Subject discrimination device, subject discrimination method, computer program, and non-transitory computer-readable medium

Abstract

To provide a device capable of enhancing correctness related to association between acquired biological information and a subject.SOLUTION: A subject determination device 1 comprises: an acquisition part for acquiring a first data set corresponding to secular change of biological information of a subject 2, and a second data set corresponding to secular change of biological information acquired at a timing different from that of acquisition of the first data set; a generation part for generating a discriminator 121 which learned any one of the first and second data sets as training data; a determination part for calculating first similarity using input information of the data set which is not used in generation of the discriminator 121 out of the first and second data sets, and first output information acquired by inputting the data set not used in generation of the discriminator 121 to the discriminator 121, then on the basis of the first similarity, determining whether the first and second data sets are acquired from the same subject 2; and an output part for outputting an output signal corresponding to a determination result by the determination part.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an apparatus and method for identifying subjects for electrocardiogram tests, etc., a computer program for causing the apparatus to execute the method, and a computer-readable medium on which the computer program is recorded. [Background technology]

[0002] An electrocardiogram test using an electrocardiograph is one test that measures the electrical activity of the heart. When performing an electrocardiogram test, a user inputs subject information such as a subject ID before starting electrocardiogram measurement. Biometric information such as electrocardiogram data is recorded in association with the input subject information (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 07-155300 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the electrocardiogram device disclosed in Patent Document 1, if there is an error in the input subject information, the acquired biological information may be associated with a different subject. If the acquired biological information is recorded in association with a different subject, the accuracy of the test will be impaired. Note that this problem is not limited to electrocardiograms, but may also occur when dealing with other parameters.

[0005] An object of the present invention is to improve the accuracy of associating acquired biometric information with a subject. [Means for solving the problem]

[0006] One aspect of the present invention to achieve the above object is a subject discrimination device, comprising: an acquisition unit that acquires a first data set corresponding to a time-dependent change in biological information of a subject and a second data set corresponding to a time-dependent change in the biological information acquired at a timing different from that of the first data set; a creation unit that creates a classifier that is trained using one of the first data set and the second data set as training data; a discrimination unit that calculates a first similarity using input information of one of the first dataset and the second dataset that has not been used in creating the classifier, and first output information obtained by inputting the dataset that has not been used in creating the classifier to the classifier, and discriminates whether the first dataset and the second dataset have been obtained from the same subject based on the first similarity; an output unit that outputs an output signal corresponding to a result of the determination by the determination unit; Equipped with.

[0007] In order to achieve the above object, a subject discrimination method according to one embodiment of the present invention comprises the steps of: A subject discrimination method executed by a subject discrimination device, comprising: acquiring a first data set corresponding to a time-dependent change in biological information of a subject, and a second data set corresponding to a time-dependent change in biological information acquired at a timing different from that of the first data set; creating a classifier trained using one of the first data set and the second data set as training data; calculating a first similarity using input information of one of the first dataset and the second dataset that has not been used to create the classifier, and first output information obtained by inputting the dataset that has not been used to create the classifier to the classifier, and determining, based on the first similarity, whether the first dataset and the second dataset have been obtained from the same subject; Includes.

[0008] A computer program according to one aspect of the present invention for achieving the above object comprises: By executing the program by the computer, the subject discrimination device acquiring a first data set corresponding to a time-dependent change in biological information of a subject, and a second data set corresponding to a time-dependent change in biological information acquired at a timing different from that of the first data set; creating a classifier that is trained using one of the first data set and the second data set as training data; A first similarity is calculated using input information of one of the first dataset and the second dataset that was not used in creating the classifier, and first output information obtained by inputting the dataset that was not used in creating the classifier to the classifier, and it is determined whether the first dataset and the second dataset were obtained from the same subject based on the first similarity.

[0009] A non-transitory computer-readable medium having the above computer program recorded thereon is also one aspect of the present invention for achieving the above object.

[0010] According to the above configuration, the biometric information acquired from the subject is input to a classifier that has learned the characteristics of biometric information that may have been acquired from the subject at a time different from the first data set, and it is determined whether the two data sets were acquired from the same subject based on the similarity between the two data sets. This makes it possible to increase the accuracy of associating the acquired biometric information with the subject. [Brief description of the drawings]

[0011] [Figure 1] 2 illustrates a functional configuration of a subject discrimination apparatus according to an embodiment. [Diagram 2] 13 illustrates a flow of processing executed by the subject discrimination apparatus. [Diagram 3] 13 shows an example of a process performed by the subject discrimination apparatus until a classifier is created. [Figure 4] 2 illustrates a process executed by the subject discrimination device when discriminating a subject. [Diagram 5]2 illustrates a process executed by the subject discrimination device when discriminating a subject. [Figure 6] 2 illustrates a process executed by the subject discrimination device when discriminating a subject. [Figure 7] 1 illustrates the process of data augmentation. [Figure 8] 13 shows another example of the process performed by the subject discrimination apparatus until a classifier is created.

[0012] Hereinafter, examples of embodiments will be described in detail with reference to the accompanying drawings.

[0013] 1 illustrates a functional configuration of a subject discrimination device 1 according to an embodiment (hereinafter, simply referred to as discrimination device 1). The discrimination device 1 includes an input interface 11, a processor 12, and an output interface .

[0014] The input interface 11 receives an input signal IS corresponding to a change over time in the biological information of the subject 2 from a sensor 3 attached to the body of the subject 2. In the following explanation, a case where the subject 2 is subjected to an electrocardiogram test will be exemplified. Note that the information acquired by the sensor 3 may include, in addition to the electrocardiogram, arterial blood oxygen saturation (SpO2), etc.

[0015] A plurality of electrodes are attached to the body of the subject 2 as the sensor 3. The input signal IS represents a change over time in the potential detected by the electrodes. The input interface 11 may include a circuit, as necessary, for converting the input signal IS into a data format that allows the processor 12 to perform the processing described below.

[0016] The processor 12 is configured to acquire a first data set D1 and a second data set D2. The first data set D1 includes electrocardiogram waveform information of the subject 2. The electrocardiogram waveform information is an example of data corresponding to a change in biological information over time. The second data set D2 includes electrocardiogram waveform information acquired earlier than the first data set D1. The second data set D2 is an example of a data set including biological information acquired at a different timing than the first data set D1. The processor 12 is an example of an acquisition unit.

[0017] The processor 12 is configured to determine whether the first data set D1 and the second data set D2 are acquired from the same subject. Details of the determination process will be described later. The processor 12 is an example of a determination unit.

[0018] The processor 12 is configured to output output data corresponding to the determination result. The output interface 13 is configured to output an output signal OS corresponding to the output data. The output signal OS may be transmitted to an external device 4. The output interface 13 may include a circuit for converting the output data into an output signal OS that can be processed by the external device, as necessary. The output interface 13 is an example of an output unit.

[0019] The external device 4 may be configured to notify the user of the determination result. The notification may be performed through at least one of a visual notification, an auditory notification, and a tactile notification.

[0020] The discrimination device 1 may include a notification unit 14. In this case, the processor 12 may transmit an output signal OS corresponding to the discrimination result to the notification unit 14. In this case, the processor 12 is an example of an output unit. The notification unit 14 may be configured to notify the user of the discrimination result based on the output signal OS. The notification may be performed through at least one of a visual notification, an auditory notification, and a tactile notification.

[0021] 2 illustrates an example of a flow of processing executed by the processor 12. First, the processor 12 acquires a first data set D1 based on the input signal IS (STEP 1).

[0022] Next, the processor 12 acquires a second data set D2 (STEP 2). For example, as shown in Fig. 3, the first data set D1 may include identification information ID of the subject 2. The identification information ID may be assigned by the discrimination device 1, or may be provided from outside together with the input signal SI. In this case, the processor 12 acquires a second data set D2 corresponding to a past test result to which the same identification information ID is assigned.

[0023] 1, the second data set D2 may be acquired from an external database 5. The processor 12 identifies a data set to which the same identification information ID as the first data set D1 has been assigned from past test results stored in the database 5, and acquires the identified data set as the second data set D2.

[0024] Alternatively, the discrimination device 1 may include a storage 15 (an example of a storage unit). The storage 15 is a storage device such as a semiconductor memory or a hard disk drive. Past test results may be stored in the storage 15. In this case, the processor 12 identifies a data set to which the same identification information ID as the first data set D1 is assigned from the past test results stored in the storage 15, and acquires the data set as the second data set D2.

[0025] Next, as illustrated in FIG. 3, the processor 12 creates a classifier 121 that has been trained using the second data set D2 as training data (STEP 3 in FIG. 2). The processor 12 is an example of a creation unit. The classifier 121 can be understood as a processing function executed by the processor 12. The classifier 121 is configured by extracting features of the second data set D2. In detail, the classifier 121 becomes a processing function in which the features of the second data set D2 are aggregated through learning using the second data set D2 as training data. For example, a data set different from the data set used as training data (the second data set D2 in this embodiment) may be input to the classifier 121. If the input data set corresponds to the property of the classifier 121 (the second data set D2), an output similar to the input of the input data set is obtained. The features of the second data set D2 are not preset by a user, but are extracted by the processor 12 through learning using training data.

[0026] The classifier 121 may be created using an autoencoder with dimensionality reduction of an intermediate layer. For example, autoencoding with dimensionality reduction from 1000 dimensions to 125 dimensions is performed using a convolutional autoencoder. Other examples of the autoencoder include an autoencoder, a denoising autoencoder, a sparse autoencoder, and a stacked autoencoder.

[0027] With this configuration, the features of the training data can be extracted efficiently.

[0028] Next, the processor 12 inputs the first data set D1 to the classifier 121, as illustrated in Fig. 4. When the first data set D1 is input to the classifier 121 in which the features of the second data set D2 are aggregated, the processor 12 acquires first output information O1, which is waveform information (STEP 4 in Fig. 2). Here, the more similar the features of the first data set D1 and the second data set D2 are, the higher the similarity between the first output information O1 and the first data set D1 becomes.

[0029] Next, the processor 12 compares the first data set D1 with the first output information O1 to obtain a first similarity S1 (STEP 5 in FIG. 2). The first similarity S1 is an index representing the strength of similarity between the first data set D1 and the first output information O1. For example, a difference between a value at a specific time point in the waveform information included in the first data set D1 and a value at the same time point in the waveform information included in the first output information O1 is obtained. The differences obtained for multiple time points are statistically processed to obtain the first similarity S1.

[0030] For example, a root mean squared error may be used as a statistical process for obtaining the first similarity S1. By such a statistical process, the strength of similarity between the first data set D1 and the first output information O1 may be evaluated. Note that the statistical process method is not limited to the root mean squared error, and may be, for example, a mean absolute error (MAE), a dynamic time warping method DTW, a mean absolute percentage error (MAPE), a mean squared error (MSE), a cross correlation function (CCF), or the like.

[0031] When the subject 2 from which the first data set D1 was obtained is the same as the subject from which the second data set D2 was obtained, the features included in the waveform information tend to match, and the first similarity S1 obtained by comparing the first data set D1 with the first output information O1 is high. On the other hand, when the subject 2 from which the first data set D1 was obtained is different from the subject from which the second data set D2 was obtained, the difference between the features of the first output information O1, in which the features of the first data set D1 are emphasized, and the first similarity S1 as a comparison result is low.

[0032] Therefore, the processor 12 can determine whether the first data set D1 and the second data set D2 are obtained from the same subject based on the first similarity S1 (STEP 6 in FIG. 2). For example, as illustrated in FIG. 5, the processor 12 compares the first similarity S1 with a predetermined threshold value St. The processor 12 can be configured to determine that the first data set D1 and the second data set D2 are obtained from the same subject when the first similarity S1 exceeds the threshold value St.

[0033] 6 illustrates the details of the flow of the discrimination process executed by the processor 12. When it is determined that the first data set D1 and the second data set D2 are obtained from the same subject (YES in STEP 61), the processor 12 outputs output data indicating the determination result (STEP 62). As a result, an output signal OS based on the output data is output, and the determination result is notified by the notification unit 14 or the external device 4.

[0034] When it is determined that the first data set D1 and the second data set D2 are obtained from different subjects (NO in STEP 61), the processor 12 outputs output data indicating the determination result (STEP 63). As a result, an output signal OS based on the output data is output, and the determination result is notified by the notification unit 14 or the external device 4.

[0035] According to the above-mentioned configuration, electrocardiogram waveform information acquired from the subject 2 is input to the classifier 121 that has learned the characteristics of electrocardiogram waveform information that may have been acquired from the subject 2 in the past, and it is determined whether the two were acquired from the same subject based on the similarity between the two. In other words, it is possible to assist in confirming that electrocardiogram waveform information associated with the same identification information ID was acquired from the same subject. Therefore, it is possible to improve the accuracy of associating the acquired electrocardiogram waveform information with the subject.

[0036] As illustrated in Fig. 2, the processor 12 may further acquire a third dataset D3 (see Fig. 4) corresponding to a change over time in biometric information acquired earlier than the first dataset D1 and the second dataset (STEP 7 in Fig. 2). The third dataset D3 is an example of a dataset including biometric information acquired at a timing different from that of the first dataset D1 and the second dataset D2. In this case, the processor 12 identifies a dataset to which the same identification information ID as the identification information ID corresponding to the first dataset D1 is assigned. Then, of the identified datasets, the processor 12 acquires a dataset acquired earlier than the second dataset as the third dataset D3.

[0037] Next, the processor 12 inputs the third data set D3 to the classifier 121, as illustrated in Fig. 4. When the third data set D3 is input to the classifier 121 in which the features of the second data set D2 are aggregated, the processor 12 acquires second output information O2, which is waveform information (STEP 8 in Fig. 2). Here, the more similar the features of the second data set D2 and the third data set D3 are, the higher the similarity between the second output information O2 and the third data set D3 becomes.

[0038] Next, the processor 12 compares the third data set D3 with the second output information O2 to obtain a second similarity S2 (STEP 9 in FIG. 2). The second similarity S2 is an index representing the strength of similarity between the third data set D3 and the second output information O2. For example, a difference between a value at a specific time point in the waveform information included in the third data set D3 and a value at the same time point in the waveform information included in the second output information O2 is obtained. The difference obtained for multiple time points is statistically processed to obtain the second similarity S2.

[0039] As a statistical process for obtaining the second similarity S2, a root mean squared error (Root Mean Squared Error) may be used. By such a statistical process, the strength of similarity between the third data set D3 and the second output information O2 may be evaluated. Note that the statistical process method is not limited to the root mean squared error, and may be, for example, a mean absolute error (MAE), a dynamic time warping method DTW, a mean absolute percentage error (MAPE), a mean squared error (MSE), a cross correlation function (CCF), or the like.

[0040] Next, the processor 12 determines whether the first data set D1 and the second data set D2 are obtained from the same subject based on the first similarity S1 and the second similarity S2 (STEP 6 in FIG. 2). For example, as illustrated in FIG. 5, the processor 12 determines whether the first data set D1 and the second data set D2 are obtained from the same subject based on the degree of discrepancy between the first similarity S1 and the second similarity S2. Specifically, the processor 12 may be configured to determine that the first data set D1 and the second data set D2 are obtained from the same subject when the degree of discrepancy between the first similarity S1 and the second similarity S2 is equal to or smaller than a predetermined value.

[0041] According to such a configuration, for example, the second similarity S2 is calculated using a third data set D3 corresponding to a time-dependent change in biological information acquired earlier than the second data set D2. Then, the processor 12 determines whether the first data set D1 and the second data set D2 are acquired from the same subject 2 based on the first similarity S1 and the second similarity S2. Since it is confirmed that the third data set D3 was acquired from the subject 2, it is highly reliable as data used for discrimination. Therefore, by using the first similarity S1 and the second similarity S2 calculated from the third data set D3, the discrimination accuracy by the processor 12 can be improved.

[0042] 6, when it is determined that the first data set D1 and the second data set D2 are obtained from the same subject (YES in STEP 61), the processor 12 may be configured to store the first data set D1 in the storage 15 as a second data set D2 that is a data set that may be training data in the next and subsequent determinations (STEP 64). Note that the second data set D2 may be stored in the database 5.

[0043] According to this configuration, when it is confirmed that the first data set D1 is acquired from the subject 2, the first data set D1 is used as the second data set D2 that becomes training data for the classifier 121 in the next and subsequent tests. In other words, the latest data set acquired from the subject 2 is used to perform discrimination on the first data set D1 acquired next and subsequent tests. This enables the processor 12 to create a highly accurate classifier 121 in the next and subsequent tests. As a result, the accuracy of the discrimination results performed by the processor 12 is improved.

[0044] For the same reason, the processor 12 may store the second data set D2 in the storage 15 as a third data set D3, which is a data set different from the first data set D1 among the data sets input to the classifier 121 (STEP 65). The third data set D3 may be stored in the database 5.

[0045] As illustrated in Fig. 4, the first data set D1 may be expanded by data augmentation. A specific example of the method will be described with reference to Fig. 7.

[0046] When the first data set D1 is biometric information acquired over a time length T1, the processor 12 extracts a data set d1 corresponding to a time length T2 from time t1. The processor 12 then acquires a data set d2 corresponding to a time length T2 from time t2 obtained by adding a time length T3 to time t1. Similarly, the processor 12 acquires a data set d3 corresponding to a time length T2 from time t3 obtained by adding a time length T3 to time t2.

[0047] The time length T1 is, for example, 10 seconds. The time length T2 is, for example, 2 seconds. The time length T3 is, for example, 0.2 seconds. In this case, the data set d1 includes biometric information from time 0 to 2 seconds. The data set d2 includes biometric information from time 0.2 to 2.2 seconds. The data set d3 includes biometric information from time 0.4 to 2.4 seconds.

[0048] By repeating such a process, a data set d41 including biometric information from 8 seconds to 10 seconds is finally obtained, and a total of 41 data sets d1 to d41 are generated. If the first data set D1 includes 60,000 pieces of sampling data, the number of sampling data included in each of the data sets d1 to d41 is 12,000. Therefore, the number of sampling data obtained as a result of data augmentation is 492,000, and it is clear that data augmentation has been performed.

[0049] Each of the data sets d1 to d41 generated as described above is input to the classifier 121. As a result, a first output information O1 is acquired for each of the data sets d1 to d41, as illustrated in Fig. 4. As a result of comparing each of the multiple pieces of first output information O1 with the data set from which it was generated, multiple first similarities S1 are calculated, as illustrated in Fig. 5.

[0050] According to such a configuration, the processor 12 compares the multiple first similarities S1 with the threshold value St, and determines whether the first data set D1 and the second data set D2 are acquired from the same subject 2. For example, the determination is performed by comparing the average value of the multiple first similarities S1 with the threshold value St. Since the comparison with the threshold value St can be performed statistically, it is less susceptible to the influence of noise, and the accuracy of the determination result performed by the processor 12 is improved.

[0051] As illustrated in Fig. 4, the processor 12 may similarly perform data augmentation on the third data set D3. In this case, as illustrated in Fig. 5, a plurality of second similarities S2 are calculated.

[0052] In this case, the processor 12 compares the multiple first similarities S1 and the multiple second similarities S2 to determine whether the first data set D1 and the second data set D2 are obtained from the same subject 2. For example, if the degree of deviation between the average value of the multiple first similarities S1 and the average value of the multiple second similarities S2 is below a threshold, it is determined that the first data set D1 and the second data set D2 are obtained from the same subject 2.

[0053] With this configuration, statistical comparison is possible using the third data set D3 that is known to have been obtained from the subject 2, so comparison using a fixed threshold value St is not necessary, and more accurate discrimination can be performed.

[0054] 3, the processor 12 may perform data augmentation on the second data set D2 and generate a classifier 121 based on the data-augmented second data set D2. A specific method of data augmentation is the same as the above-described method for the first data set D1 and the third data set D3.

[0055] According to such a configuration, the amount of training data used in creating the classifier 121 is increased by data augmentation, so that the processor 12 can create a more accurate classifier 121. As a result, the processor 12 can more accurately determine whether the first data set D1 and the second data set D2 are obtained from the same subject 2.

[0056] As illustrated in FIG. 2, the processor 12 may determine whether or not discrimination of the subject is feasible based on the acquired first data set D1 (STEP 10 in FIG. 2). A known electrocardiogram noise determination program, a known arrhythmia analysis algorithm, or the like may be used to determine whether or not discrimination is feasible. For example, when the electrocardiogram noise determination program is used, the processor 12 may determine whether or not discrimination of the subject is feasible by comparing a noise score obtained by performing statistical processing on the first data set D1 with a preset threshold value. When the obtained noise score is equal to or greater than the threshold value, the processor 12 determines that discrimination of the subject is not feasible (NO in STEP 10) and ends this process. In this case, the discrimination device 1 outputs an output signal OS indicating that the first data set D1 does not meet a predetermined condition for inputting to the discriminator 121 from the output interface 13. On the other hand, when the processor 12 determines that discrimination of the subject is feasible (YES in STEP 10), the processor 12 executes the process from STEP 2 onward.

[0057] For example, when a large organic change occurs in the subject's heart or when the subject has arrhythmia, even if the first data set D1 and the second data set D2 are data sets acquired from the same subject, the features of the first data set D1 may be significantly different from the features of the second data set D2 acquired earlier than the first data set D1. Therefore, the first data set D1 may become noise in the above-mentioned statistical processing. According to the above-mentioned configuration, the first data set D1, which may become noise, is not input to the classifier 121, so that the discrimination accuracy can be maintained.

[0058] Information used to acquire the second data set D2 is not limited to the identification information ID given to the subject 2. Fig. 8 illustrates an example in which electrocardiogram waveform information of the subject 2 is acquired as streaming data SD. The streaming data SD is stored in the storage 15 or database 5 of the discrimination device 1. In this example, the discrimination device 1 is configured to acquire streaming data SD acquired before an interruption occurs in the input of the streaming data SD. Examples of causes of the input interruption include the sensor 3 falling off the body of the subject 2 and communication failure.

[0059] 8, the streaming data SD may include time information indicating the time of acquisition. Time t11 indicates the time when acquisition of the streaming data SD started after recovery from an input interruption. That is, the processor 12 acquires the streaming data SD having time t11 as time information as the first data set D1 (STEP 1 in FIG. 2).

[0060] When an interruption occurs in the input of streaming data, the processor 12 acquires, as the second data set D2, streaming data SD having time t12, which is earlier than time t11, as time information (STEP 2 in FIG. 2).

[0061] Next, the processor 12 executes the processes of STEP 3 to STEP 6 illustrated in Fig. 2. That is, the processor 12 creates a classifier 121 that learns a second data set D2 (a data set acquired before the input interruption) that is streaming data SD having time information t12 as training data. Next, the processor 12 acquires a first similarity S1 by inputting a first data set D1 (a data set acquired after recovery from the input interruption) that is streaming data SD having time information t11 to the classifier 121. Then, the processor 12 compares the first similarity S1 with a threshold value St to determine whether the first data set D1 and the second data set D2 are acquired from the same subject.

[0062] According to the above configuration, electrocardiogram waveform information acquired from the subject 2 after the interruption of the streaming data input is input to the classifier 121 that has learned the features of electrocardiogram waveform information that may have been acquired from the subject 2 in the past, and it is determined whether the two were acquired from the same subject based on the similarity between the two. In other words, it is possible to assist in confirming that the electrocardiogram waveform information acquired before and after the interruption of the streaming data input was acquired from the same subject. Therefore, it is possible to improve the accuracy of associating the acquired electrocardiogram waveform information with the subject.

[0063] A processor having the functions described above may be realized by a general-purpose microprocessor operating in cooperation with a general-purpose memory. The processor may include a plurality of processor cores. Examples of the general-purpose memory include a ROM and a RAM. The general-purpose memory may store a computer program for executing the processes described below. The computer program may be stored in the general-purpose memory in advance, or may be downloaded from an external server via a communication network. The computer program is an example of an instruction executable by the processor. For example, the processor may specify at least a part of the computer program stored in the ROM, expand it on the RAM, and cooperate with the RAM to execute the processes described below.

[0064] In the present embodiment, a computer-readable medium may be used. A computer-readable medium refers to any type of physical memory (RAM, ROM, etc.) that can store information or data that can be read by a processor. A computer-readable medium can store instructions for execution by one or more processors. Note that the term "computer-readable medium" encompasses tangible items and excludes carrier waves and transitory signals (i.e., non-transitory).

[0065] The present invention is not limited to the above-described embodiment, and can be freely modified, improved, etc. as appropriate. The material, shape, size, numerical value, form, number, location, etc. of each component in the above-described embodiment are arbitrary and not limited as long as the present invention can be achieved.

[0066] In the above embodiment, the processor 12 creates the classifier 121 using the second data set D2 as training data. That is, the first data set D1 is an example of a data set that was not used to create the classifier (in other words, a data set that is an input to the classifier 121). However, the processor 12 may create the classifier 121 using the first data set D1 as training data. In this case, the second data set D2 is an example of a data set that was not used to create the classifier. That is, when determining the identity of a subject using the first data set D1 and the second data set D2, the classifier 121 may be created from one data set regardless of the timing of acquisition of each data set (which data set was acquired first), and the other data set may be input to the classifier 121. Note that the determination of the identity of a subject is a determination of whether the first data set D1 and the second data set D2 are acquired from the same subject.

[0067] Furthermore, the processor 12 may create the classifier 121 using the third data set D3 as training data. In this case, the first data set D1 and the second data set D2 are examples of data sets not used in creating the classifier (in other words, data sets that are input to the classifier 121). That is, when determining the identity of a subject using the first data set D1 to the third data set D3, the classifier 121 may be created from any one data set regardless of the timing of acquiring each data set (which data set was acquired first), and the other two data sets may be input to the classifier 121.

[0068] In the above embodiment, the second data set D2 is a data set acquired earlier than the first data set D1, but the first data set D1 may be a data set acquired earlier than the second data set D2. Also, the third data set D3 is a data set acquired earlier than the second data set, but the third data set D3 may be a data set acquired later in time than at least one of the first data set D1 and the second data set D2. [Explanation of symbols]

[0069] 1: subject discrimination device, 2: subject, 3: sensor, 4: external device, 5: database, 10: subject, 11: input interface, 12: processor, 13: output interface, 14: notification unit, 15: storage, 121: classifier

Claims

1. an acquisition unit that acquires a first data set corresponding to a time-dependent change in biological information of a subject and a second data set corresponding to a time-dependent change in the biological information acquired at a timing different from that of the first data set; a creation unit that creates a classifier trained using an autoencoder with one of the first data set and the second data set as training data; a discrimination unit that, when a noise score obtained by performing statistical processing on one of the first data set and the second data set that is not used for creating the classifier is less than a preset threshold, calculates a first similarity using input information of the one of the first data set and the second data set that is not used for creating the classifier and first output information obtained by inputting the data set that is not used for creating the classifier to the classifier, and discriminates whether the first data set and the second data set are obtained from the same subject based on the first similarity but not based on identification information of the subject; an output unit that outputs an output signal corresponding to a result of the determination by the determination unit; A subject discrimination device comprising:

2. the acquiring unit is further capable of acquiring a third data set corresponding to a change over time in the biological information acquired at a timing different from that of the first data set and the second data set, 2. The subject discrimination device of claim 1, wherein the discrimination unit calculates a second similarity using input information of the third dataset and second output information obtained by inputting the third dataset to the classifier, and determines whether the first dataset and the second dataset have been obtained from the same subject based on the first similarity and the second similarity.

3. the first data set includes an identity of the subject; The subject discrimination device according to claim 1 , wherein the acquisition unit acquires the second data set by referring to identification information of the subject.

4. The apparatus further comprises a storage unit capable of storing the data set; The subject discrimination device according to any one of claims 1 to 3, wherein when the discrimination unit determines that the first data set and the second data set are obtained from the same subject, a data set not used in creating the classifier is stored in the storage unit to be used as the training data.

5. The subject discrimination device according to claim 1 , wherein the first data set and the second data set are streaming data.

6. The subject discrimination device according to claim 1 , wherein a data set used to create the classifier is expanded by data augmentation.

7. The subject discrimination device according to claim 1 , wherein when a dataset not used to create the classifier does not meet a predetermined condition for inputting the dataset to the classifier, the output unit outputs an output signal indicating that the predetermined condition is not met.

8. The subject discrimination device according to claim 1 , wherein the discrimination section calculates the first similarity using a root mean square error.

9. The subject discrimination device according to claim 2 , wherein the discrimination section calculates the first similarity and the second similarity using a root mean square error.

10. A subject discrimination method executed by a subject discrimination device, comprising: acquiring a first data set corresponding to a time-dependent change in biological information of a subject, and a second data set corresponding to a time-dependent change in biological information acquired at a timing different from that of the first data set; creating a classifier trained using an autoencoder with one of the first data set and the second data set as training data; a step of: when a noise score obtained by performing statistical processing on one of the first data set and the second data set that is not used for creating the classifier is less than a predetermined threshold, calculating a first similarity using input information of the one of the first data set and the second data set that is not used for creating the classifier and first output information obtained by inputting the dataset that is not used for creating the classifier to the classifier, and determining, based on the first similarity but not based on identification information of the subject, whether the first data set and the second data set have been obtained from the same subject.

11. By executing the program by the computer, the subject discrimination device acquiring a first data set corresponding to a time-dependent change in biological information of a subject, and a second data set corresponding to a time-dependent change in biological information acquired at a timing different from that of the first data set; creating a classifier trained using an autoencoder with one of the first data set and the second data set as training data; a first similarity using input information of the first dataset and the second dataset that is not used for creating the classifier, and first output information obtained by inputting the dataset that is not used for creating the classifier, to the classifier, when a noise score obtained by performing statistical processing on the dataset out of the first dataset and the second dataset that is not used for creating the classifier is less than a predetermined threshold, and a determination is made as to whether the first dataset and the second dataset are obtained from the same subject, based on the first similarity but not based on identification information of the subject.

12. A non-transitory computer readable medium having the computer program of claim 11 recorded thereon.

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