JP2014226451A - Sleeping state measuring apparatus and sleeping state measuring method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a sleeping state measuring apparatus capable of measuring sleep depth at a high degree of accuracy.SOLUTION: A sleeping state measuring apparatus 1 comprises: abnormality signal detection means 4 for detecting a body movement signal as an abnormality signal, from output signals of a biological information detection sensor 2; abnormality signal exclusion means 5 for excluding the detection period of the abnormality signal detected by the abnormality signal detection means 4, from the output signals of the biological information detection sensor 2; respiratory cycle calculation means 6 for calculating a respiratory cycle C on the basis of the output signals from which the detection period of the abnormality signal has been excluded by the abnormality signal exclusion means 5; and sleep depth evaluation means 9 for evaluating the sleep depth on the basis of the respiratory cycle C calculated by the respiratory cycle calculation means 6.

Description

  The present invention relates to a sleep state measurement device and a sleep state measurement method capable of evaluating sleep depth.

  In medical institutions (eg, hospitals), elderly facilities, general households, etc., in order to grasp the health status of subjects such as sick people, elderly people, healthy people, infants, etc., biological information of subjects is measured. .

  Various types of biological information detection sensors are used for the measurement. For example, panel-type or sheet-type sensors that are used by being placed on the lower side of the subject on the bed are known (for example, Patent Document 1). 10). In addition, a band type attached to the upper body of the subject is also known. This type of sensor is configured to detect and output a respiratory signal, a body motion signal, and the like as a biological information signal of a subject.

  Furthermore, measuring the sleep state of the subject is important for understanding the health state of the subject in more detail. As such an apparatus, for example, an apparatus disclosed in Japanese Patent No. 3733133 (Patent Document 11) is known. This device is configured to estimate a sleep state based on an output signal of a biological information detection sensor (biological information sensor).

JP 2009-112646 A JP 2008-229361 A JP 2008-229359 A JP 2008-178738 A JP 2008-178734 A JP 2008-099957 A JP 2008-099747 A JP 2008-069779 A JP 2006-043445 A JP 2006-043445 A Japanese Patent No. 3733133

  However, in this type of device, the output signal of the biological information detection sensor is composed of an electrical signal on which a body motion signal, a respiratory signal, etc. are superimposed. Therefore, when a subject's body motion (eg, turning over) occurs during sleep. The body motion signal included in the output signal of the biological information detection sensor becomes much larger than the respiration signal, and as a result, there arises a problem that the estimation accuracy (evaluation accuracy) of the sleep state is lowered.

  This invention is made | formed in view of the technical background mentioned above, The objective is to provide the sleep state measurement apparatus and sleep state measurement method which can evaluate a sleep depth with high precision.

  The present invention provides the following means.

[1] An abnormal signal detection means for detecting a body motion signal as an abnormal signal from the output signal of the biological information detection sensor;
Abnormal signal exclusion means for excluding the detection period of the abnormal signal detected by the abnormal signal detection means from the output signal of the biological information detection sensor;
A respiratory cycle calculating means for calculating a respiratory cycle based on an output signal after the abnormal signal detection period is excluded by the abnormal signal excluding means;
Sleep depth evaluation means for evaluating sleep depth based on the respiratory cycle calculated by the respiratory cycle calculation means;
A sleep state measuring device comprising:

  [2] The sleep state measurement device according to item 1, wherein the abnormal signal detection means detects the body motion signal and further an abnormal respiratory signal as the abnormal signal from an output signal of the biological information detection sensor.

  [3] The sleep state measurement device according to item 1 or 2, wherein the sleep depth evaluation unit evaluates the sleep depth using an elliptical area indicating variation in Lorentz plots for the respiratory cycle.

[4] The apparatus further comprises a respiratory amplitude value calculating means for calculating a respiratory amplitude value based on the output signal after the abnormal signal detection period is excluded by the abnormal signal removing means.
The sleep state measuring device according to any one of the preceding items 1 to 3, wherein the sleep depth evaluation unit further evaluates the sleep depth based on the respiration amplitude value calculated by the respiration amplitude value calculation unit.

  [5] The sleep state measurement device according to item 4, wherein the sleep depth evaluation unit evaluates the sleep depth using an elliptical area indicating a variation of a Lorentz plot for the respiratory amplitude value.

[6] It further comprises body motion information calculation means for calculating body motion information based on the abnormal signal excluded by the abnormal signal exclusion means,
6. The sleep state measuring device according to any one of the preceding items 1 to 5, wherein the sleep depth evaluation unit further evaluates the sleep depth based on the body movement information calculated by the body movement information calculation unit.

[7] An abnormal signal detection step of detecting a body motion signal as an abnormal signal from the output signal of the biological information detection sensor;
An abnormal signal exclusion step of excluding a detection period of the abnormal signal detected in the abnormal signal detection step from an output signal of the biological information detection sensor;
A respiratory cycle calculating step of calculating a respiratory cycle based on the output signal after the detection period of the abnormal signal is excluded in the abnormal signal exclusion step;
A sleep depth evaluation step for evaluating the sleep depth based on the respiratory cycle calculated in the respiratory cycle calculation step;
The sleep state measuring method characterized by comprising.

  [8] The sleep state measurement method according to item 7, wherein the abnormal signal detection step detects the body motion signal and further an abnormal respiratory signal as the abnormal signal from an output signal of the biological information detection sensor.

  [9] The sleep state measuring method according to item 7 or 8, wherein the sleep depth evaluation step evaluates the sleep depth by using an elliptical area indicating variation in the Lorentz plot for the respiratory cycle.

[10] The method further includes a respiratory amplitude value calculation step of calculating a respiratory amplitude value based on the output signal after the abnormal signal detection period is excluded in the abnormal signal exclusion step,
10. The sleep depth measurement method according to any one of the preceding items 7 to 9, wherein the sleep depth evaluation step further evaluates the sleep depth based on the respiration amplitude value calculated in the respiration amplitude value calculation step.

  [11] The sleep state measurement method according to item 10 above, wherein the sleep depth evaluation step evaluates the sleep depth using an elliptical area indicating a variation of the Lorentz plot for the respiratory amplitude value.

[12] It further includes a body motion information calculation step of calculating body motion information based on the abnormal signal detected in the abnormal signal detection step,
The sleep state measuring method according to any one of 7 to 11 above, wherein the sleep depth evaluation step further evaluates the sleep depth based on the body motion information calculated in the body motion information calculation step.

  The present invention has the following effects.

  According to the sleep state measuring apparatus of [1], the respiratory cycle calculating means calculates the respiratory cycle based on the output signal of the biological information detection sensor after the detection period of the body motion signal as the abnormal signal is excluded. Therefore, the respiratory cycle can be calculated accurately. And since a sleep depth evaluation means evaluates a sleep depth based on this respiration cycle, it can evaluate a sleep depth with high precision.

  According to the previous item [2], the abnormal signal detecting means detects not only a body motion signal but also an abnormal respiratory signal as an abnormal signal, and the respiratory cycle calculating means detects the living body after the detection period of the abnormal signal is excluded. Since the respiratory cycle is calculated based on the output signal of the information detection sensor, the respiratory cycle can be calculated more accurately. Therefore, the evaluation accuracy of the sleep depth can be improved.

  According to the preceding item [3], the sleep depth evaluation means can further improve the evaluation accuracy of the sleep depth by evaluating the sleep depth using the area of the ellipse indicating the variation of the Lorentz plot for the respiratory cycle. it can.

  According to the above item [4], since the sleep depth evaluation means evaluates the sleep depth based not only on the respiratory cycle but also on the respiratory amplitude value, it is possible to further improve the evaluation accuracy of the sleep depth.

  According to the previous item [5], the sleep depth evaluation means further improves the evaluation accuracy of the sleep depth by evaluating the sleep depth using the area of the ellipse indicating the variation of the Lorentz plot for the respiratory amplitude value. Can do.

  According to the preceding item [6], the sleep depth evaluation means evaluates the sleep depth based not only on the respiratory cycle but also on the body motion information, so that the evaluation accuracy of the sleep depth can be further improved.

  The sleep state measuring methods of the preceding items [7] to [12] have the same effects as the sleep state measuring devices of the preceding items [1] to [6], respectively.

FIG. 1 is a schematic plan view showing a sleep state measuring apparatus according to an embodiment of the present invention in its use state. FIG. 2 is a block diagram showing a schematic configuration of the sleep state measuring apparatus. FIG. 3 is a main flowchart of a sleep state measurement method using the sleep state measurement apparatus. FIG. 4 is a flowchart of a method for calculating a respiratory cycle, a respiratory amplitude value, body motion information, and the like in the sleep state measurement method. FIG. 5 is a diagram illustrating sampling of the output signal. FIG. 6 is a diagram illustrating the maximum value, minimum value, amplitude value, interval, and the like of the output signal. FIG. 7 is a diagram for explaining the moving average value of the amplitude value of the output signal. FIG. 8 is a diagram for explaining a respiratory cycle, a respiratory amplitude value, and the like. FIG. 9 is a diagram illustrating a method for calculating a moving average value of respiratory amplitude values. FIG. 10 is a diagram for explaining body movement time and the like. FIG. 11 is a diagram for explaining the Lorentz plot for the respiratory cycle. FIG. 12 is a diagram for explaining the Lorentz plot for the respiratory amplitude value. FIG. 13 is a diagram illustrating an example of a chain stroke respiration signal.

  Next, an embodiment of the present invention will be described below with reference to the drawings.

  As shown in FIG. 1, a sleep state measurement apparatus 1 according to an embodiment of the present invention measures and measures at least a sleep depth as a sleep state of a subject H, and includes a biological information detection sensor 2 and a calculator 3. Etc. Examples of the subject H include sick people, elderly people, healthy people, infants and the like.

  The biological information detection sensor 2 detects and outputs at least a respiratory signal and a body motion signal as a biological information signal of the subject H. Various sensors can be used as the sensor 2, and a panel type or a sheet type is used in this embodiment. In other words, the sensor 2 is used by being placed on the lower side of the upper body (for example, the chest) of the subject H on the bed 20 as a bed, and is described in, for example, Patent Documents 1 to 10 described above. is there. More specifically, the sensor 2 detects a strain variation or a pressure variation generated in the laying plate (eg, strain generating plate) 2a due to the biological activity of the subject H, and a detection element (eg, strain gauge) attached to the laying plate 2a. , A piezoelectric film element) and a detection signal of the detection element 2b is output as a biological information signal of the subject H. Examples of the biological activity of the subject H include breathing and body movement. Therefore, the output signal of the biological information detection sensor 2 includes at least a respiratory signal and a body motion signal, and is composed of a voltage signal in which these signals are superimposed, that is, a voltage synthesized so that these signals overlap each other. It consists of signals. The unit of the output signal is “mV”, for example.

  The computing unit 3 includes a computer having a ROM, a RAM, a CPU, and the like, and performs a predetermined computation based on an output signal of the biological information detection sensor 2 transmitted from the biological information detection sensor 2 via the signal line 10. Etc. The schematic configuration of the calculator 3 is as follows.

  As shown in FIG. 2, the computing unit 3 includes an abnormal signal detection means 4, an abnormal signal exclusion means 5, a respiratory cycle calculation means 6, a respiratory amplitude value calculation means 7, a body motion information calculation means 8, a sleep depth evaluation means 9, and the like. It has.

  The abnormal signal detection means 4 detects the body motion signal and the abnormal respiratory signal from the output signal of the biological information detection sensor 2 as an abnormal signal that is not a normal respiratory signal.

  The abnormal signal exclusion unit 5 excludes the detection period of the abnormal signal detected by the abnormal signal detection unit 4 from the output signal of the biological information detection sensor 2.

  The respiratory cycle calculation means 6 calculates a respiratory cycle based on the output signal after the abnormal signal detection means 5 excludes the abnormal signal detection period.

  The respiration amplitude value calculation means 7 calculates a respiration amplitude value based on the output signal after the abnormal signal detection means 5 excludes the abnormal signal detection period.

  The body motion information calculation means 8 calculates body motion information based on the abnormal signal excluded by the abnormal signal exclusion means 5. The body motion information includes the number of body motions, the size of the body motion, and the like. In the present embodiment, the number of body movements is calculated as body movement information.

  The sleep depth evaluating means 9 is based on the respiratory cycle calculated by the respiratory cycle calculating means 6, the respiratory amplitude value calculated by the respiratory amplitude value calculating means 7, and the body motion information calculated by the body motion information calculating means 8. This is to evaluate the sleep depth.

  Further, the computing unit 3 includes an amplifying unit (not shown), a setting unit (not shown) for setting various values (e.g., predetermined coefficients, thresholds) used for the sleep depth evaluation, and the sleep depth evaluation result. And storage means (not shown) for storing data and values used for the evaluation, a timer (not shown), and the like.

  In the present embodiment, the output signal of the biological information detection sensor 2 provided to the abnormal signal detection means 4 is a signal after being amplified by the amplification means of the calculator 3. Here, if the pulsation signal (heart rate signal) is largely included in the output signal of the biological information detection sensor 2, the pulsation signal is removed from the output signal of the biological information detection sensor 2 by a predetermined filter. Therefore, it is desirable to be configured to be provided to the abnormal signal detection means 4.

  Next, a sleep state measurement method using the sleep state measurement device 1 of the present embodiment will be described below.

  As shown in FIG. 3, in step S <b> 1, predetermined coefficients and threshold values used for sleep depth evaluation are set.

  In step S2, detection of biological information by the biological information detection sensor 2 is started.

  In step S3, a respiratory cycle, a respiratory amplitude value, the number of body movements, and the like are calculated. A specific procedure for this calculation will be described later.

  In step S4, the sleep depth evaluation means 9 calculates the sleep depth evaluation value A used for the sleep depth evaluation. A specific procedure for this calculation will be described later.

  In steps S <b> 5 to S <b> 11, the sleep depth evaluation unit 9 evaluates the sleep depth using the sleep depth evaluation value A. A description of this evaluation will be given later.

  Next, the calculation method of the respiratory cycle, the respiratory amplitude value, the number of body movements, etc. performed in step S3 will be described below.

  As shown in FIG. 4, in step S20, the timer provided in the calculator 3 is reset.

  In step S21, the maximum value e and the minimum value f of the output signal of the biological information detection sensor 2 are initialized. In step S22, the abnormal signal detection means 4 searches for the maximum value e of the output signal of the biological information detection sensor 2 (more specifically, the maximum value e of each wave of the output signal) and the minimum value f. The method is as follows. That is, as shown in FIG. 5, the output signal of the biological information detection sensor 2 is sampled at a predetermined sampling frequency, and the maximum value e and the minimum value f are found from the sampling data obtained thereby. The sampling frequency is preferably set to a value in the range of 2 to 100 Hz. Note that the reciprocal of the sampling frequency is the sampling period.

  In step S23, as shown in FIG. 6, the amplitude value g (peak peak value) of the output signal and the interval d between the maximum value e and the minimum value f of the output signal are calculated and stored by the abnormal signal detection means 4. To do. Here, the amplitude value g means the difference between the maximum value e and the minimum value f adjacent to each other in the output signal (that is, the value of “maximum value e” − “minimum value f”). The interval d means the time (period) until the output signal reaches the minimum value f from the maximum value e.

  In step S24, the abnormal signal detection means 4 and the abnormal signal exclusion means 5 determine whether or not the amplitude value g and the interval d of the output signal satisfy the first respiratory condition. The determination is performed by comparing the amplitude value g and the interval d with a predetermined threshold value, and the specific determination method is as follows.

  If the amplitude value g and the interval d of the output signal satisfy both the following respiration determination expressions (1a) and (1b) in the first respiration condition, it is determined that the first respiration condition is satisfied (“YES”) and the process proceeds to step S25. move on.

1000 mV ≦ amplitude value g ≦ 5000 mV Equation (1a)
1 second ≦ interval d ≦ 6 seconds (1b)

  Here, when both of the expressions (1a) and (1b) are satisfied, the signal during this period is likely not to include an abnormal respiratory signal and a body motion signal, that is, includes only a normal respiratory signal. It means that the possibility is high. Therefore, when both of the expressions (1a) and (1b) are satisfied, it is tentatively determined that a normal respiratory signal is detected, and the signal of this period is used as the signal of the detection period of the normal respiratory signal. Temporarily adopted as an output signal used for the calculation of the respiratory amplitude value.

  When the amplitude value g of the output signal satisfies the next respiration determination formula (2) under the first respiration condition, the process proceeds to step S35 because the first respiration condition is not satisfied ("NO-1").

        5000 mV <amplitude value g (2).

  Here, the case where the expression (2) is satisfied means that since the amplitude value g is very large, the signal during this period is highly likely to contain a body motion signal. Therefore, when the expression (2) is satisfied, it is tentatively determined that a body motion signal (that is, an abnormal signal) has been detected, and the signal during this period is used as a signal for the detection period of the body motion signal (abnormal signal). Excluded from the output signal used to calculate the period and respiratory amplitude values.

  When the amplitude value g of the output signal satisfies the following respiration determination formula (3) in the first respiration condition, when both the following respiration determination formulas (4a) and (4b) are satisfied, and the following respiration determination formula When it is at least one of the cases where both (5a) and (5b) are satisfied, it is determined that the first breathing condition is not satisfied ("NO-2"), and the process returns to step S21.

Amplitude value g <1000 mV Equation (3)
1000 mV ≦ amplitude value g ≦ 5000 Equation (4a)
Interval d <1 second (Formula 4b)
1000 mV ≦ amplitude value g ≦ 5000 Equation (5a)
6 seconds <interval d ... Formula (5b).

  Here, the case of at least one case means that there is a high possibility that the signal during this period includes an abnormal breathing signal indicating abnormal breathing such as apnea or hypopnea. Therefore, in the at least one case, it is determined that an abnormal respiratory signal (that is, an abnormal signal) has been detected, and the signal of this period is used as a signal for the detection period of the abnormal respiratory signal (abnormal signal). It is excluded from the output signal used for the calculation of the amplitude value.

  In step S25, as shown in FIG. 7, a moving average value (simple moving average value in detail) mg of a plurality of recent amplitude values excluding the current amplitude value g of the output signal is determined as the abnormal signal detecting means 4 or Calculated by the abnormal signal exclusion means 5 and stored. In the present embodiment, for example, a moving average value mg of five amplitude values g1 to g5 is calculated.

  In step S26, the abnormal signal detecting means 4 and the abnormal signal excluding means 5 determine whether or not the amplitude value g of the output signal satisfies the second respiration condition. The determination is performed by comparing the amplitude value g with a predetermined threshold value using the moving average value mg of the amplitude value, and the specific determination method is as follows.

  When the amplitude value g satisfies the next respiration determination formula (6) in the second respiration condition, the process proceeds to step S27 on the assumption that the second respiration condition is satisfied (“YES”).

        Moving average value mg × 30% ≦ amplitude value g ≦ moving average value × 150% Equation (6).

  Here, when the expression (6) is satisfied, it is highly possible that the signal during this period does not include the abnormal respiratory signal and the body motion signal, that is, it is highly likely that only the normal respiratory signal is included. Means. Therefore, when the expression (6) is satisfied, it is determined that a normal respiration signal is detected, and the signal of this period is used as a signal of the detection period of the normal respiration signal to be used for the calculation of the respiration cycle and the respiration amplitude value. Adopted for output signal.

  When the amplitude value g satisfies the next respiration determination formula (7) in the second respiration condition, the process proceeds to step S35 because the second respiration condition is not satisfied ("NO-1").

        Moving average value mg × 150% <amplitude value g (7).

  Here, the case where the expression (7) is satisfied means that since the amplitude value g is very large, it is highly possible that the signal in this period includes a body motion signal. Therefore, when the expression (7) is satisfied, it is tentatively determined that a body motion signal (that is, an abnormal signal) has been detected, and the signal of this period is used as a signal for the detection period of the body motion signal (abnormal signal). Excluded from the output signal used to calculate the period and respiratory amplitude values.

  When the amplitude value g satisfies the next respiration determination formula (8) in the second respiration condition, the process returns to step S21 as not satisfying the second respiration condition (“NO-2”).

        Amplitude value g <moving average value mg × 30% (8)

  Here, when the expression (8) is satisfied, since the amplitude value g is very small, there is a high possibility that the signal during this period includes an abnormal breathing signal indicating abnormal breathing such as apnea and hypopnea. It means that. Therefore, when the expression (8) is satisfied, it is determined that an abnormal breathing signal (that is, an abnormal signal) is detected, and the signal of this period is used as a signal of the detection period of the abnormal breathing signal (abnormal signal). It is excluded from the output signal used for the calculation of the amplitude value.

  In step S27, it is determined that the output signal is a respiratory signal.

  In step S28, as shown in FIG. 8, the time of the maximum value e of the output signal (that is, the respiratory signal) is calculated as the maximum respiratory time t (n) by the respiratory cycle calculating means 6 or the respiratory amplitude value calculating means 7. Remember.

  In step S29, the respiratory amplitude value calculation means 7 calculates and stores the amplitude value g of the output signal (that is, the respiratory signal) as the respiratory amplitude value am (n).

  In step S30, a value obtained by doubling the interval d of the output signal (that is, the respiratory signal) is calculated and stored as a respiratory cycle C (n) by the respiratory cycle calculating means 6.

  In step S31, as shown in FIG. 9, the moving average value (simple moving average value in detail) ma of a plurality of most recent respiratory amplitude values am (n) is calculated by the respiratory cycle calculating means 6 or the respiratory amplitude value calculating means 7. (N) is calculated and stored. In the present embodiment, for example, a moving average value ma (n) of five respiratory amplitude values am (n) is calculated and stored.

  In step S32, it is determined by the respiration cycle calculation means 6 or the respiration amplitude value calculation means 7 whether or not, for example, 5 minutes have elapsed as a predetermined period. If it is determined that 5 minutes have not elapsed (in the case of “NO”), the process returns to step S21. On the other hand, when it is determined that 5 minutes have elapsed (in the case of “YES”), the process proceeds to step S4 (see FIG. 3). In the present invention, the predetermined period is not limited to 5 minutes, but may be 1 minute, 10 minutes, or the like.

  In step S35, the body motion information calculation unit 8 determines whether or not the amplitude value g of the output signal satisfies the body motion condition. The determination is performed by comparing the amplitude value g with a predetermined threshold value using the moving average value mg of the amplitude value, and the specific determination method is as follows.

  When the amplitude value g of the output signal satisfies the following body movement determination formula (9), it is determined that the body movement condition is satisfied (“YES”), and the process proceeds to step S36.

        Moving average value mg × 10 ≦ amplitude value g Equation (9).

  Here, the case where the expression (10) is satisfied means that since the amplitude value g is very large, it is highly possible that the signal in this period includes a body motion signal. Therefore, when the expression (9) is satisfied, it is determined that the body motion signal is detected, and the signal of this period is adopted as the signal of the body motion signal detection period.

  If the amplitude value g satisfies the next body movement determination formula (10), the body movement condition is not satisfied ("NO"), and the process returns to step S21.

        Amplitude value g <moving average value mg × 10 Equation (10).

  Here, when the expression (10) is satisfied, it means that there is a high possibility that the subject moved the limb lightly.

  When the moving average value mg is not stored at all, the body motion condition is not satisfied (“NO”), and the process returns to step S21.

  In step S36, the body motion information calculation means 8 calculates and stores the time of the maximum value e of the output signal as the body motion time h when the body motion signal is detected. Then, the number of stored body movement times is counted as the number K of body movements. Here, as shown in FIG. 10, if the time (period) from the current body movement time h to the previous body movement time h1 is within a predetermined time, for example, within 30 seconds, they are independent of each other. It is determined that each is not a single body motion but a single continuous body motion (that is, a continuous body motion), and the current body motion time h is not stored and therefore is not counted in the number K of body motions.

  Next, the calculation method of the sleep depth evaluation value A performed in step S4 will be described below with reference to FIGS.

  The sleep depth evaluation value A is an ellipse area S1 (hereinafter referred to as “LP area S1 of the respiratory cycle”) indicating the variation of the Lorenz plot (LP) for the respiratory cycle C, and the respiratory amplitude value am. It is calculated using the area S2 of the ellipse showing the variation of the Lorentz plot (hereinafter referred to as “LP area S2 of the respiratory amplitude value”) and the number K of body movements.

  The calculation of the LP area S1 of the respiratory cycle is performed in accordance with a general LP area calculation method. The calculation method will be briefly described as follows.

That is, in the plurality of respiratory cycles C obtained in step S3, the total number of respiratory cycles C N, n-th breathing cycle C _n, the n + 1 th breathing cycle and C _{n + 1.} As shown in FIG. 11, X coordinates _{C n,} the _{C n + 1} as the Y-coordinate plots all respiratory cycle C on the X-Y plane. Thereby, a Lorentz plot for the respiratory cycle C is obtained. Then, plots of all respiratory cycles C are projected onto the Y = X axis and the Y = −X axis. Thereafter, on the Y = X axis, the average M1 of the distance from the origin (0,0) and the standard deviation σ _X of the distance from the origin (0,0) are calculated, respectively, and Y = −on the X axis , The standard deviation σ _−X of the distance from the origin (0, 0) is calculated. Then, the area S1 of the ellipse having σ _X as the major axis and σ _−X as the minor axis was calculated by the following equation (11), and this value was defined as the LP area S1 of the respiratory cycle. Note that π is the circumference ratio.

S1 = π × σ _X × σ _−X Equation (11).

  Further, the calculation method of the LP area S1 of the respiratory cycle will be described more specifically as follows.

Y = projecting on the X axis: (x + y) / 2 = _xσx , (x + y) / 2 = _yσx
Y = -X-axis _{projection: x-x σx = x σx} , y-y σx = y σx
Standard deviation σ _X , σ _−X :
σ _X is calculated by the following equation (12a) by _obtaining an average value m _σx of (x _σx , y _σx ).

σ− _X is calculated by the following equation (12b) by obtaining an average value m _σ−x of (x _σ−x , y _σ−x ).

  The area S1 of the ellipse is calculated by the above formula (11).

  The average M1 of the distance from the origin (0, 0) on the Y = X axis is calculated by the following equation (12c).

  Further, the calculation of the LP area S2 of the respiratory amplitude value is performed in the same manner as the calculation method of the LP area S1 of the respiratory cycle, and the calculation method will be briefly described as follows.

That is, in the plurality of respiratory amplitude values am obtained in step S3, the total number of respiratory amplitude values am is N, the nth respiratory amplitude value is am _n , and the n + 1th respiratory cycle is am _{n + 1} . As shown in FIG. 12, all respiration amplitude values am are plotted on the XY plane, where am _n is the X coordinate and am _{n + 1} is the Y coordinate. Thereby, the Lorentz plot about the respiration amplitude value am is obtained. Then, plots of all respiration amplitude values am are projected on the Y = X axis and the Y = −X axis. Thereafter, on the Y = X axis, the average M2 of the distance from the origin (0,0) and the standard deviation σ _X of the distance from the origin (0,0) are calculated, respectively, and Y = −on the X axis , The standard deviation σ _−X of the distance from the origin (0, 0) is calculated. Then, the area S2 of the ellipse having σ _X as the major axis and σ _−X as the minor axis was calculated by the following equation (13), and this value was defined as the LP area S2 of the respiratory amplitude value.

S2 = π × σ _X × σ _−X Equation (13).

  The sleep depth evaluation value A is calculated by the following equation (14) using S1, M1, S2, M2, and K thus obtained.

    A = a3 * S1 + a4 * M1 + a7 * S2 + a8 * M2 + a9 * K (14).

  However, a3, a4, a7, a8, and a9 are coefficients related to each value, and all of these are normally set to values larger than zero. When S2 and M2 are not used for calculating the sleep depth evaluation value A, a7 and a8 are set to 0. When K is not used for calculating the sleep depth evaluation value A, a9 is set to 0. Is set.

  In general, the respiration cycle C and the respiration amplitude value am are not constant at the time of awakening, and the number of body movements K is large. Less. Therefore, the sleep depth evaluation value A means that when the value of A is large, the sleep state is deep, and when the value of A is small, sleep is deep (deep sleep).

  Here, in general, the sleep depth is roughly divided into two types: non-REM sleep and REM sleep. Furthermore, non-REM sleep is divided into four stages of stages 1 to 4 (shallow → deep) depending on the sleep depth (electroencephalogram activity). However, in this embodiment, the sleep depth is evaluated in four stages of “state 1: awakening”, “state 2: REM sleep”, “state 3: light sleep”, and “state 4: deep sleep”.

  A specific evaluation method for the sleep depth is as follows.

  The sleep depth evaluation value A is compared with three threshold values B1 to B3 (however, B1> B2> B3) as predetermined threshold values that determine the sleep depth. Then, according to the comparison result, the sleep depth is evaluated as “wakefulness”, “REM sleep”, “shallow sleep”, or “deep sleep”.

  That is, as shown in FIG. 3, in step S5, it is determined whether or not the evaluation determination formula (14a) of A> B1 is satisfied. If satisfied (“YES”), the process proceeds to step S8. It is determined as “Awakening”. Then, the process returns to step S3. On the other hand, if not satisfied (in the case of “NO”), the process proceeds to step S6.

  In step S6, it is determined whether or not the evaluation determination formula (14b) of B1 ≧ A> B2 is satisfied. If satisfied (in the case of “YES”), the process proceeds to step S9 to determine “REM sleep”. Then, the process returns to step S3. On the other hand, if not satisfied (in the case of “NO”), the process proceeds to step S7.

  In step S7, it is determined whether or not the evaluation determination formula (14c) of B2 ≧ A> B3 is satisfied. If satisfied (in the case of “YES”), the process proceeds to step S10 to determine “light sleep”. Then, the process returns to step S3. On the other hand, when not satisfied (in the case of “NO”), the process proceeds to step S11 to determine “deep sleep”. Then, the process returns to step S3. In addition, the case where it is not satisfied in step S7 means the case where A ≦ B3.

  The sleep state measurement device 1 and the sleep state measurement method of the above embodiment have the following advantages.

  Since the respiratory cycle calculating means 6 calculates the respiratory cycle C based on the output signal of the biological information detection sensor 2 after the detection period of the body motion signal as an abnormal signal is excluded, the output signal including the body motion signal The respiratory cycle C can be calculated more accurately than when the respiratory cycle is calculated based on the above. And since the sleep depth evaluation means 9 evaluates the sleep depth based on this respiration cycle C, it can evaluate the sleep depth with high accuracy.

  Furthermore, the abnormal signal detection means 4 detects not only a body motion signal but also an abnormal respiratory signal as an abnormal signal, and the respiratory cycle calculating means 6 detects the biological information detection sensor 2 after the detection period of this abnormal signal is excluded. Since the respiratory cycle C is calculated based on the output signal, the respiratory cycle C can be calculated more accurately than when the respiratory cycle is calculated based on the output signal including the abnormal respiratory signal. Therefore, the evaluation accuracy of the sleep depth can be improved.

  Furthermore, since the sleep depth evaluation means 9 evaluates the sleep depth based not only on the respiratory cycle C but also on the respiratory amplitude value am, it is possible to further improve the evaluation accuracy of the sleep depth. That is, the subject who is performing chain stroke breathing as shown in FIG. 13 generally has a shallow sleep depth, but the breathing cycle is generally constant. Therefore, when the output signal includes a chain stroke respiration signal, if the sleep depth is evaluated based only on the respiration cycle C, there is a possibility that it is evaluated as deep sleep. On the other hand, by evaluating the sleep depth based not only on the respiratory cycle C but also on the respiratory amplitude value am, the evaluation accuracy of the sleep depth can be further improved even in such a case.

  In addition, since the sleep depth evaluation means 9 evaluates the sleep depth based not only on the respiratory cycle C and the respiratory amplitude value am but also on the number of body movements K, the sleep depth evaluation accuracy can be further improved. .

  In addition, the sleep depth evaluation means 9 uses the sleep depth evaluation value A calculated using the LP area S1 of the respiratory cycle, the LP area S2 of the respiratory amplitude value, and the number of body movements K as a predetermined threshold value for determining the sleep depth. Since the sleep depth is evaluated by comparing with B1 to B3, the evaluation accuracy of the sleep depth can be reliably improved.

  Here, the sleep state measuring apparatus 1 of the present embodiment controls an external device such as an air conditioner, a wake-up device, and a lighting device for the subject H using the evaluation result of the sleep depth evaluated by the sleep depth evaluation means 9. Control means (not shown) may be provided. Some specific examples of the control mode by the control means are as follows.

  In summer, etc., when the room temperature becomes high and the sleep depth of the subject H becomes shallow, and as a result, when the sleep depth is evaluated as shallow sleep, the air conditioner (eg, air conditioner) so as to lower the room temperature. To control. In winter, when the room temperature is low and the sleep depth of the subject H becomes shallow, and as a result, when the sleep depth is evaluated as shallow sleep, an air conditioner (e.g., heating device) is used to increase the room temperature. Is controlled by the control means. In addition, when the sleep depth is evaluated as awakening for the subject H who cannot easily get up from the bed such as the bed 20 because the room temperature is low in winter or the like, the air conditioner (e.g., the room temperature is increased). The heating device is controlled by the control means.

  To enable subject H to wake up comfortably, if the sleep depth is evaluated as a shallow sleep from 30 minutes before the desired wake-up time set in advance in the alarm device (eg, alarm clock), an alarm will sound The apparatus is controlled by the control means.

  When the sleep depth is evaluated as shallow sleep or deep sleep, the lighting device is controlled so as to be turned off. Further, when the sleep depth is evaluated as awakening in the morning or the like, the lighting device is controlled by the control means so as to be lit.

  Furthermore, the sleep state measurement device 1 of the present embodiment may be configured to transmit and record the sleep depth evaluation result to a predetermined sleep recording device such as a smartphone or a personal computer via a predetermined communication network. . In this case, the evaluation result of the sleep depth of the subject H can be automatically recorded.

  Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the scope of the present invention.

  For example, in the above-described embodiment, only the number K of body movements is used as the body movement information for the evaluation of the sleep depth by the sleep depth evaluation unit 9. May be used, and the number K of body movements and the magnitude of body movement may be used together.

  INDUSTRIAL APPLICABILITY The present invention can be used for a sleep state measurement device and a sleep state measurement method that can evaluate the sleep depth.

1: Sleep state measurement device 2: Biological information detection sensor 3: Calculator 4: Abnormal signal detection means 5: Abnormal signal removal means 6: Respiration cycle calculation means 7: Respiration amplitude value calculation means 8: Body motion information calculation means 9: Sleep depth evaluation means 20: bed (bed)

Claims (12)

An abnormal signal detecting means for detecting a body movement signal as an abnormal signal from an output signal of the biological information detection sensor;
Abnormal signal exclusion means for excluding the detection period of the abnormal signal detected by the abnormal signal detection means from the output signal of the biological information detection sensor;
A respiratory cycle calculating means for calculating a respiratory cycle based on an output signal after the abnormal signal detection period is excluded by the abnormal signal excluding means;
Sleep depth evaluation means for evaluating sleep depth based on the respiratory cycle calculated by the respiratory cycle calculation means;
A sleep state measuring device comprising:   The sleep state measuring device according to claim 1, wherein the abnormal signal detecting means detects the body motion signal and further an abnormal respiratory signal as the abnormal signal from an output signal of the biological information detection sensor.   The sleep state measuring device according to claim 1 or 2, wherein the sleep depth evaluation means evaluates the sleep depth using an elliptical area indicating a variation of the Lorentz plot for the respiratory cycle. Respiration amplitude value calculating means for calculating a respiration amplitude value based on the output signal after the abnormal signal detection period is excluded by the abnormal signal exclusion means,
The sleep state measuring device according to any one of claims 1 to 3, wherein the sleep depth evaluation unit further evaluates the sleep depth based on the respiration amplitude value calculated by the respiration amplitude value calculation unit.   The sleep state measuring device according to claim 4, wherein the sleep depth evaluation unit evaluates the sleep depth using an area of an ellipse indicating a variation of a Lorentz plot for the respiratory amplitude value. Further comprising body motion information calculation means for calculating body motion information based on the abnormal signal excluded by the abnormal signal exclusion means;
The sleep state measuring device according to any one of claims 1 to 5, wherein the sleep depth evaluation unit further evaluates the sleep depth based on the body movement information calculated by the body movement information calculation unit. An abnormal signal detection step of detecting a body motion signal as an abnormal signal from the output signal of the biological information detection sensor;
An abnormal signal exclusion step of excluding a detection period of the abnormal signal detected in the abnormal signal detection step from an output signal of the biological information detection sensor;
A respiratory cycle calculating step of calculating a respiratory cycle based on the output signal after the detection period of the abnormal signal is excluded in the abnormal signal exclusion step;
A sleep depth evaluation step for evaluating the sleep depth based on the respiratory cycle calculated in the respiratory cycle calculation step;
The sleep state measuring method characterized by comprising.   The sleep state measuring method according to claim 7, wherein the abnormal signal detecting step detects the body motion signal and further an abnormal respiratory signal as the abnormal signal from an output signal of the biological information detection sensor.   The sleep state measuring method according to claim 7 or 8, wherein in the sleep depth evaluation step, the sleep depth is evaluated using an elliptical area indicating a variation of the Lorentz plot for the respiratory cycle. A breathing amplitude value calculating step of calculating a breathing amplitude value based on the output signal after the abnormal signal detection period is excluded in the abnormal signal exclusion step;
The sleep depth measuring method according to any one of claims 7 to 9, wherein the sleep depth evaluation step further evaluates the sleep depth based on the respiratory amplitude value calculated in the respiratory amplitude value calculation step.   The sleep state measuring method according to claim 10, wherein the sleep depth evaluation step evaluates the sleep depth using an area of an ellipse indicating a variation of a Lorentz plot for the respiratory amplitude value. Further comprising a body motion information calculation step of calculating body motion information based on the abnormal signal detected in the abnormal signal detection step;
The sleep state measuring method according to any one of claims 7 to 11, wherein the sleep depth evaluation step further evaluates the sleep depth based on the body movement information calculated in the body movement information calculation step.

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