KR102403752B1 - Quantification method and system for movement disorder - Google Patents

Abstract

The present invention relates to a quantification method for diagnosing dyskinesias. The first device performs a questionnaire on the patient and stores the detailed score and the summed score for each item, the first device performs a spiral drawing test on the patient, and the first device attaches the first device to the patient’s body. Receiving the SVM data from the second device, and quantifying the mobility based on the SVM data.

Description

Quantification method and system for movement disorder diagnosis

The present invention relates to a quantification method and system for diagnosing dyskinesia, in particular, It relates to a quantification method and system for diagnosing dyskinesias that objectively quantify and quantify dyskinesias so that early diagnosis is possible.

Parkinson's disease is a disease caused by the gradual loss of dopamine neurons distributed in the substantia nigra of the brain. It is a chronic degenerative disease. If Parkinson's disease is treated in the early stage, it is possible to carry out daily life without much discomfort even after a long time. In the diagnosis of Parkinson's disease, abnormal motor symptoms such as tremor (tremor of the hand), stiffness, and tremor are diagnosed depending on the observation and subjective judgment of the doctor. Currently, the most commonly used diagnostic method is a method in which a doctor evaluates exercise performance by visually observing a patient's lifestyle and simple movements (writing, drawing, etc.). However, with this method, it is not easy to distinguish between patients with essential tremor and Parkinson's disease, who have similar motor patterns in appearance, and there is room for different diagnoses by doctors for the same patient.

Therefore, the subjective judgment of the doctor is excluded and objective quantitative indicators that can be used in the diagnosis of Parkinson's disease are requested, and early diagnosis and management are requested online without the patient visiting a hospital for diagnosis.

As a related prior art, there is Republic of Korea Patent Publication No. 10-2018-0010704 "A device and method for generating an input pattern for the diagnosis of Parkinson's disease".

The problem to be solved by the present invention is to implement a quantification method and system for diagnosing abnormal movement symptoms that can be diagnosed early through an algorithm for objectively quantifying abnormal movement symptoms.

A quantification method for diagnosing abnormal motor symptoms according to an embodiment of the present invention comprises the steps of: a first device performs a questionnaire on a patient and stores detailed scores and summation scores for each question; and the first device draws a spiral to the patient performing the test, and the first device receiving SVM data from the second device attached to the patient's body, and quantifying the mobility based on the SVM data.

A quantification system for diagnosing abnormal movement symptoms according to an embodiment of the present invention includes a first device that performs a questionnaire on a patient, stores detailed scores and summed scores for each question, and performs a spiral drawing test on the patient; and a second device attached to the body of the patient to measure the patient's motion and calculate a signal vector magnitude value to generate SVM data, wherein the first device receives the SVM data generated by the second device to determine the patient's mobility. quantify

According to the present invention, early diagnosis of dyskinesia is possible by quantifying various clinical criteria performed when diagnosing Parkinson's disease patients based on engineering scales.

In addition, the diagnosis can be made through the application, enabling easier diagnosis and management for patients, and quantified data on the diagnosis of dyskinesia can contribute to research on early diagnosis of dyskinesias.

In addition, by implementing it as a quantification application, essential tremor and Parkinson's disease are quantified in real time through smart bands and applications, providing quantitative standards to medical staff, and accurate diagnosis can be expected.

1 is a block diagram of a quantification system for diagnosing abnormal movement symptoms according to an embodiment of the present invention.
2 is a flowchart illustrating a quantification method for diagnosing abnormal movement symptoms according to an embodiment of the present invention.
3 is a flowchart illustrating a motility quantification method according to an embodiment of the present invention.
4 is a conceptual diagram illustrating a spiral drawing inspection method according to an embodiment of the present invention.
5 is a flowchart illustrating a spiral drawing inspection method according to an embodiment of the present invention.
6 and 7 are graphs for explaining a spiral drawing inspection method according to an embodiment of the present invention.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed in this specification are only exemplified for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiments according to the concept of the present invention are It may be implemented in various forms and is not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention may have various changes and may have various forms, the embodiments will be illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all changes, equivalents, or substitutes included in the spirit and scope of the present invention.

The terms used herein are used only to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that a feature, number, step, operation, component, part, or combination thereof described herein exists, but one or more other features It should be understood that it does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a quantification system for diagnosing abnormal movement symptoms according to an embodiment of the present invention.

Referring to FIG. 1 , a quantification system 10 for diagnosing abnormal movement symptoms includes a first device 200 and a second device 100 . The first device 200 performs a questionnaire on the patient, stores the detailed score and the summed score for each question, and performs a spiral drawing test on the patient. The second device 100 is attached to the patient's body, measures the patient's motion, calculates a signal vector magnitude value, and generates SVM data. In this case, the first device 200 may receive the SVM data generated by the second device 100 to quantify the patient's mobility. The quantification system 10 for diagnosing abnormal motor symptoms is capable of early diagnosis of abnormal motor symptoms based on questionnaire results, spiral drawing test results, and results through quantification of mobility.

The second device 100 includes a sensor unit 110 , an SVM calculation unit 120 , and a communication unit 130 . The second device 100 may be attached to the patient's wrist in the form of a band. The sensor unit 110 may measure changes in the direction, speed, and movement distance of the patient's hand movement and tremor. The SVM calculator 120 may generate SVM data by calculating a signal vector magnitude value from the value measured by the sensor unit 110 . The communication unit 130 may transmit the generated SVM data to the first device 200 .

The first device 200 includes a display unit 210 , a questionnaire unit 220 , a spiral drawing test unit 230 , a control unit 240 , a motility quantification unit 250 , a communication unit 260 , and a storage unit 270 . . The first device 200 may be a mobile device such as a tablet PC or a smartphone, but is not limited thereto. The display unit 210 displays one of the motility test items so that the patient can view the displayed motility test and perform an operation. The questionnaire 220 may display the questionnaire already stored in the storage unit to the patient, and store and analyze the questionnaire result. The detailed score and the summed score for each question can be saved, the approximate condition of the patient can be grasped through the summed score, and the diagnosis history of a specific date can be checked through the detailed score for each question. The spiral drawing test unit 230 may diagnose by analyzing the spiral drawn by the patient on the screen. The control unit 240 controls each configuration. The mobility quantification unit 250 may quantify the mobility through the movement of the patient in conjunction with the second device. The communication unit 260 may transmit/receive data to and from the second device, and the storage unit 270 may store result values such as questionnaire items, motility digitization results, extracted tremor frequency, tremor intensity, time, error value, speed, etc. have.

2 is a flowchart illustrating a quantification method for diagnosing abnormal movement symptoms according to an embodiment of the present invention.

Referring to FIG. 2 , in the quantification method for diagnosing abnormal motor symptoms, the first device performs a questionnaire on the patient and stores detailed scores and summed scores for each question ( S210 ). The first device performs a spiral drawing test on the patient (S220). The first device receives the SVM data from the second device attached to the patient's body, and quantifies the mobility based on the SVM data (S230). Steps S210 to S230 do not necessarily operate in time series, and quantification may be performed by performing only a specific test.

The patient database can be divided into a DB that stores patient information (name, age, gender, specifics) and a DB that stores the patient's examination data. Items to examine the patient can be divided into questionnaire test, spiral drawing test, and patient motion detection. The questionnaire test stores the detailed score and the summed score for each question. Through the summed score, it is possible to grasp the approximate condition of the patient, and through the detailed score for each item, the diagnosis history for a specific date can be confirmed. The spiral drawing test brings the spiral drawn by the patient on the tablet as two-dimensional data and time values of x and y, and quantifies the patient's hand shake through various analysis methods using these values for later research or diagnosis of Parkinson's disease. In the spiral drawing test, the amplitude, frequency, and line coincidence rate of the spiral drawing test performed by the patient are calculated using an analysis algorithm, and the results can be compared with the most recent results.

The second device can measure the patient's tremor, stiffness, and movement by using an acceleration sensor. The accelerometer measures changes in the direction, speed, and movement distance of the movement and tremor of the hand wearing the second device when the patient performs the examination items specified by the doctor. In order to extract only the data necessary for measuring abnormal movement symptoms among these data, a signal vector magnitude (SVM) value is calculated and then the data is transmitted to the first device through wireless communication. In addition, the quantification function can be performed by building a library for measurement and implementing it in the second device.

3 is a flowchart illustrating a motility quantification method according to an embodiment of the present invention.

Referring to FIG. 3 , the step of quantifying the mobility based on the SVM data by the second device 100 displays at least one of the motion test items on the display unit of the first device 100 ( S310 ). The motility test item consists of six tests, and may be one of finger bumping, hand motion, stability tremor test, postural tremor test, Parkinson's disease and essential tremor discrimination test, and nose-finger pointing. Specifically, Figner Tapping diagnoses the patient's tremor by making the patient hit the index finger and thumb 10 times as quickly and as wide as possible. In the hand movement, the patient bends the elbow and points the palm to the examiner, and then makes the fist clenched to diagnose the degree of slowing of the patient's motion. Resting tremor test requires a patient to sit comfortably in a chair and observe hand tremor in a position with their hands on their knees. Postural tremor test The sms patient is asked to extend both arms straight in front of the chest and then observe the postural tremor. In the Parkinson's disease and essential tremor discrimination test, the tremor is observed and tested from the moment both arms are stretched in front of the chest. In the nose-finger pointing, the tremor of the patient is observed by having the finger follow the patient's own nose to the finger extended by the examiner.

The sensor unit of the second device measures the movement of the patient (S320). In this case, the sensor unit may be an acceleration sensor, but is not limited thereto. The SVM calculator of the second device generates SVM data by calculating the signal vector magnitude value (S330).

The communication unit of the second device transmits the SVM data to the first device (S340). The mobility quantifier of the first device 200 quantifies the patient's mobility based on the received SVM data (S350). That is, it is possible to quantify the patient's mobility by calculating the instantaneous change amount according to the passage of time on the SVM data received from the second device. The SVM data may improve accuracy of data by correcting noise through a Kalman filter.

4 is a conceptual diagram illustrating a spiral drawing inspection method according to an embodiment of the present invention.

Referring to FIG. 4 , as a conceptual diagram for the data conversion process and each measurement value in the spiral drawing test, the dyskinesia quantification algorithm uses the patient's touch input as data to measure the patient's motion. The patient draws a spiral graph presented on the screen along a line from the center with a pen. The pen path on the screen is recorded in centimeter units at 60 pixels per second in pixel coordinates on the x and y axes, and the size of one pixel is about 118 μm, and the diameter of the spiral is about 14 cm.

Patient path data is separated into tremor and motion elements using a band pass filter (BPF) with a passband frequency of 3 to 15 Hz and a low pass filter (LPF) with a pass band frequency of 3 Hz or less. do. Data that has passed through the low-pass filter is motion data, and the data that has passed through the band-pass filter is data that includes information about tremors. For tremor data, 2D data on the X and Y axes is converted into 1D data through Principal Component Analysis (PCA). A specific tremor frequency (TF) can be defined by analyzing the frequency component of the one-dimensional reduced data through ast Fourier transform (FFT). For the tremor frequency (TF), after obtaining the average and standard deviation of power in the 10 to 25 Hz band, if there is a frequency higher than the sum of the average plus 70 times the standard deviation, the frequency value is determined as the tremor frequency. The envelope of the tremor component is calculated through Hilbert Transform on the PCA result of tremor data. The average of the envelope is defined as the Tremor magnitude (TM). The motion data is defined as the error distance (ED, Error Distance) by calculating the average of the distance between the motion data and the reference line (spiral) after correcting the initial value (center coordinate of the spiral) to ‘0’. In addition, the length of the path is defined by obtaining the sum of the distances between the points of the exercise data, and the average velocity is calculated by dividing the length and the execution time (Time).

5 is a flowchart illustrating a spiral drawing inspection method according to an embodiment of the present invention.

Referring to FIG. 5 , in the step of performing the spiral drawing test on the patient by the first device, the spiral drawing test unit of the first device extracts input data based on the spiral drawn by the patient on the display unit ( S510 ).

The spiral drawing inspection unit extracts tremor information through band-pass filtering based on the extracted input data (S520). That is, data that has passed through the low-pass filter is motion data, and data that has passed through the band-pass filter is data including information on shaking.

The spiral drawing inspection unit converts input data into one-dimensional data through principal component analysis (PCA) (S530).

The spiral drawing inspection unit extracts a tremor frequency (TM) and a tremor intensity (TM) based on the input data (S540). The tremor frequency (TF) extracts the patient's tremor information so that you can know what frequency the patient trembled with. That is, a specific tremor frequency (TF) can be extracted by analyzing a frequency component of the one-dimensional data through an ast Fourier transform (FFT). At this time, by adding 70 times the mean and variance of the data of the fast Fourier transform, the largest value among the values higher than that can be defined as the tremor frequency (TF). The tremor intensity (TM) is extracted through the Hulbert transform, and data in the real domain can be expanded to data in the complex domain. That is, the envelope of the tremor element may be calculated, and the average of the envelope may be defined as a tremor magnitude (TM).

The spiral drawing test unit measures the test end time (Time), extracts tremor-free motility information through low-pass filtering, and calculates an error value (ED) ( S560 ). The error value (ED) extracts only the patient's motility information, so you can know how much you trembled from the baseline.

The spiral drawing inspection unit calculates the progressed velocity (Velocity) of completing the examination of the patient (S570). The velocity may be an average velocity during inspection.

The spiral drawing inspection unit stores tremor frequency (TF), tremor intensity (TM), time (Time), error value (ED), and velocity (Velocity) in the storage unit. The velocity may be an average velocity during inspection. Objective quantification may be possible by the above five measurement criteria.

6 and 7 are graphs for explaining a spiral drawing inspection method according to an embodiment of the present invention.

Referring to FIGS. 6 and 7 , when only a frequency domain of 3 Hz or less is calculated from the raw data of FIG. 6A through the low-pass filter (LPF), only the motion component can be extracted (FIG. 6B) . In addition, if the range of 3~15Hz is calculated as data using a bandpass filter (BPF) and then expressed as a one-dimensional graph, the Tremor Component is extracted and converted into one-dimensional data through the principal component analysis method (PCA). dimensionality can be reduced. In the case of the user without tremor (Good), it can be seen that the intensity and frequency of tremors are much smaller than those with tremors. Also, it can be seen that the intensity of the tremor increases in proportion to the tremor of the user ( FIG. 6C ).

For a user without tremor, the tremor intensity (TM) does not exceed 0.1 cm, and the tremor frequency (TF) is calculated as 0. In addition, since the error value (ED) is a value of 0.4 cm or less, it can be confirmed numerically that a spiral is drawn similarly to the reference line. In the case of a user with a slight tremor, it can be seen that the tremor intensity (TM) and tremor frequency (TF) are larger than those without tremor, but do not show much difference. In the case of the error value (ED), a larger value was derived than that of the user with a large tremor. This can be interpreted as the tremor and motor components are not completely independent. In the case of a user with a large tremor, the tremor intensity (TM) is 10 times greater than that of the case without tremor, and the tremor frequency (TF) is also distributed between 4-5 Hz on average, so it can be seen that a lot of tremors are detected. When the significance probability was calculated between each reference value, in the case of tremor frequency (TM), there was a significant difference between each result value according to the presence or absence of tremor. The tremor frequency (TF) showed a clear difference when comparing the data of users with large tremors with the data with and without tremor. The error value (ED) showed a significant difference when the data of users without tremors were compared with those with tremors.

Although the present invention has been described with reference to the embodiment shown in the drawings, which is merely exemplary, those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Accordingly, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

10; quantification system 100; 2nd device
110; sensor unit 120; SVM calculator
130; communication unit 200; 1st device
210; display unit 220; questionnaire
230; spiral drawing inspection unit 240; control
250; motility quantification unit 260; communication department
270; storage

Claims (5)

In the quantification method for diagnosing dyskinesias,
storing, by the first device, a detailed score and a summed score for each question by performing a questionnaire on the patient;
performing, by the first device, a spiral drawing test on the patient; and
a first device receiving SVM data from a second device attached to the patient's body, and quantifying the mobility based on the SVM data;
The steps to quantify motility are:
displaying at least one of the exercise test items on a display unit of the first device;
Measuring the movement of the patient by the sensor unit of the second device;
generating SVM data by calculating, by an SVM calculator of a second device, a signal vector magnitude value;
transmitting, by the communication unit of the second device, the SVM data to the first device;
Numericalizing the patient's mobility based on the received SVM data by the mobility quantifier of the first device,
The step of the first device performing the spiral drawing test on the patient,
extracting input data based on the spiral drawn by the patient on the display unit by the spiral drawing inspection unit of the first device;
extracting tremor information through band-pass filtering based on the extracted input data by the spiral drawing inspection unit;
converting the input data into one-dimensional data by the spiral drawing inspection unit through principal component analysis; and
Comprising the step of extracting the tremor frequency (TM) and tremor intensity (TM) based on the spiral drawing inspection unit input data,
For the tremor frequency, after obtaining the average and standard deviation of power in the 10 to 25 Hz band, if there is a frequency higher than the sum of the average plus 70 times the standard deviation, the frequency value is determined as the tremor frequency,
The tremor intensity is extracted through the Hulbert transform, and the data in the real domain is extended to the data in the complex domain. delete delete The method of claim 1,
A quantification method for diagnosing abnormal movement symptoms, further comprising: a spiral drawing test unit measuring an examination end time, and calculating an error value (ED) by extracting tremor-free motility information through low-pass filtering. delete

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