JP2009095486A - Apnea judging apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an apnea judging apparatus which is not influenced by individual variations or subject's body movements to judge whether the subject falls in apnea or not. <P>SOLUTION: The apnea judging apparatus comprises a pulse wave sensor 2, a pulse rate calculator 5, a frequency converting means 7, a first band-pass filter 8, a second band-pass filter 9, an operation part 10, and a judge part 11, wherein the pulse rate calculator 5 calculates temporal data about the pulse rate of a subject from a pulse wave signal of the pulse wave sensor 2; the frequency converting means 7 converts the temporal data about the pulse rate into frequency data of the pulse rate. The first band-pass filter 8 and the second band-pass filter 9 extract a first spectral signal at apnea and a second spectral signal at normal respiration, respectively, from the frequency data of the pulse rate. The processor 10 determines first signal intensity and second signal intensity which indicate the first spectral signal and the second spectral signal in their respective magnitudes, and then operates a ratio signal of the first signal intensity versus the second signal intensity. The judge part 11 determines whether the ratio signal value by the operation part 10 is beyond a given value or not and judges that the subject falls in apnea in case of an exceeding ratio signal value. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an apnea state determination device that determines an apnea state when a living body is sleeping based on a pulse group generated by the living body.

It is known to use a blood oxygen saturation meter when determining an apnea state when a living body is sleeping based on a pulse group generated by the living body. Blood oxygen saturation, or hemoglobin oxygen saturation, refers to the ratio of oxygen actually taken in and bound to the maximum oxygen binding capacity of hemoglobin in the blood. It can be determined that the oxygen deficient state in which oxygen is not sufficiently taken in, that is, the apnea state. The hemoglobin oxygen saturation is the ratio of the absorbance at a wavelength where the molar extinction coefficient of oxygen-binding hemoglobin and reduced (deoxygenated) hemoglobin is equal (equal absorption wavelength) to the molar extinction coefficient of oxygen-bound hemoglobin and reduced hemoglobin. It has been measured by measuring the absorbance at known wavelengths and solving simultaneous equations (see, for example, Patent Document 1).
JP-A-2005-233024

However, in the conventional system using the blood oxygen saturation meter, since it is necessary to use two types of light having different wavelengths, the apparatus configuration is complicated. further. Since it is necessary to synchronize the pulse data detected by each of the two types of light, the analysis processing is complicated.
The present invention has been made in view of such circumstances, and it is a main object of the present invention to provide an apnea determination device that can perform an apnea determination based on a pulse signal detected by light of one wavelength. And

  In order to solve the above problems, a pulse wave sensor that has a light emitting element and a light receiving element, is attached to a part of a living body, and measures a pulse wave of a living body, and the living body is detected from the pulse wave signal from the pulse wave sensor. An apnea condition determination device for determining an apnea condition, wherein a pulse rate calculation unit that calculates pulse rate time-series data indicating a temporal change in a pulse rate from a pulse signal from the pulse sensor, and the pulse From the pulse rate time-series data calculated by the number calculating unit to the pulse rate frequency data indicating the frequency spectrum of the pulse rate with time, and the pulse rate frequency data converted by the frequency conversion unit From the first band pass filter for extracting the first spectrum signal corresponding to the frequency band of the change in pulse rate during apnea, and the pulse rate frequency data converted by the frequency conversion means, A second band-pass filter that extracts a second spectrum signal corresponding to a frequency band of a change in pulse rate during inhalation; and a first signal intensity that indicates the overall magnitude of each spectrum signal of the first spectrum signal; Calculating a second signal intensity indicating the overall magnitude of each spectrum signal of the second spectrum signal and calculating a ratio signal of the first signal intensity with respect to the second signal intensity; A determination unit that determines whether or not the ratio signal exceeds a predetermined value; This apnea state determination apparatus determines an apnea state from the ratio of the magnitude of change over time of the pulse rate during apnea and the magnitude of change over time of the pulse rate during normal breathing.

  According to the present invention, it is possible to determine the apnea state without being affected by individual differences, even when the pulse sensor is not properly attached to the living body or when a disturbance such as body movement is received.

  The best mode for carrying out the invention will be described in detail with reference to the drawings. As shown in FIG. 1, an apnea condition determination apparatus 1 includes a pulse wave sensor 2 that is used by being worn on a human body, and an analysis apparatus 3 that determines an apnea state based on data detected by the pulse wave sensor 2. And an output unit 4. The pulse sensor 2 includes a light receiving element 20 and a light emitting element 21. The light emitting element 20 emits light, and the light reflected by the blood vessel of the fingertip is received by the light receiving element 21. The light receiving element 21 outputs a signal having a higher frequency as the amount of received light increases. The pulse sensor 2 sequentially transmits a signal (pulse wave signal) that varies due to the pulse wave to the analysis device 3 as the time output from the light receiving element 21 elapses. The pulse wave sensor 2 and the analysis device 3 transmit and receive data by wired communication, but may communicate wirelessly.

  The analysis device 3 includes a pulse rate calculation unit 5 and an apnea determination unit 6, and an analysis result is output to the output unit 5. The output unit 5 is a device that outputs data to a recording medium such as a display or paper, or a device that outputs data to another device. The analysis device 3 is configured by a dedicated device or a general-purpose computer device, and executes a program for apnea condition determination processing. The apnea determination unit 6 includes a discrete cosine conversion unit (frequency conversion unit) 7, a first band pass filter 8, a second band pass filter 9, a calculation unit 10, and a determination unit 11. . As shown in FIG. 2, the pulse rate calculation unit 5 includes a frequency filter processing unit 50 and a pattern matching unit 60. The frequency filter processing unit 50 includes a Fourier transformer (FFT) 51, a frequency filter 52, and an inverse Fourier transformer (IFFT) 53. The pattern matching unit 60 includes a search range setting unit 61, a reference point determination unit, and the like. 62, a pulse rate determination unit 63, and a waveform correction unit 64.

  Next, the operation of this embodiment will be described. FIG. 3 shows a main flow of processing performed by the analysis device 3 of the apnea state determination device 1. The pulse rate calculation unit 5 receives the pulse wave signal transmitted from the pulse wave sensor 2, and captures the pulse wave signal in the memory for a predetermined capture time. The capture time is predetermined and waits until a predetermined capture time elapses (step S101). At this time, a plurality of pulse wave signals are taken into the memory in time series, thereby creating time series data in which the signal intensity corresponding to the pulse pressure changes along the time axis. When the capture time has elapsed (Yes in step S101), frequency filter processing (step S102) is performed to reduce noise superimposed on the pulse wave and reduce low-frequency swell due to human body movement.

  Next, the waveform pattern matching process (step S103) is performed to calculate the pulse rate. The apnea determination process (step S104) is performed using the time-series data of the pulse wave number obtained by the processes so far. Thereafter, when the apnea determination process is continued (No in step S105), the process returns to step S101. When the apnea determination process ends (Yes in step S105), the process here ends.

  Details of each process will be described below. The frequency filter processing in step S102 is performed by the Fourier transformer (FFT) 51, the frequency filter 52, and the inverse Fourier transformer (IFFT) 53 of the frequency filter processing unit 50.

  Details of the frequency filter processing are shown in FIG. In the frequency filter process (step S102), first, the data size (FFT size) when the Fourier transformer 51 performs the FFT process is determined (step S201). Then, FFT processing (direct transformation) is performed on the pulse wave time-series data acquired within the FFT size range (step S202), and a frequency spectrum is acquired. This frequency spectrum is transferred to the frequency filter 52, and only a signal in the range of 0.5 to 3.0 Hz corresponding to the pulse frequency of the human body is extracted (step S203). A spectral moving average process (step S204) is performed on the extracted signal, and a moving average is calculated using past data. In the moving average, for example, the current data is weighted three times with respect to the past data and the average is taken. By acquiring the moving average, the influence of irregular fluctuations due to disturbances or the like is suppressed, and the pulse rate detection result is prevented from abruptly fluctuating.

  Further, a spectrum maximum value search (step S205) is performed on the moving averaged signal, the frequency of the maximum peak is extracted, and a temporary period Ta is calculated from this frequency. When the processing so far is completed, the inverse Fourier transform (IFFT) processing (step S206) is performed by the inverse Fourier transformer 43, and the frequency spectrum signal is directly transformed into time series data.

  A specific example of the processing so far will be described with reference to FIGS. First, FIG. 5 shows analog time-series data obtained within the FFT size. The horizontal axis shows the passage of time, and the vertical axis shows the signal intensity. This is data in which noise and undulation due to body movements are superimposed on a periodic wave caused by a pulse. When this time series waveform is orthogonally transformed by the Fourier transformer 51, a spectrum as shown in FIG. 6 is obtained. The horizontal axis represents frequency, and the vertical axis represents amplitude (intensity). If there is a peak signal having the greatest intensity between 0.5 Hz and 3.0 Hz, which is a normal human pulse frequency, and the intensity of the peak signal is twice that of other signals, the next processing is performed. Proceed to (S206). When extracting only data from 0.5 Hz to 3.0 Hz, the frequency filter 52 masks the signal below 0.5 Hz and the signal above 3.0 Hz, and the inverse Fourier transformer 53 is applied to the frequency spectrum after masking. The direct transform (inverse Fourier transform) is performed with. As a result, a waveform changing in time series as shown in FIG. 7 is created. This time-series waveform has reduced noise and reduced low-frequency swell compared to the original time-series waveform (see FIG. 5).

Next, details of the waveform pattern matching process (step S103) by the pattern matching unit 60 will be described with reference to FIG. This processing is performed in the search range setting unit 61, the reference point determination unit 62, the pulse rate determination unit 63, and the waveform correction unit 64 of the pattern matching unit 60.
First, the limit is set to the FFT data size, “0” is set as the initial value in the status (step S301), and the search range is set (step S302). As the search range, for example, a time is calculated by adding an allowable error to the provisional cycle Ta set in the spectrum maximum value search (step S205 shown in FIG. 5). This time region becomes a search range (search window SW) such as an initial point in the following processing. In addition, when a value corresponding to 25% of the provisional cycle Ta is obtained as the allowable error, an accurate result can be obtained, but the present invention is not limited to this, and a value within a range of 20 to 35% may be employed, for example. . Further, the allowable error may be varied corresponding to the stable state of the pulse of the measurement subject. For example, when the pulse is unstable, the allowable error may be increased, and the allowable error may be decreased as the pulse becomes stable.

  Further, a boundary check is performed to determine whether or not the search window SW has reached the end point of the time series waveform (step S303). If the end point has been reached, the processing here is terminated. In the initial stage, since the search window SW has not reached the end point, the process proceeds to step S304 to perform a status check (step S304). Since the status is “0” at the initial stage of processing, initial point derivation processing (step S305) is performed. In the initial point derivation process, the point having the lowest signal intensity within the provisional period Ta is determined as the initial point P1 (first reference point). In the initial point derivation process, the suitability of the initial point P1 is also checked.

  That is, when the data recognized as the initial point P1 is within a predetermined error range from the start point of the search window SW or within a predetermined error range from the end point of the search window SW, the data is actually in the middle of a downward slope. Although there is a risk that the value is regarded as the minimum value, if the initial point P1 is within this range, it is determined as nonconforming. The error range has a size corresponding to 5% of the time range of the search window SW, for example. This value can be changed in the range of 2% to 7%, for example, depending on the measurement conditions. Note that this is not the case when the person is not a healthy person, and the error range may be a larger range.

    Specifically, for example, as shown in FIG. 7, a search window SW1 starting from the start point of a waveform that changes in time series is set, and a point P1 at which the signal intensity is minimum is extracted in the search window SW1. Since the point P1 does not exist in the first error range E1 or the last error range E2 of the search window SW1, this point P1 is set as the initial point.

As shown in FIG. 8, the subsequent processing is divided according to the compatibility check result of the initial point P1 determined when the initial point P1 is derived (step S305) (step S306). The case where the check result is incompatible (No in step S306) will be described later, and the case where the initial point P1 is in conformity (Yes in step S306) will be described.
In this case, the initial point P1 is regarded as suitable, and the initial minimum point holding process (step S307) is performed. In the initial minimum point holding process, the process of holding the initial point P1 in the memory, the process of setting an area in which the initial point P1 is set as the start point and an allowable error is added as the new search window SW, and the status is “1”. And processing to set. When these processes are completed, the process returns to step S303. Note that the status “1” indicates that the initial point P1 has been determined and compatibility is recognized.

When the process is continued in step S303 returning from the initial minimum point holding process, the process proceeds from the status check in step S304 to the next point derivation process (step S310).
Details of the next point derivation process in step S310 will be described with reference to FIGS. When a new search window SW2 is set as shown in FIG. 9, the minimum point P2 is derived as the next point (second reference point) in the search window SW2 as shown in FIG. Is determined in the same manner as described above. However, the range (inspection range) in which the minimum point P2 is searched is within the minimum value search range SL2 corresponding to 75% to 100% of the search window SW. Also, an error range is set within the minimum value search range SL2 so that the middle of the downward gradient is not regarded as the minimum value.

  Further, the maximum value Q1 in the new search window SW2 is derived, and the suitability of this maximum value Q1 is determined in the same manner as described above. However, the range (inspection range) in which the maximum value Q1 is searched is within the maximum value search range SU2 corresponding to the time corresponding to 45% of the search window SW2 from the start point of the search window SW2. The error ranges E1 and E2 are also set in the maximum value search range SU2, so that the middle of the upward gradient is not regarded as the maximum value.

  If both the suitability of the minimum point P2 and the suitability of the maximum value Q1 are satisfied, the suitability of the next point derivation process as a whole is recognized, and otherwise, it is regarded as nonconforming. This is because when the initial point P1 set in the previous process is used as a reference point, it can be considered that a period shift has occurred unless the minimum point P2 appears in the range corresponding to the latter half 25% of the search window SW. . Similarly, when the initial point P1 set in the previous process is used as a reference point, if the maximum value Q1 does not appear within a range corresponding to the first 45% of the search window SW, it can be considered that a period shift has occurred. .

  As shown in FIG. 8, the subsequent processing is divided according to the compatibility check result (step S311). The case where it is determined as non-conforming (No in step S311) will be described later, and the case where the next point is conforming (Yes in step S311) will be described.

  In this case, the pulse rate determination unit 63 of the pattern matching unit 60 performs a process for deriving and holding the minimum inter-point time (step S312). Here, for example, in the pulse wave data for one cycle starting from the initial point P1 and having the next minimum value P2 as the end point, the time from the start point to the end point is calculated, and the calculation result is stored as the pulse period. The pulse rate is obtained from the pulse period. Also, the minimum value P2 of the next point is registered as the start point of the calculation for obtaining the next cycle, and an area shifted by the search window SW is set as a new search window SW. Further, a waveform correction algorithm is executed to correct a distorted pulse wave waveform.

  The waveform correction algorithm is performed by the waveform correction unit 64 of the pattern matching unit 60. Specifically, a process of converting each data of the waveform for one period with a conversion function (rotation matrix) shown below is executed.

Here, A = (P2y−P1y) / (P2x−P1x). (U (i) x, U (i) y) indicate coordinates (time, signal intensity) after waveform correction of the i-th data from the initial point P. (P1x, P1y) indicates the coordinates (time, signal strength) of the initial point P1. (P2x, P2y) indicates the coordinates (time, signal strength) of the point P2 after one cycle from the initial point P1. Therefore, the inclination angle A indicates the inclination angle of the virtual line f _A connecting minimum point together. As shown in FIG. 11, when the waveform of the pulse wave is distorted so as to decrease as a whole from the initial point P1, the signals at the points P1 and P2 are converted after the waveform conversion by the pattern matching unit 44. The intensity difference becomes zero, and the signal intensity at each point in between is corrected. There is no data change in the time direction. In the conversion function, the inclination angles A and P1y are updated according to the waveform of the pulse wave for one period.

  After performing this process, the process of verifying the validity of the waveform shown in FIG. 8 (step S313) is performed. In this process, the signal intensity difference (peak value) between the minimum value P1 or the minimum value P2 and the maximum value Q1 is examined and compared with the past peak value. In the first process, there is no past peak value, but this process is performed in the second and subsequent processes. If the peak value is within the preset allowable error, it is considered to be appropriate, and the process returns from step S314 to step S303. In this case, the above-described process is repeated, and the period is calculated in step S312 each time. On the other hand, if the current peak value is significantly different from the past peak value, the process proceeds from step S314 to step 315 as a waveform validity error.

Here, when it is determined that the above-described processing is incompatible (No in each of steps S306, S311, and S314), the pattern matching unit 44 functions as a correction unit and executes error correction processing (step S315). In this process, the search window SW is advanced by 25% of the provisional cycle Ta. Furthermore, the status is set to “0” and the initial point is derived again.
When the search window SW reaches the end point of the FFT size, the processing here is terminated and the process returns to the main flow of FIG.
As described above, the pulse rate determined to be appropriate as data for determining the apnea state is measured, and an apnea determination process to be described later is performed based on the signal of the pulse rate.

    Next, the apnea determination process (step S104) shown in FIG. 3 will be described in detail with reference to FIG. This process is performed in the apnea determination unit 6 of FIG. Apnea determination is a process of determining that an apnea has occurred after falling asleep, or examining a time zone in which apnea occurs frequently. FIG. 13 is a graph showing changes over time in pulse rate during normal breathing. FIG. 14 is a graph showing changes over time in the pulse rate when the apnea state is repeated. In both cases, the horizontal axis indicates the passage of time, and the vertical axis indicates the pulse rate. During normal breathing with a breathing cycle T1, the pulse rate changes corresponding to the breathing cycle T1. In other words, respiratory changes occur during normal breathing. On the other hand, when repeating the apnea state of the apnea cycle T2, the pulse rate changes corresponding to the apnea cycle T2. That is, when the apnea state is repeated, an apnea fluctuation occurs. In general, the respiration cycle T1 is 2 seconds or more and 5 seconds or less, and the respiration frequency band f1 during normal respiration is 0.2 Hz or more and 0.5 Hz or less. On the other hand, the apnea cycle T2 is 10 seconds or more, and the apnea frequency band f2 is 0.1 Hz or less.

  In the apnea determination process, a pulse rate signal calculated by the pulse rate calculation unit 5 is used. The pulse rate signal is pulse rate time-series data sampled by the pulse rate calculation unit 5 at the sampling time Ts. The number of pulse rate time series data used for apnea determination is N (window size), and as shown in FIG. 15, apnea determination processing is performed using the latest N pulse rate time series data for each time τ. To implement. In this embodiment, Ts = 1 second, N = 256, and τ = 10 seconds.

  First, N pulse rate time-series data are sampled and stored in a memory (step S501). Next, discrete cosine transform (DCT) is performed on the N pulse rate time-series data stored in the memory (step S502). FIG. 16 shows pulse rate frequency data obtained by discrete cosine transform of the pulse rate time series data shown in FIG. 13 or FIG. The horizontal axis indicates the frequency [Hz], and the vertical axis indicates the gain. By performing discrete cosine transform, changes in pulse rate are represented by pulse rate frequency data, which is a spectrum signal in each frequency band, and the magnitude of fluctuations in pulse rate over time (fluctuation strength) is separated for each frequency band. It becomes possible to do. Thereby, the magnitude of the fluctuation strength in the normal (steady state) respiratory frequency band f1 and the apnea frequency band f2 can be grasped. As described above, the normal respiration frequency band f1 is set to 0.2 Hz to 0.5 Hz. On the other hand, the apnea frequency band f2 is 0.1 Hz or less, but is set to 0.025 Hz or more and 0.1 Hz or less in order to simplify the filtering process described later.

  The pulse rate frequency data is filtered (extracted) with a first band-pass filter set to a pass frequency band of 0.025 Hz to 0.1 Hz (step S503). Thereby, the apnea fluctuation signal (first spectrum signal) in the apnea frequency band f2 can be extracted. Further, the pulse rate frequency data is filtered (extracted) with a second band pass filter set to a pass frequency band of 0.2 Hz to 0.5 Hz (step S504). Thereby, the respiratory fluctuation signal (second spectrum signal) in the respiratory frequency band f1 can be extracted.

  Next, in order to clarify the magnitudes of the apnea variation signal (first spectrum signal) SB1 and the breathability variation signal (second spectrum signal) SB2 expressed by the spectrum signal in the arithmetic unit 10, The total size of each spectrum signal is obtained. Specifically, the intensity of each spectrum signal in each frequency band of the apnea fluctuation signal (first spectrum signal) SB1 is squared and added together (step S505), and the respiratory fluctuation signal ( Second spectrum signal) The intensity of each spectrum signal in each frequency band of SB2 is squared and added together (step S506). The signal obtained as a result is an index value indicating the magnitude of the apnea fluctuation signal and the respiratory fluctuation signal, and the apnea frequency band power value (first signal strength) P1 and the breath frequency band power value (first number). 2 signal intensity) P2.

  Finally, the determination unit 11 divides the apnea frequency band power value (first signal intensity) P1 by the respiratory frequency band power value (second signal intensity) P2 to obtain an apnea state index (ratio signal) S. If the apnea condition index S exceeds a predetermined value, it is determined that the patient is in an apnea condition (step S507).

  Since apnea determination can be performed by processing the time-series data of the pulse rate detected by one light emitting element 20, compared to the conventional case where light of two specific wavelengths is used, Processing can be simplified. Instead of determining an apnea state based on the magnitude of the apnea frequency band power value P1, an apnea state index (ratio signal) S, which is a relative value of the apnea frequency band power value P1 and the breath frequency band power value P2. Since the apnea state is determined from the size, the apnea state can be determined without being affected by disturbances such as individual differences and body movements.

Note that the present invention can be widely applied without being limited to the above-described embodiment.
For example, the method of frequency transformation (direct transformation) is not limited to discrete cosine transformation, but may be Fourier transformation (FFT) or Wavelet transformation.

  The light emitting element may be a photoelectric element that outputs an analog signal. When calculating the period of the pulse wave, it may be possible to extract data for one period by paying attention to the maximum point of the pulse wave. The conversion function for correcting the pulse wave is not limited to a function that performs coordinate conversion so that the virtual line connecting the start point and the end point is the base, and the curve that passes through the start point and the end point is assumed to be virtual. The specified element may be used in the conversion function. At the time of apnea determination, the signal intensity after filtering processing is squared. However, since this is a technique for obtaining the magnitude of the signal, another technique such as simply taking an absolute value may be used.

  Further, the means for obtaining the pulse rate may not be a pulse group sensor having a light emitting element and a light receiving element, and may be, for example, an electrocardiograph or other pulse meter for measuring a human heart rate. Furthermore, the method for obtaining the pulse rate from the pulse group is not based on the photoelectric method, but may be a method using a piezoelectric method.

It is a block diagram of the apnea state determination apparatus in the embodiment of the present invention. It is a block diagram of the pulse rate calculation part in the embodiment of the present invention. It is a figure which shows the main flowchart in an apnea determination process. It is a flowchart which shows the detail of a frequency filter process. It is a figure which shows an example of the time series waveform obtained from a pulse group sensor. It is a figure which shows an example of the frequency spectrum obtained by Fourier-transforming a time series waveform. It is a figure explaining the process which derives | leads-out an initial point. It is a flowchart which shows the detail of a waveform pattern matching process. It is a figure explaining the processing which derives the next point. It is a figure explaining the processing which derives the next point. It is a conceptual diagram explaining the process which correct | amends a waveform. It is a figure which shows the flowchart of an apnea determination process. It is a figure which shows the change of the pulse rate at the time of normal respiration. It is a figure which shows the change of the pulse rate when repeating an apnea state. It is a figure which shows the data used for an apnea determination process. It is a figure which shows an example of the frequency spectrum obtained by carrying out discrete cosine transformation of the time series data of a pulse rate.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Apnea state determination apparatus 2 Pulse group sensor 20 Light receiving element 21 Light emitting element 3 Analysis apparatus 4 Output part 5 Pulse rate calculation part 50 Frequency filter process part 51 Fourier transformer
52 Frequency Filter 53 Inverse Fourier Transformer 60 Pattern Matching Unit 61 Search Range Setting Unit 62 Reference Point Determination Unit 63 Pulse Rate Determination Unit 64 Waveform Correction Unit 6 Apnea Determination Unit 7 Discrete Cosine Transform Unit (Frequency Conversion Unit)
8 First band-pass filter 9 Second band-pass filter 10 Calculation unit 11 Determination unit SB1 Apnea change signal (first signal)
SB2 Respiratory fluctuation signal (second signal)
P1 Apnea frequency band power value (first average signal strength)
P2 Respiration frequency band power value (second average signal strength)
S Apnea status index (ratio signal)

Claims (4)

An apnea that has a light emitting element and a light receiving element, is attached to a part of a living body, has a pulse group sensor that measures the pulse group of the living body, and determines the apnea state of the living body from the pulse group signal from the pulse group sensor A state determination device,
From the pulse signal from the pulse sensor, a pulse rate calculation unit that calculates pulse rate time-series data indicating a temporal change in the pulse rate;
A frequency conversion means for converting the pulse rate time-series data calculated by the pulse rate calculation unit into pulse rate frequency data indicating a frequency spectrum of the pulse rate with time;
A first bandpass filter for extracting a first spectrum signal corresponding to a frequency band of a change in pulse rate during apnea from the pulse rate frequency data converted by the frequency conversion unit;
A second bandpass filter that extracts a second spectrum signal corresponding to a frequency band of a pulse rate change during normal breathing from the pulse rate frequency data converted by the frequency conversion unit;
Determining a first signal intensity indicating an overall magnitude of each spectrum signal of the first spectrum signal and a second signal intensity indicating an overall magnitude of each spectrum signal of the second spectrum signal; A calculation unit for calculating a ratio signal of the first signal strength to two signal strengths;
An apnea state determination apparatus comprising: a determination unit that determines whether or not the ratio signal by the calculation unit exceeds a predetermined value, and determines that the ratio signal exceeds the predetermined value.   The apnea condition determination apparatus according to claim 1, wherein the frequency conversion means converts the pulse rate time-series data at predetermined time intervals into pulse rate frequency data.   The calculation unit squares the intensity of each spectrum signal of the first spectrum signal, adds them to calculate the first signal intensity, and the intensity of each spectrum signal of the second spectrum signal The apnea state determination apparatus according to claim 1, wherein the second signal intensity is calculated by squaring and summing them. The pulse rate calculation unit includes a reference point determination unit that extracts a signal corresponding to the start point and a signal corresponding to the end point as one reference point from the pulse signal from the pulse sensor.
The apnea state determination apparatus according to any one of claims 1 to 3, wherein a pulse rate is calculated from a time interval between the reference points.

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