WO2015178439A2 - Device and method for supporting diagnosis of central/obstructive sleep apnea, and computer-readable medium having stored thereon program for supporting diagnosis of central/obstructive sleep apnea - Google Patents

Device and method for supporting diagnosis of central/obstructive sleep apnea, and computer-readable medium having stored thereon program for supporting diagnosis of central/obstructive sleep apnea Download PDF

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WO2015178439A2
WO2015178439A2 PCT/JP2015/064546 JP2015064546W WO2015178439A2 WO 2015178439 A2 WO2015178439 A2 WO 2015178439A2 JP 2015064546 W JP2015064546 W JP 2015064546W WO 2015178439 A2 WO2015178439 A2 WO 2015178439A2
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apnea
central
sleep
psd
diagnosis support
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PCT/JP2015/064546
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French (fr)
Japanese (ja)
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WO2015178439A3 (en
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佐藤紳一
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株式会社Ａｉｎｙ
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs

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  • the present invention relates to a sleep / apnea central / occlusion type diagnosis support technique for supporting a sleep / apnea syndrome (SAS) patient's central / occlusion diagnosis by a doctor or the like.
  • SAS sleep / apnea syndrome
  • Sleep apnea is classified into three types: central sleep apnea (CSA), obstructive sleep apnea (OSA) and a mixed type (MSA: mixed sleep apnea).
  • Central sleep apnea is an apnea disease caused by abnormal function of the respiratory center
  • obstructive sleep apnea is a disease caused by obstruction of the upper airway.
  • An overnight polysomnography (PSG) test device can diagnose SAS, including central (CSA) and obstructive (OSA) determinations.
  • a respiratory belt sensor (abdominal belt sensor or chest belt sensor) is attached to the body in order to detect a respiratory state (apnea state) during sleep.
  • a respiratory state a respiratory state
  • the polarity inversion of the detection values of both the chest and abdominal belt sensors reflecting that the expansion and contraction of the chest and abdomen are in opposite phases during obstructive apnea is determined to be obstructive.
  • wearing of an abdominal belt or a chest belt may become a big burden on a test subject, and may reduce the quality of sleep.
  • the measurement signal waveform obtained by the subject's breathing has an extremely large amount of information.
  • the recorded waveform needs to be enlarged and observed for the determination of centrality (CSA) and obstructive apnea (OSA), and it takes a lot of time for doctors and technicians to diagnose CSA and OSA. It takes time and effort.
  • CSA centrality
  • OSA obstructive apnea
  • the object of the present invention is to provide quantitative analysis data using a respiratory sensor system that does not wear a breathing belt sensor to a subject during a sleep apnea test, thereby providing sleep apnea syndrome ( (SAS) It is to provide a central / occlusion type diagnosis technique of sleep apnea that supports a central / occlusion type diagnosis of a patient.
  • SAS sleep apnea syndrome
  • the present inventor has found the following characteristic facts in a raw waveform analysis of a biological signal obtained by a piezoelectric sensor.
  • the centralized apnea (CSA) and obstructive apnea (OSA) differ greatly in the PSD total value (integrated value) in the respiratory motion frequency region in the FFT analysis (the PSD total value of OSA is that of CSA). More markedly).
  • the respiratory motion frequency region can be set to 0.2 to 0.6 Hz, for example. In addition, when the respiratory motion frequency is less than 0.1 Hz, it is determined that there is apnea.
  • B In central sleep apnea (CSA) and obstructive sleep apnea (OSA), in the power spectral density (PSD) graph when FFT analysis of both is performed, from the frequency of heartbeat (for example, 1 Hz)
  • PSD power spectral density
  • C When there is arrhythmia, the cardiac cycle fluctuation becomes large, and even if it is central apnea (CSA), the PSD pattern becomes obstructive apnea (OSA) type.
  • a raw waveform data storage unit for storing raw waveform data including a subject's respiratory motion and heart sounds;
  • An apnea period detection unit that extracts a respiratory motion frequency component of the raw waveform from the raw waveform data stored in the raw waveform data storage unit and detects an apnea period;
  • An FFT processing unit that performs fast Fourier transform (FFT) processing on the data relating to the apnea period of the raw waveform data to generate a power spectral density (PSD) graph in the apnea period;
  • a PSD total value calculation unit for calculating a PSD total value in a predetermined frequency region;
  • a PSD pattern matching judgment unit for judging whether or not the generated PSD pattern matches a central apnea PSD pattern; and,
  • a display unit for displaying the raw waveform, at least a respiratory motion frequency component of the raw waveform, and the
  • a central / occlusion type diagnosis support apparatus comprising: Note that “directly placed in close contact with the subject's body” means “the sensor is placed in direct contact with the subject's skin” and “indirectly in close contact with the subject's body” "Disposed as” means that "the sensor is disposed so as to come into contact with the subject's body through sheets, pajamas, nightclothes, etc.”.
  • the sleep / apnea central / occlusion type diagnosis support apparatus is a central / occlusion type diagnosis support apparatus having a piezoelectric element as a sensing element.
  • the sleep / apnea central / occlusion type diagnosis support apparatus according to (1),
  • the raw waveform data is a sleep / apnea central / occlusion type diagnosis support apparatus further including a respiratory sound frequency component of the subject.
  • a sleep / apnea central / occlusion type diagnosis support apparatus further comprising:
  • the sleep / apnea central / occlusion type diagnosis support apparatus From the raw waveform data of the apnea period or the filtered heart sound frequency component data, the time (heart cycle) between the peaks of successive heart sounds is obtained, and one heart cycle (one sound minus one sound interval or 2 Sound-2 sound intervals) and the average of the difference between the next cardiac cycle and calculating the cardiac cycle fluctuation value,
  • a sleep / apnea central / occlusion type diagnosis support apparatus further comprising:
  • the sleep / apnea central / occlusion type diagnosis support apparatus has an analysis part,
  • the analysis unit By calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a first predetermined value or less than a second predetermined value and displaying the information on the display unit, obstructive sleep apnea (OSA) or sleep Assists in the diagnosis of temporal central apnea (CSA), Central / occlusion type diagnosis support device for sleep apnea.
  • OSA obstructive sleep apnea
  • CSA temporal central apnea
  • the sleep / apnea central / occlusion type diagnosis support apparatus When the PSD total value in the predetermined frequency region is a predetermined value or less, The analysis unit If the PSD pattern is a clear CSA type, CSA is determined and output. If the PSD pattern is not a clear CSA type, a cardiac cycle fluctuation value is further calculated, and information on whether the CSA is indeterminate or mixed apnea (MSA) is displayed on the display unit depending on whether it is greater than or equal to a predetermined value. To help determine if it is obstructive apnea (OSA), central apnea (CSA), or indeterminate (or MSA), Central / occlusion type diagnosis support device for sleep apnea.
  • OSA obstructive apnea
  • CSA central apnea
  • MSA obstructive apnea
  • a raw waveform data storage step for storing raw waveform data including a subject's breathing motion and heart sound;
  • An apnea period detection step for extracting a respiratory motion frequency component of the raw waveform from the raw waveform data stored in the raw waveform data storage step and detecting an apnea period;
  • FFT processing step of performing a fast Fourier transform process on the data related to the apnea period of the raw waveform data to generate a PSD in the predetermined frequency region, PSD sum value calculation step for calculating a power spectrum density (PSD) sum value in a predetermined frequency region;
  • a PSD pattern matching determination step of determining whether or not the generated PSD pattern matches a central apnea PSD pattern; and, Displaying the raw waveform, at least the respiratory motion frequency component of the raw waveform and the PSD;
  • a sleep / apnea central / occlusion type diagnosis support method comprising:
  • the heart rate respiration sensor unit is a central / occlusion type diagnosis support method having a piezoelectric element as a sensing element.
  • the raw waveform data is a sleep / apnea central / occlusion type diagnosis support method further including a respiratory sound frequency component of the subject.
  • a sleep / apnea central / occlusion type diagnosis support method further comprising:
  • the sleep / apnea central / occlusion type diagnosis support method From the raw waveform data of the apnea period or the filtered heart sound frequency component data, the time (heart cycle) between the peaks of successive heart sounds is obtained, and one heart cycle (one sound minus one sound interval or 2 Sound-cycle interval) and the average of the difference between the next cardiac cycle and the cardiac cycle fluctuation average calculation step to calculate the cardiac cycle fluctuation value,
  • a sleep / apnea central / occlusion type diagnosis support method further comprising:
  • the sound output unit has a sound quality adjustment function (the sound quality can be adjusted in the sound output step), and can output a heart sound / breathing sound (including snoring) and / or a sound accompanying body movement.
  • a piezoelectric sensor can be used as a heartbeat respiration sensor in order to detect heart sounds, respiratory motion, etc., and other sensors (for example, a sensor using a microphone) can also be used.
  • the heart rate respiration sensor can reinforce the central / occlusion type apnea discrimination by PSG, or the heart rate respiration sensor alone constitutes a simple type SAS monitoring device.
  • the apnea period can be determined for a period of 10 seconds or more according to the American Sleep Society (AASM) standard.
  • AASM American Sleep Society
  • the respiratory amplitude is close to zero in the signal display of the respiratory motion waveform (respiratory motion frequency component) of the normal amplitude (for example, 1/5, 1/6 of the normal respiratory motion waveform, 1/7, 1/8, 1/9, or 1/10 or less)
  • the signal is continuous for 10 seconds or more.
  • the PSD total value calculation unit calculates the power spectral density (PSD) total value as A1-A2 Hz (A1: 0.1-0.3, A2 : 0.4-0.8).
  • This frequency range is preferably 0.1-0.8 Hz, 0.1-0.7 Hz, 0.1-0.6 Hz, 0.1-0.5 Hz, 0.1-0.4 Hz,. 2-0.4Hz, 0.2-0.7Hz, 0.2-0.8Hz, 0.3-0.8Hz, 0.3-0.7Hz, 0.3-0.6Hz, 0.3- 0.5 Hz, or 0.3-0.4 Hz, particularly preferably 0.2-0.5 Hz, or 0.2-0.6 Hz.
  • the PSD pattern match determination unit in the PSD pattern match determination step, normally determines whether or not the pattern matches the central apnea PSD pattern by B1-B2 Hz of the generated PSD pattern.
  • the range may be (B1: 0.5-1.0, B2: 10-60) Hz.
  • B1 is 0.5, 0.8, 1.0
  • B2 is 10, 20, 30, 40, 50, 60.
  • the first to sixth harmonics appearing in the PSD pattern are used as objects to be matched with the CSA PSD pattern.
  • the CSA-type PSD pattern has a single peak at a height where the PSD increases about 100 times from the bottom of the peak in the PSD graph, and its harmonics are about the same up to about 10 to 40 Hz. The one that appears continuously while maintaining the peak height.
  • G In the present invention, whether CSA is undecidable or mixed apnea (MSA) is determined based on whether the cardiac cycle fluctuation value is greater than or equal to a predetermined value C1. This determination can be made based on whether the cardiac cycle fluctuation value is greater than or less than a predetermined value C1 (C1: 0.1, 0.2, 0.3, 0.4, or 0.5 seconds).
  • the present invention supports the determination of central apnea (CSA) and obstructive apnea (OSA) during each apnea period. For example, a doctor and a sleep medical certified laboratory technician comprehensively analyze these individual determinations. Thereby, doctors or the like can determine whether the patient's individual sleep apnea syndrome (SAS) is central or obstructive.
  • SAS sleep apnea syndrome
  • the breathing belt sensor is not attached to the subject during the examination, the subject is not physically burdened. Therefore, it is possible to maintain a quality equivalent to that of sleep in an individual home without disturbing sleep, and support a more accurate central / obstructive diagnosis of a sleep apnea syndrome (SAS) patient.
  • SAS sleep apnea syndrome
  • FIG. 1 is a block diagram showing a diagnosis support apparatus of the present invention.
  • FIG. 2 is an explanatory view showing a usage state of the piezoelectric sensor constituting the diagnosis support apparatus of the present invention.
  • FIG. 3 is a flowchart showing the diagnosis support method of the present invention.
  • FIG. 4 is a diagram showing a raw waveform acquired from the piezoelectric sensor constituting the diagnosis support apparatus of the present invention.
  • FIG. 5 is a diagram showing a waveform obtained by extracting a respiratory frequency component from the raw waveform of FIG.
  • FIG. 6 is a diagram showing a power spectrum density (PSD) obtained by identifying an apnea period from the waveform of FIG. 5 and obtaining from the waveform information of the apnea period.
  • PSD power spectrum density
  • FIG. 6 (A) is a diagram showing a central sleep apnea PSD.
  • FIG. 6 (B) is a diagram showing an obstructive sleep apnea PSD.
  • FIG. 7 is a flowchart showing a first example of diagnosis support.
  • FIG. 8 is a flowchart showing a second example of diagnosis support.
  • FIG. 9 is a flowchart showing a third example of diagnosis support.
  • FIG. 10 is a flowchart showing a fourth example of diagnosis support.
  • FIG. 11 is a flowchart showing a fifth example of diagnosis support.
  • FIG. 1 is an explanatory diagram showing a configuration of a sleep / apnea central / occlusion type diagnosis support apparatus (hereinafter also simply referred to as “diagnosis support apparatus”) according to the present invention.
  • the diagnosis support apparatus 1 includes a piezoelectric sensor unit 10, a raw waveform data storage unit 11, an apnea period detection unit 12, an FFT processing unit 13, a PSD total value calculation unit 14, a PSD pattern match determination unit 15, a cardiac cycle.
  • a variation average calculation unit 16 a display unit 17, a sound output unit 18, and an analysis unit 19 are provided.
  • each component is shown as a functional block, and hardware such as a CPU, storage (ROM, RAM, Hard Disk, etc.), communication circuit, display circuit, measurement board, bus, etc. is not described.
  • the blocks (“ ⁇ units”) that perform the functions of the present invention are realized by the programs stored in the hardware and storage.
  • the piezoelectric sensor unit 10 is disposed, for example, under a sheet (between the body of the subject M and the bed).
  • the piezoelectric sensor unit 10 can detect the respiratory movement and heart sound of the subject M, and send the detection result to the main body 100 of the diagnosis support apparatus 1 as raw waveform data (electrical signal).
  • the diagnosis support apparatus 1 includes a piezoelectric sensor unit 10 and a main body 100.
  • the raw waveform data storage unit 11 can receive raw waveform data from the piezoelectric sensor unit 10 via the amplifier A and store it.
  • the raw waveform data includes the respiratory sound frequency component of the subject M.
  • the apnea period detection unit 12 extracts a respiratory motion frequency component from the raw waveform data stored in the raw waveform data storage unit 11, and the amplitude fluctuation value is equal to or less than a predetermined value, which is 10 seconds or more in the present embodiment.
  • the period is detected as an apnea period.
  • the FFT processing unit 13 performs a fast Fourier transform process on the data related to the apnea period of the raw waveform data to generate PSD (power spectral density) in the apnea period.
  • PSD power spectral density
  • a raw waveform, a respiratory motion frequency component of the raw waveform, and a PSD graph are displayed on the display 172 of the display unit 17.
  • the PSD total value calculation unit 14 calculates a PSD total value (integral value) in a predetermined frequency region (0.2-0.6 Hz in the present embodiment). This calculation result is displayed numerically or graphically on the display 172 of the display unit 17 together with the PSD graph. When the total PSD value is equal to or greater than a predetermined value, it is suggested to a doctor or the like that the PSD pattern is a clear OSA type.
  • the PSD pattern match determination unit 15 determines whether or not the generated PSD pattern matches the central apnea PSD pattern when the total PSD value in the predetermined frequency region is equal to or less than the predetermined value.
  • the PSD pattern match determination unit 15 determines whether or not the PSD pattern is a clear CSA type, and whether or not the PSD pattern is a CSA type (whether “matches” or “mismatch” with the CSA type) It is displayed on the display 172 of the display unit 17. With reference to this display, doctors and the like can diagnose that sleep apnea is central (CSA) when the PSD pattern “matches” with the CSA type.
  • the cardiac cycle variation average calculation unit 16 obtains a time (cardiac cycle) between peaks of consecutive heart sounds from raw waveform data of an apnea period or filtered heart sound frequency component data. Then, the average of the difference between one heart cycle (one sound—one sound interval or two sounds—two sound intervals) and the next heart cycle is calculated to obtain the average value of heart cycle fluctuation.
  • the cardiac cycle variation average value and / or the comparison result (whether the cardiac cycle variation value is greater than or equal to the predetermined value) compared with the predetermined value is displayed on the display 172 of the display unit 17.
  • MSA mixed sleep apnea
  • the display unit 17 includes a display circuit 171 and a display 172.
  • the display circuit 171 displays the raw waveform, the respiratory motion frequency component, and the PSD graph on the display 172.
  • a doctor or the like can make an accurate diagnosis with reference to the displayed raw waveform, respiratory motion frequency component, and PSD graph.
  • the sound output unit 18 can output at least a respiratory sound component included in the raw waveform data.
  • the sound output unit 18 includes a sound circuit 181 and a speaker 182.
  • the sound circuit 181 can output from the speaker 182 heart sounds, breathing sounds (including snoring), and / or sounds accompanying body movements.
  • a signal related to a heart sound, a breathing sound, and a sound accompanying body motion can be output as an electric signal from the sound signal output terminal.
  • the analysis unit 19 can calculate whether the PSD sum value during the apnea period is greater than or equal to the first predetermined value or less than the second predetermined value. Then, by displaying the information on the display unit, diagnosis of obstructive apnea (OSA) or central apnea (CSA) is supported.
  • OSA obstructive apnea
  • CSA central apnea
  • FIG. 3 shows an embodiment of the sleep / apnea central / occlusion type diagnosis support method of the present invention.
  • Sensing step (S102) Raw waveform data is detected as an electrical signal from a piezoelectric sensor arranged in close contact with the body of the subject directly or indirectly.
  • Raw waveform data storage step (S104) Raw waveform data including breathing motion and heart sound of the subject is stored.
  • the raw waveform data further includes a respiratory sound frequency component of the subject. Whether the raw waveform data includes the respiratory sound frequency component of the subject depends on the characteristics of the sensor. The sensor used in this embodiment can extract a respiratory sound frequency component.
  • OSA obstructive apnea
  • CSA central apnea
  • FIG. 4A shows raw waveform data including respiratory motion and heart sounds of a patient with central apnea
  • FIG. 4B shows raw waveform data including respiratory motion and heart sounds of a patient with obstructive apnea.
  • the apnea period is indicated by a period P_CSA
  • the apnea period is indicated by a period P_OSA.
  • FIG. 5A shows a waveform obtained by subjecting the raw waveform data of FIG. 4A to low-pass filter processing (respiration motion frequency component extraction processing), and FIG. 5B shows the raw waveform data of FIG. The waveform which performed the low-pass filter process is shown.
  • the period P_CSA shows a waveform obtained by subjecting the raw waveform data of FIG. 4A to high-pass filter processing
  • FIG. 5D shows a waveform obtained by subjecting the raw waveform data of FIG. 4B to high-pass filter processing. Show. Breathing sounds, snoring, body movements, etc. are highlighted.
  • 5A and 5C the apnea period is indicated by a period P_CSA
  • FIGS. 5B and 5D the apnea period is indicated by a period P_OSA.
  • FIG. 6A is a diagram showing a PSD graph created by subjecting the raw waveform data of the central apnea patient of FIG. 4A to FFT processing
  • FIG. 6B is the obstructive property of FIG. 4B. It is a figure which shows the PSD graph produced by performing the FFT process to the raw waveform data of an apnea patient.
  • the area of the PSD combined value for the patient with central apnea is much smaller than the area of the PSD combined value for the patient with obstructive apnea. (See symbol a in FIGS. 6A and 6B).
  • a characteristic pattern appears in the PSD related to the patient with central apnea, but a characteristic pattern appears in the PSD related to the patient with obstructive apnea. Is not shown (see symbol b in FIGS. 6A and 6B).
  • FIG. 7 is a flowchart illustrating a first example of diagnosis support.
  • the analysis step 120A obtains the calculation result of the PSD total value (0.2 to 0.6 Hz) in the PSD total value calculation step S108 (step A1), and when the calculation result is larger than the predetermined value S (step A1).
  • “YES” in step A2) is determined as obstructive sleep apnea (OSA) (step A3), and when the calculation result is equal to or less than a predetermined value S (“NO” in step A2), central apnea ( CSA) (step A4).
  • the predetermined value S can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
  • the predetermined value S can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
  • FIG. 8 is a flowchart illustrating a second example of diagnosis support.
  • the analysis step 120B obtains the calculation result of the PSD total value (400 to 1,000 Hz) in the PSD total value calculation step S108 (step B1), and when the calculation result is larger than the predetermined value S (step B2). "YES") is determined as obstructive sleep apnea (OSA) (step B3), and when the calculation result is equal to or less than a predetermined value S ("NO" in step B2), central apnea (CSA) (Step B4).
  • the predetermined value S can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
  • FIG. 9 is a flowchart illustrating a third example of diagnosis support.
  • the analysis step 120C obtains the calculation result of the PSD total value in the PSD total value calculation step S108 (step C1), and when the calculation result is larger than the predetermined value S (in FIG. 9, when it is larger than six digits: "YES” in step C2) is determined as obstructive sleep apnea (OSA) (step C5).
  • OSA obstructive sleep apnea
  • step C3 when the PSD pattern is a clear CSA type (“YES” in step C3), it is determined that the patient has central apnea (CSA) (step C6).
  • the predetermined value S and the predetermined value T can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
  • FIG. 10 is a flowchart illustrating a fourth example of diagnosis support.
  • the analysis step 120D acquires the calculation result of the PSD total value in the PSD total value calculation step S108 (step D1), and when the calculation result is larger than the first predetermined value S1 (“YES” in step D2). Is determined to be obstructive sleep apnea (OSA) (step D4).
  • OSA sleep apnea
  • step D3 when the calculation result of the PSD total value is smaller than the second predetermined value S2 (“YES” in step D3), it is determined that the central sleep apnea (CSA) (step D6), and the PSD total value
  • the calculation result is equal to or greater than the second predetermined value S2 (“NO” in step D3), it is determined that the device is “Unknown” (CSA / OSA cannot be determined or mixed apnea (MSA)) (step D5).
  • the predetermined values S1 and S2 can be appropriately determined according to environmental conditions, physical and physiological conditions of the subject, and the like.
  • FIG. 11 is a flowchart illustrating a fifth example of diagnosis support.
  • the analysis step 120E obtains the calculation result of the PSD total value in the PSD total value calculation step S108 (step E1), and when the calculation result is larger than the first predetermined value S1 (“YES” in step E2). Is determined to be obstructive sleep apnea (OSA) (step E6).
  • OSA sleep apnea
  • step E3 when the calculation result of the PSD combined value is equal to or greater than the second predetermined value S2 (“NO” in step E3), it is determined that the device is “Unknown” (CSA / OSA cannot be determined or mixed apnea (MSA)). E8).
  • step E3 when the calculation result of the PSD total value is smaller than the second predetermined value S2 (“YES” in step E3), it is determined whether or not the PSD pattern is a clear CSA type (step E4).
  • step E4 when the PSD pattern is a clear CSA type (“YES” in step E4), it is determined as central apnea (CSA) (step E7).
  • step E4 when the PSD pattern is not a clear CSA type (“NO” in step E4), a cardiac cycle variation value (BBIV) is further calculated (step E5), and BBIV is a predetermined value T (for example, 0.2). ) (“YES” in step E5) is determined to be central apnea (CSA) (step E7), and when BBIV is equal to or smaller than a predetermined value T (“NO” in step E5), Unknown (CSA) / OSA cannot be determined or mixed apnea (MSA)) (step E8).
  • the predetermined values S1, S2 and the predetermined value T can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
  • BBIV means an average value of (n ⁇ 1) heartbeat interval differences, and is specifically obtained by the following calculation formula.
  • ABS means an absolute value.
  • k is a natural number representing the number of heartbeats, and Ik means the kth heartbeat interval (time difference between the kth heartbeat and the k + 1th heartbeat: 2 ⁇ k ⁇ n).
  • the sleep / apnea central / occlusion type diagnosis support program for executing the methods shown in FIGS. 7 to 11 can be recorded on a computer-readable recording medium.

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Abstract

　Provided is art for supporting the diagnosis of central/obstructive sleep apnea, the art enabling the accurate diagnosis of central/obstructive sleep apnea in a patient having sleep apnea syndrome (SAS).　The present invention is equipped with: a raw waveform data storage unit (11) that stores raw waveform data including the respiratory motion and heart sounds from a subject; an apnea period detection unit (12) that extracts, from the stored raw waveform data, a respiratory motion frequency component of the raw waveform, and detects an apnea period; an FFT processing unit (13) that performs a fast Fourier transform on data in the raw waveform data pertaining to the apnea period, and generates a power spectral density (PSD) for the apnea period; a PSD aggregate value calculation unit (14) that calculates a PSD aggregate value for a prescribed frequency domain; a PSD pattern matching unit (15) that evaluates whether the pattern of the generated PSD matches a PSD pattern for central apnea; and a display unit (17) that displays the raw waveform, the respiratory motion frequency component of the raw waveform, and the PSD.

Description

睡眠時無呼吸の中枢型／閉塞型診断支援装置および診断支援方法、ならびに睡眠時無呼吸の中枢型／閉塞型診断支援プログラムを記録したコンピュータ読み取り可能な記録媒体Sleep / apnea central / occlusion type diagnosis support device and diagnosis support method, and sleep apnea central / occlusion type diagnosis support program
　本発明は、医師等による睡眠時無呼吸症候群（ＳＡＳ：　Ｓｌｅｅｐ　Ａｐｎｅａ　Ｓｙｎｄｒｏｍｅ）患者の中枢型／閉塞型診断を支援する睡眠時無呼吸の中枢型／閉塞型診断支援技術に関する。 [Technical Field] The present invention relates to a sleep / apnea central / occlusion type diagnosis support technique for supporting a sleep / apnea syndrome (SAS) patient's central / occlusion diagnosis by a doctor or the like.
　睡眠時無呼吸は、中枢性（ＣＳＡ：　Ｃｅｎｔｒａｌ　Ｓｌｅｅｐ　Ａｐｎｅａ）と、閉塞性（ＯＳＡ：　Ｏｂｓｔｒｕｃｔｉｖｅ　Ｓｌｅｅｐ　Ａｐｎｅａ）およびその混合型（ＭＳＡ：　Ｍｉｘｅｄ　Ｓｌｅｅｐ　Ａｐｎｅａ）の３種類に分類される。
　中枢性睡眠時無呼吸は、呼吸中枢の機能異常により生じる無呼吸疾患であり、閉塞性睡眠時無呼吸は上気道の閉塞によって生じる疾患である。
　終夜睡眠ポリグラフ（ＰＳＧ：　Ｐｏｌｙｓｏｍｎｏｇｒａｐｈｙ）検査装置により、中枢性（ＣＳＡ）と、閉塞性（ＯＳＡ）の判断を含むＳＡＳの診断を行なうことができる。 Sleep apnea is classified into three types: central sleep apnea (CSA), obstructive sleep apnea (OSA) and a mixed type (MSA: mixed sleep apnea).
Central sleep apnea is an apnea disease caused by abnormal function of the respiratory center, and obstructive sleep apnea is a disease caused by obstruction of the upper airway.
An overnight polysomnography (PSG) test device can diagnose SAS, including central (CSA) and obstructive (OSA) determinations.
特許４５８８４６１号公報Japanese Patent No. 4588461
　ＰＳＧ検査装置では、睡眠時における呼吸状態（無呼吸状態）を検出するために、呼吸ベルトセンサ（腹ベルトセンサや胸ベルトセンサ）を身体に装着する。閉塞性無呼吸時に胸部と腹部の拡大・収縮が逆位相になることを反映した胸・腹ベルト両センサの検出値の極性反転が確認される場合、閉塞性であると判断できる。ところで、この種のＰＳＧ検査装置では、腹ベルトや胸ベルトの装着が、被験者にとって、大きな負担となり睡眠の質を低下させることがある。
　一方、被験者の呼吸により得られる測定信号波形は情報量が極めて多い。しかも、中枢性（ＣＳＡ）および閉塞性無呼吸（ＯＳＡ）の判定のために、記録した波形を拡大して観察する必要があり、医師や技師にとって、ＣＳＡおよびＯＳＡの診断のために膨大な時間と手間を要する。 In the PSG inspection device, a respiratory belt sensor (abdominal belt sensor or chest belt sensor) is attached to the body in order to detect a respiratory state (apnea state) during sleep. When the polarity inversion of the detection values of both the chest and abdominal belt sensors reflecting that the expansion and contraction of the chest and abdomen are in opposite phases during obstructive apnea is determined to be obstructive. By the way, in this kind of PSG test | inspection apparatus, mounting | wearing of an abdominal belt or a chest belt may become a big burden on a test subject, and may reduce the quality of sleep.
On the other hand, the measurement signal waveform obtained by the subject's breathing has an extremely large amount of information. Moreover, the recorded waveform needs to be enlarged and observed for the determination of centrality (CSA) and obstructive apnea (OSA), and it takes a lot of time for doctors and technicians to diagnose CSA and OSA. It takes time and effort.
　現実にはＳＡＳの判定の多くは、睡眠医療認定検査技師の目視による手作業に依存していること、およびＰＳＧで用いられるベルトセンサの緩みなどの原因により、呼吸努力検出感度が低下することから、中枢性／閉塞性の誤判断が医療の現場において皆無とはなっていない現実がある。
　本発明の目的は、睡眠時無呼吸検査に際して被検者に呼吸ベルトセンサを装着しない、呼吸センサシステムを用いて、定量的な解析データを提供することにより、医師等による睡眠時無呼吸症候群（ＳＡＳ）患者の中枢型／閉塞型診断を支援する睡眠時無呼吸の中枢型／閉塞型診断技術を提供することにある。 In reality, many SAS determinations depend on the visual inspection of a sleep medical certified laboratory technician, and the sensitivity of detecting respiratory effort decreases due to the looseness of the belt sensor used in PSG. There is a reality that central / obstructive misjudgment is not completely absent in the medical field.
The object of the present invention is to provide quantitative analysis data using a respiratory sensor system that does not wear a breathing belt sensor to a subject during a sleep apnea test, thereby providing sleep apnea syndrome ( (SAS) It is to provide a central / occlusion type diagnosis technique of sleep apnea that supports a central / occlusion type diagnosis of a patient.
　本発明者は、圧電センサにより得られた生体信号の生波形解析において、以下の特徴的な事実を見出した。
（ａ）中枢性無呼吸（ＣＳＡ）と、閉塞性無呼吸（ＯＳＡ）とでは、ＦＦＴ解析における呼吸運動周波数領域のＰＳＤ合算値（積分値）が大きく異なる（ＯＳＡのＰＳＤ合算値がＣＳＡのそれより著明に大きい）。呼吸運動周波数領域は、たとえば、０．２～０．６Ｈｚとすることができる。なお、呼吸運動周波数が、０．１Ｈｚ未満の場合には、無呼吸と判断される。
（ｂ）中枢性睡眠時無呼吸（ＣＳＡ）と、閉塞性睡眠時無呼吸（ＯＳＡ）とでは、両者をＦＦＴ解析した時のパワースペクトル密度（ＰＳＤ）グラフにおいて、心拍の周波数（たとえば１Ｈｚ）から４０Ｈｚまでの周波数範囲のＰＳＤパターンが大きく異なる。
（ｃ）不整脈がある場合、心周期変動が大きくなり、中枢性無呼吸（ＣＳＡ）であってもＰＳＤパターンは閉塞性無呼吸（ＯＳＡ）型となる。 The present inventor has found the following characteristic facts in a raw waveform analysis of a biological signal obtained by a piezoelectric sensor.
(A) The centralized apnea (CSA) and obstructive apnea (OSA) differ greatly in the PSD total value (integrated value) in the respiratory motion frequency region in the FFT analysis (the PSD total value of OSA is that of CSA). More markedly). The respiratory motion frequency region can be set to 0.2 to 0.6 Hz, for example. In addition, when the respiratory motion frequency is less than 0.1 Hz, it is determined that there is apnea.
(B) In central sleep apnea (CSA) and obstructive sleep apnea (OSA), in the power spectral density (PSD) graph when FFT analysis of both is performed, from the frequency of heartbeat (for example, 1 Hz) The PSD patterns in the frequency range up to 40 Hz are greatly different.
(C) When there is arrhythmia, the cardiac cycle fluctuation becomes large, and even if it is central apnea (CSA), the PSD pattern becomes obstructive apnea (OSA) type.
　本発明の睡眠時無呼吸の中枢型／閉塞型診断支援装置は、以下を要旨とする。
（１）
　被験者の呼吸運動および心音を含む生波形データを記憶する生波形データ記憶部、
　前記生波形データ記憶部に記憶した生波形データから前記生波形の呼吸運動周波数成分を抽出し、無呼吸期間を検出する無呼吸期間検出部、
　前記生波形データの無呼吸期間に係るデータに高速フーリエ変換（ＦＦＴ）処理を施し前記無呼吸期間におけるパワースペクトル密度（ＰＳＤ）グラフを生成するＦＦＴ処理部、
　所定周波数領域のＰＳＤ合算値を計算するＰＳＤ合算値計算部、
　前記生成したＰＳＤのパターンが、中枢性無呼吸のＰＳＤのパターンに一致するか否かを判断するＰＳＤパターン一致判断部、
　および、
　前記生波形、少なくとも、前記生波形の呼吸運動周波数成分および前記ＰＳＤグラフを表示する表示部、
を備えた睡眠時無呼吸の中枢型／閉塞型診断支援装置。
　たとえば、無呼吸期間は、アメリカ睡眠学会（ＡＡＳＭ：Ａｍｅｒｉｃａｎ　Ａｃａｄｅｍｙ　ｏｆ　Ｓｌｅｅｐ　Ｍｅｄｉｃｉｎｅ）で定義される。
　パターン一致判断部は、周知の画像認識アルゴリズムを実行する。たとえば、テンプレートマッチングにより、対象パターン（テンプレート）との照合を行うことができる。 The gist of the sleep / apnea central / occlusion type diagnosis support device of the present invention is summarized as follows.
(1)
A raw waveform data storage unit for storing raw waveform data including a subject's respiratory motion and heart sounds;
An apnea period detection unit that extracts a respiratory motion frequency component of the raw waveform from the raw waveform data stored in the raw waveform data storage unit and detects an apnea period;
An FFT processing unit that performs fast Fourier transform (FFT) processing on the data relating to the apnea period of the raw waveform data to generate a power spectral density (PSD) graph in the apnea period;
A PSD total value calculation unit for calculating a PSD total value in a predetermined frequency region;
A PSD pattern matching judgment unit for judging whether or not the generated PSD pattern matches a central apnea PSD pattern;
and,
A display unit for displaying the raw waveform, at least a respiratory motion frequency component of the raw waveform, and the PSD graph;
A sleep / apnea central / occlusion type diagnosis support apparatus comprising:
For example, the apnea period is defined by the American Academic Society of Sleep Medicine (AASM).
The pattern matching determination unit executes a known image recognition algorithm. For example, matching with a target pattern (template) can be performed by template matching.
（２）
　（１）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　さらに、前記生波形データを前記生波形に係る電気信号として検出する、直接または間接に前記被験者の身体に密着して配置される心拍呼吸センサ部、
を備えた中枢型／閉塞型診断支援装置。
　なお、「直接に前記被験者の身体に密着して配置される」とは、「センサが被験者の膚に直接接触するように配置される」の意味であり、「間接に前記被験者の身体に密着して配置される」とは、「センサが、シーツ、パジャマ・寝巻き等を介して被験者の身体に接触するように配置される」意味である。 (2)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (1),
Further, a heartbeat respiration sensor unit that detects the raw waveform data as an electrical signal related to the raw waveform, is disposed in close contact with the subject's body directly or indirectly,
A central / occlusion type diagnosis support apparatus comprising:
Note that “directly placed in close contact with the subject's body” means “the sensor is placed in direct contact with the subject's skin” and “indirectly in close contact with the subject's body” "Disposed as" means that "the sensor is disposed so as to come into contact with the subject's body through sheets, pajamas, nightclothes, etc.".
（３）
　（２）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　前記心拍呼吸センサ部は、センシング素子として圧電素子を有している中枢型／閉塞型診断支援装置。 (3)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (2),
The heartbeat respiration sensor unit is a central / occlusion type diagnosis support apparatus having a piezoelectric element as a sensing element.
（４）
　（１）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　前記生波形データは、前記被験者の呼吸音周波数成分をさらに含む睡眠時無呼吸の中枢型／閉塞型診断支援装置。 (4)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (1),
The raw waveform data is a sleep / apnea central / occlusion type diagnosis support apparatus further including a respiratory sound frequency component of the subject.
（５）
　（１）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　少なくとも前記生波形データに含まれる呼吸音成分を音出力することまたは当該呼吸音成分を電気信号として出力する音出力部、
をさらに備えた睡眠時無呼吸の中枢型／閉塞型診断支援装置。 (5)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (1),
A sound output unit for outputting at least a respiratory sound component included in the raw waveform data or outputting the respiratory sound component as an electrical signal;
A sleep / apnea central / occlusion type diagnosis support apparatus further comprising:
（６）
　（１）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　無呼吸期間の前記生波形データまたはフィルタ処理された心音周波数成分データから、連続する心音のピークとピークの間の時間（心周期）を求め、ひとつの心周期（１音―１音間隔または２音―２音間隔）と次の心周期との差の平均を計算し、心周期変動値を求める心周期変動平均計算部、
をさらに備えた睡眠時無呼吸の中枢型／閉塞型診断支援装置。 (6)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (1),
From the raw waveform data of the apnea period or the filtered heart sound frequency component data, the time (heart cycle) between the peaks of successive heart sounds is obtained, and one heart cycle (one sound minus one sound interval or 2 Sound-2 sound intervals) and the average of the difference between the next cardiac cycle and calculating the cardiac cycle fluctuation value,
A sleep / apnea central / occlusion type diagnosis support apparatus further comprising:
（７）
　（１）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　さらに、解析部を備え、
　前記解析部は、
　前記所定周波数領域における前記ＰＳＤ合算値が、第１所定値以上かまたは第２所定値以下かを計算しその情報を前記表示部に表示することによって、閉塞性睡眠時無呼吸（ＯＳＡ）か睡眠時中枢性無呼吸（ＣＳＡ）かの診断を支援する、
睡眠時無呼吸の中枢型／閉塞型診断支援装置。 (7)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (1),
Furthermore, it has an analysis part,
The analysis unit
By calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a first predetermined value or less than a second predetermined value and displaying the information on the display unit, obstructive sleep apnea (OSA) or sleep Assists in the diagnosis of temporal central apnea (CSA),
Central / occlusion type diagnosis support device for sleep apnea.
（８）
　（７）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　前記所定周波数領域における前記ＰＳＤ合算値が所定値以下の場合、
　前記解析部は、
　ＰＳＤパターンが明瞭なＣＳＡ型である場合はＣＳＡと判定出力し、
　ＰＳＤパターンが明瞭なＣＳＡ型でない場合は、さらに心周期変動値を計算し、所定値以上か以下かによってＣＳＡか判定不可能または混合型無呼吸（ＭＳＡ）かの情報を前記表示部に表示することにより、閉塞性無呼吸（ＯＳＡ）、中枢性無呼吸（ＣＳＡ）あるいは判定不可能（またはＭＳＡ）であるかの判断を支援する、
睡眠時無呼吸の中枢型／閉塞型診断支援装置。 (8)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (7),
When the PSD total value in the predetermined frequency region is a predetermined value or less,
The analysis unit
If the PSD pattern is a clear CSA type, CSA is determined and output.
If the PSD pattern is not a clear CSA type, a cardiac cycle fluctuation value is further calculated, and information on whether the CSA is indeterminate or mixed apnea (MSA) is displayed on the display unit depending on whether it is greater than or equal to a predetermined value. To help determine if it is obstructive apnea (OSA), central apnea (CSA), or indeterminate (or MSA),
Central / occlusion type diagnosis support device for sleep apnea.
（９）
　（７）または（８）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
　前記所定周波数領域前記ＰＳＤ合算値が所定値以上か以下かまたは中間値であるかを計算し、中間値の場合は判定不可能とする情報を前記表示部に追加表示することによって閉塞性無呼吸（ＯＳＡ）、中枢性無呼吸（ＣＳＡ）あるいは判定不可能（またはＭＳＡ）であるかの判断を支援する、
睡眠時無呼吸の中枢型／閉塞型診断支援装置。 (9)
The sleep / apnea central / occlusion type diagnosis support apparatus according to (7) or (8),
An obstructive apnea is calculated by calculating whether the PSD total value is greater than or less than a predetermined value or an intermediate value in the predetermined frequency region, and additionally displaying information indicating that the PSD cannot be determined if the intermediate value is an intermediate value on the display unit. (OSA) to help determine if central apnea (CSA) or indeterminate (or MSA)
Central / occlusion type diagnosis support device for sleep apnea.
　本発明の睡眠時無呼吸の中枢型／閉塞型診断支援方法は、以下を要旨とする。
（１０）
　被験者の呼吸運動および心音を含む生波形データを記憶する生波形データ記憶ステップ、
　前記生波形データ記憶ステップにおいて記憶した生波形データから前記生波形の呼吸運動周波数成分を抽出し、無呼吸期間を検出する無呼吸期間検出ステップ、
　前記生波形データの無呼吸期間に係るデータに高速フーリエ変換処理を施し前記所定周波数領域におけるＰＳＤを生成するＦＦＴ処理ステップ、
　所定周波数領域のパワースペクトル密度（ＰＳＤ）合算値を計算するＰＳＤ合算値計算ステップ、
　前記生成したＰＳＤのパターンが、中枢性無呼吸のＰＳＤのパターンに一致するか否かを判断するＰＳＤパターン一致判断ステップ、
　および、
　前記生波形、少なくとも、前記生波形の呼吸運動周波数成分および前記ＰＳＤを表示する表示ステップ、
を有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。
　たとえば、無呼吸期間は、アメリカ睡眠学会（ＡＡＳＭ：Ａｍｅｒｉｃａｎ　Ａｃａｄｅｍｙ　ｏｆ　Ｓｌｅｅｐ　Ｍｅｄｉｃｉｎｅ）で定義される。
　パターン一致判断部は、周知の画像認識アルゴリズムを実行する。たとえば、テンプレートマッチングにより、対象パターン（テンプレート）との照合を行うことができる。 The gist of the sleep / apnea central / occlusion type diagnosis support method of the present invention is summarized as follows.
(10)
A raw waveform data storage step for storing raw waveform data including a subject's breathing motion and heart sound;
An apnea period detection step for extracting a respiratory motion frequency component of the raw waveform from the raw waveform data stored in the raw waveform data storage step and detecting an apnea period;
FFT processing step of performing a fast Fourier transform process on the data related to the apnea period of the raw waveform data to generate a PSD in the predetermined frequency region,
PSD sum value calculation step for calculating a power spectrum density (PSD) sum value in a predetermined frequency region;
A PSD pattern matching determination step of determining whether or not the generated PSD pattern matches a central apnea PSD pattern;
and,
Displaying the raw waveform, at least the respiratory motion frequency component of the raw waveform and the PSD;
A sleep / apnea central / occluded diagnosis support method comprising:
For example, the apnea period is defined by the American Academic Society of Sleep Medicine (AASM).
The pattern matching determination unit executes a known image recognition algorithm. For example, matching with a target pattern (template) can be performed by template matching.
（１１）
　（１０）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　さらに、直接または間接に被験者の身体に密着して配置された心拍呼吸センサから、生波形データを電気
信号として検出するセンシングステップ、
を有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (11)
The sleep / apnea central / occlusion type diagnosis support method according to (10),
Furthermore, a sensing step of detecting raw waveform data as an electrical signal from a heartbeat respiration sensor arranged directly or indirectly in close contact with the subject's body,
A sleep / apnea central / occluded diagnosis support method comprising:
（１２）
　（１１）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　前記心拍呼吸センサ部は、センシング素子として圧電素子を有している中枢型／閉塞型
診断支援方法。 (12)
The sleep / apnea central / occlusion type diagnosis support method according to (11),
The heart rate respiration sensor unit is a central / occlusion type diagnosis support method having a piezoelectric element as a sensing element.
（１３）
　（１０）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　前記生波形データは、前記被験者の呼吸音周波数成分をさらに含む睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (13)
The sleep / apnea central / occlusion type diagnosis support method according to (10),
The raw waveform data is a sleep / apnea central / occlusion type diagnosis support method further including a respiratory sound frequency component of the subject.
（１４）
　（１０）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　少なくとも前記生波形データに含まれる呼吸音成分を音出力することまたは当該呼吸音成分を電気信号として出力する音出力ステップ、
をさらに有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (14)
The sleep / apnea central / occlusion type diagnosis support method according to (10),
A sound output step of outputting at least a respiratory sound component included in the raw waveform data or outputting the respiratory sound component as an electrical signal;
A sleep / apnea central / occlusion type diagnosis support method further comprising:
（１５）
　（１０）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　無呼吸期間の前記生波形データまたはフィルタ処理された心音周波数成分データから、連続する心音のピークとピークの間の時間（心周期）を求め、ひとつの心周期（１音―１音間隔または２音―２音間隔）と次の心周期との差の平均を計算し、心周期変動値を求める心周期変動平均計算ステップ、
をさらに有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (15)
The sleep / apnea central / occlusion type diagnosis support method according to (10),
From the raw waveform data of the apnea period or the filtered heart sound frequency component data, the time (heart cycle) between the peaks of successive heart sounds is obtained, and one heart cycle (one sound minus one sound interval or 2 Sound-cycle interval) and the average of the difference between the next cardiac cycle and the cardiac cycle fluctuation average calculation step to calculate the cardiac cycle fluctuation value,
A sleep / apnea central / occlusion type diagnosis support method further comprising:
（１６）
　（１０）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　さらに、解析ステップを備え、
　前記解析ステップでは、
　前記所定周波数領域における前記ＰＳＤ合算値が、第１所定値以上かまたは第２所定値以下かを計算しその情報を前記表示ステップに渡すことによって、閉塞性無呼吸（ＯＳＡ）か中枢性無呼吸（ＣＳＡ）かの診断を支援する、
睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (16)
The sleep / apnea central / occlusion type diagnosis support method according to (10),
And an analysis step
In the analysis step,
By calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a first predetermined value or less than a second predetermined value and passing the information to the display step, obstructive apnea (OSA) or central apnea (CSA) to support the diagnosis,
Central / obstructive diagnosis support method for sleep apnea.
（１７）
　（１６）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　前記所定周波数領域における前記ＰＳＤ合算値が所定値以下の場合、
　前記解析ステップでは、
　ＰＳＤパターンが明瞭なＣＳＡ型である場合はＣＳＡと判定出力し、
　ＰＳＤパターンが明瞭なＣＳＡ型でない場合は、さらに心周期変動値を計算し、所定値以上か以下かによってＣＳＡか判定不可能または混合型無呼吸（ＭＳＡ）かの情報を前記表示ステップに渡すことによりＯＳＡ、ＣＳＡあるいは判定不可能（またはＭＳＡ）であるかの判断を支援する、
睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (17)
The sleep / apnea central / occlusion type diagnosis support method according to (16),
When the PSD total value in the predetermined frequency region is a predetermined value or less,
In the analysis step,
If the PSD pattern is a clear CSA type, CSA is determined and output.
If the PSD pattern is not a clear CSA type, the cardiac cycle fluctuation value is further calculated, and information indicating whether the CSA is indeterminate or mixed apnea (MSA) is passed to the display step depending on whether it is greater than or less than a predetermined value. Assists in determining whether OSA, CSA or indeterminate (or MSA)
Central / obstructive diagnosis support method for sleep apnea.
（１８）
　（１５）または（１６）に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
　前記所定周波数領域における前記ＰＳＤ合算値が所定値以上か以下かまたは中間値であるかを計算し、中間値の場合は判定不可能とする情報を前記表示ステップに追加して渡すことによってＯＳＡ、ＣＳＡあるいは判定不可能（または，ＭＳＡ（ＯＳＡ／ＣＳＡ混合型））かの判断を支援することを特徴とする睡眠時無呼吸の中枢型／閉塞型診断支援方法。 (18)
(15) or the sleep / apnea central / occlusion type diagnosis support method according to (16),
OSA by calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a predetermined value or less or an intermediate value, and adding information that cannot be determined in the case of the intermediate value to the display step. A central / occluded type diagnosis support method for sleep apnea characterized by supporting judgment of CSA or determination impossible (or MSA (OSA / CSA mixed type)).
　本発明のコンピュータ読み取り可能な記録媒体は、以下を要旨とする。
（１９）
　（１０）から（１８）の何れか１項に記載の方法を実行する睡眠時無呼吸の中枢型／閉塞型診断支援プログラムを記録したコンピュータ読み取り可能な記録媒体。 The summary of the computer-readable recording medium of the present invention is as follows.
(19)
(10) A computer-readable recording medium recording a sleep / apnea central / occlusion type diagnostic support program for executing the method according to any one of (18).
（ａ）
　本発明では、音出力部は音質調整機能を有し（音出力ステップでは音質調整ができ）、心音・呼吸音（いびきを含む）およびまたは体動に付随する音を出力することもできる。
（ｂ）
　本発明においては、心音、呼吸運動等を検出するために、心拍呼吸センサとして、圧電センサを使用することもできるし、他のセンサ（たとえば、マイクロフォンを用いたセンサ）を使用することもできる。
　心拍呼吸センサは、ＰＳＧの入力に加えることにより、ＰＳＧによる中枢型／閉塞型無呼吸の判別を補強することもできるし、心拍呼吸センサ単体で簡易型ＳＡＳモニタリング装置を構成し、中枢型／閉塞型無呼吸の判別を支援することもできる。
　無呼吸期間は、ＰＳＧ組み込み式の場合は、アメリカ睡眠学会（ＡＡＳＭ）の基準に従って１０秒以上の期間を決定できる。
　簡易型ＳＡＳモニタリング装置における無呼吸期間は、通常の振幅の呼吸運動波形（呼吸運動周波数成分）の信号表示において呼吸振幅がゼロに近い（例えば通常の呼吸運動波形の１／５、１／６、１／７、１／８、１／９または１／１０以下）信号が連続する１０秒以上の期間とする。
　この無呼吸期間を拡大表示した場合は、呼吸努力およびまたは心音のアーチファクト（低周波数成分）が観測される。
（ｃ）
　本発明では、無呼吸期間検出部は（無呼吸期間検出ステップでは）、生波形に現れる無呼吸期間のうち、たとえば５．１２，たとえば１０．２４，たとえば２０．４８（×ｎ：ｎ＝１，２，３，４・・・）秒の期間を検出する。
　詳しく説明すると、ＦＦＴ解析に必要なフレームサイズは２の冪乗で指定する必要があり、したがって、このフレームサイズは、記録データのサンプリング周波数が２００Ｈｚの場合（サンプリング間隔は５ミリ秒）、１０２４ポイントでは５．１２秒、２０４８ポイントでは１０．２４秒，４０９６ポイントでは２０．４８秒となる。
（ｄ）
　本発明では、ＰＳＤ合算値計算部が（ＰＳＤ合算値計算ステップでは）、パワースペクトル密度（ＰＳＤ）合算値を計算する所定周波数領域は、Ａ１－Ａ２Ｈｚ（Ａ１：０．１－０．３、Ａ２：０．４－０．８）とすることができる。
　この周波数範囲は、好ましくは、０．１－０．８Ｈｚ、０．１－０．７Ｈｚ、０．１－０．６Ｈｚ、０．１－０．５Ｈｚ、０．１－０．４Ｈｚ、０．２－０．４Ｈｚ、０．２－０．７Ｈｚ、０．２－０．８Ｈｚ、０．３－０．８Ｈｚ、０．３－０．７Ｈｚ、０．３－０．６Ｈｚ、０．３－０．５Ｈｚ、または０．３－０．４Ｈｚ、であり、特に、好ましくは、０．２－０．５Ｈｚ、または０．２－０．６Ｈｚである。
（ｅ）
　本発明では、ＰＳＤパターン一致判断部は（ＰＳＤパターン一致判断ステップでは）、中枢性無呼吸のＰＳＤのパターンに一致するか否かの判断は、通常は、生成したＰＳＤのパターンの、Ｂ１－Ｂ２Ｈｚ（Ｂ１：０．５－１．０、Ｂ２：１０－６０）Ｈｚの領域とすることができる。
　好ましくは、たとえばＢ１は、０．５，０．８，１．０であり、たとえばＢ２は、１０，２０，３０，４０，５０，６０である。
　通常、ＰＳＤのパターンに現れる、第１から第６高調波をＣＳＡ型ＰＳＤパターンとの一致判断の対象とする。
（ｆ）
　本発明では、ＣＳＡ型ＰＳＤパターンは、ＰＳＤグラフにおいて、ピーク底部から１００倍程度ＰＳＤが増加する高さの単峰ピークが心拍数の周波数に現れるとともに、その高調波が１０ないし４０Ｈｚ程度まで同程度のピーク高を維持しながら連続的に現れるものを言う。
（ｇ）
　本発明では、ＣＳＡか判定不可能または混合型無呼吸（ＭＳＡ）かの判断は、心周期変動値が所定値Ｃ１以上か以下かによって行なう。この判断は、心周期変動値が所定値Ｃ１（Ｃ１：０．１，０．２，０．３，０．４または０．５秒）以上か以下かによって行なうことができる。
（ｈ）
　本発明においては、各無呼吸期間の中枢性無呼吸（ＣＳＡ）と閉塞性無呼吸（ＯＳＡ）の判定を支援する。たとえば、医師、睡眠医療認定検査技師が、これらの個々の判定を総合的に解析する。これにより、医師等は、患者個人の睡眠時無呼吸症候群（ＳＡＳ）が、中枢型か閉塞型かを決定することができる。 (A)
In the present invention, the sound output unit has a sound quality adjustment function (the sound quality can be adjusted in the sound output step), and can output a heart sound / breathing sound (including snoring) and / or a sound accompanying body movement.
(B)
In the present invention, a piezoelectric sensor can be used as a heartbeat respiration sensor in order to detect heart sounds, respiratory motion, etc., and other sensors (for example, a sensor using a microphone) can also be used.
By adding to the input of PSG, the heart rate respiration sensor can reinforce the central / occlusion type apnea discrimination by PSG, or the heart rate respiration sensor alone constitutes a simple type SAS monitoring device. It can also assist in determining type apnea.
In the case of the PSG built-in type, the apnea period can be determined for a period of 10 seconds or more according to the American Sleep Society (AASM) standard.
During the apnea period in the simple SAS monitoring apparatus, the respiratory amplitude is close to zero in the signal display of the respiratory motion waveform (respiratory motion frequency component) of the normal amplitude (for example, 1/5, 1/6 of the normal respiratory motion waveform, 1/7, 1/8, 1/9, or 1/10 or less) The signal is continuous for 10 seconds or more.
When this apnea period is magnified, respiratory effort and / or heart sound artifacts (low frequency components) are observed.
(C)
In the present invention, the apnea period detection unit (in the apnea period detection step) includes, for example, 5.12, for example 10.24, for example 20.48 (× n: n = 1) among apnea periods appearing in the raw waveform. , 2, 3, 4...) Seconds are detected.
More specifically, the frame size required for the FFT analysis must be specified by a power of 2. Therefore, this frame size is 1024 points when the recording data sampling frequency is 200 Hz (sampling interval is 5 milliseconds). Is 5.12 seconds, 2048 points is 10.24 seconds, and 4096 points is 20.48 seconds.
(D)
In the present invention, the PSD total value calculation unit (in the PSD total value calculation step) calculates the power spectral density (PSD) total value as A1-A2 Hz (A1: 0.1-0.3, A2 : 0.4-0.8).
This frequency range is preferably 0.1-0.8 Hz, 0.1-0.7 Hz, 0.1-0.6 Hz, 0.1-0.5 Hz, 0.1-0.4 Hz,. 2-0.4Hz, 0.2-0.7Hz, 0.2-0.8Hz, 0.3-0.8Hz, 0.3-0.7Hz, 0.3-0.6Hz, 0.3- 0.5 Hz, or 0.3-0.4 Hz, particularly preferably 0.2-0.5 Hz, or 0.2-0.6 Hz.
(E)
In the present invention, the PSD pattern match determination unit (in the PSD pattern match determination step) normally determines whether or not the pattern matches the central apnea PSD pattern by B1-B2 Hz of the generated PSD pattern. The range may be (B1: 0.5-1.0, B2: 10-60) Hz.
Preferably, for example, B1 is 0.5, 0.8, 1.0, and for example, B2 is 10, 20, 30, 40, 50, 60.
Usually, the first to sixth harmonics appearing in the PSD pattern are used as objects to be matched with the CSA PSD pattern.
(F)
In the present invention, the CSA-type PSD pattern has a single peak at a height where the PSD increases about 100 times from the bottom of the peak in the PSD graph, and its harmonics are about the same up to about 10 to 40 Hz. The one that appears continuously while maintaining the peak height.
(G)
In the present invention, whether CSA is undecidable or mixed apnea (MSA) is determined based on whether the cardiac cycle fluctuation value is greater than or equal to a predetermined value C1. This determination can be made based on whether the cardiac cycle fluctuation value is greater than or less than a predetermined value C1 (C1: 0.1, 0.2, 0.3, 0.4, or 0.5 seconds).
(H)
The present invention supports the determination of central apnea (CSA) and obstructive apnea (OSA) during each apnea period. For example, a doctor and a sleep medical certified laboratory technician comprehensively analyze these individual determinations. Thereby, doctors or the like can determine whether the patient's individual sleep apnea syndrome (SAS) is central or obstructive.
　検査に際して被検者に呼吸ベルトセンサを装着しないので、被検者に肉体的には負担が生じない。そのため、睡眠を妨害せず個人の家庭における睡眠と同等の質を維持することができ、より正確な睡眠時無呼吸症候群（ＳＡＳ）患者の中枢型／閉塞型診断を支援することができる。 Because the breathing belt sensor is not attached to the subject during the examination, the subject is not physically burdened. Therefore, it is possible to maintain a quality equivalent to that of sleep in an individual home without disturbing sleep, and support a more accurate central / obstructive diagnosis of a sleep apnea syndrome (SAS) patient.
図１は本発明の診断支援装置を示すブロック図である。FIG. 1 is a block diagram showing a diagnosis support apparatus of the present invention. 図２は本発明の診断支援装置を構成する圧電センサの使用状態を示す説明図である。FIG. 2 is an explanatory view showing a usage state of the piezoelectric sensor constituting the diagnosis support apparatus of the present invention. 図３は本発明の診断支援方法を示すフローチャートである。FIG. 3 is a flowchart showing the diagnosis support method of the present invention. 図４は本発明の診断支援装置を構成する圧電センサから取得した生波形を示す図である。FIG. 4 is a diagram showing a raw waveform acquired from the piezoelectric sensor constituting the diagnosis support apparatus of the present invention. 図５は図４の生波形から呼吸周波成分を抽出した波形を示す図である。FIG. 5 is a diagram showing a waveform obtained by extracting a respiratory frequency component from the raw waveform of FIG. 図６は図５の波形から無呼吸期間を特定し当該無呼吸期間の波形情報から得られたパワースペクトル密度（ＰＳＤ）を示す図である。　図６（Ａ）は中枢性睡眠時無呼吸のＰＳＤを示す図である。　図６（Ｂ）は閉塞性睡眠時無呼吸のＰＳＤを示す図である。FIG. 6 is a diagram showing a power spectrum density (PSD) obtained by identifying an apnea period from the waveform of FIG. 5 and obtaining from the waveform information of the apnea period. FIG. 6 (A) is a diagram showing a central sleep apnea PSD. FIG. 6 (B) is a diagram showing an obstructive sleep apnea PSD. 図７は診断支援の第１の例を示すフローチャートである。FIG. 7 is a flowchart showing a first example of diagnosis support. 図８は診断支援の第２の例を示すフローチャートである。FIG. 8 is a flowchart showing a second example of diagnosis support. 図９は診断支援の第３の例を示すフローチャートである。FIG. 9 is a flowchart showing a third example of diagnosis support. 図１０は診断支援の第４の例を示すフローチャートである。FIG. 10 is a flowchart showing a fourth example of diagnosis support. 図１１は診断支援の第５の例を示すフローチャートである。FIG. 11 is a flowchart showing a fifth example of diagnosis support.
　図１は、本発明の睡眠時無呼吸の中枢型／閉塞型診断支援装置（以下、単に「診断支援装置」とも言う）の構成を示す説明図である。
　図１において、診断支援装置１は、圧電センサ部１０、生波形データ記憶部１１、無呼吸期間検出部１２、ＦＦＴ処理部１３、ＰＳＤ合算値計算部１４、ＰＳＤパターン一致判断部１５、心周期変動平均計算部１６、表示部１７、音出力部１８、および解析部１９を備えている。 FIG. 1 is an explanatory diagram showing a configuration of a sleep / apnea central / occlusion type diagnosis support apparatus (hereinafter also simply referred to as “diagnosis support apparatus”) according to the present invention.
In FIG. 1, the diagnosis support apparatus 1 includes a piezoelectric sensor unit 10, a raw waveform data storage unit 11, an apnea period detection unit 12, an FFT processing unit 13, a PSD total value calculation unit 14, a PSD pattern match determination unit 15, a cardiac cycle. A variation average calculation unit 16, a display unit 17, a sound output unit 18, and an analysis unit 19 are provided.
　図１では、各構成要素を機能ブロックで示してあり、ＣＰＵ，ストレージ（ＲＯＭ，ＲＡＭ，Ｈａｒｄ　Ｄｉｓｋ等），通信回路，表示回路，測定ボード，バス等のハードウェアは記載していない。本発明の各機能を奏するブロック（「～部」）は、上記ハードウェアおよびストレージに記憶されたプログラムにより実現される。 In FIG. 1, each component is shown as a functional block, and hardware such as a CPU, storage (ROM, RAM, Hard Disk, etc.), communication circuit, display circuit, measurement board, bus, etc. is not described. The blocks (“˜units”) that perform the functions of the present invention are realized by the programs stored in the hardware and storage.
　圧電センサ部１０は、図２に示すように、たとえばシーツの下（被験者Ｍの身体とベッドとの間）に配置される。圧電センサ部１０は、被験者Ｍの呼吸運動および心音を検出し、検出結果を生波形データ（電気信号）として診断支援装置１の本体１００に送出することができる。図１では、診断支援装置１は、圧電センサ部１０と本体１００とからなる。 As shown in FIG. 2, the piezoelectric sensor unit 10 is disposed, for example, under a sheet (between the body of the subject M and the bed). The piezoelectric sensor unit 10 can detect the respiratory movement and heart sound of the subject M, and send the detection result to the main body 100 of the diagnosis support apparatus 1 as raw waveform data (electrical signal). In FIG. 1, the diagnosis support apparatus 1 includes a piezoelectric sensor unit 10 and a main body 100.
　生波形データ記憶部１１は、圧電センサ部１０から生波形データを増幅器Ａを介して受け取り、これを記憶することができる。生波形データには、被験者Ｍの呼吸音周波数成分が含まれていている。 The raw waveform data storage unit 11 can receive raw waveform data from the piezoelectric sensor unit 10 via the amplifier A and store it. The raw waveform data includes the respiratory sound frequency component of the subject M.
　無呼吸期間検出部１２は、生波形データ記憶部１１に記憶されている生波形データから呼吸運動周波数成分を抽出し、振幅変動値が所定値以下で、本実施形態では、１０秒以上である期間を無呼吸期間として検出する。 The apnea period detection unit 12 extracts a respiratory motion frequency component from the raw waveform data stored in the raw waveform data storage unit 11, and the amplitude fluctuation value is equal to or less than a predetermined value, which is 10 seconds or more in the present embodiment. The period is detected as an apnea period.
　ＦＦＴ処理部１３は、生波形データの無呼吸期間に係るデータに高速フーリエ変換処理を施し無呼吸期間におけるＰＳＤ（パワースペクトル密度）を生成する。後述するように、表示部１７のディスプレイ１７２には、生波形、生波形の呼吸運動周波数成分およびＰＳＤグラフが表示される。 The FFT processing unit 13 performs a fast Fourier transform process on the data related to the apnea period of the raw waveform data to generate PSD (power spectral density) in the apnea period. As will be described later, a raw waveform, a respiratory motion frequency component of the raw waveform, and a PSD graph are displayed on the display 172 of the display unit 17.
　ＰＳＤ合算値計算部１４は、所定周波数領域（本実施形態では０．２－０．６Ｈｚ）におけるＰＳＤ合算値（積分値）を計算する。この計算結果は、表示部１７のディスプレイ１７２に、ＰＳＤグラフとともに、数値表示またはグラフ表示される。
　ＰＳＤ合算値が所定値以上の場合には、ＰＳＤパターンが明瞭なＯＳＡ型であることが医師等に示唆されたことになる。 The PSD total value calculation unit 14 calculates a PSD total value (integral value) in a predetermined frequency region (0.2-0.6 Hz in the present embodiment). This calculation result is displayed numerically or graphically on the display 172 of the display unit 17 together with the PSD graph.
When the total PSD value is equal to or greater than a predetermined value, it is suggested to a doctor or the like that the PSD pattern is a clear OSA type.
　ＰＳＤパターン一致判断部１５は、所定周波数領域におけるＰＳＤ合算値が所定値以下の場合に、生成したＰＳＤのパターンが、中枢性無呼吸のＰＳＤのパターンに一致するか否かを判断する。
　ＰＳＤパターン一致判断部１５は、ＰＳＤパターンが明瞭なＣＳＡ型であるか否かを判断し、ＰＳＤパターンがＣＳＡ型であるか否か（ＣＳＡ型と「一致」するか「不一致」か）が、表示部１７のディスプレイ１７２に表示される。
　この表示を参照して、医師等は、ＰＳＤパターンがＣＳＡ型と「一致」しているときは睡眠時無呼吸が中枢型（ＣＳＡ）であるとの診断を行なうことができる。 The PSD pattern match determination unit 15 determines whether or not the generated PSD pattern matches the central apnea PSD pattern when the total PSD value in the predetermined frequency region is equal to or less than the predetermined value.
The PSD pattern match determination unit 15 determines whether or not the PSD pattern is a clear CSA type, and whether or not the PSD pattern is a CSA type (whether “matches” or “mismatch” with the CSA type) It is displayed on the display 172 of the display unit 17.
With reference to this display, doctors and the like can diagnose that sleep apnea is central (CSA) when the PSD pattern “matches” with the CSA type.
　ＰＳＤパターン一致判断部１５が、ＰＳＤパターンが明瞭なＣＳＡ型でないと判断したときは、さらに心周期変動平均計算部１６の計算結果を参照する。心周期変動平均計算部１６は、無呼吸期間の生波形データまたはフィルタ処理された心音周波数成分データから、連続する心音のピークとピークの間の時間（心周期）を求める。そして、ひとつの心周期（１音―１音間隔または２音―２音間隔）と次の心周期との差の平均を計算し、心周期変動平均値を求める。 When the PSD pattern match determination unit 15 determines that the PSD pattern is not a clear CSA type, the calculation result of the cardiac cycle variation average calculation unit 16 is further referred to. The cardiac cycle variation average calculation unit 16 obtains a time (cardiac cycle) between peaks of consecutive heart sounds from raw waveform data of an apnea period or filtered heart sound frequency component data. Then, the average of the difference between one heart cycle (one sound—one sound interval or two sounds—two sound intervals) and the next heart cycle is calculated to obtain the average value of heart cycle fluctuation.
　この心周期変動平均値およびまたは所定値と比較した比較結果（心周期変動値が所定値以上か以下か）が表示部１７のディスプレイ１７２に表示される。
　これにより、医師等は、
（ｉ）周期変動平均値が所定値以上のときは、心拍数が極めて不安定なためにＰＳＤパターンにＣＳＡ型のピークが現れないが、ＣＳＡ型である、
（ｉｉ）周期変動平均値が所定値以下のときは、ＣＳＡ型かＯＳＡ型の判定が不可能または混合型睡眠時無呼吸（ＭＳＡ）である、
との診断を行なうことができる。 The cardiac cycle variation average value and / or the comparison result (whether the cardiac cycle variation value is greater than or equal to the predetermined value) compared with the predetermined value is displayed on the display 172 of the display unit 17.
This allows doctors to
(I) When the average value of periodic fluctuations is equal to or greater than a predetermined value, the heart rate is extremely unstable, and thus no CSA type peak appears in the PSD pattern.
(Ii) When the average value of periodic fluctuations is not more than a predetermined value, determination of CSA type or OSA type is impossible or mixed sleep apnea (MSA),
Can be diagnosed.
　表示部１７は、表示回路１７１とディスプレイ１７２からなる。表示回路１７１は、生波形、呼吸運動周波数成分およびＰＳＤグラフをディスプレイ１７２に表示する。医師等は、表示された生波形、呼吸運動周波数成分およびＰＳＤグラフを参照して、的確な診断を行なうことができる。
　音出力部１８は、少なくとも前記生波形データに含まれる呼吸音成分を音出力することができる。
　音出力部１８は、サウンド回路１８１とスピーカ１８２とからなる。サウンド回路１８１は、心音や呼吸音（いびきを含む）およびまたは体動に付随する音をスピーカ１８２から出力することができる。
　また、心音や呼吸音、体動に付随する音に係る信号を電気信号として音信号出力端子から出力することができる。 The display unit 17 includes a display circuit 171 and a display 172. The display circuit 171 displays the raw waveform, the respiratory motion frequency component, and the PSD graph on the display 172. A doctor or the like can make an accurate diagnosis with reference to the displayed raw waveform, respiratory motion frequency component, and PSD graph.
The sound output unit 18 can output at least a respiratory sound component included in the raw waveform data.
The sound output unit 18 includes a sound circuit 181 and a speaker 182. The sound circuit 181 can output from the speaker 182 heart sounds, breathing sounds (including snoring), and / or sounds accompanying body movements.
In addition, a signal related to a heart sound, a breathing sound, and a sound accompanying body motion can be output as an electric signal from the sound signal output terminal.
　解析部１９は、無呼吸期間における前記ＰＳＤ合算値が、第１所定値以上かまたは第２所定値以下かを計算するこができる。
　そして、その情報を表示部に表示することによって、閉塞性無呼吸（ＯＳＡ）か中枢性無呼吸（ＣＳＡ）かの診断を支援する。 The analysis unit 19 can calculate whether the PSD sum value during the apnea period is greater than or equal to the first predetermined value or less than the second predetermined value.
Then, by displaying the information on the display unit, diagnosis of obstructive apnea (OSA) or central apnea (CSA) is supported.
　図３に本発明の睡眠時無呼吸の中枢型／閉塞型診断支援方法の一実施例を示す。
　センシングステップ（Ｓ１０２）：直接または間接に被験者の身体に密着して配置された圧電センサから、生波形データを電気信号として検出する。
　生波形データ記憶ステップ（Ｓ１０４）：被験者の呼吸運動および心音を含む生波形データを記憶する。
　無呼吸期間検出ステップ（Ｓ１０６）：生波形データ記憶ステップにおいて記憶した生波形データから生波形の呼吸運動周波数成分を抽出し、振幅変動値が所定値以下で所定時間よりも長い期間を無呼吸期間として検出する。 FIG. 3 shows an embodiment of the sleep / apnea central / occlusion type diagnosis support method of the present invention.
Sensing step (S102): Raw waveform data is detected as an electrical signal from a piezoelectric sensor arranged in close contact with the body of the subject directly or indirectly.
Raw waveform data storage step (S104): Raw waveform data including breathing motion and heart sound of the subject is stored.
Apnea period detection step (S106): The respiratory motion frequency component of the raw waveform is extracted from the raw waveform data stored in the raw waveform data storage step, and the period when the amplitude fluctuation value is equal to or less than a predetermined value and longer than the predetermined time is determined as the apnea period. Detect as.
　ＦＦＴ処理ステップ（Ｓ１０８）：生波形データの無呼吸期間に係るデータに高速フーリエ変換処理を施しＰＳＤを生成する。
　生波形データは、被験者の呼吸音周波数成分をさらに含む。生波形データが、被験者の呼吸音周波数成分を含むか否かは、センサの特性に依存する。本実施形態で使用されるセンサでは呼吸音周波数成分を抽出することができる。
　ＰＳＤ合算値計算ステップ（Ｓ１１０）：所定周波数領域のパワースペクトル密度（ＰＳＤ）合算値を計算する。
　ＰＳＤパターン一致判断ステップ（Ｓ１１２）：生成したＰＳＤのパターンが、中枢性無呼吸のＰＳＤのパターンに一致するか否かを判断する。
　心周期変動平均計算ステップ（Ｓ１１４）：無呼吸期間の生波形データまたはフィルタ処理された心音周波数成分データから、連続する心音のピークとピークの間の時間（心周期）を求め、ひとつの心周期（１音―１音間隔または２音―２音間隔）と次の心周期との差の平均を計算し心周期変動値を求める。 FFT processing step (S108): Fast Fourier transform processing is performed on the data related to the apnea period of the raw waveform data to generate a PSD.
The raw waveform data further includes a respiratory sound frequency component of the subject. Whether the raw waveform data includes the respiratory sound frequency component of the subject depends on the characteristics of the sensor. The sensor used in this embodiment can extract a respiratory sound frequency component.
PSD total value calculation step (S110): A power spectral density (PSD) total value in a predetermined frequency region is calculated.
PSD pattern matching judgment step (S112): It is judged whether or not the generated PSD pattern matches the central apnea PSD pattern.
A cardiac cycle variation average calculating step (S114): obtaining a time (cardiac cycle) between peaks of continuous heart sounds from raw waveform data of an apnea period or filtered heart sound frequency component data, and one cardiac cycle The average of the difference between (one sound—one sound interval or two sounds—two sound intervals) and the next cardiac cycle is calculated to obtain a cardiac cycle fluctuation value.
　表示ステップ（Ｓ１１６）：生波形、少なくとも、生波形の呼吸運動周波数成分およびＰＳＤを表示する。
　音出力ステップ（Ｓ１１８）：少なくとも前記生波形データに含まれる呼吸音成分を音出力することまたは当該呼吸音成分を電気信号として出力する。
　解析ステップ（Ｓ１２０）：所定周波数領域におけるＰＳＤ合算値が、第１所定値以上かまたは第２所定値以下かを計算しその情報を前記表示ステップに渡すことによって、閉塞性無呼吸（ＯＳＡ）か中枢性無呼吸（ＣＳＡ）かの診断を支援する。 Display step (S116): Display the raw waveform, at least the respiratory motion frequency component and PSD of the raw waveform.
Sound output step (S118): Output at least a respiratory sound component included in the raw waveform data, or output the respiratory sound component as an electrical signal.
Analyzing step (S120): calculating whether the PSD total value in the predetermined frequency region is greater than or equal to the first predetermined value or less than the second predetermined value and passing the information to the display step, thereby determining whether the patient is obstructive apnea (OSA) Supports diagnosis of central apnea (CSA).
　図４（Ａ）に中枢性無呼吸の患者の呼吸運動および心音を含む生波形データを示し、図４（Ｂ）に閉塞性無呼吸の患者の呼吸運動および心音を含む生波形データを示す。
　図４（Ａ）において無呼吸期間を期間Ｐ＿ＣＳＡで示し、図４（Ｂ）において無呼吸期間を期間Ｐ＿ＯＳＡで示す。 4A shows raw waveform data including respiratory motion and heart sounds of a patient with central apnea, and FIG. 4B shows raw waveform data including respiratory motion and heart sounds of a patient with obstructive apnea.
In FIG. 4A, the apnea period is indicated by a period P_CSA, and in FIG. 4B, the apnea period is indicated by a period P_OSA.
　図５（Ａ）に図４（Ａ）の生波形データにローパスフィルタ処理（呼吸運動周波数成分抽出処理）を施した波形を示し、図５（Ｂ）に図４（Ｂ）の生波形データにローパスフィルタ処理を施した波形を示す。
　期間Ｐ＿ＣＳＡに代表的に示されるように、ローパスフィルタ処理後は心音の低周波成分がアーチファクトとなって無呼吸期間に顕在化する。
　図５（Ｃ）に図４（Ａ）の生波形データにハイパスフィルタ処理を施した波形を示し、図５（Ｄ）に図４（Ｂ）の生波形データにハイパスフィルタ処理を施した波形を示す。呼吸音、いびき、体動などが強調表示される。
　図５（Ａ），図５（Ｃ）において無呼吸期間を期間Ｐ＿ＣＳＡで示し、図５（Ｂ），図５（Ｄ）において無呼吸期間を期間Ｐ＿ＯＳＡで示す。 FIG. 5A shows a waveform obtained by subjecting the raw waveform data of FIG. 4A to low-pass filter processing (respiration motion frequency component extraction processing), and FIG. 5B shows the raw waveform data of FIG. The waveform which performed the low-pass filter process is shown.
As representatively shown in the period P_CSA, after the low-pass filter processing, the low frequency component of the heart sound becomes an artifact and becomes apparent during the apnea period.
FIG. 5C shows a waveform obtained by subjecting the raw waveform data of FIG. 4A to high-pass filter processing, and FIG. 5D shows a waveform obtained by subjecting the raw waveform data of FIG. 4B to high-pass filter processing. Show. Breathing sounds, snoring, body movements, etc. are highlighted.
5A and 5C, the apnea period is indicated by a period P_CSA, and in FIGS. 5B and 5D, the apnea period is indicated by a period P_OSA.
　図６（Ａ）は図４（Ａ）の中枢性無呼吸の患者の生波形データにＦＦＴ処理を施して作成したＰＳＤグラフを示す図、図６（Ｂ）は図４（Ｂ）の閉塞性無呼吸の患者の生波形データにＦＦＴ処理を施して作成したＰＳＤグラフを示す図である。
　図６（Ａ）および図６（Ｂ）からわかるように、中枢性無呼吸の患者に係るＰＳＤ合算値の面積は、閉塞性無呼吸の患者に係るＰＳＤ合算値の面積に比べ格段に小さいことがわかる（図６（Ａ）および図６（Ｂ）の符号ａ参照）。
　また、１Ｈｚから４０Ｈｚ程度までの周波数領域では、中枢性無呼吸の患者に係るＰＳＤには特徴的なパターン（ピークパターン）が表れるが、閉塞性無呼吸の患者に係るＰＳＤには特徴的なパターンは表れていないことがわかる（図６（Ａ）および図６（Ｂ）の符号ｂ参照）。 6A is a diagram showing a PSD graph created by subjecting the raw waveform data of the central apnea patient of FIG. 4A to FFT processing, and FIG. 6B is the obstructive property of FIG. 4B. It is a figure which shows the PSD graph produced by performing the FFT process to the raw waveform data of an apnea patient.
As can be seen from FIGS. 6 (A) and 6 (B), the area of the PSD combined value for the patient with central apnea is much smaller than the area of the PSD combined value for the patient with obstructive apnea. (See symbol a in FIGS. 6A and 6B).
In the frequency range from 1 Hz to 40 Hz, a characteristic pattern (peak pattern) appears in the PSD related to the patient with central apnea, but a characteristic pattern appears in the PSD related to the patient with obstructive apnea. Is not shown (see symbol b in FIGS. 6A and 6B).
　本発明の診断支援装置１による診断支援の例を以下に示す。
　図７は、診断支援の第１の例を示すフローチャートである。
　図７において、解析ステップ１２０Ａは、ＰＳＤ合算値計算ステップＳ１０８におけるＰＳＤ合算値（０．２～０．６Ｈｚ）の計算結果を取得し（ステップＡ１）、当該計算結果が所定値Ｓより大きいとき（ステップＡ２の「ＹＥＳ」）は閉塞性睡眠時無呼吸（ＯＳＡ）と判断し（ステップＡ３）、当該計算結果が所定値Ｓ以下であるとき（ステップＡ２の「ＮＯ」）は中枢性無呼吸（ＣＳＡ）と判断する（ステップＡ４）。
　所定値Ｓは、環境条件、被験者の身体的・生理的条件等に応じて、適宜定めることができる。
　所定値Ｓは、環境条件、被験者の身体的・生理的条件等に応じて、適宜定めることができる。 An example of diagnosis support by the diagnosis support apparatus 1 of the present invention is shown below.
FIG. 7 is a flowchart illustrating a first example of diagnosis support.
In FIG. 7, the analysis step 120A obtains the calculation result of the PSD total value (0.2 to 0.6 Hz) in the PSD total value calculation step S108 (step A1), and when the calculation result is larger than the predetermined value S (step A1). “YES” in step A2) is determined as obstructive sleep apnea (OSA) (step A3), and when the calculation result is equal to or less than a predetermined value S (“NO” in step A2), central apnea ( CSA) (step A4).
The predetermined value S can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
The predetermined value S can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
　図８は、診断支援の第２の例を示すフローチャートである。
　図８において、解析ステップ１２０Ｂは、ＰＳＤ合算値計算ステップＳ１０８におけるＰＳＤ合算値（４００～１，０００Ｈｚ）の計算結果を取得し（ステップＢ１）、当該計算結果が所定値Ｓより大きいとき（ステップＢ２の「ＹＥＳ」）は閉塞性睡眠時無呼吸（ＯＳＡ）と判断し（ステップＢ３）、当該計算結果が所定値Ｓ以下であるとき（ステップＢ２の「ＮＯ」）は中枢性無呼吸（ＣＳＡ）と判断する（ステップＢ４）。
　第１の例と同様、所定値Ｓは、環境条件、被験者の身体的・生理的条件等に応じて、適宜定めることができる。 FIG. 8 is a flowchart illustrating a second example of diagnosis support.
In FIG. 8, the analysis step 120B obtains the calculation result of the PSD total value (400 to 1,000 Hz) in the PSD total value calculation step S108 (step B1), and when the calculation result is larger than the predetermined value S (step B2). "YES") is determined as obstructive sleep apnea (OSA) (step B3), and when the calculation result is equal to or less than a predetermined value S ("NO" in step B2), central apnea (CSA) (Step B4).
As in the first example, the predetermined value S can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
　図９は、診断支援の第３の例を示すフローチャートである。
　図９において、解析ステップ１２０Ｃは、ＰＳＤ合算値計算ステップＳ１０８におけるＰＳＤ合算値の計算結果を取得し（ステップＣ１）、当該計算結果が所定値Ｓより大きいとき（図９では６桁より大きいとき：ステップＣ２の「ＹＥＳ」）は閉塞性睡眠時無呼吸（ＯＳＡ）と判断し（ステップＣ５）。計算結果が所定値Ｓ以下であるときは（ステップＣ２の「ＮＯ」）、ＰＳＤパターンが明瞭なＣＳＡ型であるか否かを判断する（ステップＣ３）。
　ステップＣ３において、ＰＳＤパターンが明瞭なＣＳＡ型であるとき（ステップＣ３の「ＹＥＳ」）は中枢性無呼吸（ＣＳＡ）と判断する（ステップＣ６）。
　ステップＣ３において、ＰＳＤパターンが明瞭なＣＳＡ型でないとき（ステップＣ３の「ＮＯ」）は、さらに心周期変動値（ＢＢＩＶ）を計算し（ステップＣ４）、ＢＢＩＶが所定値Ｔ（たとえば、Ｔ＝０．２）よりも大きいとき（ステップＣ４の「ＹＥＳ」）は中枢性無呼吸（ＣＳＡ）と判断し（ステップＣ５）、ＢＢＩＶが所定値Ｔ以下のとき（ステップＣ４の「ＮＯ」）は、Ｕｎｋｎｏｗｎ（ＣＳＡ／ＯＳＡ判定不可能または混合型無呼吸（ＭＳＡ））と判定する（ステップＣ７）。
　所定値Ｓおよび所定値Ｔは、環境条件、被験者の身体的・生理的条件等に応じて、適宜定めることができる。 FIG. 9 is a flowchart illustrating a third example of diagnosis support.
9, the analysis step 120C obtains the calculation result of the PSD total value in the PSD total value calculation step S108 (step C1), and when the calculation result is larger than the predetermined value S (in FIG. 9, when it is larger than six digits: "YES" in step C2) is determined as obstructive sleep apnea (OSA) (step C5). When the calculation result is equal to or smaller than the predetermined value S (“NO” in step C2), it is determined whether or not the PSD pattern is a clear CSA type (step C3).
In step C3, when the PSD pattern is a clear CSA type (“YES” in step C3), it is determined that the patient has central apnea (CSA) (step C6).
In step C3, when the PSD pattern is not a clear CSA type (“NO” in step C3), a cardiac cycle variation value (BBIV) is further calculated (step C4), and BBIV is a predetermined value T (for example, T = 0) .2) (“YES” in step C4) is determined as central apnea (CSA) (step C5), and when BBIV is equal to or less than a predetermined value T (“NO” in step C4), Unknown It is determined that (CSA / OSA determination is impossible or mixed apnea (MSA)) (step C7).
The predetermined value S and the predetermined value T can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
　図１０は、診断支援の第４の例を示すフローチャートである。
　図１０において、解析ステップ１２０Ｄは、ＰＳＤ合算値計算ステップＳ１０８におけるＰＳＤ合算値の計算結果を取得し（ステップＤ１）、当該計算結果が第１所定値Ｓ１より大きいとき（ステップＤ２の「ＹＥＳ」）は閉塞性睡眠時睡眠時無呼吸（ＯＳＡ）と判断する（ステップＤ４）。ＰＳＤ合算値の計算結果が第１所定値Ｓ１以下であるときは（ステップＤ２の「ＮＯ」）、計算結果が第２所定値Ｓ２より小さいか否かを判断する（ステップＤ３）。ステップＤ３において、ＰＳＤ合算値の計算結果が第２所定値Ｓ２より小さいときは（ステップＤ３の「ＹＥＳ」）は中枢性睡眠時無呼吸（ＣＳＡ）と判断し（ステップＤ６）、ＰＳＤ合算値の計算結果が第２所定値Ｓ２以上のときは（ステップＤ３の「ＮＯ」）はＵｎｋｎｏｗｎ（ＣＳＡ／ＯＳＡ判定不可能または混合型無呼吸（ＭＳＡ））と判断する（ステップＤ５）。
　所定値Ｓ１，Ｓ２は、環境条件、被験者の身体的・生理的条件等に応じて、適宜定めることができる。 FIG. 10 is a flowchart illustrating a fourth example of diagnosis support.
In FIG. 10, the analysis step 120D acquires the calculation result of the PSD total value in the PSD total value calculation step S108 (step D1), and when the calculation result is larger than the first predetermined value S1 (“YES” in step D2). Is determined to be obstructive sleep apnea (OSA) (step D4). When the calculation result of the PSD total value is equal to or less than the first predetermined value S1 (“NO” in step D2), it is determined whether or not the calculation result is smaller than the second predetermined value S2 (step D3). In step D3, when the calculation result of the PSD total value is smaller than the second predetermined value S2 (“YES” in step D3), it is determined that the central sleep apnea (CSA) (step D6), and the PSD total value When the calculation result is equal to or greater than the second predetermined value S2 (“NO” in step D3), it is determined that the device is “Unknown” (CSA / OSA cannot be determined or mixed apnea (MSA)) (step D5).
The predetermined values S1 and S2 can be appropriately determined according to environmental conditions, physical and physiological conditions of the subject, and the like.
　図１１は、診断支援の第５の例を示すフローチャートである。
　図１１において、解析ステップ１２０Ｅは、ＰＳＤ合算値計算ステップＳ１０８におけるＰＳＤ合算値の計算結果を取得し（ステップＥ１）、当該計算結果が第１所定値Ｓ１より大きいとき（ステップＥ２の「ＹＥＳ」）は閉塞性睡眠時無呼吸（ＯＳＡ）と判断し（ステップＥ６）。ＰＳＤ合算値の計算結果が所定値Ｓ１以下であるときは（ステップＥ２の「ＮＯ」）、計算結果が第２所定値Ｓ２より小さいか否かを判断する（ステップＥ３）。
　ステップＥ３において、ＰＳＤ合算値の計算結果が第２所定値Ｓ２以上のときは（ステップＥ３の「ＮＯ」）Ｕｎｋｎｏｗｎ（ＣＳＡ／ＯＳＡ判定不可能または混合型無呼吸（ＭＳＡ））と判断する（ステップＥ８）。ステップＥ３において、ＰＳＤ合算値の計算結果が第２所定値Ｓ２より小さいときは（ステップＥ３の「ＹＥＳ」）はＰＳＤパターンが明瞭なＣＳＡ型であるか否かを判断する（ステップＥ４）。
　ステップＥ４において、ＰＳＤパターンが明瞭なＣＳＡ型であるとき（ステップＥ４の「ＹＥＳ」）は、中枢性無呼吸（ＣＳＡ）と判断する（ステップＥ７）。
　ステップＥ４において、ＰＳＤパターンが明瞭なＣＳＡ型でないとき（ステップＥ４の「ＮＯ」）は、さらに心周期変動値（ＢＢＩＶ）を計算し（ステップＥ５）、ＢＢＩＶが所定値Ｔ（たとえば、０．２）よりも大きいとき（ステップＥ５の「ＹＥＳ」）は中枢性無呼吸（ＣＳＡ）と判断し（ステップＥ７）、ＢＢＩＶが所定値Ｔ以下のとき（ステップＥ５の「ＮＯ」）は、Ｕｎｋｎｏｗｎ（ＣＳＡ／ＯＳＡ判定不可能または混合型無呼吸（ＭＳＡ））と判定する（ステップＥ８）。
　所定値Ｓ１，Ｓ２および所定値Ｔは、環境条件、被験者の身体的・生理的条件等に応じて、適宜定めることができる。
　ＢＢＩＶは、（ｎ－１）個の心拍間隔差の平均値を意味し、具体的には下記計算式により求められる。
Figure JPOXMLDOC01-appb-M000001
 　上式において、ＡＢＳは絶対値を意味する。
　ｋは何番目の心拍かを表す自然数であり、Ｉｋはｋ番目の心拍間隔（ｋ番目の心拍とｋ＋１番目の心拍との時間差：２≦ｋ≦ｎ）を意味する。 FIG. 11 is a flowchart illustrating a fifth example of diagnosis support.
In FIG. 11, the analysis step 120E obtains the calculation result of the PSD total value in the PSD total value calculation step S108 (step E1), and when the calculation result is larger than the first predetermined value S1 (“YES” in step E2). Is determined to be obstructive sleep apnea (OSA) (step E6). When the calculation result of the PSD total value is equal to or less than the predetermined value S1 (“NO” in step E2), it is determined whether or not the calculation result is smaller than the second predetermined value S2 (step E3).
In step E3, when the calculation result of the PSD combined value is equal to or greater than the second predetermined value S2 (“NO” in step E3), it is determined that the device is “Unknown” (CSA / OSA cannot be determined or mixed apnea (MSA)). E8). In step E3, when the calculation result of the PSD total value is smaller than the second predetermined value S2 (“YES” in step E3), it is determined whether or not the PSD pattern is a clear CSA type (step E4).
In step E4, when the PSD pattern is a clear CSA type (“YES” in step E4), it is determined as central apnea (CSA) (step E7).
In step E4, when the PSD pattern is not a clear CSA type (“NO” in step E4), a cardiac cycle variation value (BBIV) is further calculated (step E5), and BBIV is a predetermined value T (for example, 0.2). ) (“YES” in step E5) is determined to be central apnea (CSA) (step E7), and when BBIV is equal to or smaller than a predetermined value T (“NO” in step E5), Unknown (CSA) / OSA cannot be determined or mixed apnea (MSA)) (step E8).
The predetermined values S1, S2 and the predetermined value T can be appropriately determined according to environmental conditions, physical / physiological conditions of the subject, and the like.
BBIV means an average value of (n−1) heartbeat interval differences, and is specifically obtained by the following calculation formula.
Figure JPOXMLDOC01-appb-M000001
 In the above formula, ABS means an absolute value.
k is a natural number representing the number of heartbeats, and Ik means the kth heartbeat interval (time difference between the kth heartbeat and the k + 1th heartbeat: 2 ≦ k ≦ n).
　なお、図７から図１１に記載の各方法を実行する睡眠時無呼吸の中枢型／閉塞型診断支援プログラムは、コンピュータ読み取り可能な記録媒体に記録することができる。 It should be noted that the sleep / apnea central / occlusion type diagnosis support program for executing the methods shown in FIGS. 7 to 11 can be recorded on a computer-readable recording medium.
　１　睡眠時無呼吸の中枢型／閉塞型診断支援装置
　１０　圧電センサ部
　１１　生波形データ記憶部
　１２　無呼吸期間検出部
　１３　ＦＦＴ処理部
　１４　ＰＳＤ合算値計算部
　１５　ＰＳＤパターン一致判断部
　１６　心周期変動平均計算部
　１７　表示部
　１７１　表示回路
　１７２　ディスプレイ
　１８　音出力部
　１８１　サウンド回路
　１８２　スピーカ
　１９　解析部
　１００　本体 DESCRIPTION OF SYMBOLS 1 Sleep / apnea central / occlusion type diagnosis support device 10 Piezoelectric sensor unit 11 Raw waveform data storage unit 12 Apnea period detection unit 13 FFT processing unit 14 PSD total value calculation unit 15 PSD pattern coincidence determination unit 16 Cardiac cycle variation Average calculator 17 Display unit 171 Display circuit 172 Display 18 Sound output unit 181 Sound circuit 182 Speaker 19 Analysis unit 100 Main body

Claims (19)

  1. 　被験者の呼吸運動および心音を含む生波形データを記憶する生波形データ記憶部、
    　前記生波形データ記憶部に記憶した生波形データから前記生波形の呼吸運動周波数成分を抽出し、無呼吸期間を検出する無呼吸期間検出部、
    　前記生波形データの無呼吸期間に係るデータに高速フーリエ変換（ＦＦＴ）処理を施し前記無呼吸期間におけるパワースペクトル密度（ＰＳＤ）グラフを生成するＦＦＴ処理部、
    　所定周波数領域のＰＳＤ合算値を計算するＰＳＤ合算値計算部、
    　前記生成したＰＳＤのパターンが、中枢性無呼吸のＰＳＤのパターンに一致するか否かを判断するＰＳＤパターン一致判断部、
    　および、
    　前記生波形、前記生波形の呼吸運動周波数成分および前記ＰＳＤグラフを表示する表示部、
    を備えた睡眠時無呼吸の中枢型／閉塞型診断支援装置。     A raw waveform data storage unit for storing raw waveform data including a subject's respiratory motion and heart sounds;
    An apnea period detection unit that extracts a respiratory motion frequency component of the raw waveform from the raw waveform data stored in the raw waveform data storage unit and detects an apnea period;
    An FFT processing unit that performs fast Fourier transform (FFT) processing on the data relating to the apnea period of the raw waveform data to generate a power spectral density (PSD) graph in the apnea period;
    A PSD total value calculation unit for calculating a PSD total value in a predetermined frequency region;
    A PSD pattern matching judgment unit for judging whether or not the generated PSD pattern matches a central apnea PSD pattern;
    and,
    A display unit for displaying the raw waveform, a respiratory motion frequency component of the raw waveform, and the PSD graph;
    A sleep / apnea central / occlusion type diagnosis support apparatus comprising:
  2. 　請求項１に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　さらに、前記生波形データを前記生波形に係る電気信号として検出する、直接または間接に前記被験者の身体に密着して配置される心拍呼吸センサ部、
    を備えた中枢型／閉塞型診断支援装置。     The sleep / apnea central / occlusion type diagnosis support apparatus according to claim 1,
    Further, a heartbeat respiration sensor unit that detects the raw waveform data as an electrical signal related to the raw waveform, is disposed in close contact with the subject's body directly or indirectly,
    A central / occlusion type diagnosis support apparatus comprising:
  3. 　請求項１に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　前記心拍呼吸センサ部は、センシング素子として圧電素子を有している中枢型／閉塞型
    診断支援装置。     The sleep / apnea central / occlusion type diagnosis support apparatus according to claim 1,
    The heartbeat respiration sensor unit is a central / occlusion type diagnosis support apparatus having a piezoelectric element as a sensing element.
  4. 　請求項１に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　前記生波形データは、前記被験者の呼吸音周波数成分をさらに含む睡眠時無呼吸の中枢型／閉塞型診断支援装置。     The sleep / apnea central / occlusion type diagnosis support apparatus according to claim 1,
    The raw waveform data is a sleep / apnea central / occlusion type diagnosis support apparatus further including a respiratory sound frequency component of the subject.
  5. 　請求項１記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　少なくとも前記生波形データに含まれる呼吸音成分を音出力することまたは当該呼吸音成分を電気信号として出力する音出力部、
    をさらに備えた睡眠時無呼吸の中枢型／閉塞型診断支援装置。     A sleep / apnea central / occlusion type diagnostic support device according to claim 1,
    A sound output unit for outputting at least a respiratory sound component included in the raw waveform data or outputting the respiratory sound component as an electrical signal;
    A sleep / apnea central / occlusion type diagnosis support apparatus further comprising:
  6. 　請求項１に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　無呼吸期間の前記生波形データまたはフィルタ処理された心音周波数成分データから、連続する心音のピークとピークの間の時間（心周期）を求め、ひとつの心周期（１音―１音間隔または２音―２音間隔）と次の心周期との差の平均を計算し、心周期変動値を求める心周期変動平均計算部、
    をさらに備えた睡眠時無呼吸の中枢型／閉塞型診断支援装置。     The sleep / apnea central / occlusion type diagnosis support apparatus according to claim 1,
    From the raw waveform data of the apnea period or the filtered heart sound frequency component data, the time (heart cycle) between the peaks of successive heart sounds is obtained, and one heart cycle (one sound minus one sound interval or 2 Sound-2 sound intervals) and the average of the difference between the next cardiac cycle and calculating the cardiac cycle fluctuation value,
    A sleep / apnea central / occlusion type diagnosis support apparatus further comprising:
  7. 　請求項１に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　さらに、解析部を備え、
    　前記解析部は、
    　前記所定周波数領域における前記ＰＳＤ合算値が、第１所定値以上かまたは第２所定値以下かを計算しその情報を前記表示部に表示することによって、閉塞性睡眠時無呼吸（ＯＳＡ）か中枢性無呼吸（ＣＳＡ）かの診断を支援する、
    睡眠時無呼吸の中枢型／閉塞型診断支援装置。     The sleep / apnea central / occlusion type diagnosis support apparatus according to claim 1,
    Furthermore, it has an analysis part,
    The analysis unit
    By calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a first predetermined value or less than a second predetermined value and displaying the information on the display unit, obstructive sleep apnea (OSA) or central Help diagnose sexual apnea (CSA),
    Central / occlusion type diagnosis support device for sleep apnea.
  8. 　請求項７に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　前記所定周波数領域における前記ＰＳＤ合算値が所定値以下の場合、
    　前記解析部は、
    　ＰＳＤパターンが明瞭なＣＳＡ型である場合はＣＳＡと判定出力し、
    　ＰＳＤパターンが明瞭なＣＳＡ型でない場合は、さらに心周期変動値を計算し、所定値以上か以下かによってＣＳＡか判定不可能または混合型無呼吸（ＭＳＡ）かの情報を前記表示部に表示することにより、閉塞性無呼吸（ＯＳＡ）、中枢性無呼吸（ＣＳＡ）あるいは判定不可能（またはＭＳＡ）であるかの判断を支援する、
    睡眠時無呼吸の中枢型／閉塞型診断支援装置。     The sleep / apnea central / occlusion type diagnosis support device according to claim 7,
    When the PSD total value in the predetermined frequency region is a predetermined value or less,
    The analysis unit
    If the PSD pattern is a clear CSA type, CSA is determined and output.
    If the PSD pattern is not a clear CSA type, a cardiac cycle fluctuation value is further calculated, and information on whether the CSA is indeterminate or mixed apnea (MSA) is displayed on the display unit depending on whether it is greater than or equal to a predetermined value. To help determine if it is obstructive apnea (OSA), central apnea (CSA), or indeterminate (or MSA),
    Central / occlusion type diagnosis support device for sleep apnea.
  9. 　請求項７または請求項８に記載の睡眠時無呼吸の中枢型／閉塞型診断支援装置であって、
    　前記所定周波数領域前記ＰＳＤ合算値が所定値以上か以下かまたは中間値であるかを計算し、中間値の場合は判定不可能とする情報を前記表示部に追加表示することによって閉塞性無呼吸（ＯＳＡ）、中枢性無呼吸（ＣＳＡ）あるいは判定不可能（またはＭＳＡ）であるかの判断を支援する、
    睡眠時無呼吸の中枢型／閉塞型診断支援装置。     A sleep / apnea central / occlusion type diagnosis support apparatus according to claim 7 or 8,
    An obstructive apnea is calculated by calculating whether the PSD total value is greater than or less than a predetermined value or an intermediate value in the predetermined frequency region, and additionally displaying information indicating that the PSD cannot be determined if the intermediate value is an intermediate value on the display unit. (OSA) to help determine if central apnea (CSA) or indeterminate (or MSA)
    Central / occlusion type diagnosis support device for sleep apnea.
  10. 　被験者の呼吸運動および心音を含む生波形データを記憶する生波形データ記憶ステップ、
    　前記生波形データ記憶ステップにおいて記憶した生波形データから前記生波形の呼吸運動周波数成分を抽出し、無呼吸期間を検出する無呼吸期間検出ステップ、
    　前記生波形データの無呼吸期間に係るデータに高速フーリエ変換処理を施し前記所定周波数領域におけるＰＳＤを生成するＦＦＴ処理ステップ、
    　所定周波数領域のパワースペクトル密度（ＰＳＤ）合算値を計算するＰＳＤ合算値計算ステップ、
    　前記生成したＰＳＤのパターンが、中枢性無呼吸のＰＳＤのパターンに一致するか否かを判断するＰＳＤパターン一致判断ステップ、
    　および、
    　前記生波形、少なくとも、前記生波形の呼吸運動周波数成分および前記ＰＳＤを表示する表示ステップ、
    を有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。     A raw waveform data storage step for storing raw waveform data including a subject's breathing motion and heart sound;
    An apnea period detection step for extracting a respiratory motion frequency component of the raw waveform from the raw waveform data stored in the raw waveform data storage step and detecting an apnea period;
    FFT processing step of performing a fast Fourier transform process on the data related to the apnea period of the raw waveform data to generate a PSD in the predetermined frequency region,
    PSD sum value calculation step for calculating a power spectrum density (PSD) sum value in a predetermined frequency region;
    A PSD pattern matching determination step of determining whether or not the generated PSD pattern matches a central apnea PSD pattern;
    and,
    Displaying the raw waveform, at least the respiratory motion frequency component of the raw waveform and the PSD;
    A sleep / apnea central / occluded diagnosis support method comprising:
  11. 　請求項９に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　さらに、直接または間接に被験者の身体に密着して配置された心拍呼吸センサから、生波形データを電気信号として検出するセンシングステップ、
    を有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 9,
    Furthermore, a sensing step of detecting raw waveform data as an electrical signal from a heartbeat respiration sensor arranged directly or indirectly in close contact with the subject's body,
    A sleep / apnea central / occluded diagnosis support method comprising:
  12. 　請求項１１に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　前記心拍呼吸センサ部は、センシング素子として圧電素子を有している中枢型／閉塞型
    診断支援方法。     A sleep / apnea central / occlusion type diagnosis support method according to claim 11,
    The heart rate respiration sensor unit is a central / occlusion type diagnosis support method having a piezoelectric element as a sensing element.
  13. 　請求項１０に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　前記生波形データは、前記被験者の呼吸音周波数成分をさらに含む睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 10,
    The raw waveform data is a sleep / apnea central / occlusion type diagnosis support method further including a respiratory sound frequency component of the subject.
  14. 　請求項１０に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　少なくとも前記生波形データに含まれる呼吸音成分を音出力することまたは当該呼吸音成分を電気信号として出力する音出力ステップ、
    をさらに有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 10,
    A sound output step of outputting at least a respiratory sound component included in the raw waveform data or outputting the respiratory sound component as an electrical signal;
    A sleep / apnea central / occlusion type diagnosis support method further comprising:
  15. 　請求項１０に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　無呼吸期間の前記生波形データまたはフィルタ処理された心音周波数成分データから、連続する心音のピークとピークの間の時間（心周期）を求め、ひとつの心周期（１音―１音間隔または２音―２音間隔）と次の心周期との差の平均を計算し、心周期変動値を求める心周期変動平均計算ステップ、
    をさらに有する睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 10,
    From the raw waveform data of the apnea period or the filtered heart sound frequency component data, the time (heart cycle) between the peaks of successive heart sounds is obtained, and one heart cycle (one sound minus one sound interval or 2 Sound-cycle interval) and the average of the difference between the next cardiac cycle and the cardiac cycle fluctuation average calculation step to calculate the cardiac cycle fluctuation value,
    A sleep / apnea central / occlusion type diagnosis support method further comprising:
  16. 　請求項１０に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　さらに、解析ステップを備え、
    　前記解析ステップでは、
    　前記所定周波数領域における前記ＰＳＤ合算値が、第１所定値以上かまたは第２所定値以下かを計算しその情報を前記表示ステップに渡すことによって、閉塞性無呼吸（ＯＳＡ）か中枢性無呼吸（ＣＳＡ）かの診断を支援する、
    睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 10,
    And an analysis step
    In the analysis step,
    By calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a first predetermined value or less than a second predetermined value and passing the information to the display step, obstructive apnea (OSA) or central apnea (CSA) to support the diagnosis,
    Central / obstructive diagnosis support method for sleep apnea.
  17. 　請求項１６に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　前記所定周波数領域における前記ＰＳＤ合算値が所定値以下の場合、
    　前記解析ステップでは、
    　ＰＳＤパターンが明瞭なＣＳＡ型である場合はＣＳＡと判定出力し、
    　ＰＳＤパターンが明瞭なＣＳＡ型でない場合は、さらに心周期変動値を計算し、所定値以上か以下かによってＣＳＡか判定不可能または混合型無呼吸（ＭＳＡ）かの情報を前記表示ステップに渡すことによりＯＳＡ、ＣＳＡあるいは判定不可能（またはＭＳＡ）であるかの判断を支援する、
    睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 16,
    When the PSD total value in the predetermined frequency region is a predetermined value or less,
    In the analysis step,
    If the PSD pattern is a clear CSA type, CSA is determined and output.
    If the PSD pattern is not a clear CSA type, the cardiac cycle fluctuation value is further calculated, and information indicating whether the CSA is indeterminate or mixed apnea (MSA) is passed to the display step depending on whether it is greater than or less than a predetermined value. Assists in determining whether OSA, CSA or indeterminate (or MSA)
    Central / obstructive diagnosis support method for sleep apnea.
  18. 　請求項１５または請求項１６に記載の睡眠時無呼吸の中枢型／閉塞型診断支援方法であって、
    　前記所定周波数領域における前記ＰＳＤ合算値が所定値以上か以下かまたは中間値であるかを計算し、中間値の場合は判定不可能とする情報を前記表示ステップに追加して渡すことによってＯＳＡ、ＣＳＡあるいは判定不可能（または，ＭＳＡ（ＯＳＡ／ＣＳＡ混合型））かの判断を支援することを特徴とする睡眠時無呼吸の中枢型／閉塞型診断支援方法。     The sleep / apnea central / occlusion type diagnosis support method according to claim 15 or 16,
    OSA by calculating whether the PSD total value in the predetermined frequency region is greater than or equal to a predetermined value or less or an intermediate value, and adding information that cannot be determined in the case of the intermediate value to the display step. A central / occluded type diagnosis support method for sleep apnea characterized by supporting judgment of CSA or determination impossible (or MSA (OSA / CSA mixed type)).
  19. 　請求項１０から１８の何れか１項に記載の方法を実行する睡眠時無呼吸の中枢型／閉塞型診断支援プログラムを記録したコンピュータ読み取り可能な記録媒体。 A computer-readable recording medium in which a sleep / apnea central / occlusion type diagnosis support program for executing the method according to any one of claims 10 to 18 is recorded.
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