Systems and methods for specifying rem and wake states

Abstract

The present disclosure provides systems and methods of analyzing a sleep session as a REM sleep session or a wake session. According to aspects of the present disclosure, a computer- implemented method includes accessing cardiopulmonary coupling data throughout a sleep session of a person, identifying a period in the sleep session 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 period, and designating the period as a REM sleep session or a wake session based on the high frequency coupling data and / or the low frequency coupling data corresponding to the period, wherein the period is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Application No. 62 / 903,833, filed September 21, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to sleep analysis, and more specifically, to the analysis of cardiopulmonary coupling (CPC) data or CPC data in conjunction with physiological data during a person's sleep period to specify REM and wakefulness states during the sleep period. Background Technology

[0004] Cardiopulmonary coupling is a technique for assessing sleep quality by performing quantitative analysis between two physiological signals—from an NN-interval sequence of heart rate variability coupled to a corresponding direct or derived respiratory signal—to determine the coherent cross-power of these two signals. Cardiopulmonary coupling is described in U.S. Patent Nos. 7,324,845, 7,734,334, 8,403,848, and 8,401,626, the entire contents of which are incorporated herein by reference.

[0005] Cardiopulmonary coupling can be characterized by coupling frequency. High-frequency coupling represents stable sleep, which is a biomarker of integrated stable NREM sleep and is associated with stable respiratory cycles, high vagal tone, typically non-periodic alternation patterns on electroencephalography (EEG), high relative delta power, physiological blood pressure decrease (in a healthy state), and / or a stable arousal threshold. In high-frequency coupling (HFC), the coupling frequency is greater than 0.1 Hz.

[0006] Low-frequency coupling represents unstable sleep and is a biomarker of overall unstable NREM sleep, exhibiting characteristics opposite to stable sleep. Unstable sleep is associated with EEG activity known as periodic alternation pattern (CAP), cyclical fluctuations in breathing patterns (tidal volume fluctuations), periodic changes in heart rate (CVHR), lack of blood pressure decrease, and / or variable arousal threshold. Fragmented REM sleep exhibits 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.

[0007] Very low frequency coupling (vLFC) represents both REM sleep and wakefulness. The frequency range below 0.01 Hz is defined as vLFC. From the perspective of polysomnography (PSG) and electrooculography, the primary tools used to distinguish between these two states, REM and wakefulness physiology are closely related. In cardiopulmonary coupling (CPC), REM and wakefulness exhibit very similar characteristics, manifesting as vLFC.

[0008] There is interest in further developing and improving sleep analysis techniques to specify various sleep states based on cardiopulmonary coupling data. Summary of the Invention

[0009] This disclosure relates to sleep analysis, and more specifically, to the analysis of cardiopulmonary coupling (CPC) data or CPC data and physiological data during a person's sleep period to specify REM and wake states during the sleep period.

[0010] According to various aspects of this disclosure, a computer-implemented method includes accessing cardiopulmonary coupling data throughout a person's sleep period, identifying an epoch in the sleep period that includes very low-frequency coupling data from the cardiopulmonary coupling data, accessing at least one of high-frequency coupling data and low-frequency coupling data corresponding to the epoch in the cardiopulmonary coupling data, and designating the epoch as a REM sleep epoch or a wake-up 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 the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

[0011] In various embodiments of the method, the period exhibits low-frequency coupling dominance, and the method further includes comparing the power of extremely low-frequency coupling during the period with a threshold.

[0012] In various embodiments of the method, the threshold is based on at least one of the following: the person, the person's condition, and the group that includes the person.

[0013] In various embodiments of the method, designating the period includes designating the period as a REM sleep period based on the following conditions: (i) low-frequency coupling is dominant, and (ii) the power of very low-frequency coupling exceeds a threshold during the period.

[0014] In various embodiments of this method, this period exhibits dominance of extremely low-frequency coupling.

[0015] In various embodiments of the method, the dominance of the very low frequency coupling during this period is based on the dominance in a predefined upper range of the very low frequency coupling range in the cardiopulmonary coupling data corresponding to this period.

[0016] In various embodiments of the method, designating the period includes designating the period as a REM sleep period based on the following conditions: (i) extremely low frequency coupling dominates in a predefined higher range of the extremely low frequency coupling range, and (ii) the power of at least one of the low frequency coupling and high frequency coupling in at least one of the high frequency coupling data and low frequency coupling data corresponding to the period exceeds a threshold.

[0017] In various embodiments of the method, designating the period includes designating the period as a REM sleep period based on the following conditions: (i) very low-frequency coupling dominates in the period, and (ii) there is an elevated low-frequency coupling narrowband in the low-frequency coupling data corresponding to the period.

[0018] According to various aspects of this disclosure, a system includes one or more processors and at least one memory storing instructions. When executed by the one or more processors, the instructions cause the system to access cardiopulmonary coupling data throughout a person's sleep period, identify a period in the sleep period that includes very low-frequency coupling data from the cardiopulmonary coupling data, access at least one of high-frequency or low-frequency coupling data from the cardiopulmonary coupling data corresponding to that period, and designate that period as a REM sleep period or a wake-up period based on at least one of the high-frequency or low-frequency coupling data corresponding to that period, wherein the period is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

[0019] In various embodiments of the system, this period is characterized by low-frequency coupling dominance, and when the instruction is executed by one or more processors, it causes the system to compare the power of the extremely low-frequency coupling during that period with a threshold.

[0020] In various embodiments of the system, the threshold is based on at least one of the following: the person, the person's condition, and the group that includes the person.

[0021] In various embodiments of the system, when the instruction is executed by one or more processors during the designated period, the system designates the period as a REM sleep period based on the following conditions: (i) low-frequency coupling is dominant, and (ii) the power of very low-frequency coupling exceeds a threshold during the period.

[0022] In various embodiments of the system, this period is characterized by extremely low-frequency coupling dominance.

[0023] In various embodiments of the system, the dominance of the extremely low frequency coupling during this period is based on the dominance of a predefined higher range of the extremely low frequency coupling range in the cardiopulmonary coupling data corresponding to this period.

[0024] In various embodiments of the system, when the instruction is executed by one or more processors during the specified period, the system designates the period as a REM sleep period based on the following conditions: (i) extremely low frequency coupling dominates in a predefined higher range of the extremely low frequency coupling range, and (ii) the power of at least one of the low frequency coupling or high frequency coupling in at least one of the high frequency coupling data or low frequency coupling data corresponding to the period exceeds a threshold.

[0025] In various embodiments of the system, when specifying the period, the instruction, when executed by one or more processors, causes the system to designate the period as a REM sleep period based on the following conditions: (i) extremely low-frequency coupling dominates in the period, and (ii) there is an elevated low-frequency coupling narrowband in the low-frequency coupling data corresponding to the period.

[0026] According to various aspects of the present invention, a computer-implemented method includes: accessing cardiopulmonary coupling data throughout a person's sleep period; classifying periods in the sleep period into very low frequency coupling (vLFC) periods based on the cardiopulmonary coupling data; accessing human actionography data corresponding to the vLFC periods; and designating the vLFC periods as REM periods based on a predetermined percentage of actionography measurements in the actionography data corresponding to the vLFC periods that indicate movement below a movement threshold.

[0027] In various embodiments of the method, the predetermined percentage is 95% of the motion recorder measurements in the motion recorder data corresponding to the vLFC period, and the movement threshold is 0.01 G / s.

[0028] In various embodiments of the method, the method includes changing at least one of a predetermined percentage or a movement threshold for different motion recorder sensors.

[0029] According to various aspects of this disclosure, a system includes one or more processors and at least one memory storing instructions. When executed by the one or more processors, the instructions cause the system to access cardiopulmonary coupling data throughout a person's sleep period, classify periods within the sleep period into very low frequency coupling (vLFC) periods based on the cardiopulmonary coupling data, access human motion recorder data corresponding to the vLFC periods, and designate the vLFC periods as REM periods based on a predetermined percentage of motion recorder measurements indicating movement below a movement threshold in the motion recorder data corresponding to the vLFC periods.

[0030] In various embodiments of the system, the predetermined percentage is 95% of the motion recorder measurements in the motion recorder data corresponding to the vLFC period, and the movement threshold is 0.01 G / s.

[0031] In various embodiments of the system, when executed by one or more processors, the instruction also causes the system to change at least one of a predetermined percentage and a movement threshold for different motion recorder sensors.

[0032] According to various aspects of this disclosure, a computer-implemented method includes: accessing cardiopulmonary coupling data throughout a person's sleep period; classifying periods in the sleep period into very low frequency coupling (vLFC) periods based on the cardiopulmonary coupling data; accessing pseudokinetic data of the person corresponding to the vLFC periods, wherein the pseudokinetic data is based on physiological measurements of the person rather than on kinematic measurements; and designating the vLFC periods as REM periods or wake-up periods based on the pseudokinetic data corresponding to the vLFC periods.

[0033] In various embodiments of the method, the method includes generating pseudo-body motion recorder data corresponding to vLFC periods based on the signal quality of physiological measurements.

[0034] In various embodiments of the method, generating pseudo-motion recorder data includes generating data corresponding to larger motions when the signal quality is low, and generating data corresponding to smaller motions when the signal quality is high.

[0035] In various embodiments of the method, the physiological measurements include at least one of human ECG measurements and plethysmography measurements.

[0036] In various embodiments of the method, generating pseudo-motion recorder data includes: processing physiological measurements to detect peaks during the vLFC period; generating data corresponding to smaller movements when the count of detected peaks is below a predetermined threshold and when the shape of detected peaks matches an expected peak shape; and generating data indicating larger movements when the count of detected peaks is above a predetermined threshold and when the shape of detected peaks differs from an expected peak shape.

[0037] In various embodiments of this method, physiological measurements include oxygen saturation measurements.

[0038] According to various aspects of this disclosure, a system includes one or more processors and at least one memory storing instructions. When executed by the one or more processors, the instructions cause the system to access cardiopulmonary coupling data throughout a person's sleep period, classify periods within the sleep period into very low frequency coupled (vLFC) periods based on the cardiopulmonary coupling data, access pseudo-motion recorder data corresponding to the vLFC periods, wherein the pseudo-motion recorder data is based on human physiological measurements rather than on motion recorder measurements, and designate the vLFC periods as REM periods or wakefulness periods based on the pseudo-motion recorder data corresponding to the vLFC periods.

[0039] In various embodiments of the system, when executed by one or more processors, the instruction causes the system to generate pseudo-physical motion recorder data corresponding to the vLFC period based on the signal quality of physiological measurements.

[0040] In various embodiments of the system, when generating pseudo-motion recorder data, the instruction, when executed by one or more processors, causes the system to generate data corresponding to larger movements when signal quality is low, and to generate data corresponding to smaller movements when signal quality is high.

[0041] In various embodiments of the system, physiological measurements include at least one of human ECG measurements and plethysmography measurements.

[0042] In various embodiments of the system, in generating pseudo-motion recorder data, when executed by one or more processors, the instructions cause the system to: process physiological measurements to detect peaks during the vLFC period, generate data corresponding to smaller movements when the count of detected peaks is below a predetermined threshold and when the shape of detected peaks matches an expected peak shape, and generate data indicating larger movements when the count of detected peaks is above a predetermined threshold and when the shape of detected peaks differs from an expected peak shape.

[0043] In various embodiments of this system, physiological measurements include oxygen saturation measurements.

[0044] According to various aspects of the present invention, a computer-implemented method includes: accessing cardiopulmonary coupling data throughout a person's sleep period; classifying periods in the sleep period into very low frequency coupling (vLFC) periods based on the cardiopulmonary coupling data; accessing physiological data of the person corresponding to the vLFC periods, wherein the physiological data includes physiological measurements but excludes motion recorder measurements; and designating the vLFC periods as REM periods based on indications of sleep apnea based on the physiological data corresponding to the vLFC periods.

[0045] In various embodiments of the method, physiological measurements include oxygen saturation measurements, and the method includes processing oxygen saturation measurements to identify sleep apnea events during the vLFC period.

[0046] According to various aspects of this disclosure, a system includes one or more processors and at least one memory storing instructions. When executed by the one or more processors, the instructions cause the system to access cardiopulmonary coupling data throughout a person's sleep period, classify periods within the sleep period into very low frequency coupled (vLFC) periods based on the cardiopulmonary coupling data, access physiological data corresponding to the vLFC periods, wherein the physiological data includes physiological measurements but excludes motion recorder measurements, and indicate sleep apnea based on the physiological data corresponding to the vLFC periods, designating the vLFC periods as REM periods.

[0047] In various embodiments of the system, physiological measurements include oxygen saturation measurements, and the instructions, when executed by one or more processors, cause the system to process the oxygen saturation measurements to identify sleep apnea events during the vLFC period. Attached Figure Description

[0048] When reading the description of various embodiments of the systems and methods of this disclosure with reference to the accompanying drawings, those skilled in the art will understand the purpose and features of the systems and methods of this disclosure, wherein:

[0049] Figure 1 These are illustrations of exemplary measurement systems according to various aspects of this disclosure;

[0050] Figure 2 This is a flowchart illustrating exemplary operations for specifying REM / wake states based on cardiopulmonary coupling data, according to various aspects of this disclosure;

[0051] Figure 3 This is a diagram of exemplary periods with non-zero vLFC power during LFC dominance, based on various aspects of this disclosure;

[0052] Figure 4 It is based on various aspects of this disclosure regarding the presence of eLFC during the vLFC-dominated period. NB A diagram of an exemplary period;

[0053] Figure 5 This is a flowchart illustrating exemplary operations for specifying REM / wake states based on CPC data and motion recorder data, according to various aspects of this disclosure;

[0054] Figure 6 It is a diagram of an exemplary period of motion recorder signal indicating movement, which is dominated by vLFC according to various aspects of this disclosure;

[0055] Figure 7 This is a flowchart illustrating exemplary operations for specifying REM / wake-up states based on CPC data and pseudo-motion recorder data, according to various aspects of this disclosure;

[0056] Figure 8 These are exemplary signal quality measurements and motion recorder signal graphs based on various aspects of this disclosure;

[0057] Figure 9 These are block diagrams of exemplary computing systems according to various aspects of this disclosure; and

[0058] Figure 10 This is a flowchart illustrating exemplary operations for specifying REM / wake states based on CPC data and various physiological signals, according to various aspects of this disclosure. Detailed Implementation

[0059] This disclosure relates to the analysis of cardiopulmonary coupling (CPC) data or CPC data combined with physiological data during a person's sleep period to identify REM and arousal states during sleep. In REM, the subject remains almost motionless or in a state of "skeletal muscle paralysis," in which the primary mechanical movement is in the eyes. Because REM is characterized by vLFC and there should be no significant movement, this disclosure provides a method for identifying REM states based on vLFC in the absence of sufficient motion recorders, and for identifying arousal states based on vLFC in the presence of sufficient motion recorders. Other aspects of this disclosure do not use motion recorder data to identify REM or arousal states. For example, pseudo-motion recorder data, as described in more detail later herein, may be used. Other aspects of the invention use only cardiopulmonary coupling data without using any non-CPC physiological data to identify REM or arousal states, which will be described later herein.

[0060] Now for reference Figure 1 The illustration shows an exemplary measurement system 100 according to various aspects of this disclosure. The measurement system 100 can be attached to a person during sleep to obtain physiological measurements that can be used to calculate cardiopulmonary coupling (“CPC”), such as electrocardiogram measurements or other physiological measurements. The measurement system 100 also obtains various measurements, such as ECG measurements, plethysmography measurements, oxygen saturation measurements, and / or motion recorder measurements, which will be described later herein. Figure 1 This is exemplary, and various sensors can be located in different parts of the human body, including Figure 1The portions are not shown. For example, various sensors may be located on the human torso, head, and / or limbs, as well as other locations. Those skilled in the art will understand the various sensors used to detect physiological signals. For example, in various embodiments, the sensor may be a sensor that touches the human body, or it may be a non-touch sensor that does not directly touch the human body (e.g., a pericardial imaging-based sensor). Physiological measurements may be recorded in a storage medium, such as a disk drive, flash drive, solid-state drive, or other storage medium. In various embodiments, various physiological measurements may be recorded in parallel. In various embodiments, the data of each record may be tagged or associated with a timestamp. By tagging or associating the recorded data with a timestamp, the measurements of various records can be correlated in time. Consideration is given to the possibility of using other methods to correlate the recorded measurements in time.

[0061] One aspect of this disclosure relates to systems and methods for specifying REM or wake-up states based on cardiopulmonary coupling spectral analysis without using non-CPC data. As described above, very low frequency coupling (vLFC) represents a REM sleep or wake-up state. Figure 2 An exemplary procedure is illustrated for analyzing CPC data from a person's sleep periods to distinguish between REM sleep and wakefulness without using non-CPC physiological data. In the description herein, sleep periods may be divided into segments referred to herein as "periods". In various embodiments, different periods may have the same duration, or different periods may have different durations.

[0062] refer to Figure 2 In box 210, this operation involves accessing cardiopulmonary coupling data throughout a person's sleep period. CPC data can, for example, be obtained from... Figure 1 The measurement system can be accessed or accessed from another system. In box 220, the operation involves identifying a period in sleep that includes very low frequency coupling (vLFC) data within the cardiopulmonary coupling data. As described in more detail below, a period including vLFC may be vLFC-dominant (i.e., the highest frequency coupling power is in the vLFC band) or may not be vLFC-dominant. In box 230, the operation involves accessing high-frequency coupling data and / or low-frequency coupling data within the cardiopulmonary coupling data corresponding to that period. In box 240, the operation involves designating that period as a REM sleep period or a wakefulness period based on the high-frequency coupling data and / or low-frequency coupling data corresponding to that period without using non-cardiopulmonary coupling physiological data. Figure 2 Operations can be performed by (such as) Figure 9 The computing system Figure 9 The computing system (which will be described later in this paper) is implemented using the computing system. The following describes... Figure 2 An example of the operation.

[0063] The following description pertains to periods containing vLFC coupling, but in which the dominant CPC state of that period is classified as low-frequency coupling (LFC) (i.e., the highest frequency coupling power is in the LFC band). During such periods, the power of vLFC is non-zero and less than the power of LFC.

[0064] According to various aspects of this disclosure, periods of non-zero vLFC power and LFC dominance can be characterized as fragmented REM, the opposite of unstable NREM. During periods of fragmented REM, and in the absence of upper vLFC frequency band dominance, such periods can be designated as REM sleep states based on dynamic thresholds applied in the vLFC bands, where the dynamic thresholds may vary for different individuals. Figure 3 An example of such a period is shown, in which period 310 is shown with non-zero vLFC power and LFC dominance.

[0065] In periods with non-zero vLFC power and LFC dominance, REM classification can be decoupled from fixed thresholds, given that the designation is based on non-zero vLFC power rather than vLFC dominance. Instead, dynamic thresholds allow for more accurate designation of REM sleep and wakefulness states. For example, a specific fixed threshold might be suitable for individuals with healthy sleep, but for those with unhealthy sleep where their condition worsens and affects the vLFC band, that specific threshold might not accurately designate REM sleep and wakefulness states. Therefore, among other things, dynamic thresholds applicable to different conditions, individuals, or groups can be used to designate REM sleep and wakefulness during periods with non-zero vLFC power and LFC dominance (e.g., Figure 3 In various embodiments, the dynamic threshold can be based on the average of a particular population. For example, if a period is LFC-dominated and the vLFC power is higher than the average vLFC power of a particular population, then that period can be classified as REM. Other types of dynamic thresholds are anticipated within the scope of this disclosure.

[0066] The following description pertains to the period in which the dominant CPC state has been classified as vLFC. According to various aspects of this disclosure, designating a vLFC period as a REM state or a wake-up state is based on analysis of the CPC band after the dominant CPC state has been classified as vLFC.

[0067] In various embodiments, a period with vLFC dominance can be designated as a REM state when there is activation (e.g., non-zero power) in the LFC and / or HFC bands and when the dominant CPC frequency is in the higher range of the vLFC band (e.g., close to but not exceeding 0.01 Hz). Figure 4 Examples of such periods 410 and 420 are shown.

[0068] In various embodiments, when there is also an elevated low-frequency coupling narrowband (eLFC) NB When vLFC dominates, the period can be designated as the REM state. Low-frequency coupling can be further classified as low-frequency coupled broadband (eLFC). BB or fragmentation, elevated low-frequency coupling narrowband (eLFC) NB (eLFC) or periodic, or un-elevated low-frequency coupling. NB It is a marker of periodicity and is associated with periodic breathing, Cheyne-Stokes breathing, and central apnea. eLFC BB It can be caused by other conditions, such as pain or other disturbances during fragmented sleep, while eLFC NB It can be caused by periodic limb movements.

[0069] Figure 4 It shows that eLFC also exists. NB Examples of periods 410 and 420 with vLFC dominance. During testing, such periods were designated as REM states based on polysomnography data and were also precisely designated based solely on CPC data. As used herein, the term elevated extremely low frequency coupled narrowband (eVLFC) is used. NB ), or “periodic REM” sleep, will be used to identify the elevated low-frequency coupled narrowband (eLFC) during the period when it dominates in the vLFC band. NB The emergence of eVLFC is understandable. NB Or periodic REM sleep, can be used as a new CPC state. Therefore, the method for specifying REM sleep and wake states is configured to use eVLFC. NB Specify REM sleep state. If eVLFC does not exist. NB As mentioned above, the dominant power in the higher range of the vLFC band (e.g., CPC frequency power exceeding 0.05 but less than the total power in the vLFC band) can be used to designate that period as a REM sleep state.

[0070] therefore, Figures 2 to 4 The above description illustrates an embodiment in which a period can be designated as a REM sleep state by analyzing only CPC data without using non-CPC data. The embodiments described above and Figures 2 to 4 The embodiments described are exemplary and do not limit the scope of this disclosure.

[0071] Another aspect of this disclosure relates to systems and methods for specifying REM or awake state based on the analysis of cardiopulmonary data and various physiological signals, including kinesiology, oxygen saturation, and / or pseudokinesiology signals such as ECG and plethysmography. (This will be combined with...) Figure 5 and Figure 6 Describes the use of CPC data in conjunction with motion recorder data. This will be combined... Figure 7 and Figure 10 Describe the use of CPC data in conjunction with spurious movement physiological data and / or oxygen saturation data.

[0072] Based on all aspects of this disclosure, Figure 5 A flowchart illustrating an exemplary operation for specifying a REM or wake state based on the analysis of cardiopulmonary data together with motion recorder data is shown. This operation applies a threshold to the measurement of motion to specify the period as a REM or wake state. In various embodiments, to collect information about movement, 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; in this disclosure, the object is a human. According to various aspects of this disclosure, raw motion recorder signals can be processed to generate a report of an acceleration quantity with a specific unit of measurement. A commonly used unit of measurement is m / s². 2 Or G-force. For example, it can be used. Figure 1 The system is used to acquire and store motion recorder data.

[0073] Continue to refer to Figure 5 In box 510, the operation involves accessing cardiopulmonary coupling data throughout a person's sleep period. In box 520, the operation involves classifying periods within the sleep period into very low frequency coupling (vLFC) periods based on the cardiopulmonary coupling data. The classification can be based on vLFC dominance within the period. In box 530, the operation involves accessing the person's motion recorder data corresponding to the vLFC periods. The motion recorder data can be accessed from a storage or computing system, which will combine... Figure 9 The description is as follows. For example, motion recorder data corresponding to a vLFC period can be identified based on a timestamp. In box 540, this operation includes designating the vLFC period as a REM period based on a predetermined percentage of motion recorder measurements in the motion recorder data corresponding to the vLFC period that indicate movement below a movement threshold. An example is provided below.

[0074] In various embodiments, to specify REM and wake states, a threshold of 0.01 G / s can be used, such that accelerations below 0.01 G / s are considered to indicate REM sleep, while accelerations above 0.01 G / s are considered to indicate a wake state. Specific values ​​for the threshold are exemplary, and other values ​​can be used. In various embodiments, the number of acceleration samples exceeding the threshold is compared to the total number of samples in that period to generate a metric for designating that period as either a REM sleep state or a wake state. In various embodiments, if 95% of the acceleration samples in a period are below the threshold, then that period can be designated as a REM state. Otherwise, the period will be designated as a wake state. The percentage threshold is exemplary and can be another value. In various embodiments, the length of the period or cycle to be analyzed can be modified to increase determinism and derive measurements of fragmentation or fragmentation gaps.

[0075] In various embodiments, the acceleration threshold may need to be modified based on accelerometer hardware and firmware specifications (e.g., dynamic range, sampling rate, etc.). For example, it may be necessary to analyze a new accelerometer sensor and compare it with a reference device to set a REM / wake-up specified acceleration threshold.

[0076] Figure 6 It shows that it is based on Figure 5 The operation specifies examples of CPC and physiological data for REM / wake states. For comparison with the PSG reference, the purple box shows the wakefulness of PSG scores and the extended periods of all REM cycles.

[0077] Based on all aspects of this disclosure, Figure 7 A flowchart illustrating an exemplary operation for specifying a REM or awake state based on the analysis of cardiopulmonary data together with pseudokinetic recorder data is provided. As used herein, the term "pseudokinetic recorder signal" or data refers to a nonkinetic recording physiological signal having certain characteristics indicative of motion recording. Pseudokinetic recorder signals may include, for example, ECG signals, plethysmography signals, and oxygen saturation signals. In various embodiments, the pseudokinetic recorder signal may be a physiological signal whose signal quality increases when the person is in a REM state and decreases when the person is in an awake state. Signal quality can be decreased, for example, by variations affecting signal strength, signal validity, or signal presence. Based on these aspects, intermittent degradation of signal quality is associated with motion artifacts and can be used as a pseudokinetic recorder. In various embodiments, intermittent degradation of signal quality during vLFC dominance can be specified as an awake state, while near-original or original signal quality during vLFC dominance can be specified as a REM state. Therefore, the disclosed systems and methods can analyze pseudokinetic recorder signals to specify periods as REM or awake states.

[0078] Continue to refer to Figure 7 In box 710, the operation involves accessing cardiopulmonary coupling data throughout a person's sleep period. In box 720, the operation involves classifying periods within the sleep period into very low frequency coupled (vLFC) periods based on the cardiopulmonary coupling data. For example, vLFC periods may exhibit vLFC dominance. In box 730, the operation involves accessing pseudokinetic data corresponding to the vLFC periods. The pseudokinetic data is based on human physiological measurements, not on kinematic measurements. As mentioned above, pseudokinetic data may include ECG signals, plethysmography signals, and oxygen saturation signals, etc. In box 740, the operation involves designating the vLFC periods as REM periods or wakefulness periods based on the pseudokinetic data corresponding to the vLFC periods. Figure 7 Operations can be performed in (such as) Figure 9 The computing system Figure 9 The computing system (which will be described later in this paper) is implemented in the computing system. The following describes... Figure 7 Various embodiments of the operation.

[0079] According to various aspects of this disclosure, with respect to ECG and plethysmography signals, signal quality can be quantified by evaluating the performance of feature extraction from ECG and plethysmography signals. For ECG, these features include, but are not limited to, R peaks, P waves, ST segments, and / or QRS complexes. For plethysmography, these features include, but are not limited to, systolic peaks, diastolic peaks, and / or dictrotic notches. Signal quality results are degraded when signal deterioration and / or the absence of detectable features.

[0080] In various embodiments, certain features can be rejected. During periods of weak signals, the detector may fail to detect features of the signal, resulting in "missing features." In various embodiments, the features of each marker can be compared with a preset template, and the correlation with the template can be calculated. During motion artifacts, the signal becomes distorted, and the feature of the marker has a low correlation with the template; this feature may be rejected as a "rejected feature."

[0081] In various embodiments, signal quality can be quantified by comparing the number of detected features over a given time period with the expected number of detected features. The length of the time period can be varied according to the desired granularity. In various embodiments, signal quality can be expressed as the percentage of the labeled features that are expected to be present over the selected time period.

[0082] In various embodiments, when the detected feature is a signal peak, the signal quality can be quantified as data corresponding to a smaller motion when the count of the detected peaks is below a predetermined threshold and when the shape of the detected peaks matches the expected peak shape, and the signal quality can be quantified as data indicating a larger motion when the count of the detected peaks is above a predetermined threshold and when the shape of the detected peaks is different from the expected peak shape.

[0083] According to various aspects of this disclosure, regarding the oxygen saturation signal, the signal quality of the oxygen saturation signal is based on the assessed value and the rate of change. The oxygen saturation report indicates that blood oxygen saturation is within the range of [0%, 100%]. In the event of a complete disconnection of the sensor 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 disconnection from the object, such as due to movement, may result in poor sensor contact but not a complete disconnection. In this case, the oxygen saturation value will drop rapidly at a rate that is unbelievable in actual human physiology.

[0084] According to various aspects of the invention, the rate of decrease in oxygen saturation is evaluated and compared with a threshold (e.g., a threshold of 3% change per second (i.e., 0.03 / s) or another value). In various embodiments, during periods when the threshold is violated, the signal quality value can be set to zero (0), otherwise, the signal quality value can be set to one (1). Other ways of scoring signal quality within the scope of this disclosure are conceivable. The 3% threshold is exemplary, and other values ​​can be used. In various embodiments, the threshold can be modified to change the sensitivity, and care can be taken not to set the threshold such that false negative desaturation overwhelms true desaturation.

[0085] In various embodiments, signal quality can be quantified by comparing the number of detected features within a given time period to the expected number of detected features. The length of the time period can be varied according to the desired granularity. In various embodiments, signal quality can be expressed as the percentage of the labeled features that are expected to be present during the selected time period.

[0086] Therefore, various examples of pseudo-motion recorder signals, including ECG, polysomnography, and oxygen saturation, are described. These examples are provided for illustrative purposes and do not limit the scope of this disclosure. Other physiological signals can be used as pseudo-motion recorder signals, and they are contemplated within the scope of this disclosure. The embodiments described herein for determining the signal quality of physiological signals are exemplary, and other ways of determining signal quality as a pseudo-motion recorder signal are contemplated within the scope of this disclosure.

[0087] Figure 8This is a graph showing exemplary signal quality of a human motion recorder signal and a pseudo-motion recorder signal, where the signal quality score for each time period is on the left y-axis, the motion recorder score is on the right y-axis in G / S units, and the sample number is on the x-axis. Figure 8 As shown, the signal quality of motion recorder signals and pseudo-motion recorder signals are inversely correlated. The graph illustrates how the number of high signal quality features decreases with increasing motion recorder count.

[0088] Based on all aspects of this disclosure, and continuing to refer to Figure 7 When oxygen saturation data is available, the disclosed systems and methods can analyze the data to designate periods as REM or wakefulness states. According to various aspects of the invention, measurements of blood oxygen saturation (SO2, SaO2, SpO2, etc.) can be analyzed to detect the periodicity of oxygen desaturation events frequently associated with sleep apnea. The occurrence of such events during periods dominated by vLFC indicates REM sleep. Various techniques and methods can be used to identify oxygen desaturation events and / or sleep apnea events, such as those described in International Application Publication No. WO 2020061014A1, the entire contents of which are incorporated herein by reference. Other techniques and methods for identifying oxygen desaturation events and / or sleep apnea events using oxygen saturation data are contemplated within the scope of this disclosure.

[0089] Despite the above technologies (e.g., Figure 2 , Figure 5 , Figure 7 Each of these techniques can be used independently, but they can also be combined to increase determinism, accuracy, and / or flexibility based on available signals to help identify REM / wake states. These techniques can also aid in the diagnosis of sleep disorders specific to the REM sleep state. For example, “REM apnea” is considered a subcategory of sleep-disordered breathing, defined as apnea / hypopnea events occurring during REM sleep. For this purpose, having an oxygen saturation signal can improve the accuracy of disease classification. Furthermore, the complete absence of any time periods classified as REM, indicated by the use of a motion recorder or by analyzing CPC spectral data during periods indicative of REM, can indicate the presence of REM behavioral disorder (RBD), where periods of REM are associated with mechanical movements (including sleepwalking).

[0090] Figure 10 This is a flowchart illustrating an exemplary operation for determining which signals to use for REM / wake-up classification. Figure 10 The operation can be performed on computing systems (such as those described later). Figure 9This is implemented on a computing system. In box 1010, the operation involves reading a data file to access available signals. In box 1020, the operation involves determining whether ECG and / or plethysmography signals are available. If not, the operation can end at box 1022. If such signals are present, then in box 1030, the operation involves performing CPC processing to generate an array of CPC periods, and each period is classified into one of three CPC states based on the dominant frequency band: HFC, LFC, or vLFC. This process can be referred to as "basic labeling". Additionally, each period can be classified as having no eLFC, eLFC, or vLFC. BB or eLFC NB This process can be called "expanded labeling". In box 1040, for each period classified as vLFC, if the expanded labeling has already been classified as eLFC... NB If so, the period can be designated as a REM state, as described above. If no REM / wake-up state is specified in box 1030, the operation continues to box 1040.

[0091] In box 1040, this operation determines whether motion recorder data exists. If so, the operation involves specifying a period as a REM state if the motion artifacts are below a predetermined threshold for a sufficient number of samples, as described above. Figure 5 Otherwise, the period is designated as the wake-up state. If the motion recorder data is unavailable, the operation continues to box 1050.

[0092] In box 1050, this operation involves using pseudo-motion recorder signals to specify the REM / wake-up state, such as in conjunction with... Figure 7 For example, if the sum of the detected excess and missing NN intervals is below a predetermined threshold, and the oxygen saturation signal quality (if present) is free of artifacts, then the period can be designated as a REM state.

[0093] In box 1060, this operation involves determining whether an oxygen saturation signal is present. If so, the operation determines whether a desaturation event is present and whether there are any artifacts. If there is a desaturation event and no artifacts, the operation can specify the period as REM state.

[0094] Therefore, an array 1070 for sleep stage classification is generated based on the above operations. The operations described above are exemplary, and variations are contemplated within the scope of this disclosure. For example, in various embodiments, the presence of a desaturation event determined by block 1060 may override the decision of blocks 1040 and / or 1050, or may cause blocks 1040 and / or 1050 to designate the period as an "unknown" state.

[0095] Figure 10The operation is exemplary, and other methods of using a combination of physiological data and CPC data to determine REM / wake state are contemplated within the scope of this disclosure.

[0096] The aspects and embodiments of this disclosure can be implemented in one or more computing systems capable of performing the functions described herein. References Figure 9 An example of a computing system 900 for implementing this disclosure is shown. Various embodiments of this disclosure described herein can be implemented by the computing system 900. However, it will be apparent to those skilled in the art how to implement this disclosure using other computer systems and / or computer architectures.

[0097] The computing system 900 includes one or more processors, such as processor 904. Processor 904 is connected to communication infrastructure 906 (e.g., a communication bus, crossover bar, or network).

[0098] 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 a variety of measurements and metrics described herein, including CPC data, motion recorder data, oxygen saturation data, ECG data, and / or plethysmography data, etc. In various embodiments, the display 930 may present graphical and numerical representations. Presentations and reports may include some or all of the various metrics disclosed above.

[0099] The computing system 900 also includes a main memory 908, preferably random access memory (RAM), and may also include auxiliary memory 910. Auxiliary 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 the removable storage unit 918 in a known manner. The removable storage unit 918 represents a floppy disk, magnetic tape, optical disk, etc., read from and written to by the removable storage drive 914. As will be understood, removable storage 918 includes computer-usable storage media in which computer software (e.g., programs or other instructions) and / or data are stored.

[0100] In various embodiments, auxiliary memory 910 may include other similar devices for allowing computer software and / or data to be loaded into computing system 900. Such devices may include, for example, removable memory 922 and interface 920. Such examples may include a program box and box interface (as found in conventional devices), a removable memory chip (e.g., EPROM or PROM) and associated socket, as well as other removable storage devices 922 and interfaces 920 that allow software and data to be transferred from removable storage device 922 to computing system 900.

[0101] The computing system 900 may also include a communication interface 924. The communication interface 924 allows the transfer of software and data between the computing system 900 and external devices. Examples of the communication interface 924 may include a modem, a network interface (such as an Ethernet or WiFi card), a communication port, a PCMCIA or SD or other slot and card, and other components. The software and data transferred via the communication interface 924 are in the form of signals 928, which may be electronic, electromagnetic, optical, 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 may be implemented using wires or cables, optical fibers, telephone lines, cellular telephone links, RF links, free-space optics, and / or other communication channels.

[0102] As used herein, the terms "computer program media" and "computer-usable media" are generally used to refer to media such as removable storage 918, removable storage 922, hard disks installed in hard disk drives 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.

[0103] Computer programs (also referred to as computer control logic or computer-readable program code) are stored in main memory 908 and / or auxiliary memory 910. Computer programs can also be received via communication interface 924. When these computer programs are executed, they enable computing system 900 to implement the present disclosure discussed herein. Specifically, when the computer programs are executed, they enable processor 904 to implement the processing and operations of the present disclosure, for example, as described above. Figure 2 , Figure 5 and / or Figure 7 Various operations. Therefore, such a computer program represents the controller of the computing system 900.

[0104] In embodiments where this disclosure is implemented using software, the software may be stored in a computer program product and loaded into a computing system 900 using a removable storage drive 914, a hard disk drive 912, an interface 920, or a communication interface 924. When executed by a processor 904, control logic (software) causes the processor 904 to perform the functions of this disclosure as described herein. Therefore, the technology of this disclosure can be provided as software for a medical device (SaMD) or as non-medical software. In various embodiments, the software may include a cloud-based application.

[0105] The embodiments disclosed herein are examples of this disclosure and can be implemented in various forms. For example, although some embodiments herein are described as separate embodiments, each embodiment herein may be combined with one or more other embodiments herein. The specific structural and functional details disclosed herein should not be construed as limiting, but rather as a representative basis for teaching those skilled in the art to use this disclosure differently in virtually any suitable detailed structure.

[0106] 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 according to this disclosure. A phrase of the form “A or B” means “(A), (B), or (A and B).” A phrase of the form “at least one of A, B, and C” means “(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).”

[0107] Any of the methods, programs, algorithms, or code described herein can be translated into or represented in a programming language or computer program. The terms "programming language" and "computer program" as used herein include any language used to specify computer instructions, and include (but are not limited to) the following languages ​​and their derivatives: assembly language, basic languages, batch files, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command languages, Pascal, Perl, PL1, Python, scripting languages, Visual Basic, meta-languages ​​that specify the program itself, and all first, second, third, fourth, fifth, or next-generation computer languages. Databases and other data schemas, and any other meta-languages, are also included. There is no distinction between languages ​​that are interpreted, compiled, or use both compilation and interpretation methods simultaneously. There is no distinction between a compiled version of a program and a source version. Therefore, a program, in which a programming language can exist in more than one state (such as source, compiled, object, or linked), is a reference to any and all of these states.

[0108] The system described herein can also utilize one or more controllers to receive various information and transform the received information to generate output. The controller can include any type of computing device, computing circuit, or any type of processor or processing circuit capable of executing a series of instructions stored in memory. The controller can include multiple processors and / or a multi-core central processing unit (CPU), and can include any type of processor, such as a microprocessor, digital signal processor, microcontroller, programmable logic device (PLD), field-programmable gate array (FPGA), etc. The controller can 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.

[0109] It should be understood that the foregoing description is merely illustrative of this disclosure. Various alternatives and modifications can be devised by those skilled in the art without departing from this disclosure. Therefore, this disclosure is intended to encompass all such alternatives, modifications, and differences. The embodiments described with reference to the accompanying drawings are presented merely to illustrate certain examples of this disclosure. Other elements, steps, methods, and techniques that are not substantially different from those described above are also intended to be within the scope of this disclosure.

Claims

1. A computer-implemented method, comprising: Access cardiopulmonary coupling data throughout a person's sleep period; Based on the cardiopulmonary coupling data, the periods in the sleep time are classified as very low frequency coupling (vLFC) periods; Access pseudokinetic data from the person corresponding to the vLFC period, wherein the pseudokinetic data is based on physiological measurements of the person rather than on kinematic measurements, wherein the physiological measurements on which the pseudokinetic data is based are separate from and different from the cardiopulmonary coupling data, and wherein the pseudokinetic data indicates movement; and Based on the pseudo-motion recorder data corresponding to the vLFC period, the vLFC period is designated as a REM period or a wake-up period.

2. The computer-implemented method according to claim 1 further includes generating pseudo-body motion recorder data corresponding to the vLFC period based on the signal quality of the physiological measurement.

3. The computer-implemented method according to claim 2, wherein, Generating the pseudo-motion recorder data includes quantifying the signal quality of the physiological measurements, wherein the signal quality is inversely correlated with the motion recorder data.

4. The computer-implemented method according to claim 3, wherein, The physiological measurements include at least one of the person's ECG measurements and plethysmography measurements.

5. The computer-implemented method according to claim 4, wherein, Quantifying the signal quality of the physiological measurements includes: The physiological measurements are processed to detect peaks during the vLFC period; When the count of detected peaks is below a predetermined threshold and when the shape of the detected peaks matches the expected peak shape, data indicating higher signal quality is generated; and When the count of the detected peaks is greater than the predetermined threshold and when the shape of the detected peaks is different from the expected peak shape, data indicating lower signal quality is generated.

6. The computer-implemented method according to claim 3, wherein, The physiological measurements include oxygen saturation measurements.

7. A system comprising: One or more processors; and At least one memory stores instructions that, when executed by the one or more processors, cause the system to: Access cardiopulmonary coupling data throughout a person's sleep period; Based on the cardiopulmonary coupling data, the periods in the sleep time are classified as very low frequency coupling (vLFC) periods; Access pseudokinetic data from the person corresponding to the vLFC period, wherein the pseudokinetic data is based on physiological measurements of the person rather than on kinematic measurements, wherein the physiological measurements on which the pseudokinetic data is based are separate from and different from the cardiopulmonary coupling data, and wherein the pseudokinetic data indicates movement; and Based on the pseudo-motion recorder data corresponding to the vLFC period, the vLFC period is designated as a REM period or a wake-up period.

8. The system according to claim 7, wherein, When executed by the one or more processors, the instructions also cause the system to generate pseudokinetic data corresponding to the vLFC period based on the signal quality of the physiological measurement.

9. The system according to claim 8, wherein, In generating the pseudo-motion recorder data, the instructions, when executed by the one or more processors, cause the system to quantify the signal quality of the physiological measurement, wherein the signal quality is inversely correlated with the motion recorder.

10. The system according to claim 9, wherein, The physiological measurements include at least one of the person's ECG measurements and plethysmography measurements.

11. The system according to claim 10, wherein, In generating the pseudo-motion recorder data, the instructions, when executed by the one or more processors, cause the system to: The physiological measurements are processed to detect peaks during the vLFC period; When the count of detected peaks is below a predetermined threshold and the shape of the detected peaks matches the expected peak shape, data indicating higher signal quality is generated; and When the count of the detected peaks is greater than the predetermined threshold and when the shape of the detected peaks is different from the expected peak shape, data indicating lower signal quality is generated.

12. The system according to claim 9, wherein, The physiological measurements include oxygen saturation measurements.

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