A system and method for specifying REM states and waking states.

Abstract

This invention provides a system and method for analyzing whether a sleep epoch is a REM sleep epoch or an awake epoch. [Solution] According to aspects of the present disclosure, a computer-based method includes accessing cardiopulmonary coupling data over a person's sleep period; identifying sleep period epochs corresponding to very low frequency coupling in the cardiopulmonary coupling data; accessing high frequency coupling data and / or low frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch; and designating the epoch as a REM sleep epoch or an awake epoch based on the high frequency coupling data and / or low frequency coupling data corresponding to the epoch, wherein the epoch is designated based on cardiopulmonary coupling data without using non-cardiopulmonary physiological data.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 903,833, filed on 21 September 2019, which is incorporated herein by reference in its entirety.

[0002] This disclosure relates to sleep analysis, and more particularly to the analysis of cardiopulmonary connectivity (CPC) data during human sleep, or the analysis of CPC data and physiological data, for specifying REM and wakefulness during sleep. [Background technology]

[0003] Cardiopulmonary coupling is a technique for assessing sleep quality by performing a quantitative analysis between two physiological signals—a NN interval series from a corresponding direct or derived respiratory signal and coupled heart rate variability—to determine the coherent cross-power of these two signals. Cardiopulmonary coupling is described, among other things, in U.S. Patents 7,324,845, 7,734,334, 8,403,848, and 8,401,626, all of which are incorporated herein by reference in their entirety.

[0004] Cardiopulmonary coupling is characterized in terms of coupling frequency. High-frequency coupling represents stable sleep, which is a biomarker of integrated, stable N-REM sleep, and is associated with periods of stable breathing, high vagal tone, generally aperiodic alternating patterns on electroencephalograms (EEGs), high relative delta power, physiological blood pressure reduction (healthy), and / or a stable arousal threshold. In high-frequency coupling (HFC), the coupling frequency is greater than 0.1 Hz.

[0005] Low-frequency coupling represents unstable sleep, a biomarker of integrated, unstable N-REM sleep that has characteristics opposite to stable sleep. Unstable sleep is associated with EEG activity called periodic alternating patterns (CAP), periods of fluctuating respiratory patterns (variable tidal volume), periodic variability in heart rate (CVHR), non-decreasing blood pressure, and / or variable arousal threshold. Fragmented REM sleep has low-frequency coupling characteristics. In low-frequency coupling (LFC), the coupling frequency is in the range of [0.01, 0.1] Hz. Low-frequency coupling can be further classified as elevated low-frequency coupling broadband or elevated low-frequency coupling narrowband.

[0006] Very low frequency coupling (vLFC) represents REM sleep and wakefulness. A frequency range of less than 0.01 Hz is defined as very low frequency coupling (vLFC). The physiology of REM and wakefulness is closely related to electrooculography (PSG), which is used as a primary tool for distinguishing between the two states. REM and wakefulness have very similar appearances in cardiopulmonary coupling (CPC) and manifest as very low frequency coupling (vLFC). [Overview of the project] [Problems that the invention aims to solve]

[0007] There is interest in further developing and improving sleep analysis techniques to specify various sleep states based on cardiopulmonary connectivity data. [Means for solving the problem]

[0008] This disclosure relates to sleep analysis, and more particularly to the analysis of cardiopulmonary connectivity (CPC) data during human sleep, or the analysis of CPC data and physiological data, for specifying REM and wakefulness during sleep.

[0009] According to aspects of the present disclosure, a computer-based method includes accessing cardiopulmonary coupling data over a person's sleep period; identifying epochs in the sleep period that include very low-frequency coupling in the cardiopulmonary coupling data; accessing at least one of high-frequency coupling data or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch; and designating the epoch as a REM sleep epoch or an awake epoch based on at least one of the high-frequency coupling data or low-frequency coupling data corresponding to the epoch, wherein the epoch is designated based on cardiopulmonary coupling data without using non-cardiopulmonary physiological data.

[0010] In various embodiments of the method, the epoch exhibits the advantage of low-frequency coupling, and the method further includes comparing the power of the ultra-low-frequency coupling during the epoch with a threshold.

[0011] In various embodiments of the method, the threshold is based on at least one of a person, a person's state, or a population containing a person.

[0012] In various embodiments of the method, designating the epoch includes designating the epoch as a REM sleep epoch based on (i) the dominance of low-frequency coupling and (ii) the power of the ultra-low-frequency coupling during the epoch exceeding a threshold.

[0013] In various embodiments of the method, the epoch exhibits the advantage of ultra-low frequency coupling.

[0014] In various embodiments of the method, the superiority of the ultra-low frequency coupling of the epoch is based on the superiority within a predetermined upper limit range of the ultra-low frequency coupling range in the cardiopulmonary coupling data corresponding to the epoch.

[0015] In various embodiments of the method, designating the epoch includes designating the epoch as a REM sleep epoch based on (i) the superiority of ultra-low frequency coupling within a predetermined upper limit range of ultra-low frequency coupling, and (ii) that in at least one of the high-frequency coupling data or low-frequency coupling data corresponding to the epoch, the power of at least one of the low-frequency coupling or high-frequency coupling exceeds a threshold.

[0016] In various embodiments of the method, designating the epoch includes designating the epoch as a REM sleep epoch based on (i) the dominance of ultra-low frequency coupling in the epoch, and (ii) the presence of elevated low-frequency coupling narrowband in the low-frequency coupling data corresponding to the epoch.

[0017] According to aspects of the present disclosure, the system includes one or more processors and at least one memory for storing instructions. When executed by one or more processors, the instructions cause the system to access cardiopulmonary coupling data over a person's sleep period, to identify epochs in the sleep period including very low-frequency coupling in the cardiopulmonary coupling data, to access at least one of high-frequency coupling data or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch, to designate the epoch as a REM sleep epoch or an awakening epoch based on at least one of the high-frequency coupling data or low-frequency coupling data corresponding to the epoch, the epochs being designated based on cardiopulmonary coupling data without using non-cardiopulmonary physiological data.

[0018] In various embodiments of the system, an epoch exhibits a low-frequency coupling advantage, and when an instruction is executed by one or more processors, the system causes the system to compare the power of the ultra-low-frequency coupling during the epoch with a threshold.

[0019] In various embodiments of the system, the threshold is based on at least one of a person, a person's state, or a population containing a person.

[0020] In various embodiments of the system, in specifying the epoch, when the instructions are executed by one or more processors, the system is caused to specify the epoch as a REM sleep epoch based on (i) the predominance of low-frequency coupling and (ii) the power of ultra-low-frequency coupling during the epoch exceeding a threshold.

[0021] In various embodiments of the system, an epoch exhibits the predominance of ultra-low-frequency coupling.

[0022] In various embodiments of the system, the predominance of ultra-low-frequency coupling of the epoch is based on the predominance within a predetermined upper limit range of the ultra-low-frequency coupling range in the cardiopulmonary coupling data corresponding to the epoch.

[0023] In various embodiments of the system, in specifying the epoch, when the instructions are executed by one or more processors, the system is caused to specify the epoch as a REM sleep epoch based on (i) the predominance of ultra-low-frequency coupling within a predetermined upper limit range of the ultra-low-frequency coupling range and (ii) at least one of the low-frequency coupling or high-frequency coupling power exceeding a threshold in at least one of the high-frequency coupling data or low-frequency coupling data corresponding to the epoch.

[0024] In various embodiments of the system, in specifying the epoch, when the instructions are executed by one or more processors, the system is caused to specify the epoch as a REM sleep epoch based on (i) the predominance of ultra-low-frequency coupling in the epoch and (ii) the presence of an increased low-frequency coupling narrow band in the low-frequency coupling data corresponding to the epoch.

[0025] According to aspects of the present disclosure, a computer-based method includes accessing cardiopulmonary coupling data over a person's sleep period, classifying sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data, accessing the person's actigraphy data corresponding to the vLFC epochs, and designating the vLFC epochs as rem epochs based on the fact that a predetermined percentage of actigraphy measurements in the actigraphy data corresponding to the vLFC epochs indicates exercise below the exercise threshold.

[0026] In various embodiments of the method, a predetermined percentage is 95% of the actigraphy measurement in the actigraphy data corresponding to the vLFC epoch, and the motion threshold is 0.01 G / s.

[0027] In various embodiments of the method, the method includes varying at least one of the predetermined percentages or motion thresholds for various actigraphy sensors.

[0028] According to aspects of the present disclosure, the system includes one or more processors and at least one memory for storing instructions. When an instruction is executed by one or more processors, it causes the system to access cardiopulmonary coupling data over a person's sleep period, to classify sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data, to access the person's actigraphy data corresponding to the vLFC epochs, and to designate the vLFC epochs as rem epochs based on the fact that a predetermined percentage of actigraphy measurements in the actigraphy data corresponding to the vLFC epochs indicates exercise below the exercise threshold.

[0029] In various embodiments of the system, a predetermined percentage is 95% of the actigraphy measurement in the actigraphy data corresponding to the vLFC epoch, and the motion threshold is 0.01 G / s.

[0030] In various embodiments of the system, when an instruction is executed by one or more processors, the system further changes at least one of the predetermined percentages or motion thresholds for various actigraphy sensors.

[0031] According to aspects of the present disclosure, a computer-based method includes accessing cardiopulmonary coupling data over a person's sleep period; classifying sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data; accessing pseudo-actigraphy data of a person corresponding to the vLFC epochs, which is based on physiological measurements of a person and not on actigraphy measurements; and designating the vLFC epochs as rem epochs or wakeful epochs based on the pseudo-actigraphy data corresponding to the vLFC epochs.

[0032] In various embodiments of the method, the method includes generating pseudo-actigraphy data corresponding to the vLFC epoch based on the signal quality of physiological measurements.

[0033] In various embodiments of the method, generating pseudo-actigraphy data includes generating data corresponding to larger movements when signal quality is lower and generating data corresponding to smaller movements when signal quality is higher.

[0034] In various embodiments of the method, the physiological measurements include at least one of a person's ECG measurement or plethysmography measurement.

[0035] In various embodiments of the method, generating pseudo-actigraphy data includes processing physiological measurements to detect peaks during a vLFC epoch, generating data corresponding to smaller movements when the count of detected peaks falls below a predetermined threshold and the shape of the detected peaks matches the shape of the expected peaks, and generating data indicating larger movements when the count of detected peaks exceeds a predetermined threshold and the shape of the detected peaks differs from the shape of the expected peaks.

[0036] In various embodiments of the method, physiological measurements include oxygen saturation measurements.

[0037] According to aspects of the present disclosure, the system includes one or more processors and at least one memory for storing instructions. When executed by one or more processors, the instructions cause the system to access cardiopulmonary coupling data over a person's sleep period, to classify sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data, to access pseudo-actigraphy data of a person corresponding to the vLFC epochs, which is based on physiological measurements of a person and not on actigraphy measurements, and to designate the vLFC epochs as rem epochs or wakefulness epochs based on the pseudo-actigraphy data corresponding to the vLFC epochs.

[0038] In various embodiments of the system, when an instruction is executed by one or more processors, it causes the system to generate pseudo-actigraphy data corresponding to a vLFC epoch based on the signal quality of physiological measurements.

[0039] In various embodiments of the system, when generating pseudo-actigraphy data, instructions, when executed by one or more processors, cause the system to generate data corresponding to larger movements when signal quality is lower and data corresponding to smaller movements when signal quality is higher.

[0040] In various embodiments of the system, the physiological measurements include at least one of a person's ECG measurement or plethysmography measurement.

[0041] In various embodiments of the system, when generating pseudo-actigraphy data, an instruction, when executed by one or more processors, causes the system to process physiological measurements to detect peaks during the vLFC epoch, generate data corresponding to smaller movements when the count of detected peaks is below a predetermined threshold and the shape of the detected peaks matches the shape of the expected peaks, and generate data indicating larger movements when the count of detected peaks is above a predetermined threshold and the shape of the detected peaks differs from the shape of the expected peaks.

[0042] In various embodiments of the system, physiological measurements include oxygen saturation measurements.

[0043] According to aspects of the present disclosure, a computer-based method includes accessing cardiopulmonary coupling data over a person's sleep period; classifying sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data; accessing physiological data of the person corresponding to the vLFC epochs, including physiological measurements but not actigraphy measurements; and designating the vLFC epochs as rem epochs based on physiological data corresponding to vLFC epochs exhibiting sleep-disordered breathing.

[0044] In various embodiments of the method, the physiological measurements include oxygen saturation measurements, and the method includes processing the oxygen saturation measurements to identify sleep-disordered breathing events during a vLFC epoch.

[0045] According to aspects of the present disclosure, the system includes one or more processors and at least one memory for storing instructions. When executed by one or more processors, the instructions cause the system to access cardiopulmonary coupling data over a person's sleep period, to classify sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data, to access physiological data of the person corresponding to the vLFC epochs, including physiological measurements but not actigraphy measurements, and to designate the vLFC epochs as rem epochs based on the physiological data corresponding to vLFC epochs exhibiting sleep-disordered breathing.

[0046] In various embodiments of the system, physiological measurements include oxygen saturation measurements, and instructions, when executed by one or more processors, cause the system to process the oxygen saturation measurements to identify sleep-disordered breathing events during a vLFC epoch.

[0047] The purpose and features of the systems and methods of this disclosure will become apparent to those skilled in the art when the descriptions of their various embodiments are read with reference to the accompanying drawings. [Brief explanation of the drawing]

[0048] [Figure 1] This is a diagram illustrating an exemplary measurement system according to an aspect of the present disclosure. [Figure 2] This is a flowchart illustrating exemplary operations for specifying REM / awake states based on cardiopulmonary connectivity data, according to aspects of the present disclosure. [Figure 3] This is a diagram illustrating an exemplary epoch having non-zero vLFC power during LFC dominance, according to an aspect of the present disclosure. [Figure 4] This is a diagram illustrating an exemplary epoch having eLFCNB during vLFC dominance, according to aspects of the present disclosure. [Figure 5] This is a flowchart illustrating exemplary operations for specifying REM / awake states based on CPC data and actigraphy data, according to aspects of this disclosure. [Figure 6]This is an exemplary epoch diagram corresponding to a vLFC advantage and an actigraphy signal showing motion, according to an aspect of the present disclosure. [Figure 7] This is a flowchart illustrating exemplary operations for specifying REM / awake states based on CPC data and pseudo-actigraphy data, according to aspects of this disclosure. [Figure 8] This is an exemplary graph of signal quality measurement and actigraphy signals according to an aspect of the present disclosure. [Figure 9] This is a block diagram of an exemplary computing system according to the aspects of this disclosure. [Figure 10] This is a flowchart illustrating exemplary operation for specifying REM / awake states based on CPC data and various physiological signals, according to aspects of this disclosure. [Modes for carrying out the invention]

[0049] This disclosure relates to the analysis of cardiopulmonary connectivity (CPC) data during human sleep, or the analysis of CPC data and physiological data, for specifying REM and waking states during sleep. During REM sleep, the subject is mostly motionless or in a state of "skeletal muscle paralysis," with the primary mechanical movement being the eyeballs. Since REM manifests as vLFC and should not involve any significant movement, one method according to this disclosure identifies REM states based on vLFC without sufficient actigraphy and identifies waking states based on vLFC with sufficient actigraphy. Other aspects of this disclosure do not use actigraphy data to specify REM or waking states. For example, pseudo-actigraphy data can be used, as will be described in more detail later herein. Other aspects of this disclosure use only cardiopulmonary connectivity data to specify REM or waking states without using non-CPC physiological data, which will be described later herein.

[0050] Referring here to Figure 1, a diagram of an exemplary measurement system 100 according to an aspect of this disclosure can be attached to a person in sleep to acquire physiological measurements that can be used to calculate cardiopulmonary connectivity ("CPC"), such as electrocardiogram measurements or other physiological measurements. The measurement system 100 also acquires various measurements, such as ECG measurements, plethysmography measurements, oxygen saturation measurements, and / or actigraphy measurements, the use of which will be described later herein. Figure 1 shows exemplary various sensors that may be placed on various parts of the human body, including parts not shown in Figure 1. For example, various sensors may be placed on the torso, head, and / or limbs of a person, among other places. Various sensors for detecting physiological signals will be understood by those skilled in the art. For example, in various embodiments, the sensor may be a sensor that touches the human body or a touchless sensor that does not directly touch the person (e.g., a sensor based on ballistic cardiography). Physiological measurements may be recorded on a storage medium such as a disk drive, flash drive, solid-state drive, or other storage medium. In various embodiments, various physiological measurements can be recorded in parallel. In various embodiments, each recorded data can be tagged with or associated with a timestamp. By tagging or associating timestamps with the recorded data, various recorded measurements can be correlated in time. It is intended that other methods for correlating recorded measurements in time may be used.

[0051] One aspect of this disclosure relates to a system and method for specifying REM sleep or wakefulness based on cardiopulmonary coupled spectral analysis without using non-CPC data. As previously stated, very low frequency coupling (vLFC) represents REM sleep or wakefulness. Figure 2 shows exemplary operation of analyzing CPC data from a person's sleep period to distinguish between REM sleep and wakefulness without using non-CPC physiological data. In this description, a sleep period can be divided into segments, which may be referred to herein as “epochs.” In various embodiments, different epochs may have the same duration, or different epochs may have different durations.

[0052] Referring to Figure 2, in block 210, the operation includes accessing cardiopulmonary coupling data over a person's sleep period. CPC data can be accessed, for example, from the measurement system in Figure 1 or another system. In block 220, the operation includes identifying epochs in the sleep period that include very low frequency coupling in the cardiopulmonary coupling data. As will be described in more detail below, epochs that include very low frequency coupling (vLFC) may or may not be vLFC dominant (i.e., the highest frequency coupling power is in the vLFC band). In block 230, the operation includes accessing high frequency coupling data and / or low frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch. In block 240, the operation includes designating the epoch as a REM sleep epoch or an awake epoch based on the high frequency coupling data and / or low frequency coupling data corresponding to the epoch, without using non-cardiopulmonary physiological data. The operation in Figure 2 can be performed by a computing system, such as the computing system in Figure 9, which will be described later in this specification. The following describes the operation embodiment shown in Figure 2.

[0053] The following explanation includes vLFC coupling, but concerns epochs where the dominant CPC state is classified as low-frequency coupling (LFC), i.e., epochs where the highest frequency coupling power is in the LFC band. In such epochs, the vLFC power is non-zero and smaller than the LFC power.

[0054] According to aspects of this disclosure, in the case of an epoch with non-zero vLFC power and LFC dominance, such an epoch is characterized as fragmentary REM rather than unstable N-REM. During a fragmentary REM epoch, and when there is no dominance of the vLFC frequency band within the upper limit range, such an epoch can be designated as a REM sleep state based on a dynamic threshold applied to the vLFC band, which may vary from person to person. An example of such an epoch is shown in Figure 3, where epoch 310 is shown to have non-zero vLFC power and LFC dominance.

[0055] In such epochs with non-zero vLFC power and LFC dominance, REM classification may not be based on a fixed threshold, in that the designation is based on non-zero vLFC power rather than vLFC dominance. Rather, dynamic thresholds allow for a more accurate representation of REM sleep and wakefulness states. For example, a particular fixed threshold may be suitable for a person with a healthy sleep state, but that particular threshold may not accurately designate REM sleep and wakefulness states in a person with an unhealthy sleep state where the worsened state may affect the vLFC band. Thus, REM sleep and wakefulness can be designated in epochs with non-zero vLFC power and LFC dominance (e.g., Figure 3), among other things, using dynamic thresholds that are appropriate for various conditions, people, or populations. In various embodiments, the dynamic threshold may be based on the mean of a particular population. For example, if an epoch is LFC dominant and the vLFC power is above the mean vLFC power of a particular population, that epoch can be classified as REM. Other types of dynamic thresholds are intended to be within the scope of this disclosure.

[0056] The following description pertains to epochs in which the dominant CPC state is classified as vLFC. In aspects of this disclosure, designating a vLFC epoch as a REM state or a waking state is based on an analysis of the CPC frequency band after the dominant CPC state has been classified as vLFC.

[0057] In various embodiments, a vLFC-dominant epoch can be designated as a REM state when activation (e.g., non-zero power) is present in the LFC and / or HFC bands, and the dominant CPC frequency is within the upper limit of the vLFC band (e.g., close to but not exceeding 0.01 Hz). Figure 4 shows examples of such epochs 410 and 420.

[0058] In various embodiments, the epoch in which vLFC is dominant is an increased low-frequency coupled narrowband (eLFC) NB When ) also exists, it can be designated as a REM state. Low-frequency coupling is elevated low-frequency coupled broadband (eLFC). BB ) or fragmentation, elevated low-frequency coupled narrowband (eLFC) NB ) or can be subdivided as periodic, or as lacking elevated low-frequency coupling. eLFC NB eLFC is a periodic marker associated with periodic respiration, Cheyne-Stokes respiration, and central apnea. BB This can be caused by other disorders such as sleep pain or other disturbances that trigger fragmentation, while eLFC NB This can be caused by periodic limb movements.

[0059] Figure 4 shows eLFC NB Examples of epochs 410 and 420, in which vLFC dominance exists, are shown. During the trials, such epochs were designated as REM states based on polysomnography data, and also precisely designated based solely on CPC data. Elevated very low frequency coupled narrowband (eVLFC) as used herein. NB The term "periodic REM" sleep refers to elevated low-frequency coupled narrowband (eLFC) sleep, which is predominant in the vLFC frequency band.NB ) is used to identify the occurrence of eVLFC NB or periodic REMs will be understood to serve as a new CPC state. Thus, the method for specifying the REM sleep state and the wake state is that eVLFC NB is configured to be specified as the REM sleep state. eVLFC NB If it does not exist, as described above, the dominance within the upper limit range of the vLFC band (for example, the CPC frequency power exceeds 0.05 but is lower than the total power in the vLFC band) can be used to specify the epoch as the REM sleep state.

[0060] Thus, FIGS. 2-4 and the above description show embodiments in which epochs can be specified as the REM sleep state by analyzing only CPC data without using non-CPC data. The above-described embodiments and the embodiments of FIGS. 2-4 are exemplary and do not limit the scope of the present disclosure.

[0061] Another aspect of the present disclosure relates to a system and method for specifying the REM state or the wake state based on analyzing cardiopulmonary data, various physiological signals such as actigraphy, oxygen saturation, and / or pseudo-actigraphy physiological signals such as ECG and plethysmography. The use of CPC data and actigraphy data is described in connection with FIGS. 5 and 6. The use of CPC data and pseudo-actigraphy physiological data and / or oxygen saturation data will be described in connection with FIGS. 7 and 10.

[0062] According to aspects of this disclosure, Figure 5 shows a flowchart of an exemplary operation for specifying REM or awake states based on the analysis of cardiopulmonary and actigraphy data. The operation applies a threshold to motion measurements to designate an epoch as a REM or awake state. In various embodiments, to collect motion information, the recording device (e.g., hardware) includes an accelerometer sensor. An accelerometer is a sensor device that measures the acceleration (rate of change of velocity) of an object, which is a person in this disclosure. According to aspects of this disclosure, the raw actigraphy signal can be processed to generate a quantity that reports acceleration in a specific unit of measurement. A common unit of measurement is m / s². 2 Or it is the force of G. Actigraphy data can be acquired and stored using, for example, the system shown in Figure 1.

[0063] Continuing to refer to Figure 5, in block 510, the operation includes accessing cardiopulmonary coupling data over a person's sleep period. In block 520, the operation includes classifying sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data. The classification may be based on the vLFC dominance in the epoch. In block 530, the operation includes accessing the person's actigraphy data corresponding to the vLFC epoch. The actigraphy data may be accessed from a storage or computing system, which will be explained in relation to Figure 9. Actigraphy data corresponding to vLFC epochs may be identified, for example, based on a timestamp. In block 540, the operation includes designating the vLFC epoch as a rem epoch based on the fact that a predetermined percentage of actigraphy measurements in the actigraphy data corresponding to the vLFC epoch indicate exercise below the exercise threshold. An example is provided below.

[0064] In various embodiments, a threshold of 0.01 G / s can be used to specify REM sleep and wakefulness, where accelerations below 0.01 G / s are treated as indicators of REM sleep, and accelerations of 0.01 G / s or greater are treated as indicators of wakefulness. The specific threshold values ​​are illustrative, and other values ​​may be used. In various embodiments, the number of acceleration samples exceeding the threshold is compared to the total number of samples in an epoch to generate a measure for specifying the epoch as either REM sleep or wakefulness. In various embodiments, if 95% of the acceleration samples in an epoch are below the threshold, the epoch can be specified as REM sleep; otherwise, the epoch is specified as wakefulness. The percentage threshold is illustrative, and other values ​​may be used. In various embodiments, the length of the epoch or period analyzed can be modified to increase certainty and derive fragmentation measurements or their absence.

[0065] In various embodiments, the acceleration threshold may need to be modified based on the specifications of the accelerometer's hardware and firmware (e.g., dynamic range, sampling rate, etc.). For example, a new accelerometer sensor may need to be analyzed and compared to a reference device to set an acceleration threshold for specifying REM / Arousal.

[0066] Figure 6 shows an example of CPC and physiological data indicating REM / awake states based on the behavior in Figure 5. For comparison with PSG reference, the duration of awakening and all REM periods scored by PSG are shown in purple boxes.

[0067] According to aspects of this disclosure, Figure 7 shows a flowchart of exemplary operation for specifying REM or wakefulness based on the analysis of cardiopulmonary data and pseudo-actigraphy data. As used herein, the terms “pseudo-actigraphy signal” or data refer to non-actigraphic physiological signals having specific features that indicate actigraphy. Pseudo-actigraphy signals may include, for example, ECG signals, plethysmography signals, and oxygen saturation signals, among others. In various embodiments, pseudo-actigraphy signals may be physiological signals whose signal quality is higher when a person is in REM state and lower when a person is wakeful. Signal quality may be degraded by changes that affect, for example, signal intensity, signal effectiveness, or the presence of the signal, among others. According to some embodiments, intermittent degradation of signal quality correlates with motion artifacts and can be utilized as pseudo-actigraphy. In various embodiments, intermittent degradation of signal quality with a vLFC predominance can be designated as an awakened state, while near-original or original signal quality with a vLFC predominance can be designated as a REM state. Accordingly, the disclosed systems and methods can analyze the pseudoactigraphy signal to designate an epoch as either a REM or awakened state.

[0068] Continuing to refer to Figure 7, in block 710, the operation includes accessing cardiopulmonary coupling data over a person's sleep period. In block 720, the operation includes classifying sleep period epochs as very low frequency coupling (vLFC) epochs based on the cardiopulmonary coupling data. For example, a vLFC epoch may exhibit vLFC dominance. In block 730, the operation includes accessing pseudo-actigraphy data of a person corresponding to a vLFC epoch. The pseudo-actigraphy data is based on a person's physiological measurements, not on actigraphy measurements. As previously mentioned, the pseudo-actigraphy data may include, among other things, ECG signals, plethysmography signals, and oxygen saturation signals. In block 740, the operation includes designating a vLFC epoch as a rem epoch or an awakening epoch based on the pseudo-actigraphy data corresponding to the vLFC epoch. The operation in Figure 7 can be performed on a computing system such as the computing system in Figure 9, which will be discussed further in this specification. Various embodiments of the operation shown in Figure 7 are described below.

[0069] In aspects of this disclosure, with respect to ECG and plethysmography signals, signal quality can be quantified by evaluating the feature extraction performance of the ECG and plethysmography signals. In the case of ECG, such features include, but are not limited to, R peaks, P waves, ST segments, and / or QRS complex waves, among others. In the case of plethysmography, such features include, but are not limited to, systolic peaks, diastolic peaks, and / or overlapping notches, among others. When the signal degrades and / or the features that are detected disappear, the signal quality result deteriorates.

[0070] In various embodiments, certain features may be rejected. During periods of weak signal, the detector may fail to detect features in the signal, resulting in "feature loss." In various embodiments, all marked features can be compared to a preset template, and the correlation with the template can be calculated. During periods of motion artifacts, the signal may be distorted, resulting in a low correlation between the marked features and the template, and the features may be rejected as "rejected features."

[0071] In various embodiments, signal quality can be quantified by comparing the amount of detected features with the expected number of features detected over a given time segment. The length of the time segment can be varied according to the desired granularity. In various embodiments, signal quality can be expressed as a percentage of the expected features over a selected time segment, which are marked features.

[0072] In various embodiments, when the detected feature is a signal peak, signal quality can be quantified as data indicating less movement when the count of detected peaks is below a predetermined threshold and the shape of the detected peaks matches the shape of the expected peaks, and as data indicating greater movement when the count of detected peaks is above a predetermined threshold and the shape of the detected peaks differs from the shape of the expected peaks.

[0073] According to aspects of this disclosure, with respect to the oxygen saturation signal, the signal quality of the oxygen saturation signal is based on evaluating the value and rate of change. Oxygen saturation reports blood oxygen saturation within the range of [0%, 100%]. If the sensor is completely disconnected from the person (e.g., due to movement), the oxygen saturation value is expected to drop to 0%. During these periods, the signal quality will be zero (0). Intermittent disconnections from the subject due to movement, etc., may cause poor contact in the sensor, but not complete disconnection. In these situations, the oxygen saturation value will drop rapidly at a rate that is not possible in actual human physiology.

[0074] According to aspects of this disclosure, the oxygen saturation depletion rate is evaluated and compared to a threshold, such as a 3% / second threshold change (i.e., 0.03 / s) or another value. In various embodiments, the signal quality value may be set to 0 during the period when the threshold is exceeded, and to 1 otherwise. It is intended that other methods for scoring signal quality are within the scope of this disclosure. The 3% threshold is illustrative, and other values ​​may be used. In various embodiments, the threshold may be changed to vary sensitivity, and care can be taken not to set the threshold so that false negative saturation depletion outweighs true saturation depletion.

[0075] In various embodiments, signal quality can be quantified by comparing the amount of detected features with the expected number of features detected over a given time segment. The length of the time segment can be varied according to the desired granularity. In various embodiments, signal quality can be expressed as a percentage of the expected features over a selected time segment, which are marked features.

[0076] Accordingly, various examples of pseudoactigraphy signals, including ECG, polysomnography, and oxygen saturation, are described. These examples are provided for illustrative purposes only and are not intended to limit the scope of this disclosure. Other physiological signals may be used as pseudoactigraphy signals and are intended to be within the scope of this disclosure. The embodiments described herein for determining the signal quality of physiological signals are illustrative, and other methods for determining signal quality as a pseudoactigraphy signal are intended to be within the scope of this disclosure.

[0077] Figure 8 is a graph of exemplary signal quality for an exemplary actigraphy signal and a simulated human actigraphy signal, with the signal quality score across each time segment on the left y-axis, actigraphy (G / s) on the right y-axis, and sample number on the x-axis. As shown in Figure 8, the graph shows how the number of high signal quality features decreases with increasing actigraphy, indicating that the signal quality of the actigraphy signal and the simulated actigraphy signal are inversely correlated.

[0078] According to aspects of this disclosure, and continuing with reference to Figure 7, when oxygen saturation data is available, the disclosed system and method can analyze oxygen saturation data to designate an epoch as either a REM or waking state. According to aspects of this disclosure, blood oxygen saturation (SO2, SaO2, SpO2, etc.) measurements can be analyzed to detect periods of oxygen desaturation events, which are often associated with sleep-disordered breathing. The presence of such events during vLFC-dominant periods indicates REM sleep. Various techniques and methods can be used to identify oxygen desaturation events and / or sleep-disordered breathing events, such as the technique described in International Patent Application Publication No. WO2020061014A1, which is incorporated entirely herein by reference. Other techniques and methods for identifying oxygen desaturation events and / or sleep-disordered breathing events using oxygen saturation data are intended to be within the scope of this disclosure.

[0079] Each of the aforementioned techniques (e.g., Figures 2, 5, and 7) can be used independently, but they can also be used in combination to enhance certainty, accuracy, and / or flexibility based on the available signals to help specify REM / wakeful states. These techniques can also assist in the diagnosis of sleep disorders specific to REM sleep. For example, "REM apnea" is considered a subcategory of sleep-disordered breathing in which apnea / hypopnea events occur during REM sleep. For this purpose, the presence of an oxygen saturation signal may improve the accuracy of disorder classification. Furthermore, the absence of any period classified as REM through the use of actigraphy or analysis of actigraphic signals during periods in which CPC spectral analysis indicates REM may indicate the presence of REM behavior disorder (RBD), in which REM periods are associated with mechanical movements (including sleepwalking).

[0080] Figure 10 is a flowchart illustrating an exemplary operation for determining which signals to use for REM / Awakening classification. The operation in Figure 10 can be performed on a computing system such as the computing system in Figure 9, which will be discussed later. In block 1010, the operation includes reading data files to access available signals. In block 1020, the operation includes determining whether ECG signals and / or plethysmography signals are available. If they are not available, the operation can terminate in block 1022. If such signals are present, in block 1030, the operation includes performing CPC processing to generate an array of CPC epochs, each epoch being classified into one of three CPC states based on the dominant frequency band: HFC, LFC, or vLFC. This process can be called "basic labeling". Furthermore, each epoch is classified into no eLFC, eLFC BB , or eLFC NB It can be classified as such, and this process can be called “extended labeling”. In block 1040, for each epoch classified as vLFC as described herein, the extended label is eLFC NBIf classified as such, the epoch can be designated as a REM state. If no REM / awake state is indicated in block 1030, the action continues to block 140.

[0081] In block 1040, the operation determines whether actigraphy data is present. If present, the operation includes designating the epoch as a REM state if the motion artifacts fall below a predetermined threshold for a sufficient number of samples, as described above in relation to Figure 5. Otherwise, the epoch is designated as an awake state. If actigraphy data is unavailable, the operation proceeds to block 1050.

[0082] In block 1050, the operation involves using a pseudo-actigraphy signal to specify a REM / awake state, as described in relation to Figure 7. For example, if the sum of detected excess and missing NN intervals falls below a predetermined threshold and there are no artifacts in the oxygen saturation signal quality (if any), that epoch can be specified as a REM state.

[0083] In block 1060, the operation includes determining whether an oxygen saturation signal is present. If present, the operation determines whether a desaturation event is present and whether there are no artifacts. If a desaturation event is present and there are no artifacts, the operation may designate the epoch as a REM state.

[0084] Therefore, an array of sleep stage classifications 1070 is generated based on the above operation. The above operation is illustrative, and it is intended that variations are within the scope of this disclosure. For example, in various embodiments, the presence of a saturation depletion event, such as that determined by block 1060, may override the determination of block 1040 and / or 1050, or block 1040 and / or 1050 may designate the epoch as "unknown".

[0085] The operation shown in Figure 10 is illustrative, and it is intended that other methods using a combination of physiological and CPC data to determine REM / awake states are within the scope of this disclosure.

[0086] The aspects and embodiments of this disclosure can be implemented on one or more computing systems capable of performing the functions described herein. Referring to Figure 9, an example of a computing system 900 for implementing this disclosure is shown. Various embodiments of this disclosure described herein can be carried out by computing system 900. However, how this disclosure can be carried out using other computer systems and / or computer architectures will be obvious to those skilled in the art.

[0087] The computing system 900 includes one or more processors, such as a processor 904. The processor 904 is connected to a communication infrastructure 906 (e.g., a communication bus, crossover bar, or network).

[0088] The computing system 900 may include a display 930 that receives graphics, text, and other data from a communication infrastructure 906 (or from a frame buffer not shown) for display. In various embodiments, the display 930 may present various measurements and metrics described herein, including, among other things, CPC data, actigraphy data, oxygen saturation data, ECG data, and / or plethysmography data. In various embodiments, the display 930 may present graphs and numerical representations. Presentations and reports may include some or all of the various metrics disclosed above herein.

[0089] The computing system 900 also includes main memory 908, preferably random access memory (RAM), and may also include secondary memory 910. The secondary memory 910 may include, for example, a hard disk drive 912 and / or a removable storage drive 914 representing a floppy disk drive, magnetic tape drive, optical disk drive, etc. The removable storage drive 914 reads from and / or writes to a removable storage unit 918 in a well-known manner. The removable storage unit 918 represents a floppy disk, magnetic tape, optical disk, etc., which is read from and written to by the removable storage drive 914. As understood, the removable storage 918 includes a computer-usable storage medium that stores computer software (e.g., programs or other instructions) and / or data.

[0090] In various embodiments, the secondary memory 910 may include other similar devices to enable loading computer software and / or data into the computing system 900. Such devices may include, for example, removable storage 922 and an interface 920. Such examples may include a program cartridge and cartridge interface (such as those found in legacy devices), a removable memory chip (such as an EPROM or PROM) and associated socket, as well as other removable storage devices 922, and an interface 920 that enables the transfer of software and data from the removable storage devices 922 to the computing system 900.

[0091] The computing system 900 may also include a communication interface 924. The communication interface 924 enables the transfer of software and data between the computing system 900 and external devices. Examples of the communication interface 924 include, among other components, a modem, a network interface (such as an Ethernet or WiFi card), a communication port, a PCMCIA or SD or other slot, and a card. The software and data transferred via the communication interface 924 are in the form of signals 928, which may be electronic signals, electromagnetic signals, optical signals, or other signals that can be received by the communication interface 924. These signals 928 are provided to the communication interface 924 via a communication path (i.e., channel) 926. The communication path 926 carries the signals 928 and can be implemented using wires or cables, optical fibers, telephone lines, cellular phone links, RF links, free-space optical communication, and / or other communication channels.

[0092] As used herein, the terms “computer program medium” and “computer-usable medium” generally refer to media such as removable storage 918, removable storage 922, a hard disk contained in a hard disk drive 912, and signals 928. These computer program products are devices for providing software to a computing system 900. This disclosure includes such computer program products.

[0093] The computer program (also called computer control logic or computer-readable program code) is stored in the main memory 908 and / or secondary memory 910. The computer program can also be received via the communication interface 924. When such a computer program is executed, it enables the computing system 900 to carry out the present disclosure as described herein. In particular, when the computer program is executed, it enables the processor 904 to carry out the processes and operations of the present disclosure, such as the various steps of methods 200, 300, 400, 500, and 600 described above. Thus, such a computer program represents the controller of the computing system 900.

[0094] In embodiments in which the Disclosure is implemented using software, the software can be stored in a computer program product and loaded into a computing system 900 using a removable storage drive 914, a hard drive 912, an interface 920, or a communication interface 924. The control logic (software), once executed by the processor 904, causes the processor 904 to perform the functions of the Disclosure described herein. Thus, the technology of the Disclosure may be provided as a software medical device (SaMD) or as non-medical software. In various embodiments, the software may include a cloud-based application.

[0095] The embodiments disclosed herein are examples of the disclosure and can be embodied in various forms. For example, certain embodiments herein are described as separate embodiments, but each of the embodiments herein can be combined with one or more other embodiments herein. Certain structural and functional details disclosed herein are not limiting and should be construed as representative grounds to teach those skilled in the art to employ the disclosure in a variety of substantially any appropriately detailed structures.

[0096] The phrases “In one embodiment,” “In an embodiment,” “In various embodiments,” “In some embodiments,” or “In other embodiments” may each refer to one or more of the same or different embodiments provided herein. The phrase “A or B” means “(A), (B), or (A and B).” The phrase “At least one of A, B, or C” means “(A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C).”

[0097] Any method, program, algorithm, or code described herein may be translated into a programming language or computer program, or expressed in a programming language or computer program. As used herein, the terms “programming language” and “computer program” include, respectively, any language used to specify instructions to a computer, and include, but are not limited to, the following languages ​​and their derivatives: assembler, Basic, batch file, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command languages, Pascal, Perl, PL1, Python, scripting languages, Visual Basic, metalanguages ​​for specifying programs, and all first, second, third, fourth, fifth, or further generations of computer languages. Databases and other data schemas and any other metalanguages ​​are also included. Languages ​​are not distinguished as interpreted, compiled, or using both compiled and interpreted approaches. Compiled versions of programs are not distinguished as source versions. Therefore, a program is a reference to any and all such states (source, compiled, object, or linked, etc.) in which a programming language can exist.

[0098] The systems described herein may also utilize one or more controllers to receive various information, transform the received information, and generate output. A controller may include any type of computing device, computing circuit, or any type of processor or processing circuit capable of executing a set of instructions stored in memory. A controller may include multiple processors and / or a multicore central processing unit (CPU), and may include any type of processor, such as a microprocessor, digital signal processor, microcontroller, programmable logic device (PLD), or field-programmable gate array (FPGA). A controller may also include memory for storing data and / or instructions that, when executed by one or more processors, cause one or more processors to execute one or more methods and / or algorithms.

[0099] It should be understood that the foregoing description merely illustrates the present disclosure. Various alternatives and modifications can be devised by those skilled in the art without departing from the present disclosure. Accordingly, the present disclosure is intended to encompass all such alternatives, modifications, and variations. Embodiments described with reference to the accompanying drawings are presented solely to illustrate specific examples of the present disclosure. Other elements, steps, methods, and techniques that differ only slightly from those described above are also intended to be within the scope of the present disclosure.

Claims

1. A method performed by computer, Accessing cardiopulmonary connectivity data over a person's sleep period, To identify the epoch of sleep period including ultra-low frequency coupling in the cardiopulmonary coupling data, Accessing at least one of the high-frequency coupled data or low-frequency coupled data in the cardiopulmonary coupled data corresponding to the aforementioned epoch, The epoch is designated as a REM sleep epoch or a wakefulness epoch based on at least one of the high-frequency coupled data or the low-frequency coupled data corresponding to the epoch, This includes, and the epoch is specified based on the cardiopulmonary data without using non-cardiopulmonary physiological data. method.

2. The computer-based method according to claim 1, wherein the epoch exhibits the advantage of low-frequency coupling, and the method further comprises comparing the power of the ultra-low-frequency coupling during the epoch with a threshold.

3. The computer-based method according to claim 2, wherein the threshold is based on at least one of a person, a person's state, or a population containing a person.

4. The computer-based method according to claim 3, wherein designating the epoch is a REM sleep epoch based on (i) the superiority of the low-frequency coupling and (ii) the power of the very low-frequency coupling during the epoch exceeds a threshold.

5. The method, performed by a computer according to claim 1, exhibits the advantages of ultra-low frequency coupling.

6. The computer-based method according to claim 5, wherein the superiority of the ultra-low frequency coupling of the epoch is based on superiority within a predetermined upper range of the ultra-low frequency coupling range in the cardiopulmonary coupling data corresponding to the epoch.

7. The computer-based method according to claim 6, wherein designating the epoch is a REM sleep epoch based on (i) the superiority of the ultra-low frequency coupling within a predetermined upper range of the ultra-low frequency coupling range, and (ii) the power of at least one of the low-frequency coupling or the high-frequency coupling in at least one of the high-frequency coupling data or low-frequency coupling data corresponding to the epoch exceeds a threshold.

8. The computer-based method according to claim 5, wherein designating the epoch includes designating the epoch as a REM sleep epoch based on (i) the superiority of the ultra-low frequency coupling in the epoch, and (ii) the presence of an elevated low-frequency coupling narrowband in the low-frequency coupling data corresponding to the epoch.

9. It is a system, One or more processors, At least one memory for storing instructions, The system is provided with such that when the instruction is executed by one or more processors, the system By giving access to cardiopulmonary connectivity data over a person's sleep period, To identify the epoch of the sleep period including ultra-low frequency coupling in the cardiopulmonary coupling data, Access at least one of the high-frequency coupled data or low-frequency coupled data in the cardiopulmonary coupling data corresponding to the epoch, Based on at least one of the high-frequency coupled data or the low-frequency coupled data corresponding to the epoch, the epoch is designated as a REM sleep epoch or an awakening epoch, and the epoch is designated based on the cardiopulmonary coupled data without using non-cardiopulmonary coupled physiological data. system.

10. The system according to claim 9, wherein the epoch exhibits the advantage of low-frequency coupling, and when the instruction is executed by the one or more processors, the system further causes the system to compare the power of the ultra-low-frequency coupling during the epoch with a threshold.

11. The system according to claim 10, wherein the threshold is based on at least one of a person, a person's state, or a population containing a person.

12. The system according to claim 11, wherein, in designating the epoch, the instruction, when executed by one or more processors, causes the system to designate the epoch as a REM sleep epoch based on (i) the superiority of the low-frequency coupling and (ii) the power of the ultra-low-frequency coupling during the epoch exceeding the threshold.

13. The system according to claim 9, wherein the aforementioned epoch exhibits the advantage of ultra-low frequency coupling.

14. The system according to claim 13, wherein the superiority of the ultra-low frequency coupling of the epoch is based on superiority within a predetermined upper range of the ultra-low frequency coupling range in the cardiopulmonary coupling data corresponding to the epoch.

15. The system according to claim 14, wherein, in designating the epoch, when the instruction is executed by the one or more processors, the system designates the epoch as a REM sleep epoch based on (i) the superiority of the ultra-low frequency coupling within a predetermined upper range of the ultra-low frequency coupling range, and (ii) that in at least one of the high-frequency coupling data or the low-frequency coupling data corresponding to the epoch, the power of at least one of the low-frequency coupling or high-frequency coupling exceeds a threshold.

16. The system according to claim 13, wherein, in designating the epoch, the instruction, when executed by the one or more processors, causes the system to designate the epoch as a REM sleep epoch based on (i) the superiority of the ultra-low frequency coupling in the epoch, and (ii) the presence of an elevated low-frequency coupling narrowband in the low-frequency coupling data corresponding to the epoch.

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