Mild cognitive impairment screening method, and computer program and screening device associated with method thereof

Abstract

Provided is a mild cognitive impairment (MCI) screening method that can be used to easily screen MCI in a short amount of time without using a large device that is complicated and expensive and without requiring specialized knowledge. Also provided are a computer program and screening device that are associated with the method thereof.　A mild cognitive impairment screening method according to the present invention comprises: a measurement step S1 for measuring a finger tapping motion that is an opening / closing motion of two fingers; an arithmetic calculation step S3 for calculating at least one of four feature amounts including a number of tapping times, an average value of a tapping interval, the standard deviation of the tapping interval, and a number of times of freezing, on the basis of measurement data obtained in the measurement step S1 within a prescribed time during alternating finger tapping with both hands; and determination steps S4-S6 for making a determination regarding mild cognitive impairment on the basis of a prescribed threshold value relating to the feature amount calculated in the arithmetic calculation step S3.

Description

Mild cognitive impairment screening method, computer program associated with said method, and screening device

[0001] The present invention relates to a method for screening for mild cognitive impairment, and in particular to a method for measuring finger tapping movements and screening for mild cognitive impairment using the measurement results, as well as a computer program and screening device that accompany the method.

[0002] As the aging society progresses, the number of patients with Alzheimer's disease is increasing year by year, and if it is detected early, the progression of the disease can be slowed with medication. However, it can be difficult to distinguish between symptoms associated with aging, such as forgetfulness, and the disease itself, and in many cases, patients only visit a hospital once the condition has progressed to a severe stage.

[0003] In this situation, conventional screening tests for the early detection of Alzheimer's disease have included blood tests, olfactory tests, and tests that replicate a doctor's interview on a tablet device. However, these tests have presented problems such as the pain of blood sampling and the length of the test time, which places a heavy burden on the subject. Meanwhile, cognitive function assessments based on button presses or measuring finger movements of one hand using a tablet device have also been conducted as less burdensome tests, but these have the drawback of not achieving sufficient test accuracy. A simple screening test that is highly accurate and less burdensome for the subject could lead to the early detection of Alzheimer's disease, improve patients' quality of life, and contribute to reducing medical and nursing care costs.

[0004] In recent years, it has become clear that finger tapping, the opening and closing movements of two fingers using the thumb and index finger of both hands, can reveal movement patterns specific to Alzheimer's disease. High correlations have been confirmed between finger tapping measurements and dementia tests based on general interviews. These findings are believed to be the result of finger tapping measurements capturing the decline in rhythmic motor function of both hands due to brain atrophy in Alzheimer's disease. Furthermore, the hands are said to be the second brain, and many brain regions are involved in finger function. Finger movements are also associated with Alzheimer's disease, cerebrovascular and Lewy body dementia, Parkinson's disease, and developmental coordination disorder (e.g., difficulty skipping or jumping rope). In other words, finger tapping can reveal brain status. Furthermore, finger tapping can be used as a "ruler" for brain health, allowing for quantification of fine motor function, which can be used in a variety of fields, including healthcare, rehabilitation, and lifestyle assistance.

[0005] As a method for accurately measuring and evaluating finger tapping movements, for example, Patent Document 1 discloses a motor function evaluation system that includes a motor function measurement device that calculates motor data based on the relative distance between a pair of a transmitter coil and a receiver coil attached to a moving part of a living body, and an evaluation device that evaluates the motor function of the living body based on the motor data received from the motor function measurement device. Specifically, Patent Document 1 shows that a magnetic sensor attached to the fingertips converts changes in magnetic force that fluctuate during tapping movements with two fingers into electrical signals, and measures and quantifies the movements to capture feature quantities that indicate the characteristics of the finger movements, thereby revealing the state of brain function.

[0006] JP 2016-49123 A

[0007] As mentioned above, early detection of dementia is extremely important, and to do so, it is also necessary to distinguish between mild cognitive impairment (MCI), which is a precursor to dementia.

[0008] Such screening for MCI has traditionally been carried out through a diagnosis by a dementia specialist, a comprehensive geriatric assessment (CGA) by a clinical psychologist, psychological tests (e.g., FAB, RCPM, ADAS), magnetic resonance imaging (MRI), single-photon emission computed tomography, or electrocardiogram and blood tests, with the dementia specialist ultimately making a comprehensive judgment based on the results of these tests.

[0009] However, MCI screening using such methods is accompanied by various problems, such as the tests being invasive, taking a long time to perform, requiring specialized knowledge, and requiring the use of complex, large, and expensive equipment.

[0010] The present invention has been made in consideration of the above circumstances, and aims to provide a mild cognitive impairment screening method, a computer program associated with the method, and a screening device that enable screening for MCI to be performed easily and in a short time without using complex, large, and expensive equipment or requiring specialized knowledge.

[0011] In order to solve the above-mentioned problems, the mild cognitive impairment screening method of the present invention is characterized by comprising: a measurement step of measuring finger tapping movement, which is the opening and closing movement of two fingers; a calculation step of calculating at least one of four feature quantities, consisting of the tapping count, which is the total number of finger taps, the average value of the tapping interval, which is the time interval between each tap, the standard deviation of the tapping interval, and the freezing count, which is the number of times the finger movement stops due to hesitation in the tapping movement, based on measurement data obtained by the measurement step within a predetermined time when fingers are tapped alternately with both hands; and a determination step of making a determination regarding mild cognitive impairment based on a predetermined threshold value for the feature quantities calculated in the calculation step.

[0012] The present inventors conducted clinical research focusing on finger tapping movements and succeeded in extracting movement patterns specific to individuals with MCI from tapping movements involving the thumb and index finger of both hands. This was achieved by utilizing technology that quantifies dexterous finger movements using a magnetic sensor to analyze the diverse characteristics of the tapping movement, which involves repeatedly opening and closing two fingers. Furthermore, the present inventors confirmed that by comparing these results with values ​​for healthy elderly people (cutoff values ​​(thresholds) for each feature), cognitive decline characteristic of MCI can be detected with high accuracy. Based on these results, the present invention provides a testing method for supporting the early detection of individuals with MCI, i.e., a mild cognitive impairment screening method.

[0013] Specifically, the inventors measured the finger tapping movements of 173 MCI patients and 173 healthy elderly people, and by comparing the feature amounts of these two groups, they found that significant differences were observed in four feature amounts, in particular: the number of tappings, which is the total number of finger taps within a specified time period when alternately tapping with both hands; the average value of the tapping interval, which is the time interval between each tap; the standard deviation of the tapping interval; and the number of freezings, which is the number of times the finger movement stops due to hesitation in the tapping movement. In fact, the correlation coefficient r (which is an index measuring the strength of the relationship between two numerical values, and is "1" if there is a perfect positive correlation and "-1" if there is a perfect negative correlation) was 0.51 for the number of tappings, 0.45 for the average value of the tapping interval, 0.43 for the standard deviation of the tapping interval, and 0.41 for the number of freezings. The correlation coefficients of these four feature quantities were significantly larger than the 168 feature quantities obtained from simultaneous and alternating finger tapping with both hands. Based on this result, the inventors calculated four feature quantities based on the measurement data obtained by measuring finger tapping within a predetermined time during alternating finger tapping with both hands: the number of tappings, which is the total number of finger taps; the average tapping interval, which is the time interval between each tap; the standard deviation of the tapping interval; and the number of freezings, which is the number of times the finger movement stops due to hesitation in the tapping movement. It was discovered that by comparing these calculated feature quantities with a predetermined threshold, a diagnosis of mild cognitive impairment can be made.

[0014] According to the above-described configuration of the present invention, among the many feature quantities obtained by various finger tapping movements, four feature quantities that show particularly significant differences between healthy subjects and MCI subjects, namely, the number of taps within a predetermined time during alternating double tapping, the average value of the tapping interval, the standard deviation of the tapping interval, and the number of freezing movements, are selected, and MCI is diagnosed based on the corresponding thresholds (cutoff values). This makes it possible to easily and quickly screen for MCI with high accuracy without requiring specialized knowledge. Moreover, the finger tapping measurement itself can be performed non-invasively, easily, and using a simple and compact device, allowing for measurement and evaluation with little burden on the subject in a short measurement time.

[0015] In the above configuration, the measurement step of measuring finger tapping movements, which are the opening and closing movements of two fingers, may use any measurement method as long as it can measure finger tapping movements. For example, it may be a measurement method using magnetic sensors attached to two fingers, a measurement method that processes image data obtained by photographing the finger movements, or a measurement method based on detecting finger movements tapping on a touch panel. Furthermore, in the above configuration, the "determination of mild cognitive impairment" includes a determination of whether or not the subject has MCI, a determination of whether the likelihood of MCI is high or low, and presentation (determination) of the probability of MCI and the degree of MCI.

[0016] In the above configuration, the assessment step may assess MCI using any one of the four feature quantities alone, or may assess MCI by using the four feature quantities in various combinations, or may assess MCI by scoring or quantifying the results based on predetermined thresholds for each feature quantity. For example, in one embodiment, the calculation step may calculate all four feature quantities, and the assessment step may compare each of the four feature quantities with its corresponding threshold and assess MCI based on the number of feature quantities that exceed the threshold. Furthermore, the assessment step may assess the degree of MCI based on the magnitude of the difference between the feature quantity and its threshold.

[0017] In addition to the above-mentioned mild cognitive impairment screening method (for example, by processing by a computer according to a predetermined algorithm, etc.), the present invention also provides a computer program that causes a computer to execute the method, and a mild cognitive impairment screening device that can execute the method.

[0018] According to the mild cognitive impairment screening method, computer program, and device of the present invention, MCI screening can be performed easily and in a short time without using complex, large, and expensive equipment and without requiring specialized knowledge.

[0019] 1 is a block diagram showing a schematic configuration of a mild cognitive impairment screening device according to one embodiment of the present invention. It is a schematic diagram showing both hands of a subject with tapping sensors attached to the thumb and index finger. It is a flowchart showing an example of a mild cognitive impairment screening method according to one embodiment of the present invention that can be executed by the mild cognitive impairment screening device of FIG. 1. (a) shows an example of left-hand finger-tapping movement waveform data obtained by measurement using a tapping sensor, and (b) shows an example of right-hand finger-tapping movement waveform data measured using a tapping sensor. (a) is a diagram comparing the number of tappings, which is a feature value in alternate finger-tapping movement with both hands, between individuals with MCI and healthy elderly people, and (b) is an ROC analysis diagram for the number of tappings. (a) is a diagram comparing the average value of tapping intervals, which is a feature value in alternate finger-tapping movement with both hands, between individuals with MCI and healthy elderly people, and (b) is an ROC analysis diagram for the average value of tapping intervals. (a) is a graph comparing the standard deviation of tapping intervals, a feature of alternate finger tapping exercises performed with both hands, between individuals with MCI and healthy elderly people, (b) is an ROC analysis graph for the standard deviation of tapping intervals. (a) is a graph comparing the number of freezing events, a feature of alternate finger tapping exercises performed with both hands, between individuals with MCI and healthy elderly people, and (b) is an ROC analysis graph for the number of freezing events.

[0020] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In this embodiment, by providing the following technology, highly advanced technology contributes to the development of medical care and the realization of a healthy society. The realization of this mild cognitive impairment screening method (device and computer program) contributes to "9. Build resilient infrastructure, promote inclusive and sustainable industrialization, promote innovation and build resilient infrastructure" of the Sustainable Development Goals (SDGs) advocated by the United Nations.

[0021] In addition, in the following embodiments, a mild cognitive impairment screening method (apparatus) will be described, but the present invention may also be configured as a computer program that enables the processing performed by the mild cognitive impairment screening method (apparatus) to be performed by a computer.

[0022] 1 shows a schematic configuration of a mild cognitive impairment screening device 1 according to one embodiment of the present invention. As shown in the figure, this mild cognitive impairment screening device 1 includes a measurement unit 10 having a tapping sensor 2 that magnetically detects finger tapping movements, which are opening and closing movements of two fingers, and a processor 30 that processes measurement data measured by the measurement unit 10.

[0023] The measurement unit 10 measures finger movement data based on the relative distance between a pair of a transmitter coil and a receiver coil attached to a finger (or other movable part) of a living body, and detects, for example, time-series information on the subject's finger movement. The measurement unit 10 can acquire time-series data (waveform data) of the subject's movement information relating to at least one of distance, speed, acceleration, and jerk (acceleration differentiated with respect to time). Figure 4 shows an example of such time-series (waveform) data. In Figure 4, (a) shows an example of left-hand finger tapping movement waveform data measured by the tapping sensor 2, and (b) shows an example of right-hand finger tapping movement waveform data measured by the tapping sensor 2.

[0024] The measurement unit 10 includes a tapping sensor 2, first and second switching circuits 4 and 5, an AC generator 6 for generating AC, an amplifier / filter circuit 7, an A / D converter 8, a detector 9, a downsampler 10 for downsampling, and a controller 11 for controlling the operations of these components.

[0025] The tapping sensor 2 is composed of a pair of a transmitter coil 2A (2A') and a receiver coil 2B (2B') (or multiple rows of pairs of coils) and is attached to the fingers (e.g., nails) of the subject's hand 100 (100A, 100A') using, for example, double-sided tape or a fixing band, as shown in FIG. 2 . Specifically, in FIG. 2 , a pair of a transmitter coil 2A and a receiver coil 2B is attached to the thumb 100a and index finger 100b of the subject's right hand 100A, and a pair of a transmitter coil 2A' and a receiver coil 2B' is attached to the thumb 100a and index finger 100b of the subject's left hand 100A' (these may be reversed or attached to other fingers). In this case, the transmitter coil 2A (2A') emits a magnetic field, and the receiver coil 2B (2B') receives (detects) the magnetic field emitted by the transmitter coil 2A (2A').

[0026] One AC generator 6 is connected to the transmitter coil 2A (2A') via a first switching circuit 4. Through the switching operation of the first switching circuit 4, AC current (e.g., a 20 kHz current) from the AC generator 6 flows sequentially through the transmitter coil 2A (2A'), causing the transmitter coil 2A (2A') through which the AC current flows to generate an AC magnetic field. The AC generator 6 generates AC current of a predetermined frequency, and the timing of the current flow is controlled by a controller 11. The signal generated by the AC generator 6 is used as a reference signal for the detection operation of the detector 9.

[0027] The controller 11 generates a synchronization signal for controlling the first and second switching circuits 4 and 5. This synchronization signal enables the first switching circuit 4 and the second switching circuit 5 to be switched simultaneously, and they operate sequentially for each pair of the transmitting coil 2A (2A') and the receiving coil 2B (2B').

[0028] The receiving coil 2B (2B') is also connected to an amplifier / filter circuit 7 via a second switching circuit 5, and the output signal from the amplifier / filter circuit 7 is converted into a digital signal by an A / D converter 8, which is then transmitted to a detector 9. The conversion of analog data into digital data by the A / D converter 8 facilitates subsequent processing (downsampling, etc.). The detector 9 also performs processing to delete a predetermined period of the AC magnetic field waveform (noise portion) detected by the receiving coil 2B (2B') immediately after switching by the second switching circuit 5.

[0029] Furthermore, the time of the deletion process in the AC magnetic field waveform of each receiving coil 2B (2B') is precisely controlled by the controller 11. After this deletion process, the detector 9 performs full-wave rectification and filtering (mainly processing using a low-pass filter (LPF)) using the aforementioned reference signal. Finally, the digital signal processed by the detector 9 is converted (downsampled) by the downsampler 10 into coarse data with a sampling frequency (e.g., 200 Hz) that is approximately 1 / 1000 (a predetermined ratio) of the sampling frequency (e.g., 200 kHz) of the A / D converter 8. This reduces the overall data volume. Therefore, the output signal can be transmitted at high speed as data from multiple receiving coils even with limited communication capacity. In other words, because the communication interface 12 of the measurement unit 10 receives a small amount of data from the downsampler 10, finger movement data related to multiple receiving coils can be transferred to the processor 30 (via the communication interface 31 of the processor 30) at once, wirelessly or via a wire.

[0030] The processor 30, which processes the measurement data measured by the measurement unit 10, calculates at least one of four feature quantities based on detection information detected by the tapping sensor 2, specifically, based on measurement data output from the measurement unit 10, which measures the finger tapping movement of both hands alternately using the tapping sensor 2: the tapping count, which is the total number of finger taps within a predetermined time period; the average tapping interval, which is the time interval between each tap; the standard deviation of the tapping interval; and the freezing count, which is the number of times the finger movement stops due to hesitation in the tapping movement (the feature quantity or the number of features to be calculated may be selected automatically based on a predetermined algorithm, or may be selected based on a command input via an operation input interface 38, which will be described later); and the processor 30 has an arithmetic circuit 33, which calculates at least one of four feature quantities based on detection information detected by the tapping sensor 2, specifically, based on measurement data output from the measurement unit 10, which measures the finger tapping movement of both hands alternately using the tapping sensor 2 (the selection of the feature quantity or the number of features to be calculated may be performed automatically based on a predetermined algorithm, or may be performed based on a command input via an operation input interface 38, which will be described later), and a judgment circuit 34, which makes a judgment regarding mild cognitive impairment based on a predetermined threshold value for the feature quantity calculated by the arithmetic circuit 33.

[0031] In addition, the mild cognitive impairment screening device 1 further includes a display 37 that displays various data (such as the calculation results calculated by the calculation circuit 33) including the judgment results by the judgment circuit 34 of the processor 30, a memory 36 that stores the various data, and an operation input interface 38 that can input necessary data and commands to the processor 30 by operation.

[0032] In the above configuration, the processor 30 is composed of a CPU and the like, and executes programs such as an operating system (OS) and various operation control applications stored in the memory 36, thereby performing operation control processing for the various circuits 33 and 34 described above and controlling the startup operations of various applications.

[0033] The memory 36 is configured with a flash memory or the like, and stores programs such as an operating system and applications for controlling the operation of various processes such as images, audio, documents, displays, measurements, etc. The memory 36 also stores information data such as base data required for basic operations by the operating system and file data used by various applications.

[0034] The processing by the processor 30 may be stored as a single application, and the measurement of finger movements and the calculation and analysis of various feature quantities may be performed by activating the application. Alternatively, an external server device with high computing performance and large capacity may receive the measurement results from the information processing terminal and calculate and analyze the feature quantities.

[0035] The operation input interface 38 generally uses input means such as a keyboard, key buttons, touch keys, etc., but may also use, for example, gesture operation or voice input, and is used to set and input the information that the subject should enter.

[0036] Furthermore, the communication interface 31 may not only receive measurement results from the measurement unit 10, but may also wirelessly communicate with a server device or the like located in a different location via short-range wireless communication, a wireless LAN, or base station communication. In this case, measurement data, analyzed and calculated features, and the like may be transmitted and received from the server device or the like via the transmitting / receiving antenna 39 during wireless communication. While short-range wireless communication is performed using, for example, an electronic tag, this is not limited thereto, and wireless LANs such as Bluetooth (registered trademark), IrDA (Infrared Data Association, registered trademark), Zigbee (registered trademark), HomeRF (Home Radio Frequency, registered trademark), or Wi-Fi (registered trademark) may also be used, as long as they are capable of at least wireless communication when located near other information terminals. Furthermore, for base station communication, long-distance wireless communication such as W-CDMA (Wideband Code Division Multiple Access) or GSM (Global System for Mobile communications) may be used. It is also possible to detect the positional relationship and orientation between terminals using an ultra-wide band (UWB) system. Although not shown, the communication interface 31 may use other methods as wireless communication means, such as optical communication or acoustic wave communication. In this case, a light emitting / receiving unit and an acoustic wave output / input interface are used instead of the transmitting / receiving antenna 39, respectively.

[0037] In this embodiment, the measuring unit 10 and the processor 30 have each of the aforementioned components individually, but they may also be provided with a functional unit that integrates at least some or all of these components; in short, any configuration is acceptable as long as the functions of each of these components are ensured.

[0038] The mild cognitive impairment screening device 1 configured as described above is non-invasive and has a compact design that is small, lightweight, and highly biosafe, allowing for measurement and evaluation in a short measurement time with minimal burden on the subject.

[0039] Next, an example of the operation of the mild cognitive impairment screening device 1 (mild cognitive impairment screening method) configured as described above will be described in more detail with reference to the flowchart of FIG. 3 and the empirical data of FIGS.

[0040] 3 shows an example of processing steps (steps S1 to S7 of the mild cognitive impairment screening method) executed by the mild cognitive impairment screening device 1 (steps S2 and onward show an example of processing steps executed by the processor 30). As shown in the figure, in the mild cognitive impairment screening device 1 (mild cognitive impairment screening method) of this embodiment, first, the measurement unit 10 measures the finger tapping movement performed by the subject (measurement step S1). Specifically, the measurement unit 10 magnetically measures (detects) the finger tapping movement of both hands alternately using the tapping sensors 2. During this measurement process, the processor 30 acquires detection data (measurement data) from the tapping sensors 2 (step S2).

[0041] After the subject's finger tapping movement is measured by the measurement unit 10 and the measurement data is received by the processor 30, the arithmetic circuit 33 of the processor 30 calculates, based on the received measurement data, at least one of four feature quantities: the tapping count, which is the total number of finger taps, the average tapping interval, which is the time interval between each tap, the standard deviation of the tapping interval, and the freezing count, which is the number of times the subject hesitates to continue the tapping movement and stops moving the finger (calculation step S3). Once these feature quantities have been calculated by the arithmetic circuit 33, the judgment circuit 34 of the processor 30 then makes a judgment regarding MCI based on a predetermined threshold value for the calculated feature quantities.

[0042] Specifically, in this embodiment, the determination circuitry 34 determines whether a predetermined condition based on a threshold is satisfied (step S4), and if the determination result is affirmative, determines that the subject has MCI (determination step S5), and if the determination result is negative, determines that the subject does not have MCI (determination step S6). Here, the predetermined condition based on a threshold may be, for example, a comparison of the feature amount calculated by the arithmetic circuitry 33 with a threshold set for that feature amount, and that the feature amount is equal to or greater than the set threshold or less than the threshold. Alternatively, the predetermined condition based on a threshold may be a calculation result obtained by converting the result based on a predetermined threshold for each of the four feature amounts into a score or a numerical value, and that the result is equal to or greater than a predetermined value or less than a predetermined value.

[0043] Alternatively, the determination circuitry 34 may determine whether the probability of MCI is high or low, or may present (determine) the probability of MCI or the degree of MCI, depending on how the threshold is set. The degree of MCI may be determined based on the magnitude of the difference between the feature and the threshold.

[0044] In the aforementioned example in which MCI is determined by comparing the calculated feature value with a predetermined threshold, the threshold is set to, for example, 30 (times) when the feature value is the number of tappings, 0.472 (seconds) when the feature value is the average tapping interval, 0.065 (seconds) when the feature value is the standard deviation of the tapping interval, and 32.5 (times) when the feature value is the number of freezings. These values ​​are based on the empirical data shown in Figures 5, 6, 7, and 8.

[0045] As mentioned above, the inventors measured the finger tapping movements of 173 MCI patients and 173 healthy elderly people, and by comparing the features of these two groups, they found significant differences in four feature values ​​in particular: the number of taps, which is the total number of finger taps within a specified time when alternately tapping with both hands; the average tapping interval, which is the time interval between each tap; the standard deviation of the tapping interval; and the number of freezing episodes, which is the number of times the fingers hesitate to continue the tapping movement and stop moving (when the finger motor function of MCI patients was compared with that of healthy elderly people, fine motor impairment, reduced finger dexterity, and a reduced number of taps were observed). In fact, the correlation coefficient r, an index that measures the strength of the correlation between two numerical values, was 0.51 for the number of tappings, 0.45 for the average value of the tapping interval, 0.43 for the standard deviation of the tapping interval, and 0.41 for the number of freezings. The correlation coefficients of these four feature quantities were significantly larger than those of the 168 feature quantities obtained from simultaneous and alternating finger tapping with both hands.

[0046] Figures 5(a), 6(a), 7(a), and 8(a) show the results of measuring alternating finger tapping movements of both hands for an average of 15 seconds in 173 MCI patients and 173 healthy elderly subjects, respectively, and comparing the four feature values ​​obtained from the measurements between the MCI group and the healthy elderly group. Figure 5 shows the results for the number of tappings, Figure 6 shows the results for the mean tapping interval, Figure 7 shows the results for the standard deviation of the tapping interval, and Figure 8 shows the results for the number of freezings. Figures 5(b), 6(b), 7(b), and 8(b) show the results of receiver operating characteristic (ROC) analysis, a method for evaluating the accuracy of model predictions. In this case, the vertical axis represents sensitivity, and the horizontal axis represents specificity. The ROC curve is a collection of points obtained by varying the threshold (cutoff value). In the figure, an inverted triangle mark on the ROC curve indicates the position of the aforementioned threshold used in the MCI diagnosis in this embodiment. The values ​​of sensitivity, specificity, AUC (Area Under the Curve), and threshold (cutoff value) at this position are shown as numerical data in the lower right corner of the graph. Here, sensitivity is one of the indicators that determine the characteristics of a clinical test and is a value defined as "the probability of correctly determining a positive result for a given test." Specificity is also one of the indicators that determine the characteristics of a clinical test and is a value defined as "the probability of correctly determining a negative result for a given test." When an ROC curve is created, AUC represents the area below the curve on the graph, and takes a value between 0 and 1, with values ​​closer to 1 indicating higher discriminatory ability.

[0047] As can be seen from the empirical data shown in Figures 5 to 8, when the threshold for the number of tappings (Figure 5) is set to 30 times (sensitivity 0.769, specificity 0.665), the AUC is 0.79, which means that it is possible to distinguish between the MCI group and the healthy elderly group with a 79% probability. Also, when the threshold for the average tapping interval (Figure 6) is set to 0.472 seconds (sensitivity 0.711, specificity 0.723), the AUC is 0.79, which means that it is possible to distinguish between the MCI group and the healthy elderly group with a 79% probability. Furthermore, for the standard deviation of the tapping interval (FIG. 7), when the threshold is set to 0.065 seconds (sensitivity 0.676, specificity 0.734), the AUC is 0.77, so it can be said that it is possible to distinguish between the MCI group and the healthy elderly group with a probability of 77%. For the number of freezings (FIG. 8), when the threshold is set to 32.5 times (sensitivity 0.688, specificity 0.717), the AUC is 0.74, so it can be said that it is possible to distinguish between the MCI group and the healthy elderly group with a probability of 74%.

[0048] Therefore, based on the above empirical data, in this embodiment, as an example, when the number of tappings is calculated as the feature, a threshold is set to 30, and when the number of tappings is below 30 based on comparison with this threshold, MCI is determined. When the average value of tapping intervals is calculated as the feature, a threshold is set to 0.472, and when the average value of tapping intervals exceeds 0.64 seconds based on comparison with this threshold, MCI is determined. When the standard deviation of tapping intervals is calculated as the feature, a threshold is set to 0.065 seconds, and when the standard deviation of tapping intervals exceeds 0.065 seconds based on comparison with this threshold, MCI is determined. When the number of freezings is calculated as the feature, a threshold is set to 32.5, and when the number of freezings exceeds 32.5 based on comparison with this threshold, MCI is determined. Alternatively, MCI may be determined when two or more, three or more, or four of these four feature values ​​exceed (or fall below) the threshold. That is, the calculation circuit 33 may calculate all four of the aforementioned features, and the judgment circuit 34 may compare each of the four features with its corresponding threshold, and make a judgment regarding MCI depending on the number of features that exceed the threshold.

[0049] When the MCI determination is performed in the above manner, the determination result is displayed on the display 37 (step S7). In addition, by inputting a predetermined command, for example, through the operation input interface 38, data corresponding to the command, such as calculation data calculated by the arithmetic circuit 33 (or measurement data output from the measurement unit 10), is also displayed on the display 37.

[0050] Although the embodiments of the present invention have been described above with reference to the drawings, the present invention is not limited to the above-described embodiments and can include various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0051] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the program, table, and file that implements each function may be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD, or may be stored in a device on a communication network.

[0052] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected.

[0053] 2 tapping sensor 10 measurement unit 30 processor 33 calculation circuit 34 determination circuit

Claims

1. A method for screening for mild cognitive impairment, comprising: a measurement step of measuring finger tapping movements, which are opening and closing movements of two fingers; a calculation step of calculating at least one of four feature quantities, based on measurement data obtained by the measurement step during finger tapping with both hands alternately, the four feature quantities being the tapping count, which is the total number of finger taps, the average value of the tapping intervals, which is the time interval between each tap, the standard deviation of the tapping intervals, and the freezing count, which is the number of times the finger movement stops due to hesitation in the tapping movement; and a determination step of making a determination regarding mild cognitive impairment based on a predetermined threshold value for the feature quantities calculated in the calculation step.

2. The mild cognitive impairment screening method of claim 1, characterized in that the calculation step calculates all of the four feature amounts, and the judgment step compares each of the four feature amounts with its corresponding threshold, and makes a judgment regarding mild cognitive impairment based on the number of feature amounts that exceed the threshold.

3. A mild cognitive impairment screening method as described in claim 1 or 2, characterized in that the judgment step judges the degree of mild cognitive impairment based on the magnitude of the difference between the feature and its threshold.

4. A computer program for processing measurement data of finger tapping movement, which is the opening and closing movement of two fingers, characterized by causing a computer to execute the following steps: a calculation step for calculating at least one of four feature quantities, consisting of the tapping count, which is the total number of finger taps, the average tapping interval, which is the time interval between each tap, the standard deviation of the tapping interval, and the freezing count, which is the number of times the finger movement stops due to hesitation in the tapping movement, based on the measurement data obtained within a predetermined time when tapping the fingers alternately with both hands; and a judgment step for making a judgment regarding mild cognitive impairment based on a predetermined threshold value for the feature quantities calculated in the calculation step.

5. The computer program according to claim 4, characterized in that the calculation step calculates all of the four feature amounts, and the judgment step compares each of the four feature amounts with its corresponding threshold, and makes a judgment regarding mild cognitive impairment depending on the number of feature amounts that exceed the threshold.

6. A computer program according to claim 4 or 5, characterized in that the determination step determines the degree of mild cognitive impairment based on the magnitude of the difference between the feature and its threshold.

7. A mild cognitive impairment screening device comprising: a measuring unit that measures finger tapping movements, which are opening and closing movements of two fingers; and a processor that processes measurement data obtained by measurement by the measuring unit, wherein the processor comprises: an arithmetic circuit that calculates at least one of four feature quantities based on the measurement data during alternate finger tapping with both hands within a predetermined time period: the tapping count, which is the total number of finger taps; the average tapping interval, which is the time interval between each tap; the standard deviation of the tapping interval; and the freezing count, which is the number of times the finger movement stops due to hesitation in the tapping movement; and a judgment circuit that makes a judgment regarding mild cognitive impairment based on a predetermined threshold value for the feature quantities calculated by the arithmetic circuit.

8. The mild cognitive impairment screening device of claim 7, characterized in that the calculation circuit calculates all of the four feature amounts, and the judgment circuit compares each of the four feature amounts with its corresponding threshold, and makes a judgment regarding mild cognitive impairment based on the number of feature amounts that exceed the threshold.

9. A mild cognitive impairment screening device as described in claim 7 or 8, characterized in that the judgment circuit judges the degree of mild cognitive impairment based on the magnitude of the difference between the feature and its threshold.

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