the at least one second time point may, for example, correspond to a diastole in a second cardiac cycle different from the first cardiac cycle. Alternatively, the at least one first time point corresponds to, for example, a systole or diastole in a cardiac cycle, and the at least one second time point corresponds to any time point within the same or a different cardiac cycle.

[0060] As mentioned above, light intensity can be obtained by photoplethysmography in the following steps: -emitting light of wavelength by a light source; - collecting, by photoplethysmography, propagated light on a sensor corresponding to wavelengths of light transmitted or reflected as it propagates through an examination volume of the individual at two or more time points; and - determining the light intensity of the propagated light on the sensor at two or more points in time.

[0061] Photoplethysmography technology uses a simple, noninvasive setup probe or biosensor. The photoplethysmography biosensor noninvasively measures changes in pulsatile blood volume in a test volume by collecting photoplethysmography signals, thereby assessing PAT. The light source can be, for example, an LED or any other suitable light source that can be miniaturized to fit into the photoplethysmography biosensor. This wavelength can be, for example, in the red spectrum. Alternatively, this wavelength can be in the infrared spectrum. The physical distance between the light source and the sensor can be, for example, a few mm, e.g., less than 3 mm.

[0062] The evaluation of a signal indicative of an individual's peripheral arterial tone (PAT) to detect sleep disorder events will now be described in detail with reference to the drawings.

[0063] FIG. 1A shows a simplified block diagram of a system according to an exemplary embodiment of the present disclosure. The system 100 includes a device 102 configured to measure variations in arterial blood volume of an individual's test volume by photoplethysmography and an apparatus 104 configured to acquire light intensities measured by the device 102 and process the acquired light intensities to detect sleep disorder events 14 therefrom. The device 102 is placed, for example, on the individual's finger. Thus, the device 102 measures the light intensity of light propagating through the individual's finger over time. As detailed above and shown, for example, in Equation (10), the measured light intensity reflects variations in the individual's arterial blood volume. Furthermore, as shown in Equations (11) and (12), the measured light intensity empirically provides information about the individual's peripheral arterial tone. Thus, the device 102 outputs light intensities 12 that reflect the individual's peripheral arterial tone measured over time. The measured light intensities are provided to the apparatus 104. The device 104 includes at least one processor and at least one memory configured to store algorithms for operating the device, stored in the at least one memory in the form of software or program instructions. In addition to storing the software, the at least one memory may also store any data generated by the device and any other data necessary for its proper operation. However, this data may also be stored in a separate memory external to the device. The at least one processor may execute the program instructions stored in the at least one memory to control the operation of the device. In other words, in one embodiment, the device 104 includes a computing system including hardware and software components for processing the measured and acquired light intensities 12 and determining sleep disorder events 14 therefrom.

[0064] 1B illustrates various steps performed by the device 104 to detect a sleep disorder event 14. In the first step, step 111, a peripheral arterial tone signal, or PAT, is measured. channelis derived from the acquired light intensity and the measured photoplethysmography signal, as detailed above and shown in equations (11) and (12). In summary, apparatus 104 acquires light intensities 12 acquired over time by device 102, e.g., by photoplethysmography, at two or more time points. Apparatus 104 then determines from the acquired light intensities 12 the change in arterial blood volume within the test volume between the two or more time points. This is accomplished by determining the logarithm of a function of light intensity, or an approximation of that function, which may optionally be divided by a function that depends on SpO2. The result, as shown in equations (11) and (12), is a measure of the individual's PAT. channel It's a signal.

[0065] In the next step, step 112, the vasoconstriction event is determined by PAT channel As mentioned above, the vasoconstriction event is determined by the PAT signal. channel It is characterized by the fluctuation of the amplitude of the signal. More specifically, PAT channel A vasoconstriction event in the signal is characterized by a decrease in amplitude followed by an increase in amplitude. channel The vasoconstriction events in the signal can be determined by any suitable signal processing algorithm. channel The signal is divided into event segments characterized by a signal amplitude drop followed by a signal amplitude rise. Each of these event segments reflects vasoconstriction, i.e., contraction of an artery in the test volume. In other words, the event segments are used to measure the PAT channel These vasoconstriction events correspond to vasoconstriction events observed in the signal. These vasoconstriction events may be associated with, for example, respiratory events such as apnea or hypopnea, respiration-related arousal events or RERA events, periodic or non-periodic limb movements, bruxism events, or sleep disturbance events such as snoring.

[0066] FIG. 2 shows an example signal illustrating an individual's peripheral arterial tone (PAT), highlighting episodes of rapid arterial tone changes characterized by amplitude drops followed by amplitude increases. Each detected event segment can be seen to be characterized by an amplitude drop followed by an amplitude increase. The figure further shows that the peripheral arterial tone fluctuations for each event segment correlate to some extent with the individual's oxygen saturation estimate and SpO2 fluctuations.

[0067] In the next step, step 120, the apparatus proceeds to characterize each event segment, i.e., vasoconstriction event, based on the characterization features. Characterization features include, for example, the magnitude 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 drop period and / or the amplitude rise period of the event segment; the steepness of the amplitude drop period and / or the steepness of the amplitude rise period of the event segment; the full width at half maximum of the event segment; etc. However, it is necessary to calculate the characterization features for each event segment in a way that significantly reduces the impact of VAR episodes on the amplitude within the event segment.

[0068] For this purpose, the apparatus performs a PAT channel Calculate a baseline-invariant version of the signal, i.e., the baseline-invariant signal. The baseline-invariant signal is calculated using the PAT channel By relating the amplitude of the signal to a baseline level, e.g., PAT channel It can be derived by dividing the signal by: PAT channel the baseline signal corresponding to the high amplitude of the signal, i.e., its peak envelope or peak baseline; PAT channel the baseline signal corresponding to the low amplitude of the signal, i.e., its trough envelope or valley baseline; The baseline signal corresponding to the mean values ​​of the high and low amplitudes of the PAT channel, i.e., its peak-to-trough mean envelope; · The baseline signal corresponding to any percentile amplitude value of the PAT channel, i.e., the percentile envelope; A smoothed version of any of the above, PAT channel A smoothed version of the signal, e.g., with a window size longer than the duration of the vasoconstriction event, and PAT channel PAT with a window sliding process corresponding to one or more samples of the signal channel A calculation of a moving or sliding average version of a signal.

[0069] If the baseline signal is one of the smoothed signals above, the event segment or PAT due to this smoothed signal channel It is important to ensure that the time variations, e.g., characterized by a time constant, of the smoothed signal are slow enough so that the obtained smoothed signal does not follow the shape of the event segment to the extent that signal division would result in a baseline-invariant signal with significant loss of information related to the morphology of the original event segment. In other words, the variations of the derived baseline-invariant signal due to peripheral arterial tone should be largely preserved, while the variations due to VAR episodes should be largely reduced.

[0070] Figure 3 shows an example of a signal indicating peripheral arterial tone (PAT) in an individual experiencing a VAR episode due to venous blood pooling induced by a finger arm drop, where the photoplethysmography (PPG) signal is measured below heart level. It highlights how the PAT channel amplitude is suppressed during a VAR episode. More specifically, the top two plots show how the amplitude of the PPG signal 210 and its filtered, normalized version 220 are suppressed during a VAR episode. The third plot shows how the amplitude of the PAT channel 230, derived as shown in equations (11) or (12), drops during a VAR episode. The fourth plot shows how dividing each sample of the PAT channel by the baseline value yields a baseline-invariant version of the PAT channel 240, in which the effects of VAR amplitude modulation are substantially eliminated. As can be seen, amplitude variations due to peripheral arterial tone in event segments 110_1 to 110_3 and 110_n in the original PAT channel 220 and the baseline-invariant version of PAT channel 240 are maintained, while amplitude modulations due to VAR episodes are substantially eliminated in the baseline-invariant version of PAT channel 240.

[0071] Figure 4A shows the PAT channel Each sample of the signal 230 is channel 2 is a detailed diagram of the effect of dividing by a sample of the extracted peak envelope 231, trough envelope, average peak-to-trough envelope, and smoothed version of the signal. channel The peak-to-trough envelope corresponds to the local maximum and minimum amplitudes in the signal, while the peak-to-trough average envelope corresponds to the average value of the peak and trough envelopes. As can be seen, the original PAT channelA baseline invariant signal 232, generated by dividing samples of signal 230 by the high or low amplitude, the average of the high and low amplitudes, or the amplitude of a smoothed version of the signal, substantially eliminates the amplitude suppression caused by VAR while preserving the morphological integrity and relative amplitude variation of each event segment.

[0072] Once the baseline-invariant signal has been calculated, the device proceeds to characterize the event segments. To this end, the device derives various features characterizing the event segments from the baseline-invariant version of the signal. The characterization features include, for example, the magnitude of the event segment, i.e., the maximum and / or minimum amplitude of the event segment; the median and / or mean amplitude of the event segment; the quartile amplitude of the event segment; the duration of the vasoconstriction event; the duration of the amplitude drop period; the duration of the amplitude rise period of the vasoconstriction event; the steepness of the amplitude drop period; the steepness of the amplitude rise period of the vasoconstriction event; the full width at half maximum of the event segment, etc.

[0073] To derive these features, the device calculates various feature points from the portion of the baseline-invariant signal corresponding to the event segment in step 122. The feature points may be described in terms of amplitude and / or time. For example, the feature points may be the sample of the event segment having the maximum amplitude value, the sample of the event segment having the minimum amplitude value, the first and last samples of the event segment, etc.

[0074] Next, the device derives various features characterizing the event segment using information obtained from the derived feature points. To this end, in step 123, the device associates the obtained information to derive various features. For example, the magnitude of an event segment can be calculated as the difference between the maximum and minimum amplitude values ​​observed in the event segment. In other words, the magnitude of the amplitude drop of the event segment can be calculated as the difference between the amplitude value of the first sample or the sample with the maximum amplitude value and the amplitude value of the sample with the minimum amplitude value. Typically, the first sample coincides with or substantially coincides with the sample with the maximum amplitude value. Here, the first sample or the sample with the maximum amplitude value serves as a reference point, and the sample with the minimum amplitude value serves as a baseline point, with their respective amplitude values ​​serving as the reference amplitude value and the baseline amplitude value. 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 decline of an event segment may be calculated as the ratio of the amplitude difference to the time difference between the first sample or the sample with the largest amplitude value and the sample with the smallest amplitude value.

[0075] FIG. 5 shows an example event segment 310 of the baseline-invariant signal 240 illustrating various feature points that allow for the calculation of various features characterizing the event segment. In this example, the reference and baseline amplitude values ​​correspond to samples 313 and 311 of the signal having the maximum and minimum amplitude values, respectively. The duration 322 of the event segment is identified by samples 313 and 312, which correspond to the first and last samples in the event segment. The amplitude drop portion of the event segment and the amplitude rise portion of the event segment correspond to slopes 325 and 326, having durations 323 and 324, respectively. In this example, the steepness of the amplitude drop is simply indicated as the absolute magnitude 321 of the amplitude drop. Similarly, the steepness of the amplitude rise period is now indicated as the absolute magnitude between samples 312 and 311.

[0076] Alternatively, features characterizing an event segment on an absolute scale can be derived by relating the amplitude of the signal portion corresponding to the event segment to a baseline level. For example, this can be done by comparing the amplitude of the PAT corresponding to the event segment at the baseline level of the event segment. channel This can be achieved by dividing the signal portion. For example, the baseline level of the event segment can be: The maximum amplitude value of the event segment, i.e., its peak value; The minimum amplitude value of the event segment, i.e., its trough value; or · Mean or median amplitude value of the event segment; quartile or any percentile amplitude value of the event segment; or the amplitude value of any other characteristic point of the event segment, such as the point of maximum upward or downward slope of the event segment; or · Smoothed version of event segments.

[0077] As can be seen from this list, the baseline level of an event segment can be a baseline value or a baseline signal. Figure 4B shows, for example, a PAT at a baseline level corresponding to an event segment. channel Figure 1 shows an example of possible baseline values ​​for two event segments that can be used to relate the amplitude of the event segment to a baseline level by dividing a portion of the signal. In this figure, each event segment is represented by a pair of dashed lines. Amplitude values ​​indicating the maximum, minimum, mean, median, and lower quartile amplitude values ​​(lgr), as well as amplitude values ​​at the maximum and minimum slopes of the event segment, are shown.

[0078] PAT corresponding to the event segment channelThe baseline-invariant portion of each event segment is obtained by dividing the signal portion by the baseline amplitude value of the event segment. Thus, the output of 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 apparatus proceeds to steps 122 and 123 to derive characteristic features for each event, as described above. That is, in step 122, various feature points are derived from each baseline-invariant signal portion corresponding to each event segment. Next, in step 123, the apparatus uses information obtained from the feature points of each event segment to derive various features characterizing each event segment, as described above. For example, the magnitude of an event segment can be calculated as the difference between the maximum amplitude value and the minimum amplitude value. The magnitude of the amplitude drop of an event segment can be calculated as the difference between the amplitude value of the first sample or the sample with the maximum amplitude value and the amplitude value of the sample with the minimum amplitude value, etc.

[0079] Alternatively, any characteristic feature related to magnitude, such as a feature calculated using one or more amplitude values ​​of the signal portion corresponding to the event segment, may be obtained on a relative scale rather than an absolute scale. In this case, step 121 is omitted and the apparatus calculates the original unmodified PAT, as shown in the third plot of FIG. channelVarious characteristic features are derived from the signal 230. In this case, characteristic features affected by VAR, such as magnitude and steepness features, are calculated on a relative scale to correct for amplitude fluctuations due to VAR episodes. To this end, characteristic amplitude points of the event segment, such as peak amplitude, trough amplitude, peak-to-trough average amplitude, or percentile values, are derived, as described above with reference to FIG. 5. Unlike the above, here, the amplitude of the event segment is derived by, for example, calculating the relative difference between a reference amplitude value and a baseline amplitude value. Similarly, other amplitude-related features characterizing the event segment, such as a steepness feature, may be derived on a relative scale based on one or more amplitude values ​​derived from the signal portion corresponding to the event segment.

[0080] Thus, the output of step 120 is one or more features that characterize the event segment in terms of at least a magnitude measure that characterizes the intensity of the event segment, and possibly one or more additional features such as the duration of the event segment, the duration of the amplitude drop period of the event segment, the duration of the amplitude rise period of the event segment, the steepness of the amplitude drop period of the event segment, the steepness of the amplitude rise period of the event segment, and the full width at half maximum of the event segment.

[0081] In the final step of the method, step 130, the method proceeds to detecting a sleep disorder event 14 based on one or more determined features characterizing the vasoconstriction event. A sleep disorder event may be detected, for example, based on at least a magnitude measure of the vasoconstriction event. If the magnitude measure is higher than a predetermined threshold, the vasoconstriction event is considered to be a sleep disorder event. In addition to the magnitude measure, other features characterizing the vasoconstriction event may be considered. These additional features may further characterize the vasoconstriction event based on its morphological profile. The morphological profile may be linked, for example, to 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 drop period of the event segment, the duration of the amplitude rise period of the event segment, the steepness of the amplitude drop period of the event segment, the steepness of the amplitude rise period of the event segment, and the full width at half maximum of the event segment may be further considered. In this case, a parameterized cost function that takes into account the considered features may be evaluated to assess whether the vasoconstriction event is a sleep disorder event.

[0082] The step of detecting sleep disorder events can be performed by a classifier developed for performing the step of detecting sleep disorder events. Thus, the classifier implements a parameterized cost function by designing and inputting a set of rules for detection. As described above, these rules may be based on thresholds for each of one or more features or combinations of features to check how well the features of an event fit the criteria defined by the rules. The thresholds for each feature can be derived, for example, based on measurements obtained during a clinical trial. From these measurements, sleep disorder events are first identified by manual or computer-assisted scoring, and then the thresholds for each feature are determined from the identified sleep disorder events. Furthermore, features can be ordered based on their accuracy in detecting a particular sleep disorder event, and one or more optimal features can then be selected for detecting the corresponding sleep disorder event. Using only the optimal features for detecting sleep disorder events can simplify the implementation of the classifier without sacrificing detection accuracy. The classifier developed in this way can then evaluate a parameterized cost function implementing these rules to determine whether an event is a sleep disorder event.

[0083] The device 104 may also provide an indication of a particular medical or physiological condition based on the co-occurrence of sleep disorder events characterized by certain characteristics. For example, methods may be used that analyze the co-occurrence of vasoconstriction events with certain characteristics, such as a certain minimum event duration and a certain minimum distance between the minimum and maximum sample values, i.e., the duration of the event's amplitude drop, and / or characteristics observed in other physiological signals, such as an increase in pulse or heart rate, a decrease in blood oxygen saturation, and / or limb movement during the event. The limb movement may be voluntary or involuntary and may be picked up, for example, by an accelerometer. This co-occurrence of sleep disorder events and additional physiological characteristics may further assist a medical professional in determining an individual's particular medical or physiological condition.

[0084] FIG. 6 shows an example of a respiratory event, here an apnea event. As can be seen, the end of the apnea event is PAT channel Consistent with a decrease in signal amplitude and / or an increase in pulse rate (PR) and / or a decrease in SpO2.

[0085] As discussed above, the solutions of the present invention can improve the accuracy of determining various characterizing features from physiological signals indicative of peripheral arterial tone, and therefore improve the accuracy of detecting sleep disorder events from such physiological signals. Also, as discussed above, the methods can provide an indication of the co-occurrence of sleep disorder events and, possibly, features observed in other physiological signals, providing additional information that can assist medical professionals in determining an individual's particular medical and physiological condition.

[0086] Furthermore, although the above observations and methodologies are described in relation to photoplethysmography, those skilled in the art will recognize that similar observations and methodologies can readily be applied to physiological signals acquired by pneumatic plethysmography.

[0087] FIG. 7 illustrates a suitable computing system 600 on which embodiments of methods for detecting sleep disorder events in accordance with the present invention may be implemented. The computing system 600 may generally be formed as a suitable general-purpose computer and may include a bus 610, a processor 602, a local memory 604, one or more optional input interfaces 614, one or more optional output interfaces 616, a communications interface 612, a storage device interface 606, and one or more storage devices 608. The bus 610 may comprise one or more conductors that enable communication between components of the computing system 600. The processor 602 may include any type of conventional processor or microprocessor that interprets and executes programming instructions. The local memory 604 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions to be executed by the processor 602, and / or a read-only memory (ROM) or another type of static storage device that stores static information and instructions used by the processor 602. Thus, the processor may execute instructions stored in the local memory to perform the various steps of the methods described above. The input interface 614 may comprise one or more conventional mechanisms that allow an operator or user to input information into the computing device 600, such as a keyboard 620, a mouse 630, a pen, voice recognition, etc., and / or one or more PPG sensors. The output interface 616 may comprise one or more conventional mechanisms for outputting information to an operator or user, such as a display 640. The communication interface 612 may comprise any transceiver-like mechanism, such as one or more Ethernet interfaces, that allows the computing system 600 to communicate with other devices and / or systems, such as other computing devices 701, 702, 703. Thus, processing of signals obtained from one or more PPG sensors may be processed remotely by other computing devices.The communication interface 612 of the computing system 600 may be connected to such other computing systems by a local area network (LAN) or a wide area network (WAN) such as the Internet. The storage element interface 606 may comprise a storage interface, such as a Serial Advanced Technology Attachment (SATA) interface or a Small Computer System Interface (SCSI), for connecting the bus 610 to one or more storage elements 608, e.g., one or more local disks such as SATA disk drives, and for controlling the reading and / or writing of data from and / or to these storage elements 608. Although the storage element 608 is described above as a local disk, any other suitable computer-readable medium may generally be used, such as a removable magnetic disk, an optical storage medium such as a CD or DVD, a ROM disk, a solid-state drive, a flash memory card, or the like. Accordingly, the computing system 600 may correspond to circuitry for processing signals obtained from one or more PPG sensors to detect sleep disorder events therefrom, as described above with reference to FIG. 1 .

[0088] As used herein, the term "circuitry" may refer to one or more or all of the following: (a) hardware-only circuit implementations, such as implementations using only analog and / or digital circuitry; (b) A combination of hardware circuitry and software (if applicable), e.g. (i) a combination of analog and / or digital hardware circuitry and software / firmware; and (ii) software, and any portion of a hardware processor (including a digital signal processor) with software and memory that cooperates to cause a device, such as a mobile phone or server, to perform various functions; (c) Hardware circuits and / or processors, such as microprocessors or portions of microprocessors, that require software (e.g., firmware) to operate, but may not require software to operate. This definition of "circuit" applies wherever the term is used in this application, including in the claims. As a further example, the term "circuit," as used herein, encompasses a simple hardware circuit, a processor (or processors), a portion of a hardware circuit or processor, and its (or their) accompanying software and / or firmware implementations. The term "circuit" may also refer, for example, to a baseband integrated circuit or processor integrated circuit in a mobile terminal, as applicable to certain elements recited in the claims, or to similar integrated circuits in a server, cellular network device, or other computing or network device.

[0089] While the present invention has been described with reference to particular embodiments, it will be apparent to those skilled in the art that the invention is not limited to the details of the illustrative embodiments described above, and that the invention can be embodied with various changes and modifications without departing from the scope of the present invention. The present embodiments are to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than the foregoing description, and all changes that come within the scope of the claims are therefore intended to be embraced therein.

[0090] Readers of this patent application will understand that the words "comprising" or "comprise" do not exclude other elements or steps, that the words "a" or "an" do not exclude a plurality, and that a single element, such as a computer system, processor, or another integrated unit, may perform the functions of several means recited in the claims. Any reference numerals in the claims should not be construed as limiting the respective claims. When used in the specification or claims, terms such as "first," "second," "third," "a," "b," "c," etc., are introduced to distinguish between similar elements or steps and do not necessarily describe a sequential or chronological order. Similarly, terms such as "top," "bottom," "upper," and "lower" are introduced for explanatory purposes and do not necessarily indicate relative positions. It should be understood that terms so used are interchangeable in appropriate circumstances, and that embodiments of the invention can operate in accordance with the invention in other orders or directions different from those described or illustrated above. In order to maintain the disclosure of the present application as originally filed, the contents of claims 1 to 15 as originally filed are added below. (Claim 1) 1. A computer-implemented method for detecting sleep disorder events from a signal (230) indicative of peripheral arterial tone of an individual affected by venous arteriolar reflex, comprising: A determination step (110) of determining a vasoconstriction event (310, 110_1 to 110_n) from a change in the signals (230, 240); a step (122) of deriving a reference amplitude value (313) and a baseline amplitude value (311) of the vasoconstriction event (310, 110_1 to 110_n), wherein the reference amplitude value is different from the baseline amplitude value; a correlating step (123) of correlating the reference amplitude value (311) of the vasoconstriction event with the baseline amplitude value (313) of the vasoconstriction event to obtain a magnitude measure (321) of the vasoconstriction event; detecting a sleep disorder event therefrom (130); 20. A computer-implemented method comprising: (Claim 2) 2. The computer-implemented method of claim 1, wherein the deriving step (122) comprises calculating a baseline invariant signal (232) of the vasoconstriction event and selecting therefrom the reference amplitude value (313) and the baseline amplitude value (311) of the vasoconstriction event, the reference amplitude value and the baseline amplitude value corresponding to amplitude values ​​of the vasoconstriction event that enable calculation of the magnitude measure of the vasoconstriction event. (Claim 3) 3. The computer-implemented method of claim 2, wherein the calculating step comprises dividing the signal portion (310) corresponding to the vasoconstriction event by a baseline (231, 313) of the vasoconstriction event. (Claim 4) 4. The computer-implemented method of claim 2 or 3, wherein the associating step (123) comprises calculating an absolute magnitude (321) of the vasoconstriction event from the reference amplitude value (311) and the baseline amplitude value (313). (Claim 5) 5. The computer-implemented method of claim 2, wherein the baseline (231, 313) of the vasoconstriction event is derived by calculating an envelope of the signal characterizing the venous-arteriolar reflection in the signal, such as a peak envelope (231), a trough envelope, a peak-to-trough average envelope, an envelope based on percentile values, or a smoothed version of the signal, or by calculating an amplitude value of the vasoconstriction event characterizing the venous-arteriolar reflection of the vasoconstriction event, such as a peak amplitude (311), a through amplitude, a peak-to-trough average amplitude, or a percentile value of the vasoconstriction event. (Claim 6) 2. The computer-implemented method of claim 1, wherein the reference amplitude value and the baseline amplitude value of the vasoconstriction event are derived by calculating the peak amplitude, the through amplitude, the average peak-to-trough amplitude, or a percentile value of the vasoconstriction event, respectively. (Claim 7) 7. The computer-implemented method of claim 6, wherein the correlating step (123) comprises calculating a relative magnitude of the vasoconstriction event from the reference amplitude value and the baseline amplitude value of the vasoconstriction event. (Claim 8) 8. The computer-implemented method of claim 1, wherein the detecting step (130) comprises identifying vasoconstriction events (310, 110_1 to 110_n) characterized by a magnitude measure (321) higher than a predetermined value. (Claim 9) 9. The computer-implemented method of claim 8, wherein the identifying step further considers at least one of the duration of the vasoconstriction event (322), the duration of the amplitude decrease period (323) and / or the duration of the amplitude increase period (324) of the vasoconstriction event, the steepness of the amplitude decrease period (325) and / or the steepness of the amplitude increase period (326) of the vasoconstriction event. (Claim 10) 10. The computer-implemented method of claim 1, wherein the determining step (110) comprises identifying a portion of the signal (230, 240) characterized by an amplitude drop (311) followed by an amplitude rise (312). (Claim 11) 11. The computer-implemented method of claim 10, wherein the determining step (110) further considers at least one of the durations (322) of the amplitude drop periods and the amplitude rise periods of the signal portion, the durations (323) of the amplitude drop periods and / or the durations (324) of the amplitude rise periods of the signal portion, the steepness (325) of the amplitude drop periods and / or the steepness (326) of the amplitude rise periods of the signal portion. (Claim 12) 12. The computer-implemented method of claim 1, further comprising obtaining a signal indicative of changes in pulsatile blood volume at a selected anatomical location of the patient by plethysmography, and deriving therefrom said signal indicative of changes in peripheral arterial tone (230). (Claim 13) 1. An apparatus configured to detect a sleep disorder event from a signal (230) indicative of peripheral arterial tone of an individual affected by a venous arteriolar reflex, the apparatus comprising: A determination step (110) of determining a vasoconstriction event (310, 110_1 to 110_n) from a change in the signals (230, 240); a step (122) of deriving a reference amplitude value (313) and a baseline amplitude value (311) of the vasoconstriction event (310, 110_1 to 110_n), wherein the reference amplitude value is different from the baseline amplitude value; a correlating step (123) of correlating the reference amplitude value (311) of the vasoconstriction event with the baseline amplitude value (313) of the vasoconstriction event to obtain a magnitude measure (321) of the vasoconstriction event; detecting a sleep disorder event therefrom (130); 10. An apparatus comprising: means configured to perform (Claim 14) A computer program product comprising computer-executable instructions for causing a computer to perform the computer-implemented method of any one of claims 1 to 12. (Claim 15) A computer-readable storage medium containing computer-executable instructions for performing the computer-implemented method of any one of claims 1 to 12 when the program is run on a computer.

Claims

1. 1. A computer-implemented method for detecting sleep disorder events from a signal indicative of peripheral arterial tone in an individual affected by venous arteriolar reflex, comprising: a determining step in which one or more processors determine a vasoconstriction event from a change in the signal; deriving, by the one or more processors, a reference amplitude value and a baseline amplitude value for the vasoconstriction event, the reference amplitude value and the baseline amplitude value being different; a correlating step, by the one or more processors, correlating the reference amplitude value of the vasoconstriction event to the baseline amplitude value of the vasoconstriction event to obtain a magnitude measure of the vasoconstriction event; a detecting step in which the one or more processors detect a sleep disorder event from the magnitude measure; 20. A computer-implemented method comprising:

2. 2. The computer-implemented method of claim 1, wherein the deriving step includes calculating a baseline invariant signal of the vasoconstriction event and selecting the reference amplitude value and the baseline amplitude value of the vasoconstriction event from the baseline invariant signal, the reference amplitude value and the baseline amplitude value corresponding to amplitude values ​​of the vasoconstriction event that enable calculation of the magnitude measure of the vasoconstriction event.

3. 3. The computer-implemented method of claim 2, wherein the calculating step comprises dividing the signal portion corresponding to the vasoconstriction event by a baseline for the vasoconstriction event.

4. 4. The computer-implemented method of claim 2 or 3, wherein the correlating step includes calculating an absolute magnitude of the vasoconstriction event from the reference amplitude value and the baseline amplitude value.

5. 5. The computer-implemented method of claim 2, wherein the baseline of the vasoconstriction event is derived by calculating an envelope of the signal that characterizes the venous arteriolar reflection in the signal, such as a peak envelope, a trough envelope, a peak-to-trough average envelope, an envelope based on percentile values, or a smoothed version of the signal, or by calculating an amplitude value of the vasoconstriction event that characterizes the venous arteriolar reflection of the vasoconstriction event, such as a peak amplitude, a through amplitude, a peak-to-trough average amplitude, or a percentile value of the vasoconstriction event.

6. 2. The computer-implemented method of claim 1, wherein the reference amplitude value and the baseline amplitude value of the vasoconstriction event are derived by calculating the peak amplitude, the through amplitude, the average peak-to-trough amplitude, or a percentile value of the vasoconstriction event, respectively.

7. 7. The computer-implemented method of claim 6, wherein the correlating step includes calculating a relative magnitude of the vasoconstriction event from the reference amplitude value and the baseline amplitude value of the vasoconstriction event.

8. 8. The computer-implemented method of claim 1, wherein the detecting step includes identifying the vasoconstriction event when a magnitude measure is higher than a predetermined value.

9. 9. The computer-implemented method of claim 8, wherein the identifying step further considers at least one of the duration of the vasoconstriction event, the duration of the amplitude decrease period and / or the duration of the amplitude increase period of the vasoconstriction event, the steepness of the amplitude decrease period and / or the steepness of the amplitude increase period of the vasoconstriction event.

10. 10. The computer-implemented method of claim 1, wherein the determining step comprises identifying a portion of the signal characterized by an amplitude drop followed by an amplitude rise.

11. 11. The computer-implemented method of claim 10, wherein the determining step further considers at least one of the durations of the amplitude drops and periods of the amplitude increases of the portion of the signal, the durations of the amplitude drops and / or periods of the amplitude increases of the portion of the signal, and the steepness of the amplitude drops and / or periods of the amplitude increases of the portion of the signal.

12. 12. The computer-implemented method of claim 1, further comprising obtaining a signal indicative of changes in pulsatile blood volume at a selected anatomical location of the individual by plethysmography, and deriving the signal indicative of peripheral arterial tone from the signal indicative of changes in pulsatile blood volume.

13. A computer-readable storage medium containing computer-executable instructions for performing the computer-implemented method of any one of claims 1 to 12 when the computer-executable instructions are run as a program on a computer.

14. 1. An apparatus configured to detect a sleep disorder event from a signal indicative of peripheral arterial tone in an individual affected by a venous arteriolar reflex, the apparatus comprising: one or more processors; determining a vasoconstriction event from a change in said signal; deriving a reference amplitude value and a baseline amplitude value of the vasoconstriction event, the reference amplitude value and the baseline amplitude value being different; correlating the reference amplitude value of the vasoconstriction event with the baseline amplitude value of the vasoconstriction event to obtain a magnitude measure of the vasoconstriction event; Detecting sleep disorder events from the magnitude measures The apparatus is configured to:

15. 15. The apparatus of claim 14, wherein the deriving comprises calculating a baseline invariant signal of the vasoconstriction event and selecting a reference amplitude value and a baseline amplitude value of the vasoconstriction event from the baseline invariant signal, the reference amplitude value and the baseline amplitude value corresponding to amplitude values ​​of the vasoconstriction event that enable calculation of a magnitude measure of the vasoconstriction event.

16. 16. The apparatus of claim 15, wherein the calculating comprises dividing the portion of the signal corresponding to the vasoconstriction event by a baseline for the vasoconstriction event.

17. 17. The apparatus of claim 15 or 16, wherein said correlating comprises calculating an absolute magnitude of the vasoconstriction event from the reference amplitude value and the baseline amplitude value.

18. 18. The apparatus of claim 15, wherein the baseline of the vasoconstriction event is derived by calculating an envelope of the signal that characterizes the venous arteriolar reflection in the signal, such as a peak envelope, a trough envelope, a peak-to-trough average envelope, or an envelope based on percentile values, or by calculating an amplitude value of the vasoconstriction event that characterizes the venous arteriolar reflection of the vasoconstriction event, such as a smoothed version of the signal, the peak amplitude, the through amplitude, the peak-to-trough average amplitude, or a percentile value of the vasoconstriction event.

19. 15. The apparatus of claim 14, wherein the reference amplitude value and the baseline amplitude value of the vasoconstriction event are derived by calculating a peak amplitude value, a through amplitude, a peak-to-trough mean amplitude, or a percentile value, respectively, of the vasoconstriction event.

20. 20. The apparatus of claim 19, wherein the correlating comprises calculating a relative magnitude of the vasoconstriction event from the reference amplitude value and the baseline amplitude value of the vasoconstriction event.

21. 21. The apparatus of any one of claims 14 to 20, wherein the detecting comprises identifying the vasoconstriction event when a magnitude measure exceeds a predetermined value.

22. 22. The apparatus of claim 21, further comprising a photoplethysmography biosensor configured to measure changes in pulsatile blood volume to produce a signal indicative of peripheral arterial tone of the individual.

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