Sleep state analysis system

Abstract

An analysis system is provided that can grasp the disease attributes of a subject based on the subject's past and current clinical information and various information obtained during testing, and improve the accuracy of sleep stage analysis results. [Solution] A sleep state analysis system that calculates heart rate variability from at least the measurement values ​​of the heart rate sensor 21 of the subject and analyzes the sleep stage of the subject from the heart rate variability, and is equipped with a disease attribute estimation unit 40 that estimates the disease attribute of the subject from all or part of the measurement values ​​of the heart rate sensor 21, the measurement values ​​of other sensors, and the subject's clinical information, and an algorithm change unit 50 that changes the algorithm for analyzing the sleep stage from the disease attribute of the subject estimated by the disease attribute estimation unit 40.

Description

[Technical Field]

[0001] The present invention relates to a sleep state analysis system that analyzes a subject's sleep stages based on the subject's heart rate information and the like. [Background technology]

[0002] As a sleep state analysis system of this type, as disclosed in Patent Document 1 below, a sleep determination device is known that uses heart rate variability parameters based on heart rate data to determine which of multiple sleep stages a user belongs to. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Republished Publication No. 2018-221750 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when analyzing a subject's sleep stages, it is known that the results of sleep stage analysis vary depending on the subject's health condition, etc., and there is a need to establish an analysis system that can accurately reflect the subject's past and present clinical information and various information from the test in the sleep stage analysis results.

[0005] In view of the above circumstances, the present invention aims to provide an analysis system that can grasp the disease attributes of a subject based on the subject's past and present clinical information and various information obtained during examinations, and can improve the accuracy of sleep stage analysis results. [Means for solving the problem]

[0006] A sleep state analysis system that calculates heart rate variability from at least a measurement value of a heart rate sensor of a subject and analyzes a sleep stage of the subject from the heart rate variability, a disease attribute estimation unit that estimates a disease attribute of the subject from all or part of the measurement value of the heart rate sensor, the measurement values ​​of other sensors, and clinical information of the subject; an algorithm change unit that changes an algorithm for analyzing a sleep stage based on the disease attribute of the subject estimated by the disease attribute estimation unit; The present invention is characterized by comprising:

[0007] According to the sleep state analysis system of the first invention, the disease attributes of a subject can be estimated from all or part of the measurement values ​​of the heart rate sensor, the measurement values ​​of other sensors, and the subject's clinical information, and the optimal analysis algorithm can be selected from various algorithms according to the disease attributes, thereby enabling more accurate sleep state analysis.

[0008] That is, according to the sleep state analyzing system of the first invention, the disease attributes of the subject can be grasped from the subject's past and present clinical information and various information at the time of examination, and the accuracy of the sleep stage analysis results can be improved.

[0009] The sleep state analysis system of the second invention is the first invention, The disease attribute estimation unit estimates sleep apnea syndrome from either or both of the airflow measurement value of the airflow sensor as the other sensor and the blood oxygen saturation level of the SPO2 sensor, and if the disease attribute estimation unit estimates sleep apnea syndrome, the algorithm change unit changes the algorithm for analyzing sleep stages to an algorithm for sleep apnea syndrome. It is characterized by:

[0010] According to the sleep state analysis system of the second invention, sleep apnea syndrome can be estimated from either or both of the airflow measurement values ​​of the airflow sensor and the blood oxygen saturation level of the SPO2 sensor for the subject, and based on that information, the algorithm for analyzing sleep stages can be changed to an algorithm compatible with sleep apnea syndrome, thereby enabling optimal sleep state analysis for the subject.

[0011] In this way, the sleep state analyzing system of the second invention can perform optimal sleep state analysis for the subject based on the test information of the subject.

[0012] The sleep state analysis system of the third invention is the first invention, The disease attribute estimation unit determines whether the disease attribute of the subject corresponds to dementia by referring to clinical information of the subject, and if so, the algorithm change unit changes the algorithm for analyzing sleep stages to a dementia-compatible algorithm. It is characterized by:

[0013] According to the sleep state analysis system of the third invention, the subject's clinical information can be referenced to determine whether the subject's disease attribute corresponds to dementia, and based on that information, the algorithm for analyzing sleep stages can be changed to an algorithm that is compatible with dementia, thereby enabling sleep state analysis that is optimal for the subject.

[0014] In this way, the sleep state analyzing system of the third invention can perform optimal sleep state analysis for a subject based on the subject's clinical information.

[0015] A sleep state analysis system according to a fourth aspect of the present invention is the sleep state analysis system according to the first aspect of the present invention, The disease attribute estimation unit determines whether the disease attribute of the subject corresponds to Parkinson's disease by referring to the clinical information of the subject, and if so, the algorithm change unit changes the algorithm for analyzing sleep stages to an algorithm for Parkinson's disease. It is characterized by:

[0016] According to the sleep state analysis system of the fourth invention, the subject's clinical information can be referenced to determine whether the subject's disease attribute corresponds to Parkinson's disease, and based on that information, the algorithm for analyzing sleep stages can be changed to an algorithm compatible with Parkinson's disease, thereby enabling sleep state analysis that is optimal for the subject.

[0017] In this way, the sleep state analyzing system of the fourth aspect of the invention can perform optimal sleep state analysis for a subject based on the subject's clinical information. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a block diagram showing the overall configuration of a sleep state analysis system according to an embodiment of the present invention; [Figure 2] FIG. 2 is an explanatory diagram showing an application example of the sleep state analysis system of FIG. 1. [Figure 3] FIG. 2 is an explanatory diagram showing another application example of the sleep state analysis system of FIG. 1. [Figure 4] FIG. 2 is an explanatory diagram showing yet another application example of the sleep state analysis system of FIG. [Figure 5] FIG. 2 is an explanatory diagram showing an example of sleep state analysis in the sleep state analysis system according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] The overall configuration of a sleep state analysis system according to one embodiment of the present invention will be described below with reference to FIG.

[0020] As shown in FIG. 1, the sleep state analysis system 10 includes a sensor unit 20 including a heart rate sensor 21, a clinical information storage unit 30, a disease attribute estimation unit 40, an algorithm modification unit 50, and a sleep stage analysis unit 60.

[0021] Although the sensor unit 20 is shown as one sensor group for convenience, in reality, each sensor is attached to a different part of the subject and outputs detection signal data. Therefore, the sleep state analysis system 10 may be provided with a data storage unit, etc., as needed.

[0022] The heartbeat sensor 21 detects the electrocardiogram signal or pulse wave signal of the subject by contacting its electrode terminals with a plurality of points on the surface of the subject's body, and various known systems can be used.

[0023] The airflow sensor 22 is placed near the head of the subject to sense the subject's respiratory airflow, and various known systems can be used.

[0024] The SPO2 sensor 23 is attached to the subject's fingertip or the like and accurately detects the oxygen saturation in the subject's blood using a sensor that detects using red light and infrared light, and various known systems can be used.

[0025] The acceleration sensor 24 is attached to the limbs of the subject to detect the subject's physical activity, and various known systems can be used.

[0026] The past and present clinical information of the subject is stored in the clinical information storage unit 30 using an input unit (not shown) or the like.

[0027] The disease attribute estimation unit 40 estimates the disease attribute of the subject from all or part of the measurement values ​​of the heart rate sensor 21, the measurement values ​​of other sensors such as the airflow sensor 22, and the clinical information of the subject.

[0028] Here, among the disease attributes of the subject, sleep apnea syndrome (hereinafter also referred to as SAS) is estimated from either or both of the airflow measurement value of the airflow sensor 22 and the blood oxygen saturation level of the SPO2 sensor 23, and dementia and Parkinson's disease are determined by referring to the clinical information stored in the clinical information storage unit 30. The SAS determination criteria are set in accordance with the US SAS guidelines, etc. The disease attributes may also include the subject's age, hypertension, diabetes, estimated disease severity, and the severity of the corresponding disease.

[0029] The algorithm change unit 50 is provided with various algorithms that are optimal for the disease attributes of each subject, and an appropriate algorithm is selected based on the judgment of the disease attribute estimation unit 40.

[0030] Here, algorithm 1 is applied to healthy individuals, and algorithms 2 to 4 are applied to SAS, dementia, and Parkinson's disease patients, respectively. Various methods can be adopted to configure each algorithm stored in the algorithm modification unit 50.

[0031] The sleep stage analysis unit 60 employs the appropriate algorithm selected by the algorithm change unit 50 to calculate the heart rate variability of the subject from the measurements of the heart rate sensor 21, and analyzes the sleep stage of the subject from the heart rate variability. Information about the subject's physical activity measured by the acceleration sensor 24 can also be reflected as additional information in the analysis of the sleep stage.

[0032] The sleep analysis in the sleep stage analysis unit 60 can distinguish between, for example, four stages including wakefulness, and the analysis is performed using a machine learning system that has learned the results of a large amount of analysis data.

[0033] The results of the sleep stage analysis are then output by an output / display unit (not shown).

[0034] The above is the configuration of the sleep state analysis system. Note that in the above configuration, the sleep state analysis system 10 may be an integrated device including each component, or may be realized as a system in which, for example, some or all of the components other than the sensor unit 20 are built on the cloud.

[0035] Next, an application example of the sleep state analysis system of this embodiment will be described in detail with reference to FIGS.

[0036] FIG. 2 shows a configuration for estimating sleep apnea syndrome (SAS) from either or both of the airflow measurement value of the airflow sensor 22 and the blood oxygen saturation level of the SPO2 sensor 23.

[0037] Here, using either or both of the airflow measurement value and the blood oxygen saturation level, the disease attribute estimation unit 40 estimates whether or not the subject has SAS in accordance with the US SAS guidelines, and notifies the algorithm change unit 50 of the estimation result. If SAS is estimated, the algorithm change unit 50 adopts algorithm 2 as the applicable algorithm. If SAS is not estimated, algorithm 1 for healthy subjects is adopted.

[0038] In the former case, the sleep stage analysis unit 60 applies algorithm 2 based on the measurement values ​​of the heart rate sensor 21 to analyze the sleep state of the subject, and in the latter case, applies algorithm 1.

[0039] Figure 3 shows the configuration when dementia is suspected.

[0040] In Fig. 3, the disease attribute estimation unit 40 determines whether or not the subject has dementia based on the clinical information of the subject stored in the clinical information storage unit 30, and notifies the algorithm change unit 50 of the determination result. If the determination is positive, the algorithm change unit 50 adopts algorithm 3 as the applicable algorithm. If the determination is negative, algorithm 1 for healthy subjects is adopted.

[0041] In the former case, the sleep stage analysis unit 60 applies algorithm 3 based on the measurement values ​​of the heart rate sensor 21 to analyze the sleep state of the subject, and in the latter case, applies algorithm 1.

[0042] Figure 4 shows the configuration when Parkinson's disease is suspected.

[0043] 3, in FIG. 4, the disease attribute estimation unit 40 determines whether or not the subject has Parkinson's disease based on the subject's clinical information stored in the clinical information storage unit 30, and notifies the algorithm change unit 50 of the determination result. If the determination is positive, the algorithm change unit 50 adopts algorithm 4 as the applicable algorithm. If the determination is negative, algorithm 1 for healthy subjects is adopted.

[0044] In the former case, the sleep stage analysis unit 60 applies algorithm 4 based on the measurement values ​​of the heart rate sensor 21 to analyze the sleep state of the subject, and in the latter case, algorithm 1 is applied.

[0045] Here, an example of sleep state analysis in the sleep stage analysis section 60 will be described with reference to FIG.

[0046] Figure 5 shows the results of checking the suitability of applying algorithms 1 to 4 to healthy individuals and individuals with each disease. Figure 5 shows the relative accuracy of sleep stages determined by sleep analysis.

[0047] As is clear from Figure 5, (A) Algorithm 1 is designed for healthy individuals, so it provides good sleep analysis accuracy for healthy individuals (1), but when used on SAS patients, the accuracy decreases (2). On the other hand, when SAS-specific algorithm 2 is used on SAS patients, the analysis can be performed more accurately than for healthy individuals (3).

[0048] In addition, (B) Algorithm 1 is designed for healthy individuals, so it provides good sleep analysis accuracy for healthy individuals (1), but the accuracy decreases when used on dementia patients (4). On the other hand, when using algorithm 3 for dementia patients, it can provide more accurate analysis than for healthy individuals (5).

[0049] Similarly, (C) Algorithm 1 is designed for healthy individuals, so it provides good sleep analysis accuracy for healthy individuals (1), but the accuracy decreases when used on Parkinson's disease patients (6). On the other hand, when Parkinson's disease patients are analyzed using Algorithm 4 for Parkinson's disease, the analysis is more accurate than that for healthy individuals (7).

[0050] The above is the processing content of the sleep state analysis system of this embodiment. According to this sleep state analysis system, it is possible to realize an analysis system that can grasp the disease attributes of a subject based on the subject's past and present clinical information and various information at the time of examination, and can improve the accuracy of the sleep stage analysis results. [Explanation of symbols]

[0051] 1...Subject, 10...Sleep state analysis system, 21...Heart rate sensor, 22...Airflow sensor, 23...SPO2 sensor, 30...Clinical information storage unit, 40...Disease attribute estimation unit, 50...Algorithm modification unit, 60...Sleep stage analysis unit.

Claims

1. A sleep state analysis system that calculates heart rate variability from at least a measurement value of a heart rate sensor of a subject and analyzes a sleep stage of the subject from the heart rate variability, a disease attribute estimation unit that estimates a disease attribute of the subject from all or part of the measurement values ​​of the heartbeat sensor, the measurement values ​​of other sensors, and clinical information of the subject; an algorithm change unit that changes an algorithm for analyzing a sleep stage based on the disease attribute of the subject estimated by the disease attribute estimation unit; A sleep state analysis system comprising:

2. 2. The sleep state analysis system according to claim 1, A sleep state analysis system characterized in that the disease attribute estimation unit estimates sleep apnea syndrome from either or both of the airflow measurement value of the airflow sensor as the other sensor and the blood oxygen saturation level of the SPO2 sensor, and if an estimate is made, the algorithm change unit changes the algorithm for analyzing sleep stages to an algorithm compatible with sleep apnea syndrome.

3. 2. The sleep state analysis system according to claim 1, A sleep state analysis system characterized in that the disease attribute estimation unit determines whether the subject's disease attribute corresponds to dementia by referring to the subject's clinical information, and if so, the algorithm change unit changes the algorithm for analyzing sleep stages to an algorithm for dementia.

4. 2. The sleep state analysis system according to claim 1, A sleep state analysis system characterized in that the disease attribute estimation unit determines whether the subject's disease attribute corresponds to Parkinson's disease by referring to the subject's clinical information, and if so, the algorithm change unit changes the algorithm for analyzing sleep stages to an algorithm compatible with Parkinson's disease.

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