Information processing device, information processing program, and information processing method

Abstract

To provide supplementary information to be used for assisting reading of a uniform Cardio Toco Graph (CTG).SOLUTION: An information processor 1 generates supplementary information for reading CTG. The information processor has: an acquisition part 1311 acquiring CTG; a waveform signal extraction part 1312 extracting a waveform signal of a fetal cardiac rate waveform included in the CTG; a low band extraction part 1314 extracting a low frequency component signal from the waveform signal; and a baseline section identification part 132 which identifies as the supplementary information a baseline determination target section for determining a baseline on the basis of the low frequency component signal. The baseline section identification part extracts as a first determination section a section until the first prescribed time passes from a prescribed first starting point in the low frequency component signal, and when dispersion value of the low frequency component signal in the first determination section is smaller than first threshold, the first determination section is identified as a first configuration section configuring the baseline determination target section.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing program, and an information processing method. [Background technology]

[0002] A fetal heart rate electrocardiogram (CTG: cardiotocography) is a waveform diagram that continuously records the fetal heart rate and uterine contractions (labor pains) in a time series, measured by a fetal monitoring device. Doctors estimate the condition of the fetus by checking the changes in uterine contractions and fetal heart rate based on the CTG. During delivery, the fetus may fall into a hypoxic state due to various factors (e.g., placental abruption, umbilical cord compression, etc.). As a result, the fetus may develop ischemic encephalopathy due to acidosis (acidification of the blood) during the fetal and postnatal period, which may lead to physical disabilities and developmental disorders in the future. Therefore, during the delivery process and pregnancy of pregnant women, continuous and intermittent observations are performed based on the CTG to check for signs of fetal hypoxia.

[0003] Regarding the interpretation of CTG, the Japan Society of Obstetrics and Gynecology has provided criteria for the baseline heart rate, baseline fine fluctuations in heart rate, and transient fluctuations. Doctors make their judgments based on these criteria. However, CTG is interpreted visually by obstetricians and midwives, and there is a large degree of individual variation in interpretation based on the experience of obstetricians and midwives. Therefore, despite the existence of such criteria, there are still many cases of ischemic encephalopathy.

[0004] Technology has been proposed to determine parameters such as baseline fluctuations and transient fluctuations in heart rate based on fetal heart rate and maternal uterine activity, and to indicate the level of risk to the fetus based on non-reassuring characteristics (abnormalities) defined for each parameter (see, for example, Patent Document 1).

[0005] The technology disclosed in Patent Document 1 judges non-reassuring characteristics based on standard thresholds of parameters such as heart rate baseline fluctuations and transient fluctuations. However, the technology does not disclose specific details of extracting and judging parameters such as heart rate baseline fluctuations and transient fluctuations from fetal heart rate. Therefore, there is a demand for technology that can uniformly judge and identify heart rate baselines, heart rate fluctuations, and transient fluctuations. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Special Publication No. 2020-500077 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention aims to provide auxiliary information that can be used to aid in the interpretation of CTG. [Means for solving the problem]

[0008] The information processing device of the present invention is an information processing device that generates auxiliary information used to assist in interpreting a fetal heart rate tocogram, and comprises an acquisition unit that acquires the fetal heart rate tocogram, a waveform signal extraction unit that extracts a waveform signal of a fetal heart rate waveform included in the acquired fetal heart rate tocogram, a low-frequency extraction unit that extracts a predetermined low-frequency component signal from the extracted waveform signal, and a baseline interval determination unit that identifies a baseline determination target interval for determining a fetal heart rate baseline as auxiliary information based on the extracted low-frequency component signal, wherein the baseline interval determination unit extracts, as a first determination interval, a section from a predetermined first starting point in the low-frequency component signal to a first predetermined time required to determine the fetal heart rate baseline, and when the variance value of the low-frequency component signal in the first determination interval is smaller than a first threshold, determines the first determination interval as a first constituent interval constituting the baseline determination target interval. Effect of the Invention

[0009] According to the present invention, it is possible to provide auxiliary information that is used to assist in the interpretation of CTG. [Brief description of the drawings]

[0010] [Figure 1] FIG. 2 is a schematic diagram showing an example of a cardiotocogram used in the information processing device according to the present invention. [Diagram 2] 1 is a network connection diagram showing an embodiment of an information processing device according to the present invention; [Diagram 3] 1 is a functional block diagram showing an embodiment of an information processing apparatus according to the present invention; [Figure 4] 4 is a functional block diagram of a noise removal unit included in the information processing device of FIG. 3. [Diagram 5] 1 is a flowchart illustrating an embodiment of an information processing method according to the present invention. [Figure 6] 6 is a flowchart of a pre-processing included in the information processing method of FIG. 5. [Figure 7] 6A to 6C are schematic diagrams showing examples of signal states at each stage of the pre-processing in FIG. 5. [Figure 8] 6 is a flowchart of a portion of a baseline determination process included in the information processing method of FIG. 5. [Figure 9] 9 is a flowchart of another part of the baseline determination process in FIG. 8 . [Figure 10] 9 is a schematic diagram showing an example of main processing of the baseline determination processing in FIG. 8; [Figure 11] 6 is a flowchart of a fine variation determination process included in the information processing method of FIG. 5. [Figure 12] 12 is a schematic diagram showing an example of main processing of the fine variation determination processing of FIG. 11. [Figure 13] 6 is a flowchart of a part of a transient fluctuation learning process included in the information processing method of FIG. 5. [Figure 14] 14 is a flowchart of another part of the transient fluctuation learning process of FIG. 13. [Figure 15] 14 is a flowchart of still another part of the transient fluctuation learning process of FIG. 13 . [Figure 16] 14 is a schematic diagram showing an example of main processing of the transient fluctuation section identification processing of FIG. 13. [Figure 17] 14 is a schematic diagram illustrating a method of acquiring learning data in the transient fluctuation learning process of FIG. 13. [Figure 18] 6 is a flowchart of a transient fluctuation identification process included in the information processing method of FIG. 5. [Figure 19] 19 is a schematic diagram illustrating each discrimination interval of the transient fluctuation discrimination process of FIG. 18. [Figure 20] FIG. 13 is a schematic diagram showing an example of a determination result when the present method is applied to an actual cardiotocography. [Figure 21] FIG. 9 is a schematic diagram showing the result of identifying a baseline determination target section when the baseline determination process of FIG. 8 is performed on an actual cardiotocogram. [Figure 22] FIG. 9 is a schematic diagram showing the fetal heart rate baseline determination result when the baseline determination process of FIG. 8 is performed on an actual fetal cardiotocography. [Diagram 23] 12 is a schematic diagram showing the determination result of fetal heart rate baseline fine fluctuations when the fine fluctuation determination process of FIG. 11 is performed on an actual fetal heart rate-tocogram. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Below, with reference to the drawings, we will explain embodiments of an information processing device (hereinafter referred to as "this device"), an information processing program (hereinafter referred to as "this program"), and an information processing method (hereinafter referred to as "this method") according to the present invention.

[0012] The present invention relates to the generation of auxiliary information for use in aiding in the interpretation of a cardiotocogram. The present invention also relates to determining a fetal heart rate baseline (hereinafter referred to as "baseline"), determining fetal heart rate baseline fine fluctuations (hereinafter referred to as "fine fluctuations"), generating a classifier for transient fluctuations, and classifying transient fluctuations.

[0013] A "cardiotocography (CTG)" is a waveform diagram that continuously records the fetal heart rate and uterine contractions (labor) measured by a labor monitoring device in a chronological order, and shows the changes in uterine contractions and fetal heart rate to estimate the state of the fetus. A cardiotocography (hereinafter referred to as "CTG") includes a waveform corresponding to the fetal heart rate (hereinafter referred to as "fetal heart rate waveform") and a waveform corresponding to uterine contractions.

[0014] FIG. 1 is a schematic diagram showing an example of a CTG used in an information processing device according to the present invention. In the figure, the upper waveform shows the fetal heart rate waveform, and the lower waveform shows the waveform corresponding to uterine contractions. The vertical axis of the upper part of the figure shows the fetal heart rate (bpm: beats per minute), and the horizontal axis shows the elapsed time (sec).

[0015] "Auxiliary information" is information used to assist in interpreting the CTG. In this embodiment, the auxiliary information includes a baseline determination target section, a fine variation determination target section, and a transient variation section. Furthermore, in this embodiment, the auxiliary information includes not only the baseline determination result, the fine variation determination result, and the transient variation identification result obtained by the present invention, but also learning data required for identifying the transient variation (i.e., data that assists in the identification).

[0016] The "baseline (fetal heart rate baseline)" is the approximate average heart rate of the fetus over a 10-minute period. Medically, the baseline is determined as a section that continues for 2 minutes or more (i.e., the baseline determination section) among sections of the waveform corresponding to the fetal heart rate, excluding the sections of transient fluctuations and the sections of increased fine fluctuations. In this embodiment, the baseline is determined as one of three types: "normal," "tachycardia," and "bradycardia." The baseline is determined as "bradycardia" when it is less than 110 bpm, as "tachycardia" when it exceeds 160 bpm (beats per minute), and as "normal" when it is between 110 bpm and 160 bpm. Since "normal," "tachycardia," and "bradycardia" are known terms for the baseline, a description thereof will be omitted.

[0017] "Fetal heart rate baseline fine fluctuations" are fine fluctuations in the baseline, and by definition refer to fetal heart rate fluctuations of two or more cycles per minute that have no regularity in either amplitude or frequency. Medically, fine fluctuations are determined in sections where the baseline is legible. In this embodiment, fine fluctuations are determined as one of four types: "moderate (normal)", "increased", "decreased" or "absent". Since "moderate (normal)", "increased", "decreased" and "absent" in fine fluctuations are well known, their explanation will be omitted.

[0018] "Transient fluctuation" refers to a rapid change ("increase" or "decrease") in the fetal heart rate. In this embodiment, the transient fluctuation includes five types: "transient tachycardia," "early transient bradycardia," "late transient bradycardia," "variable transient bradycardia," and "prolonged transient bradycardia." Each type of transient fluctuation is well known, so a description thereof will be omitted.

[0019] The "baseline determination target section" is a section that is specified as a baseline in the present invention, and is a section for which the baseline is determined in the present invention. Details of the baseline determination target section will be described later.

[0020] The "fine fluctuation determination target section" is a section for determining fine fluctuations in the present invention. The fine fluctuation determination target section is specified within the baseline determination target section. The fine fluctuation determination target section will be described in detail later.

[0021] The term "transient fluctuation section" refers to a section that is specified in the present invention as a section in which a transient fluctuation occurs. The details of the transient fluctuation section will be described later.

[0022] The "classifier" in the present invention is a model based on a machine learning algorithm (for example, a known machine learning algorithm such as CNN (Convolutional Neural Network)) that identifies transient fluctuations. In the present invention, the classifier identifies "other than transient fluctuations," "transient tachycardia," "early transient bradycardia and late transient bradycardia," "variable transient bradycardia (mild)," "variable transient bradycardia (severe)," and "prolonged transient bradycardia." In this embodiment, the learning data for the classifier is generated based on the transient fluctuation section, as described below.

[0023] <<Information processing devices>> First, an embodiment of the present device will be described.

[0024] FIG. 2 is a network connection diagram showing an embodiment of the present device.

[0025] This device 1 generates auxiliary information used to assist in interpreting the CTG. This device 1 is connected to an external device 2 via a network N. The specific configuration and operation of this device 1 will be described later.

[0026] The external device 2 is a device that generates and / or stores a CTG, for example a fetal monitoring device.

[0027] The network N is, for example, a communication network such as the Internet, a mobile communication network, a local area network (LAN), a wide area network (WAN), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0028] <Configuration of information processing device> FIG. 3 is a functional block diagram showing an embodiment of the present device 1. As shown in FIG.

[0029] The present device 1 is realized, for example, by a personal computer. The present program runs on the present device 1, and the present program cooperates with the hardware resources of the present device 1 to realize the present method.

[0030] Here, by causing a computer (not shown) to execute this program, the program can cause the computer to function in the same manner as the device 1, and to execute the method.

[0031] The device 1 includes a communication unit 11, a storage unit 12, a control unit 13, an operation unit 14, and a display unit 15.

[0032] The communication unit 11 acquires the CTG from the external device 2 via the network N. The communication unit 11 is configured with, for example, a communication module and a communication interface. The CTG is stored in the storage unit 12.

[0033] The storage unit 12 stores information (e.g., various thresholds, etc.) necessary for the device 1 to execute the method described later. The storage unit 12 is composed of, for example, a recording device such as a hard disk drive (HDD) or a solid state drive (SSD) provided in the device 1, a volatile memory such as a random access memory (RAM), and / or a portable storage medium such as a flash memory.

[0034] The control unit 13 controls the operation of the entire device 1 and executes the method described below. The control unit 13 is composed of, for example, a CPU (Central Processing Unit) included in the device 1, a volatile memory such as a RAM that functions as a working area for the CPU, and a non-volatile memory such as a ROM (Read Only Memory) that stores various information such as the program. The control unit 13 includes a preprocessing unit 131, a baseline section identifying unit 132, a baseline determination unit 133, a fine fluctuation section identifying unit 134, a fine fluctuation determination unit 135, a transient fluctuation section identifying unit 136, a data acquisition unit 137, a classifier generation unit 138, and a classifier 139.

[0035] The preprocessing unit 131 performs preprocessing on the CTG, i.e., acquiring the CTG, extracting a waveform signal described below, removing noise, extracting low-frequency components, and extracting high-frequency components. The preprocessing unit 131 includes an acquiring unit 1311, a waveform signal extracting unit 1312, a noise removing unit 1313, a low-frequency extracting unit 1314, and a high-frequency extracting unit 1315.

[0036] The acquiring unit 1311 acquires the CTG from the external device 2 via the communication unit 11. In this embodiment, the acquiring unit 1311 acquires the CTG in real time. A specific operation of the acquiring unit 1311 will be described later.

[0037] Waveform signal extraction unit 1312 extracts a fetal heart rate waveform signal (hereinafter referred to as a "waveform signal") from the CTG acquired by acquisition unit 1311. A specific operation of waveform signal extraction unit 1312 will be described later.

[0038] FIG. 4 is a functional block diagram of the noise removal unit 1313.

[0039] The noise removal unit 1313 removes noise from the waveform signal extracted by the waveform signal extraction unit 1312 (see FIG. 3). The noise removal unit 1313 includes a first filter 1313a, a first low-pass filter 1313b, a first calculation unit 1313c, a second low-pass filter 1313d, a second calculation unit 1313e, and a second filter 1313f. In this embodiment, the noise removal unit 1313 executes the program, for example, to function as the first filter 1313a, the first low-pass filter 1313b, the first calculation unit 1313c, the second low-pass filter 1313d, the second calculation unit 1313e, and the second filter 1313f. The specific operation of the noise removal unit 1313 will be described later.

[0040] Return to Figure 3. The low frequency extractor 1314 extracts a low frequency component signal in a predetermined range from the waveform signal from which noise has been removed. The specific operation of the low frequency extractor 1314 will be described later.

[0041] The high frequency extraction section 1315 extracts a high frequency component signal in a predetermined range from the waveform signal from which noise has been removed. The specific operation of the high frequency extraction section 1315 will be described later.

[0042] The baseline section identifying unit 132 identifies a baseline determination target section for determining a baseline as auxiliary information based on the extracted low frequency component signal. A specific operation of the baseline section identifying unit 132 will be described later.

[0043] The baseline determination unit 133 determines a baseline in the baseline determination target section based on the identified baseline determination target section. Specific operations of the baseline determination unit 133 will be described later.

[0044] The fine fluctuation section identifying unit 134 identifies a fine fluctuation determination target section for determining fine fluctuations as auxiliary information based on the extracted high frequency component signal. A specific operation of the fine fluctuation section identifying unit 134 will be described later.

[0045] Based on the identified fine fluctuation determination target section, the fine fluctuation determination unit 135 determines fine fluctuations in the fine fluctuation determination target section. Specific operations of the fine fluctuation determination unit 135 will be described later.

[0046] The transient fluctuation section identifying unit 136 identifies one or more transient fluctuation sections as auxiliary information for acquiring learning data for a classifier that identifies a transient fluctuation, based on the extracted low-frequency component signal. A specific operation of the transient fluctuation section identifying unit 136 will be described later.

[0047] The data acquisition unit 137 acquires learning data for the classifier based on the identified transient fluctuation section. A specific operation of the data acquisition unit 137 will be described later.

[0048] The "learning data" is training data used for training the classifier to identify transient fluctuations, and is, for example, waveform data (low-frequency component signal) of a portion of the low-frequency component signal (including at least a portion of the transient fluctuation portion).

[0049] The classifier generating unit 138 generates a classifier based on the training data. The specific operation of the classifier generating unit 138 will be described later.

[0050] The discrimination unit 139 discriminates the transient fluctuation by using a discriminator. A specific operation of the discrimination unit 139 will be described later.

[0051] <<Information processing method>> Next, the operation of the present device 1, i.e., the present method executed by the present device 1, will be described with reference to Figs.

[0052] FIG. 5 is a flow chart illustrating an embodiment of the method.

[0053] The present device 1 executes pre-processing (S1), baseline determination processing (S2), fine fluctuation determination processing (S3), transient fluctuation learning processing (S4), and transient fluctuation identification processing (S5). That is, the present method includes pre-processing (S1), baseline determination processing (S2), fine fluctuation determination processing (S3), transient fluctuation learning processing (S4), and transient fluctuation identification processing (S5). Here, the baseline determination processing (S2), fine fluctuation determination processing (S3), and transient fluctuation identification processing (S5) are processing executed mainly in obstetrics. On the other hand, the transient fluctuation learning processing (S4) is processing executed in the learning site of the classifier, and is processing executed in advance before the transient fluctuation identification processing (S5). The pre-processing (S1) is common to the baseline determination process (S2), the fine fluctuation determination process (S3), the transient fluctuation learning process (S4), and the transient fluctuation identification process (S5), and is executed in advance.

[0054] <Pretreatment> FIG. 6 is a flowchart of the pre-processing (S1). FIG. 7 is a schematic diagram showing an example of the state of a signal at each stage of pre-processing (S1).

[0055] "Pre-processing (S1)" is a process of extracting a waveform signal from the CTG, removing noise from the waveform signal, and generating a low-frequency component signal and a high-frequency component signal.

[0056] First, the acquiring unit 1311 acquires a CTG from the external device 2 via the communication unit 11 (S101). In this embodiment, the acquiring unit 1311 is assumed to continuously acquire a CTG for one pregnant woman.

[0057] Next, the waveform signal extraction unit 1312 extracts a waveform signal of a fetal heart rate waveform included in the acquired CTG (S102).

[0058] Next, the noise removal unit 1313 applies a primary filter 1313a to the waveform signal to remove the main abrupt noise from the waveform signal (S103). The threshold of the primary filter 1313a is, for example, 10 bpm to 190 bpm. That is, the primary filter 1313a removes abrupt noise less than 10 bpm and abrupt noise exceeding 190 bpm from the waveform signal.

[0059] Next, the noise removal unit 1313 applies a primary low-pass filter 1313b to the waveform signal from which the main abrupt noise has been removed (hereinafter referred to as the "primary noise-removed signal"), and extracts a low-frequency band component signal (hereinafter referred to as the "primary low-frequency component signal") from the primary noise-removed signal (S104). As a result, a trend of the waveform signal that does not include the abrupt noise that has escaped the processing S103 by the primary filter 1313a is extracted. The cutoff frequency of the primary low-pass filter 1313b is, for example, 0.03 Hz.

[0060] Next, the noise removal unit 1313 uses the primary calculation unit 1313c to calculate the absolute value of the difference between the primary noise-removed signal and the primary low-frequency component signal (hereinafter referred to as the "primary difference") (S105). As a result, the fluctuation corresponding to the baseline is canceled to some extent, and the noise remaining after the process S104 becomes clear.

[0061] Next, the noise removal unit 1313 applies a secondary low-pass filter 1313d to a signal obtained from the absolute value of the calculated primary difference (hereinafter referred to as the "primary difference signal") to extract a low-frequency band component signal (hereinafter referred to as the "secondary low-frequency component signal") from the primary difference signal (S106). As a result, a fluctuation component corresponding to the baseline containing noise clarified by the process S105 is extracted. The cutoff frequency of the secondary low-pass filter 1313d is, for example, 0.06 Hz.

[0062] Next, the noise removal unit 1313 uses the secondary calculation unit 1313e to calculate the difference between the first-order difference signal and the second-order low-frequency component signal (hereinafter referred to as the "second-order difference") (S107). As a result, the aforementioned fluctuation component is cancelled. Therefore, the noise that has been made clear by the process S105 becomes clearer and detectable.

[0063] Next, the noise removal unit 1313 applies a secondary filter 1313f to the signal obtained by the secondary difference (hereinafter referred to as the "secondary difference signal") to extract a secondary difference signal (hereinafter referred to as the "noise-removed signal") from which minute abrupt noise remaining after the process S103 has been removed (S108). Here, the threshold of the secondary filter is 10 bpm. That is, the secondary filter 1313f removes abrupt noise exceeding 10 bpm from the secondary difference signal.

[0064] Next, the low frequency extraction unit 1314 applies a predetermined low pass filter to the noise-removed signal to extract a predetermined low frequency component signal from the noise-removed signal (S109). Here, the low pass filter applied in this process S109 is, for example, a Butterworth type low pass filter (order "4"). The cutoff frequency of the low pass filter is, for example, 0.03 Hz. The extracted low frequency component signal is stored in, for example, the storage unit 12.

[0065] On the other hand, the high frequency extraction unit 1315 applies a band pass filter of a predetermined range to the noise removed signal to extract a high frequency component signal of a predetermined range from the noise removed signal (S110). Here, the band pass filter applied in this process S110 is, for example, a Butterworth type band pass filter (order "4"). The cut-off frequency (band pass frequency) of the band pass filter is 0.02 Hz to 0.8 Hz. The extracted high frequency component signal is stored in, for example, the storage unit 12.

[0066] Here, when a doctor interprets a CTG, the doctor does not clearly distinguish between the low frequency components (slow fluctuations) and high frequency components (fine fluctuations) of the fetal heart rate waveform to interpret the CTG, but interprets the CTG by using both components appropriately depending on the purpose. That is, a part of the shape of the fetal heart rate waveform for which the doctor judges the baseline overlaps with a part of the shape of the fetal heart rate waveform for which the doctor judges the fine fluctuations. Therefore, in this embodiment, the high frequency component signal includes a higher frequency band than the low frequency component signal, and the low frequency component signal includes a lower frequency band than the high frequency component signal. That is, a part of the frequency band of the high frequency component signal is common to a part of the frequency band of the low frequency component signal. As a result, the present device 1 can realize judgments (judgment of the baseline, judgment of fine fluctuations) close to those of a doctor's visual observation.

[0067] The pre-processing may be performed continuously in time series, or may be performed collectively for each waveform signal within a predetermined time period (eg, 3 minutes).

[0068] Also, for example, the cutoff frequency of each filter included in the noise removal unit of the present invention is not limited to that of the present embodiment as long as low frequency component signals and high frequency component signals necessary for the present method (preprocessing) can be obtained.

[0069] Furthermore, in the pre-processing, if the remaining noise is clear enough to be detectable (e.g., to be extractable by a secondary filter) at the time when the absolute value of the first-order difference is calculated, the noise removal unit may omit steps S106 and S107.

[0070] <Baseline determination process> FIG. 8 is a flowchart of a part of the baseline determination process (S2). FIG. 9 is a flowchart of another part of the baseline determination process (S2). FIG. 10 is a schematic diagram showing an example of main processing of the baseline determination process (S2).

[0071] In FIG. 10, the first constituent section, which will be described later, is indicated by a solid arrow, and the first non-constituent section, which will be described later, is indicated by a dashed arrow. For ease of explanation, the first constituent section and the first non-constituent section are numbered chronologically from "1" to "10" in the figure. The figure shows that the first constituent sections "1" to "3" are consecutive. The figure also shows that the first non-constituent sections "4" to "10" are seven consecutive. Furthermore, the figure shows that the first constituent sections "1" to "3" are identified as the section subject to baseline determination.

[0072] The "baseline determination process (S2)" is a process of identifying a baseline determination target section for determining a baseline based on a low-frequency component signal, and determining a baseline in the baseline determination target section based on the baseline determination target section.

[0073] First, the baseline interval determination unit 132 reads out from the storage unit 12 a low frequency component signal corresponding to a fetal heart rate waveform (waveform signal) for which a baseline is to be determined (S201).

[0074] Next, the baseline section identification unit 132 sets the first point on the time series in the low frequency component signal as a starting point, and extracts a section from the starting point until a first predetermined time has elapsed as a first determination section (S202). Here, the starting point in the baseline determination process (S2) is an example of the first starting point in the present invention.

[0075] The "first predetermined time" is the time required to determine the baseline, and is two minutes in medical terms. Here, the fetal heart rate waveform in the CTG may not have a two-minute baseline determination target section. In this case, the doctor determines the baseline based on a baseline determination target section that is less than two minutes but close to two minutes (e.g., 90 seconds). Therefore, the first predetermined time is preferably about 70 to 110 seconds, and in this embodiment, the first predetermined time is set to 80 seconds. That is, in this embodiment, the first determination section is a section from the starting point to 80 seconds.

[0076] The starting point set by the baseline section determination unit in the present invention is not limited to the first point on the time series. That is, for example, the baseline section determination unit in the present invention may set a point selected by the user of the device as the starting point.

[0077] Next, the baseline section identification unit 132 calculates a variance value of the low frequency component signal in the first determination section (hereinafter referred to as a "first variance value") (S203).

[0078] Next, the baseline section identifying unit 132 compares the first variance value with a predetermined first threshold value to determine whether the first variance value is smaller than the first threshold value (S204).

[0079] The "first threshold" is a threshold for determining whether or not the first determination section is appropriate as a baseline determination target section. In this embodiment, the first threshold is set to "7." The first threshold is, for example, set in advance by a user of the device 1 and stored in the storage unit 12.

[0080] When the first variance value is smaller than the first threshold value ("Y" in S204), the baseline section identification unit 132 identifies the first determination section as a section constituting the baseline determination target section (hereinafter referred to as the "first constituent section") (S205).

[0081] On the other hand, when the first variance value is greater than or equal to the first threshold value ("N" in S204), the baseline section identification unit 132 identifies the first determination section as a section that does not constitute the baseline determination target section (hereinafter referred to as the "first non-constituent section") (S206).

[0082] Next, the baseline section identification unit 132 determines whether or not the first non-constituent section occurs consecutively a predetermined number of times (S207).

[0083] The "predetermined number of times" is the number of times that a first non-constituent section successively occurs such that some sections of two successive first constituent sections in the time series no longer have anything in common. In other words, the predetermined number of times is the number of times that a common section disappears between a certain first constituent section and the next first constituent section in the time series (the two first constituent sections are separated). The predetermined number of times is set, for example, based on the first predetermined time. That is, for example, when the first predetermined time is 80 seconds and the second predetermined time described below is 10 seconds, the predetermined number of times is set to 9 times.

[0084] When the first non-constituent interval does not occur consecutively the predetermined number of times ("N" in S207), the baseline interval identifying unit 132 determines whether or not an interval subsequent to the first determination interval in the time series continues for a second predetermined time or more in the low frequency component signal (S208). In other words, the baseline interval identifying unit 132 determines whether or not the acquiring unit 1311 has continuously acquired the fetal heart rate waveform for the second predetermined time from the end point of the first determination interval.

[0085] The "second predetermined time" is a time shorter than the first predetermined time, and is set to shift the first judgment section later in the time series. The second predetermined time is set so that two consecutive first judgment sections in the time series have a part in common. In the present embodiment, the second predetermined time is set to 10 seconds.

[0086] It should be noted that the second predetermined time in the present invention is not limited to 10 seconds. That is, for example, the second predetermined time in the present invention may be 5 seconds or 15 seconds.

[0087] When the later section in the time series continues for more than the second predetermined time ("Y" in S208), the baseline section identification unit 132 sets the point where the second predetermined time has elapsed from the start point as a new start point, and extracts the section from the new start point to the point where the first predetermined time has elapsed as a new first judgment section (S209). In other words, the baseline section identification unit 132 updates the position of the start point of the first judgment section to the position of a point the second predetermined time after the same position. In other words, the baseline section identification unit 132 shifts the first judgment section back in the time series by the second predetermined time. Next, the baseline determination process (S2) returns to process S203.

[0088] When the first non-constituent sections are consecutive a predetermined number of times ("Y" in S207), if a plurality of first constituent sections are identified, the baseline section identification unit 132 identifies a section obtained by connecting first constituent sections having a common section among the plurality of first constituent sections as a baseline determination target section (S210). On the other hand, if one first constituent section is identified, the baseline section identification unit 132 identifies the first constituent section as a baseline determination target section. In this way, by identifying the baseline determination target section, the present device 1 can identify a section that is close to the section in which the doctor actually determines the baseline as the baseline determination target section. The baseline determination target section is associated with, for example, a low frequency component signal and a waveform signal on which the low frequency component signal is based, and stored in the storage unit 12 as auxiliary information.

[0089] Next, the baseline determination unit 133 calculates an average heart rate for each first component section that constitutes the baseline determination target section in the low frequency component signal (S211). The average heart rate is associated with the first component section and the baseline determination target section constituted by the first component section, for example, and is stored in the storage unit 12 as auxiliary information.

[0090] Next, the baseline determination unit 133 compares the average heart rate for each first constituent section with a predetermined threshold value to determine the baseline for each first constituent section (S212). Specifically, when the average heart rate is smaller than "110 bpm", the baseline determination unit 133 determines that "the fetal heart rate of the baseline corresponding to the first constituent section is bradycardia", when the average heart rate is larger than "160 bpm", the baseline determination unit 133 determines that "the fetal heart rate of the baseline corresponding to the first constituent section is tachycardia", and when the average heart rate is equal to or larger than "110 bpm" and equal to or smaller than "160 bpm", the baseline determination unit 133 determines that "the fetal heart rate of the baseline corresponding to the first constituent section is normal". Here, the threshold value for determining the baseline is medically defined as "110 bpm" and "160 bpm", and is stored in the storage unit 12 in advance. The determination result is stored in the storage unit 12 as auxiliary information, for example, in association with the first constituent section and the baseline determination target section constituted by the first constituent section. Next, the baseline determination process (S2) returns to process S208.

[0091] When the later section in the time series does not continue for a second predetermined time or longer ("N" in S208), the baseline section identification unit 132 determines whether the value of a standby count described below is "0" or "1" (S213).

[0092] When the standby count value is "0", the baseline section identification unit 132 adds "1" to the standby count after waiting for a second predetermined time (S214) and executes process S208. On the other hand, when the standby count value is "1", the baseline section identification unit 132 determines whether or not there is a first component section that has not been identified as a baseline determination target section in sections subsequent to the section last identified as a baseline determination target section (S215).

[0093] If there is a first component section that has not been specified as a baseline determination target section ("Y" in S215), the baseline determination process (S2) returns to process S210. On the other hand, if there is no first component section ("N" in S215), the baseline determination process (S2) ends.

[0094] <Fine change judgment process> FIG. 11 is a flowchart of the fine variation determination process (S3). FIG. 12 is a schematic diagram showing an example of main processing of the fine variation determination process (S3).

[0095] The "fine fluctuation determination process (S3)" is a process for determining fine fluctuations based on a fine fluctuation determination target section, which is a section in a high frequency component signal that corresponds to a baseline determination target section. The fine fluctuation determination process (S3) is executed, for example, after the baseline determination target section is determined in the baseline determination process (S2).

[0096] First, the fine fluctuation section determination unit 134 reads out from the memory unit 12 a high frequency component signal corresponding to the fetal heart rate waveform (waveform signal) for which fine fluctuations are to be determined, and a baseline determination target section associated with a low frequency component signal corresponding to the same waveform signal (S301).

[0097] Next, the fine fluctuation section identifying unit 134 identifies a section corresponding to the baseline determination target section in the high frequency component signal as a fine fluctuation determination target section (S302).

[0098] Next, the fine fluctuation section identification unit 134 sets the first point (start point) of the fine fluctuation judgment section as the start point, and extracts the section from the start point until the third predetermined time has elapsed as the second judgment section (S303). Here, the start point in the fine fluctuation judgment process (S3) is an example of the second start point in the present invention.

[0099] The "third predetermined time" is a time required to judge a minute fluctuation, and is set to a time shorter than the first predetermined time. The third predetermined time is set to a time during which the judgment result of a minute fluctuation does not change frequently within the minute fluctuation judgment target section, for example. In this embodiment, the third predetermined time is set to 40 seconds. That is, in this embodiment, the second judgment section is a section of 40 seconds from the starting point.

[0100] The third predetermined time in the present invention is not limited to 40 seconds. If the third predetermined time is longer than 50 seconds, small fluctuations are averaged, and the accuracy of the judgment of the small fluctuations deteriorates. On the other hand, if the third predetermined time is shorter than 30 seconds, a plurality of small fluctuations are judged individually, and the judgment result frequently changes. Therefore, it is preferable that the third predetermined time in the present invention is between 30 seconds and 50 seconds.

[0101] Next, the fine variation determining unit 135 calculates a variance value of the high frequency components in the second determination section (hereinafter referred to as the "second variance value") (S304).

[0102] Next, the fine fluctuation determination unit 135 compares the second variance value with a predetermined second threshold value to determine the fine fluctuation in the second judgment interval (S305). Specifically, when the second variance value is equal to or greater than "1.3" and equal to or less than "20", the fine fluctuation determination unit 135 determines that "the fine fluctuation in the second judgment interval is moderate", when the second variance value is greater than "20" and equal to or less than "25", the fine fluctuation determination unit 135 determines that "the fine fluctuation in the second judgment interval is increasing", when the second variance value is equal to or greater than "0.07" and less than "1.3", the fine fluctuation determination unit 135 determines that "the fine fluctuation in the second judgment interval is a phenomenon", and when the second variance value is less than "0.07", the fine fluctuation determination unit 135 determines that "the fine fluctuation in the second judgment interval has disappeared". The determination result is stored in the storage unit 12 as auxiliary information, for example, in association with the corresponding second judgment interval.

[0103] Next, the fine fluctuation section identifying unit 134 determines whether or not a section subsequent to the second determination section in the baseline determination target section in the time series continues for a fourth predetermined time or longer (S306).

[0104] The "fourth predetermined time" is a time shorter than the third predetermined time, and is a time by which the second judgment section is shifted later in the time series. The fourth predetermined time is set so that two consecutive second judgment sections in the time series have a part in common. In the present embodiment, the fourth predetermined time is set to 10 seconds.

[0105] It should be noted that the fourth predetermined time period in the present invention is not limited to 10 seconds. That is, for example, the fourth predetermined time period in the present invention may be 5 seconds or 15 seconds.

[0106] When the latter section continues for a fourth predetermined time or more in the time series ("Y" in S306), the fine fluctuation section identifying unit 134 sets the point where the fourth predetermined time has elapsed from the start point as a new start point, and extracts the section from the new start point until the third predetermined time has elapsed as a new second judgment section (S307). In other words, the fine fluctuation section identifying unit 134 updates the position of the start point of the second judgment section to the position of the point where the fourth predetermined time has elapsed from the same position. That is, the fine fluctuation section identifying unit 134 shifts the second judgment section backward in the time series by the fourth predetermined time. Then, the fine fluctuation judgment process (S3) returns to process S304. In this way, by judging fine fluctuations while shifting the second judgment section by the fourth predetermined time, the present device 1 can judge fine fluctuations in a section of the baseline that is close to the section that a doctor actually visually observes to judge fine fluctuations from the baseline.

[0107] On the other hand, when the latter section in the time series does not continue for the fourth predetermined time or longer ("N" in S306), the minute variation determination process (S3) ends.

[0108] The fine fluctuation determination process may be executed each time a baseline determination target section is identified. That is, the fine fluctuation determination process may be executed in parallel with the baseline determination target process. In this case, the fine fluctuation determination process is executed, for example, each time a baseline determination target section is identified.

[0109] <Transient Fluctuation Learning Process> FIG. 13 is a flowchart of a part of the transient fluctuation learning process (S4). FIG. 14 is a flowchart of another part of the transient fluctuation learning process (S4). FIG. 15 is a flowchart of still another part of the transient fluctuation learning process (S4). FIG. 16 is a schematic diagram showing an example of main processes of the transient fluctuation learning process (S4).

[0110] The "transient fluctuation learning process (S4)" is a process for identifying a transient fluctuation section based on a low-frequency component signal, and acquiring learning data for a classifier based on the transient fluctuation section. As described above, the transient fluctuation learning process (S4) is a process executed before the transient fluctuation identification process (S5).

[0111] First, the transient fluctuation section identifying unit 136 reads out from the storage unit 12 a low frequency component signal corresponding to a fetal heart rate waveform (waveform signal) from which a transient fluctuation section is to be extracted (S401). The low frequency component signal is, for example, acquired in advance by pre-processing (S1) and stored in the storage unit 12.

[0112] Next, the transient fluctuation section identifying unit 136 sets the first point on the time series in the low frequency component signal as a starting point, and extracts a section from the starting point until a fifth predetermined time has elapsed as a third judgment section (S402). Here, the starting point in the transient fluctuation section identifying process (S4) is an example of the third starting point in the present invention.

[0113] The "fifth predetermined time" is a time set for extracting a transient fluctuation section, and is set to a time shorter than the first predetermined time. Usually, a doctor judges one transient fluctuation for one transient fluctuation section. Therefore, the fifth predetermined time is set to a time when the judgment result of the transient fluctuation in the transient fluctuation section does not change frequently, for example. In this embodiment, the fifth predetermined time is set to 40 seconds. That is, in this embodiment, the third judgment section is a section of 40 seconds from the starting point.

[0114] The fifth predetermined time in the present invention is not limited to 40 seconds. If the fifth predetermined time is longer than 50 seconds, many sections (such as a baseline) in which no transient fluctuation occurs may be included near the start and end points of the transient fluctuation section. In addition, a plurality of short transient fluctuation sections may be combined into one transient fluctuation section. As a result, the accuracy of extracting the transient fluctuation judgment section may deteriorate. On the other hand, if the fifth predetermined time is shorter than 30 seconds, a situation may occur in which the proportion of sections with small heart rate fluctuations in the third judgment section increases. As a result, the amount of fluctuation in the third judgment section may be averaged, and the third judgment section may not be identified as a section that constitutes a transient fluctuation section. Therefore, it is preferable that the fifth predetermined time in the present invention is between 30 seconds and 50 seconds.

[0115] Next, the transient fluctuation section identifying unit 136 calculates a variance value of the low-frequency components in the third judgment section (hereinafter referred to as a "third variance value") (S403).

[0116] Next, the transient fluctuation section identifying unit 136 compares the third variance value with a predetermined third threshold value to determine whether the third variance value is greater than the third threshold value (S404).

[0117] The "third threshold" is a threshold for determining whether the third judgment section is appropriate as a transient fluctuation section. In the present embodiment, the third threshold is set to "15." The third threshold is, for example, set in advance by a user of the device 1 and stored in the storage unit 12.

[0118] When the third variance value is greater than the third threshold value ("Y" in S404), the transient fluctuation interval identifying unit 136 identifies the third judgment interval as a second constituent interval that constitutes a transient fluctuation interval (S405).

[0119] On the other hand, when the third variance value is equal to or less than the third threshold value ("N" in S404), the transient fluctuation interval identifying unit 136 identifies the third judgment interval as a second non-constituting interval that does not constitute a transient fluctuation interval (S406).

[0120] Next, the transient fluctuation interval identifying unit 136 determines whether or not the second non-constituting interval occurs consecutively a predetermined number of times (S407).

[0121] The "predetermined number of times" is the number of times that a second non-constituent section successively occurs such that a portion of two adjacent second constituent sections in the time series no longer has a common section. In other words, the predetermined number of times is the number of times that a common section disappears between a second constituent section and the next second constituent section in the time series (the two second constituent sections are separated). The predetermined number of times is set, for example, based on the fifth predetermined time. In other words, for example, when the fifth predetermined time is 40 seconds, the predetermined number of times is set to 3 times.

[0122] When the second non-constituent interval does not occur consecutively the predetermined number of times ("N" in S407), the transient fluctuation interval identifying unit 136 determines whether or not an interval subsequent to the third determination interval in the time series continues for a sixth predetermined time or more in the low frequency component signal (S408). In other words, the transient fluctuation interval identifying unit 136 determines whether or not the acquiring unit 1311 has acquired a fetal heart rate waveform for the sixth predetermined time from the end point of the fifth determination interval.

[0123] The "sixth predetermined time" is a time shorter than the fifth predetermined time, and is set to shift the third judgment section later in the time series. The sixth predetermined time is set so that two consecutive second judgment sections in the time series have a part in common. In the present embodiment, the sixth predetermined time is set to 10 seconds.

[0124] It should be noted that the sixth predetermined time period in the present invention is not limited to 10 seconds. That is, for example, the sixth predetermined time period in the present invention may be 5 seconds or 15 seconds.

[0125] When the latter section continues for the second predetermined time or more in the time series ("Y" in S408), the transient fluctuation section identifying unit 136 sets the point where the sixth predetermined time has elapsed from the start point as a new start point, and extracts the section from the new start point to the fifth predetermined time as a new third judgment section (S409). In other words, the transient fluctuation section identifying unit 136 updates the position of the start point of the third judgment section to the position of the point six predetermined time after the same position. That is, the transient fluctuation section identifying unit 136 shifts the third judgment section backward in the time series by the sixth predetermined time. Next, the transient fluctuation section identifying process (S4) returns to process S403.

[0126] When the second non-constituent intervals are consecutive a predetermined number of times ("Y" in S407), the transient fluctuation interval identifying unit 136 identifies an interval obtained by connecting second constituent intervals having a common interval among the plurality of second constituent intervals as a transient fluctuation interval (S410). In other words, the transient fluctuation interval identifying unit 136 identifies an interval constituted by one or more consecutive second constituent intervals as a transient fluctuation interval.

[0127] Next, the transient fluctuation interval identification unit 136 extracts, from the transient fluctuation interval, a interval from the first point on the time series (the start point of the first second constituent interval) to the elapse of a seventh predetermined time as a start interval, and extracts, from the last point on the time series (the end point of the last second constituent interval) back to the seventh predetermined time as an end interval (S411).

[0128] The "seventh predetermined time" is a time shorter than the fifth predetermined time, and is set in order to extract a section (start section, end section) that may include a section in which no transient fluctuation occurs from among the transient fluctuation sections. The seventh predetermined time is set so that both ends of the transient fluctuation section do not include sections in which no transient fluctuation occurs (so that such sections are minimized). In this embodiment, the seventh predetermined time is set to 10 seconds.

[0129] It should be noted that the seventh predetermined time period in the present invention is not limited to 10 seconds. That is, for example, the seventh predetermined time period in the present invention may be 5 seconds or 15 seconds.

[0130] Next, the transient fluctuation interval identifying unit 136 calculates the variance value of the start interval (hereinafter referred to as the "fourth variance value") and the variance value of the end interval (hereinafter referred to as the "fifth variance value") (S412).

[0131] Next, the transient fluctuation section identifying unit 136 compares the fourth variance value with a predetermined fourth threshold value to determine whether the fourth variance value is smaller than the fourth threshold value (S413).

[0132] The "fourth threshold" is a threshold that is set to determine whether or not a section in which no transient fluctuation occurs is included in the start section. In the present embodiment, the fourth threshold is set to "3." The fourth threshold is, for example, set in advance by a user of the present device 1 and stored in the storage unit 12.

[0133] When the fourth variance value is smaller than the fourth threshold value ("Y" in S413), the transient fluctuation interval identifying unit 136 excludes the start interval from the transient fluctuation interval (S414).

[0134] On the other hand, when the fourth variance value is equal to or greater than the fourth threshold value ("N" in S413), the transient fluctuation interval identifying unit 136 maintains the start interval as part of the transient fluctuation interval (S415).

[0135] Next, the transient fluctuation section identifying unit 136 compares the fifth variance value with a predetermined fifth threshold value to determine whether the fifth variance value is smaller than the fifth threshold value (S416).

[0136] The "fifth threshold" is a threshold set to determine whether or not a section in which no transient fluctuation occurs is included in the end section. In the present embodiment, the fifth threshold is set to "3." The fifth threshold is, for example, set in advance by a user of the present device 1 and stored in the storage unit 12.

[0137] When the fifth variance value is smaller than the fifth threshold value ("Y" in S416), the transient fluctuation interval identifying unit 136 excludes the end interval from the transient fluctuation interval (S417).

[0138] On the other hand, when the fifth variance value is equal to or greater than the fifth threshold value ("N" in S416), the transient fluctuation interval identifying unit 136 maintains the end interval as part of the transient fluctuation interval (S418).

[0139] Next, the transient fluctuation interval identifying unit 136 updates the transient fluctuation interval based on the results of steps S414, S415, S417, and S418 (S419). The updated transient fluctuation interval is stored in the storage unit 12 as auxiliary information. By identifying and updating the transient fluctuation interval in this manner, the device 1 can identify, as the transient fluctuation interval, an interval that is close to the interval in which the doctor actually judges the transient fluctuation.

[0140] Next, the data acquisition unit 137 reads out the transient fluctuation interval updated in step S419 (S420).

[0141] Next, the data acquiring unit 137 acquires, as first learning data, a low-frequency component signal in a section going back from the last point on the time series of the transient fluctuation section (the end point of the transient fluctuation section) to an eighth predetermined time (S421).

[0142] The "eighth predetermined time" is a time set to acquire, as learning data, a section in which a transient fluctuation that appears in a relatively short time occurs among the transient fluctuations. Medically, a transient fluctuation is specified to end in less than two minutes. However, in reality, a transient fluctuation may end in a relatively short time of about 90 seconds. The eighth predetermined time is a time set to deal with this transient fluctuation that appears in a short time, and is preferably between 80 and 100 seconds. In this embodiment, the eighth predetermined time is set to 90 seconds. The eighth predetermined time is, for example, set in advance by the user of the present device 1 and stored in the storage unit 12.

[0143] Next, the data acquiring unit 137 acquires, as second learning data, low-frequency component signals in a section going back from the last point on the time series of the transient fluctuation section to the ninth predetermined time (S422).

[0144] The "ninth predetermined time" is a time set to acquire, as learning data, a section in which a transient fluctuation that appears for a relatively long time occurs among the transient fluctuations. Medically, a transient fluctuation is defined as ending in less than two minutes. However, in reality, a transient fluctuation may end in a relatively long time exceeding two minutes. The ninth predetermined time is a time set to deal with this long-term transient fluctuation, and is preferably between 150 and 200 seconds. In this embodiment, the ninth predetermined time is set to 180 seconds. The ninth predetermined time is, for example, set in advance by the user of the present device 1 and stored in the storage unit 12.

[0145] 17 is a schematic diagram illustrating a method for acquiring the first learning data and the second learning data, which shows that for each transient fluctuation interval, the interval from the last point of the interval up to the 8th predetermined time going back is acquired as the first learning data, and the interval from the last point of the interval up to the 9th predetermined time going back is acquired as the second learning data.

[0146] Return to Figures 13 to 16. Next, the data acquiring unit 137 causes the display unit 15 to display the first learning data and the second learning data, and options for the labels corresponding to the types of transient fluctuations (S423).

[0147] The "label" is an identification mark that is set in advance for each type of transient variation and stored in the storage unit 12. In this embodiment, the label of a variation other than a transient variation is set to "0", the label of a transient tachycardia is set to "1", the label of an early transient bradycardia and a late transient bradycardia is set to "2", the label of a variable transient bradycardia (mild) is set to "3", the label of a variable transient bradycardia (severe) is set to "4", and the label of a persistent transient bradycardia is set to "5".

[0148] Next, for example, the user of the present device 1 selects labels corresponding to the first learning data and the second learning data, respectively, using, for example, the operation unit 14. At this time, the data acquisition unit 137 associates the first learning data with the label selected corresponding to the first learning data, associates the second learning data with the label selected corresponding to the second learning data, and stores them as auxiliary information (auxiliary information that indirectly assists in classification) in the storage unit 12 (S424). Next, the transient fluctuation learning process (S4) returns to process S408.

[0149] When the later section in the time series has not elapsed for the sixth predetermined time or more ("N" in S408), the transient fluctuation section identification unit 136 determines whether the value of the standby count described below is "0" or "1" (S425).

[0150] When the value of the standby count is "0", the transient fluctuation interval identifying unit 136 adds "1" to the standby count after waiting for a sixth predetermined time (S426) and executes the process S408. On the other hand, when the value of the standby count is "1", the transient fluctuation interval identifying unit 136 determines whether or not there is a second constituent interval that has not been identified as a transient fluctuation interval in the intervals following the interval last identified as a transient fluctuation interval (S427).

[0151] When there is a second component interval that has not been identified as a transient fluctuation interval ("Y" in S427), the transient fluctuation learning process (S4) returns to process S410. On the other hand, when there is no second component interval ("N" in S427), the transient fluctuation learning process (S4) ends.

[0152] In the data acquisition process, the data acquisition unit of the present invention may only acquire and store the plurality of first learning data and the plurality of second learning data. In this case, the association with each label is performed, for example, by a separate process.

[0153] In the data acquisition process, the data acquisition unit of the present invention may continuously acquire the first learning data and the second learning data for a plurality of transient fluctuation intervals.

[0154] <Transient Fluctuation Identification Processing> FIG. 18 is a flowchart of the transient fluctuation identifying process (S5). FIG. 19 is a schematic diagram for explaining each discrimination interval of the transient fluctuation discrimination process (S5).

[0155] The "transient fluctuation identification process (S5)" is a process of identifying a transient fluctuation using a classifier generated based on the first learning data and the second learning data.

[0156] In the following description, it is assumed that the storage unit 12 stores a first classifier and a second classifier in advance. Here, the first classifier is a classifier generated in advance by the classifier generation unit 138 based on, for example, a plurality of first learning data associated with a label. The second classifier is a classifier generated in advance by the classifier generation unit 138 based on, for example, a plurality of second learning data associated with a label.

[0157] First, the classifier 139 reads out the first classifier and the second classifier from the storage unit 12 (S501).

[0158] Next, the discrimination unit 139 reads out from the storage unit 12 a low frequency component signal corresponding to the fetal heart rate waveform (waveform signal) for which a transient variation is to be discriminated (S502).

[0159] Next, the discrimination unit 139 sets the first point on the time series in the low frequency component signal as the start point of the discrimination interval, extracts the interval from the start point to the elapse of an eighth predetermined time as the first discrimination interval, and extracts the interval from the start point to the elapse of a ninth predetermined time as the second discrimination interval (S503).

[0160] Next, the discrimination unit 139 discriminates a transient fluctuation from the low frequency component signal in the first discrimination interval using the first discriminator, and discriminates a transient fluctuation from the low frequency component signal in the second discrimination interval using the second discriminator (S504).

[0161] Next, the classification unit 139 determines the final classification results (final classification results) of the first and second classification intervals based on the classification result of the first classifier (first classification result) and the classification result of the second classifier (second classification result) (S505). At this time, for example, when the first classification result and the second classification result match, the classification unit 139 determines the first classification result as the final classification result of the first classification interval and determines the second classification result as the final classification result of the second classification interval. Also, for example, when the first classification result and the second classification result each classify different transient fluctuations, the classification unit 139 determines both the first classification result and the second classification result as the final classification results of the first and second classification intervals. On the other hand, when only one of the first classification result and the second classification result identifies a transient fluctuation, the classification unit 139 determines that transient fluctuation as the final classification result of the first and second classification intervals. The final classification result is associated with the corresponding position (section) of the low frequency component signal and stored in the storage unit 12 as auxiliary information.

[0162] In addition, when the first and second classification results each identify different transient fluctuations, the classification unit in the present invention may simply determine the classification result that there is a transient fluctuation as the final classification result for each of the first and second classification intervals.

[0163] Next, the discrimination unit 139 determines whether or not a section subsequent to the second discrimination section in the time series continues for a tenth predetermined time or longer in the low frequency component signal (S506).

[0164] The "tenth predetermined time" is a time by which the first discrimination section and the second discrimination section are shifted back in time. The tenth predetermined time is set so that the transient fluctuation is judged without excess or deficiency. In the present embodiment, the tenth predetermined time is set to 3 seconds.

[0165] When the latter section in the time series continues for the tenth predetermined time or longer ("Y" in S506), the identification unit 139 executes the process S503.

[0166] When the latter section in the time series has not elapsed the tenth predetermined time or more ("N" in S506), the discrimination unit 139 determines whether the value of a standby count described below is "0" or "1" (S507).

[0167] When the value of the standby count is "0", the identification unit 139 adds "1" to the standby count after waiting for the tenth predetermined time (S508) and executes the process S506. On the other hand, when the value of the standby count is "1", the transient fluctuation identification process (S5) ends.

[0168] The identification unit in the present invention may execute the identification process only for the transient fluctuation section. In this case, the identification unit in the present invention may identify the transient fluctuation every time a transient fluctuation section is identified in the transient fluctuation section identification process.

[0169] <<Example>> Next, an embodiment of the present invention will be described.

[0170] FIG. 20 is a schematic diagram showing an example of a determination result when the baseline determination process (S2) and the fine variation determination process (S3) are executed on an actual CTG.

[0171] Of the two-row numbers in the figure, the underlined numbers in the top row indicate the baseline label ("0" is normal), and the numbers in the bottom row indicate the fine fluctuation label ("0" is moderate, "2" is decreased). The figure shows that six baseline determination target sections, "a" through "f," have been identified. The figure also shows that the determination of fine fluctuations changes within the baseline determination target sections, "b," "d," "e," and "f." Furthermore, the figure shows that in the baseline determination target section, "c" through "e," the doctor has made corrections to the positions of the start and end points of the baseline determination target section and the fine fluctuation determination results.

[0172] FIG. 21 is a schematic diagram showing the result of identifying a baseline determination target section when the baseline determination process (S2) is executed on an actual CTG.

[0173] The "total number of sections" is the number of sections that the doctor determined to be the baseline. The "number of unspecified" is the number of baseline determination target sections that the device 1 did not identify among the sections that the doctor determined to be the baseline. The "number of fine adjustments" is the number of baseline determination target sections whose starting point and / or ending point positions were fine-tuned by the doctor among the baseline determination target sections that the device 1 determined. The "number of over-specified" is the number of baseline determination target sections that the doctor did not determine to be the baseline among the baseline determination target sections that the device 1 determined. The "intersection accuracy rate" is the accuracy rate calculated by (total number of sections - number of over-specified) / (total number of sections + number of unspecified). As shown in Figure 21, the accuracy rate of identifying the baseline determination target sections by the device 1 was approximately 96%.

[0174] FIG. 22 is a schematic diagram showing the result of baseline determination when the baseline determination process (S2) is executed on an actual CTG.

[0175] "Number of correct answers" and "Number of incorrect answers" indicate the number of correct answers and incorrect answers of the device 1 in comparison with the doctor's judgment. As shown in Fig. 22, the accuracy rate of the baseline judgment by the device 1 was 100%. No bradycardia was observed in the fetal heart rate of the CTG used in this embodiment.

[0176] FIG. 23 is a schematic diagram showing the result of fine fluctuation determination when the fine fluctuation determination process (S3) is executed on an actual CTG.

[0177] "Number of correct answers" and "Number of incorrect answers" indicate the number of correct answers and incorrect answers of the device 1 in comparison with the doctor's judgment. As shown in Fig. 20, the accuracy rate of the device 1 in judging fine fluctuations was about 99% for moderate, about 92% for decreased, and 100% for absent. No increase in fine fluctuations occurred in the fetal heart rate of the CTG used in this embodiment.

[0178] The classification accuracy of the transient fluctuation classification by the classifier generated by this device was about 79%. At this time, the number of training data of label "0" was 308, the number of training data of label "1" was 161, the number of training data of label "2" was 76, the number of training data of label "3" was 321, the number of training data of label "4" was 83, and the number of training data of label "5" was 33.

[0179] Thus, the present invention achieves an extremely high accuracy rate of 100% in determining the baseline and 90% to 100% in determining fine fluctuations. The present invention also achieves a high accuracy rate of approximately 79% in identifying transient fluctuations. In other words, the present invention enables uniform (automatic) highly accurate interpretation of CTG. As a result, the present invention can assist doctors in interpreting CTG by providing these pieces of information to them as auxiliary information.

[0180] <Summary> According to the embodiment described above, the present device 1 includes a low-frequency extraction unit 1314 that extracts a low-frequency component signal from a waveform signal, and a baseline section identification unit 132 that identifies a baseline determination target section based on the low-frequency component signal. The baseline section identification unit 132 extracts a first determination section in the low-frequency component signal, and identifies the baseline determination target section as auxiliary information based on the low-frequency component signal in the first determination section. According to this configuration, the present device 1 extracts a low-frequency component signal in which baseline characteristics are likely to appear from the CTG, and identifies the baseline determination target section as auxiliary information based on the low-frequency component signal. That is, the present device 1 can uniformly (automatically) identify auxiliary information (baseline determination target section) from the CTG. As a result, the present device 1 can provide auxiliary information used to uniformly assist in the interpretation of the CTG, regardless of the individual differences of the person interpreting the CTG.

[0181] Furthermore, according to the embodiment described above, the baseline section identification unit 132 identifies the first judgment section as a first constituent section constituting a baseline judgment target section when the variance value (first variance value) of the low frequency component signal in the first judgment section is smaller than the first threshold value. According to this configuration, the present device 1 identifies the first constituent section constituting a baseline judgment target section based on an index (variance value of the low frequency component signal) different from the memory of the CTG confirmed visually when a doctor interprets a CTG. As a result, the present device 1 can provide auxiliary information used to assist in uniform interpretation of the CTG.

[0182] Further, according to the embodiment described above, the baseline section identification unit 132 sets a point at which a second predetermined time shorter than the first predetermined time has elapsed from the first starting point as a new first starting point, and extracts a section from the new first starting point until the first predetermined time has elapsed as a new first judgment section. That is, the baseline section identification unit 132 shifts the first judgment section back in time by the second predetermined time. Then, when a plurality of first constituent sections are identified, the baseline section identification unit 132 identifies a section obtained by connecting first constituent sections having a common section among the plurality of first constituent sections as a baseline judgment target section. According to this configuration, the present device 1 can identify the first constituent section while shifting the first judgment section back little by little in time. As a result, the present device 1 can accurately identify the start point and end point of the baseline judgment target section, and identify a section that is close to the section in which a doctor actually judges the baseline as the baseline judgment target section.

[0183] Furthermore, according to the embodiment described above, the baseline determination unit 133 specifies the average heart rate for each first constituent section constituting the baseline determination target section in the low frequency component signal as the baseline fetal heart rate, and determines the baseline fetal heart rate based on the average heart rate. That is, the present device 1 always determines the baseline fetal heart rate for each first constituent section, regardless of the length of the baseline determination target section. With this configuration, the present device 1 can always capture the fluctuation of the low frequency component signal in a constant section (first determination section), regardless of the length of the baseline determination target section. Therefore, the present device 1 can stably capture the fluctuation of the low frequency component signal in the baseline determination target section. As a result, the present device 1 can achieve a stable and uniform determination of the baseline.

[0184] Furthermore, according to the embodiment described above, the present device 1 includes a high frequency extraction unit 1315 that extracts a high frequency component signal from a waveform signal, and a fine fluctuation section identification unit 134 that identifies a fine fluctuation determination target section as auxiliary information based on the high frequency component signal. The fine fluctuation section identification unit 134 identifies a section in the high frequency component signal that corresponds to the baseline determination target section as a fine fluctuation determination target section. According to this configuration, the present device 1 extracts a high frequency component signal in which characteristics of fine fluctuations are likely to appear from the CTG, and identifies the fine fluctuation determination target section as auxiliary information based on the high frequency component signal. That is, the present device 1 can uniformly (automatically) identify auxiliary information (fine fluctuation determination target section) from the CTG. As a result, the present device 1 can provide auxiliary information used to assist in uniform interpretation of the CTG.

[0185] Furthermore, according to the embodiment described above, the device 1 includes a fine fluctuation determination unit 135 that determines fine fluctuations. The fine fluctuation determination unit 135 extracts a second determination section from the fine fluctuation determination section, and determines fine fluctuations based on the high frequency component signal in the second determination section. With this configuration, the device 1 can always capture fluctuations in the high frequency component signal in a constant section (the second determination section) regardless of the length of the fine fluctuation determination section, and can therefore stably capture fluctuations in the high frequency component signal in the fine fluctuation determination section. As a result, the device 1 can achieve stable determination of fine fluctuations.

[0186] Furthermore, according to the embodiment described above, the fine fluctuation judgment unit 135 sets the point at which a fourth predetermined time has elapsed from the second starting point as a new second starting point, extracts the section from the new second starting point to the section at which a third predetermined time has elapsed as a new second judgment section, and judges fine fluctuations for each second judgment section. That is, the fine fluctuation judgment unit 135 judges fine fluctuations for each second judgment section while shifting the second judgment section little by little in the time series. According to this configuration, the present device 1 can always capture the fluctuation of the high frequency component signal in a certain section (second judgment section) regardless of the length of the fine fluctuation judgment section, and can stably capture the fluctuation of the high frequency component signal in the fine fluctuation judgment section. In addition, by judging fine fluctuations while shifting the second judgment section by the fourth predetermined time, the present device 1 can judge fine fluctuations in a section of the baseline that is close to the section that a doctor actually visually observes to judge fine fluctuations from the baseline. As a result, the present device 1 can realize stable judgment of fine fluctuations.

[0187] Furthermore, according to the embodiment described above, the fine fluctuation determination unit 135 determines fine fluctuations as either "moderate," "increased," "decreased," or "disappeared" based on the variance value of the high frequency component signal in the second determination section and a second threshold value consisting of multiple types of threshold values. According to this configuration, the present device 1 determines fine fluctuations based on an index (variance value of the high frequency component signal) different from the CTG memory visually confirmed when a doctor interprets a CTG. As a result, the present device 1 can provide auxiliary information (determination result of fine fluctuations) used to assist in uniform interpretation of CTG.

[0188] Furthermore, according to the embodiment described above, the high frequency component signal includes a higher frequency band than the low frequency component signal. Meanwhile, the low frequency component signal includes a lower frequency band than the high frequency component signal. Also, a part of the frequency band of the high frequency component signal is common to a part of the frequency band of the low frequency component signal. Usually, when judging the baseline or fine fluctuations, a doctor does not uniformly distinguish between the high frequency components and the low frequency components of a waveform signal. Therefore, according to this configuration, the present device 1 can obtain a judgment result closer to a judgment made by a doctor's visual inspection, compared to a case where the low frequency component signal and the high frequency component signal are classified by a single threshold value.

[0189] Furthermore, according to the embodiment described above, the device 1 includes a transient fluctuation section identifying unit 136 that identifies one or more transient fluctuation sections in which a transient fluctuation occurs as auxiliary information based on a low-frequency component signal. According to this configuration, the device 1 extracts a low-frequency component signal in which the characteristics of a transient fluctuation are likely to appear from the CTG, and identifies the transient fluctuation section as auxiliary information based on the low-frequency component signal. That is, the device 1 can uniformly (automatically) identify auxiliary information (transient fluctuation section) from the CTG. As a result, the device 1 can provide auxiliary information used to uniformly assist in the interpretation of the CTG, regardless of the individual differences of the person interpreting the CTG.

[0190] Furthermore, according to the embodiment described above, the transient fluctuation section identifying unit 136 identifies the third judgment section as a second constituent section constituting a transient fluctuation section when the variance value (third variance value) of the low frequency component signal in the third judgment section is greater than the third threshold. According to this configuration, the present device 1 identifies the second constituent section constituting a transient fluctuation section based on an index (variance value of the low frequency component signal) different from the CTG memory visually confirmed when a doctor interprets a CTG. As a result, the present device 1 can provide auxiliary information used to assist in uniform CTG interpretation, independent of individual differences in the person interpreting the CTG.

[0191] Furthermore, according to the embodiment described above, the transient fluctuation section identifying unit 136 sets a point at which a sixth predetermined time shorter than the fifth predetermined time has elapsed from the third starting point as a new third starting point, and extracts a section from the new third starting point until the fifth predetermined time has elapsed as a new third judgment section. That is, the transient fluctuation section identifying unit 136 shifts the third judgment section back by the sixth predetermined time in the time series. Then, when a plurality of second constituent sections are identified, the transient fluctuation section identifying unit 136 identifies a section obtained by connecting the second constituent sections having a common section among the plurality of second constituent sections as a transient fluctuation section. According to this configuration, the present device 1 can identify the second constituent section while shifting the third judgment section back little by little in the time series. By identifying and updating the transient fluctuation section in this way, the present device 1 can identify a section that is close to the section in which a doctor actually judges a transient fluctuation as a transient fluctuation section. As a result, the device 1 can accurately identify the start and end points of the transient fluctuation section.

[0192] Furthermore, according to the embodiment described above, the transient fluctuation section identifying unit 136 extracts, from the transient fluctuation section, a section from the first point on the time series until the seventh predetermined time has elapsed as a start section, and extracts, from the last point on the time series until the seventh predetermined time has elapsed, as an end section. Then, when the fourth variance value of the start section is smaller than the fourth threshold, the transient fluctuation section identifying unit 136 excludes the start section from the transient fluctuation section, and when the fifth variance value of the end section is smaller than the fourth threshold, excludes the end section from the transient fluctuation section, and updates the transient fluctuation section. According to this configuration, the present device 1 performs re-determination on a section that may include a section in which a transient fluctuation does not occur in the transient fluctuation section, and can identify the start point and end point of the transient fluctuation section more accurately.

[0193] Furthermore, according to the embodiment described above, the device 1 includes a data acquisition unit 137 that acquires learning data for a classifier that identifies a transient fluctuation based on the transient fluctuation section. The data acquisition unit 137 extracts, as learning data (first learning data), a low-frequency component signal from a section of the transient fluctuation section from the last point on the time series to the eighth predetermined time back, as the learning data. Medically, a transient fluctuation is defined as ending in less than two minutes. Therefore, when identifying a transient fluctuation, learning data acquired from the end point of the transient fluctuation section as a starting point is more likely to include the characteristics of the transient fluctuation than learning data acquired from the start point of the transient fluctuation section as a starting point. Therefore, according to this configuration, the device 1 can obtain learning data including the characteristics of the transient fluctuation as auxiliary information that assists in identifying the transient fluctuation.

[0194] Furthermore, according to the embodiment described above, the device 1 includes a discrimination unit 139 that discriminates transient variations using a discriminator generated based on learning data. The discrimination unit 139 automatically discriminates transient variations based on low-frequency component signals. According to this configuration, the device 1 acquires not only the baseline determination target section but also the discrimination result of the transient variation as auxiliary information. As a result, the device 1 can provide auxiliary information used to assist in uniform CTG interpretation, regardless of the individual differences of the person interpreting the CTG.

[0195] Furthermore, according to the embodiment described above, the device 1 includes a noise removal unit 1313 that removes noise from the waveform signal. After removing the main sudden noise, the noise removal unit 1313 repeats the application of a low-pass filter and the calculation of the signal difference twice to clarify and remove the remaining noise. With this configuration, the device 1 can remove most of the noise that is often included in the waveform signal of the fetal heart rate of the CTG. As a result, the device 1 can accurately identify the baseline determination target section and the transient fluctuation section, and can accurately determine the baseline and fine fluctuation.

[0196] Note that the present device is not limited to the configuration of the present device as long as it can specify at least the baseline determination target section as auxiliary information. That is, for example, the present device does not need to include a classifier generating unit and a classifying unit, and further does not need to include a transient fluctuation section specifying unit and a data acquiring unit, or further does not need to include a fine fluctuation section specifying unit and a fine fluctuation determining unit.

[0197] In addition, the noise removal unit of the present invention only needs to be able to remove abrupt noise, and the configuration of the noise removal unit of the present invention is not limited to that of the present embodiment. That is, for example, the noise removal unit of the present invention does not need to include a secondary low-pass filter and a secondary calculation unit.

[0198] Furthermore, the control unit in the present invention may not include a noise removal unit. In this case, the present device may include, for example, a dedicated circuit that functions as the noise removal unit. Also, for example, if the waveform signal has little noise, it is not necessary to remove noise from the waveform signal.

[0199] Furthermore, each threshold value in this method may be determined based on the accuracy rate of each judgment result and classification result, and is not limited to the present embodiment.

[0200] Furthermore, the method executed by the device is not limited to the present embodiment. That is, for example, the device may not execute the transient fluctuation learning process and / or the transient fluctuation identification process, and may not execute the fine fluctuation determination process.

[0201] Furthermore, in the transient variation learning process, the data acquiring unit of the present invention only needs to acquire the first learning data and the second learning data, and does not need to associate the first learning data with the second learning data with corresponding labels (types of transient variations). In this case, for example, labeling of the first learning data and the second learning data may be performed separately.

[0202] Furthermore, the present device may be incorporated in an external device, or an external device may function as the present device by executing the present program.

[0203] Furthermore, in the above-described embodiment, the present device 1 is configured by one computer. Instead of this, the present device may be configured by multiple computers. That is, for example, the present device may be configured by a group of multiple computers that function as the present device. Specifically, for example, the present device (group of computers) may be configured by a computer having a storage unit and a computer having a control unit that executes the present method. Also, for example, the multiple computers may have the functions of a preprocessing unit, a baseline section identification unit, a baseline determination unit, a fine fluctuation determination section identification unit, a fine fluctuation determination unit, a transient fluctuation section identification unit, a data acquisition unit, a classifier generation unit, and a classification unit in a distributed manner. In this case, the multiple computers that constitute the group of computers may transmit and receive information through a communication line, or may transfer information using a portable storage medium. [Explanation of symbols]

[0204] 1. Information processing device 12 Storage section 1311 Acquisition Department 1312 Waveform signal extraction unit 1314 Low-frequency extraction section 1315 High-frequency Extraction Section 132 Baseline Section Identification Section 133 Baseline determination section 134 Minor fluctuation section identification section 135 Fine fluctuation judgment section 136 Transient Fluctuation Section Identification Unit 137 Data Acquisition Section 139 Identification Unit

Claims

1. 1. An information processing device for generating auxiliary information used to assist in interpretation of a cardiotocography, An acquisition unit for acquiring the cardiotocography; a waveform signal extraction unit that extracts a waveform signal of a fetal heart rate waveform included in the acquired fetal cardiotocography; a low frequency extraction unit that extracts a predetermined low frequency component signal from the waveform signal; a baseline interval specifying unit that specifies, as the auxiliary information, a baseline determination target interval for determining a fetal heart rate baseline based on the extracted low frequency component signal; and The baseline section identification unit is extracting, from the low frequency component signal, a section from a first predetermined start point to a first predetermined time required for determining the fetal heart rate baseline as a first determination section; when a variance value of the low frequency component signal in the first judgment section is smaller than a first threshold, the first judgment section is identified as a first constituent section constituting the baseline judgment target section; 23. An information processing apparatus comprising:

2. The baseline section identification unit is a point at which a second predetermined time shorter than the first predetermined time has elapsed from the first starting point is set as a new first starting point, and a section from the new first starting point to the elapse of the first predetermined time is extracted as a new first determination section; When a plurality of the first component sections are identified, a section obtained by connecting the first component sections having a common section among the plurality of the first component sections is identified as the baseline determination target section.

2. The information processing device according to claim 1.

3. a baseline determination unit that determines the fetal heart rate baseline in the baseline determination target section based on the identified baseline determination target section; and the baseline determination unit specifies, in the low frequency component signal, an average heart rate for each of the first constituent sections constituting the baseline determination target section as a fetal heart rate of the fetal heart rate baseline, and determines the fetal heart rate baseline based on the average heart rate.

3. The information processing device according to claim 1 or 2.

4. a high frequency extraction unit that extracts a high frequency component signal within a predetermined range from the waveform signal; a fine fluctuation section specifying unit for specifying a fine fluctuation determination target section for determining a baseline fine fluctuation of a fetal heart rate based on the extracted high frequency component signal as the auxiliary information; and The fine fluctuation section identification unit identifying a section corresponding to the baseline determination section as the fine fluctuation determination section in the extracted high frequency component signal; 2. The information processing device according to claim 1.

5. a fine fluctuation determination unit that determines the fetal heart rate baseline fine fluctuation in the fine fluctuation determination target section based on the specified fine fluctuation determination target section; and The fine variation determination unit A start point of the minute fluctuation determination target section is set as a second start point, and a section from the second start point until a third predetermined time has elapsed is extracted as a second determination section; determining the fetal heart rate baseline fluctuation as being either moderate, increased, decreased, or absent based on a variance value of the high frequency component signal in the second judgment section and a second threshold value consisting of a plurality of threshold values; 5. The information processing device according to claim 4.

6. The fine variation determination unit setting a point at which a fourth predetermined time has elapsed since the second start point as a new second start point, and extracting a section from the new second start point to the time at which the third predetermined time has elapsed as a new second determination section; determining the fetal heart rate baseline variation for each of the second determination intervals; 6. The information processing device according to claim 5.

7. the high frequency component signal includes a higher frequency band than the low frequency component signal; the low frequency component signal includes a frequency band lower than the high frequency component signal; A part of a frequency band of the high frequency component signal is common to a part of a frequency band of the low frequency component signal.

5. The information processing device according to claim 4.

8. a transient fluctuation interval identifying unit that identifies, as the auxiliary information, one or more transient fluctuation intervals in which a transient fluctuation occurs, based on the extracted low frequency component signal; and The transient fluctuation section identification unit is setting the first starting point as a third starting point in the low frequency component signal, and extracting a section from the third starting point until a fifth predetermined time has elapsed as a third determination section; when a variance value of the low frequency component signal in the third judgment section is greater than a third threshold, the third judgment section is identified as a second constituent section constituting the transient fluctuation section.

2. The information processing device according to claim 1.

9. The transient fluctuation section identification unit is setting a point at which a sixth predetermined time has elapsed since the third start point as a new third start point, and extracting a section from the new third start point to the point at which the fifth predetermined time has elapsed as a new third determination section; Identifying a section constituted by one or more consecutive second constituent sections as the transient fluctuation section; 9. The information processing device according to claim 8.

10. The transient fluctuation section identification unit is Among the transient fluctuation sections, A section from the first point on the time series until a seventh predetermined time has elapsed is extracted as a start section; extracting a section from the last point on the time series back to the seventh predetermined time as an end section; when the fourth variance value of the start section is smaller than a fourth threshold, the start section is excluded from the transient fluctuation section, and when the fifth variance value of the end section is smaller than the fourth threshold, the end section is excluded from the transient fluctuation section, and the transient fluctuation section is updated.

9. The information processing device according to claim 8.

11. a data acquisition unit that acquires learning data for a classifier that identifies the transient fluctuation based on the transient fluctuation section; and The data acquisition unit extracts, as the learning data, the low-frequency component signal in a section from a last point on the time series to an eighth predetermined time back from the transient fluctuation section.

9. The information processing device according to claim 8.

12. A computer is caused to function as the information processing device according to claim 1.

2. An information processing program comprising:

13. 1. An information processing method executed by an information processing device for generating auxiliary information used to assist in interpretation of a cardiotocography, comprising: The information processing device, acquiring said cardiotocography; A waveform extraction step of extracting a waveform signal of a fetal heart rate waveform included in the acquired fetal cardiotocography; a low frequency extraction step of extracting a predetermined low frequency component signal from the waveform signal; a baseline determination target section specifying step of specifying, as the auxiliary information, a baseline determination target section for determining a fetal heart rate baseline based on the extracted low frequency component signal; Including, The baseline determination target section specifying step includes: extracting, from the low frequency component signal, a section from a first predetermined start point to a first predetermined time required for determining the fetal heart rate baseline as a first determination section; identifying the baseline determination section based on the low frequency component signal in the first determination section; Including, 23. An information processing method comprising:

Images