CN110729056A - Remote intelligent epileptic seizure monitoring system based on distributed pressure sensor - Google Patents
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Abstract
The utility model provides a remote epileptic seizure intelligent monitoring system based on distributed pressure sensors, which comprises a server end and a client end which are mutually communicated and connected, wherein the server end comprises a signal acquisition device and a first data terminal, the signal acquisition device comprises a substrate and a plurality of pressure sensors which are arranged on the substrate in an array manner, the pressure sensors are used for transmitting pressure state data to the first data terminal in real time, a second data terminal in the client end constructs a matrix according to the arrangement manner of the pressure sensors on the substrate, and a stress distribution diagram is drawn to monitor the posture and the stress distribution state of a patient in real time, thereby realizing the real-time remote monitoring of epileptic seizure; according to the method, the pressure signal replaces an EEG signal, remote intelligent epileptic seizure monitoring is realized based on the increasing and checking operations of the distributed pressure sensor, the data driving model and the database, potential abnormity is alarmed, and misdiagnosis or missed detection is avoided as far as possible while the working efficiency of medical staff is improved.
Description
Technical Field
The disclosure relates to the technical field of epileptic seizure monitoring, in particular to a remote epileptic seizure intelligent monitoring system based on a distributed pressure sensor.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Intelligent monitoring of epileptic seizures is a key technology for diagnosing epileptic seizures in clinical medicine, and the results directly influence the timely diagnosis and subsequent treatment of epileptic patients.
The inventor of the present disclosure finds that the current monitoring of epilepsy in clinical medicine relies on the visual observation and subjective decision of medical staff on electroencephalogram (EEG), which brings about the following limitations: (1) the monitoring data of the electroencephalogram signal for a long time is huge, medical workers need to traverse all electroencephalogram records through naked eyes when searching for abnormal changes of the electroencephalogram signal manually, so that the medical workers need to spend a great deal of time and energy to complete the process, great detection delay is inevitably caused, and the clinical requirement of real-time detection of changes of the electroencephalogram signal cannot be met; (2) experience decisions of different medical staff have certain subjective differences, and objective and unified decision criteria are lacked, so that detection results have differences; (3) the EEG signals used for diagnosis have the characteristics of high dimension and low signal-to-noise ratio, and the EEG signals are non-stationary random signals without ergodicity, which bring great difficulties in signal processing, modeling, analysis and disease diagnosis.
Disclosure of Invention
In order to solve the defects of the prior art, the remote intelligent epileptic seizure monitoring system based on the distributed pressure sensor is provided, pressure signals are used for replacing EEG signals, remote intelligent epileptic seizure monitoring is achieved based on the distributed pressure sensor, a data driving model and an increasing and checking operation of a database, potential abnormity is alarmed, and misdiagnosis or missed detection is avoided as far as possible while the working efficiency of medical staff is improved.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a remote intelligent epileptic seizure monitoring system based on distributed pressure sensors comprises at least one server side and at least one client side, wherein the server side comprises a signal acquisition device and a first data terminal, the signal acquisition device comprises a substrate and a plurality of pressure sensors which are arranged on the substrate in an array mode, and the pressure sensors are used for transmitting pressure state data to the first data terminal in real time;
the client is internally provided with a second data terminal in communication connection with the first data terminal, the second data terminal calls pressure state data in the first data terminal in real time, a matrix is constructed according to the arrangement mode of the pressure sensors on the substrate, so that the positions of the sensors and the elements of the matrix form a one-to-one correspondence relationship, a stress distribution diagram is drawn on the basis of the constructed matrix to monitor the posture and the stress distribution state of a patient in real time, and the real-time remote monitoring of epileptic seizure is realized.
As some possible implementation manners, the first data terminal serializes the collected pressure state data into a binary format and inserts the binary format into a relational database, the second data terminal cyclically reads the latest recorded data in the database and deserializes the latest recorded data into a numerical type, and the deserialized data is configured on the matrix-type stress distribution diagram in real time
By way of further limitation, the first data terminal takes time with 1/100 resolution as a main key, serializes the acquired pressure state data into a binary format and inserts into the relational database MySQL.
As some possible implementation manners, when the second data terminal performs drawing of the stress map, each element of the matrix is mapped to a different color block according to the magnitude of the value of the element, and the color block is refreshed at a frequency of 0.01 s.
As some possible implementation manners, the substrate is a square substrate made of a PVC material, N × N pressure sensors are uniformly distributed on the substrate, the pressure sensors convert pressure conversion into resistance change, convert the resistance change into voltage change through a voltage conversion module, and perform synchronous acquisition of voltage change data through a multi-channel data acquisition card in a multi-thread manner and transmit the voltage change data to the first data terminal in real time.
As some possible implementation manners, the second data terminal establishes a data description model or a prediction model for the acquired multidimensional signal, performs comparison on the established mathematical model and the actually observed signal at the current moment to complete quantification of the abnormal score, adopts a hypothesis testing method based on gaussian distribution to make a prejudgment on the state of the patient at the current moment and send alarm information for the abnormality, and analyzes the state of the patient according to the alarm information and the stress distribution diagram.
As a further limitation, the multidimensional data is fused from the data layer or the characteristic layer by using an information fusion method, and then a data description model or a prediction model is established.
As a further limitation, for the data description model, the distance between the model observing the data in real time and the model at the previous moment is used as the abnormal score of the current moment.
As a further limitation, for the data prediction model, an absolute residual between a current time prediction value output by the model and an actual observed value is used as an abnormal score at the current time.
As a further limitation, a hypothesis test based on gaussian distribution is used to determine the state of the patient at the current time, specifically:
wherein H0Indicating that there may be an abnormality in the patient's condition at that time and a warning is required, H1Indicating that the patient is in a stable state at the moment,
score a sequence of { s } for historical anomalies1,s2,…,st-1Mean of samples of }, σt-1Is a sequence s1,s2,…,st-1Standard deviation of.
As a further limitation, the missed detection rate is reduced by reducing the confidence interval range;
further, n is 3.
Compared with the prior art, the beneficial effect of this disclosure is:
1. the system disclosed by the disclosure replaces EEG signals with pressure signals, avoids the difficulties in processing, modeling and analyzing the high-dimensional and low signal-to-noise ratio signals, and greatly improves the data processing speed and the monitoring effect.
2. The system realizes remote intelligent monitoring of epileptic seizure based on the increasing and checking operations of the distributed pressure sensor, the data driving model and the database, gives an alarm to potential abnormality, and avoids misdiagnosis or missed detection as far as possible while improving the working efficiency of medical staff.
3. The system disclosed by the disclosure is used for comparing the established mathematical model with the signal actually observed at the current moment to finish the quantification of the abnormal score, pre-judging the state of the patient at the current moment and giving an alarm for the abnormality by adopting a hypothesis testing method based on Gaussian distribution, and finally evaluating the state of the patient by combining a stress distribution diagram, so that the error caused by visual observation only of medical staff is avoided, and the disease monitoring effect on epileptics is greatly improved.
4. According to the first data terminal, the time with 1/100 resolution is used as a main key, the collected pressure state data are serialized into a binary format and are inserted into a relational database MySQL, and the stress distribution diagram can be updated in real time as the resolution is smaller than the persistence time 1/24 s; meanwhile, each element of the matrix is mapped to different colors according to the value of the element to finish the drawing of the stress graph, so that medical personnel can visually check the stress distribution condition and the change condition in real time.
Drawings
Fig. 1 is a schematic structural diagram of a remote intelligent epileptic seizure monitoring system based on a distributed pressure sensor according to embodiment 1 of the present disclosure.
Fig. 2 is a schematic structural diagram of a signal acquisition device provided in embodiment 1 of the present disclosure.
Fig. 3 is a schematic diagram of stress distribution provided in embodiment 1 of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present disclosure provides a remote intelligent epileptic seizure monitoring system based on a distributed pressure sensor, which includes a server and a client, where the server includes a signal acquisition device and a first data terminal;
the signal acquisition device is signal acquisition equipment based on a distributed film pressure sensor, as shown in fig. 2, the right side is provided with the pressure sensor which is arranged by taking PVC as a substrate, the sensors are arranged in a circular area, and wiring is carried out in a grid area; the left side is the information acquisition process of a single sensor, the resistance of the sensor changes along with the change of the pressure acting on the sensor, then the change of the resistance is converted into the change of the voltage through the voltage conversion module and is input into a certain channel in the data acquisition card, and finally the acquired data is transmitted to a data terminal in a ward through USB data.
The collected pressure state data are serialized into a binary format by taking the time with the resolution of 1/100 (less than the persistence time of vision 1/24 seconds) as a main key in a first data terminal in a server side (a ward) and are inserted into a relational database MySQL (MySQL) through Insert operation, and a second data terminal in a client side (a monitoring station) reads the latest recorded data in the database through a Select operation cycle to complete subsequent remote analysis.
And the second data terminal deserializes the latest data read from the database into numerical types and reconstructs the numerical types into an N multiplied by N matrix according to the arrangement mode of the sensors on the substrate, so that the positions of the sensors and the elements of the matrix form a one-to-one corresponding relationship.
And then, on one hand, a stress distribution diagram is drawn on the basis of the established matrix to monitor the posture and the stress distribution state of the patient in real time, and on the other hand, data description or predictive modeling can be directly constructed on the multi-dimensional signals, or the model establishment is carried out after the multi-dimensional pressure state data is fused by adopting an information fusion method.
The second data terminal compares the established mathematical model with the actual observed signal at the current moment to finish the quantification of the abnormal score, and adopts a hypothesis testing method based on Gaussian distribution to pre-judge the patient state at the current moment and send an alarm for the abnormality, and medical personnel can make final evaluation on the patient state by combining a stress distribution diagram, and the specific steps are as follows:
(1) real-time display of stress profiles: calibrating the corresponding relation between each sensor and the data acquisition card channel, establishing the read data into a matrix corresponding to the sensor positions one by one, respectively mapping each element of the matrix to different colors such as blue, green, yellow and the like according to the value of each element of the matrix to finish the drawing of a stress diagram, and refreshing the stress diagram at the frequency of 0.01s, wherein the stress distribution diagram is shown in figure 3.
(2) Modeling data: model G capable of directly describing multidimensional signals acquired at t momenttOr establishing a data prediction model f, namely x't＝f(xt-1) Wherein x is the observed value and x' is the predicted value.
Or an information fusion method can be adopted to fuse the multidimensional data from the data layer or the characteristic layer and then establish a data description model or a prediction model.
(3) Abnormal score quantification: for the data description model, a model G for observing data in real time can be usedtAnd the last time model Gt-1The distance between them is taken as the abnormal score s at time ttI.e. st＝|Gt-Gt-1|dWherein | · non-dIs a distance metric.
For the data prediction model, the absolute residual error between the predicted value at the time t and the actual observed value output by the model can be used as the abnormal score s at the momenttI.e. st＝|x’t-xt|＝|f(xt-1)-xt|；
(4) Abnormity early warning: a hypothesis test based on gaussian distribution is used to determine the state of the patient at the current time:
wherein H0Indicating that there may be an abnormality in the patient's condition at that time and a warning is required, H1Indicating that the patient is in a stable state at the moment,
score a sequence of { s } for historical anomalies1,s2,…,st-1Mean of samples of }, σt-1Is a sequence of
{s1,s2,…,st-1Standard deviation of. In addition, in order to reduce the detection of the leak as much as possible, the confidence interval range n × σ (n > 0, where n is set to 3 by default) may be further narrowed.
Based on the time points provided by the monitoring system, which may have abnormalities, the medical staff can make a final assessment of the status of the epileptic patient by integrating the stress profile, clinical experience, and expertise.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. The remote intelligent epileptic seizure monitoring system based on the distributed pressure sensors is characterized by comprising at least one server side and at least one client side, wherein the server side comprises a signal acquisition device and a first data terminal, the signal acquisition device comprises a substrate and a plurality of pressure sensors which are arranged on the substrate in an array mode, and the pressure sensors are used for transmitting pressure state data to the first data terminal in real time;
the client is internally provided with a second data terminal in communication connection with the first data terminal, the second data terminal calls pressure state data in the first data terminal in real time, a matrix is constructed according to the arrangement mode of the pressure sensors on the substrate, so that the positions of the sensors and the elements of the matrix form a one-to-one correspondence relationship, a stress distribution diagram is drawn on the basis of the constructed matrix to monitor the posture and the stress distribution state of a patient in real time, and the real-time remote monitoring of epileptic seizure is realized.
2. The distributed pressure sensor-based remote seizure monitoring system of claim 1 wherein the first data terminal serializes the collected pressure status data into a binary format and inserts into a relational database, and the second data terminal cyclically reads the most recently recorded data in the database and deserializes into a numerical type and distributes the deserialized data onto the matrixed stress profile in real time.
3. The distributed pressure sensor-based remote seizure monitoring system of claim 2 wherein the first data terminal is keyed to time at 1/100 resolution, serializes the collected pressure status data into binary format and inserts into relational database MySQL.
4. The distributed pressure sensor-based remote epileptic seizure monitoring system of claim 1 wherein the second data terminal, when performing the mapping of the stress map, maps each element of the matrix to a different color block according to the magnitude of the value thereof, and refreshes the color block at a frequency of 0.01 s.
5. The distributed pressure sensor-based remote epileptic seizure monitoring system according to claim 1, wherein the substrate is a square substrate made of PVC material, N x N pressure sensors are uniformly distributed on the substrate, the pressure sensors convert pressure transformation into resistance change, voltage transformation is performed through a voltage transformation module, voltage change data are synchronously acquired through a multi-channel data acquisition card in a multi-thread mode and are transmitted to the first data terminal in real time.
6. The distributed pressure sensor-based remote seizure monitoring system of claim 1 wherein the second data terminal constructs a data description model or a prediction model for the acquired multidimensional signals, quantifies the abnormality score based on the established mathematical model compared to the actual observed signals at the current time, and uses a gaussian distribution-based hypothesis testing method to make a pre-determination on the patient state at the current time and send an alarm message for the abnormality, and analyzes the patient state based on the alarm message and the stress distribution map.
7. The distributed pressure sensor-based remote seizure monitoring system of claim 6 wherein the data description model or the prediction model is established after the multi-dimensional data is fused from the data layer or the feature layer by using an information fusion method.
8. The distributed pressure sensor-based remote seizure monitoring system of claim 6 wherein, for the data description model, the distance between the model observing data in real time and the model at the previous moment is taken as the anomaly score at the current moment;
alternatively, the first and second electrodes may be,
and regarding the data prediction model, taking the absolute residual error between the current time predicted value and the actual observed value output by the model as the abnormal score of the current time.
9. The distributed pressure sensor-based remote seizure monitoring system of claim 8 wherein a gaussian distribution based hypothesis test is used to determine the patient's state at the current time, specifically:
H0:
H1:
wherein H0Indicating that there may be an abnormality in the patient's condition at that time and a warning is required, H1Indicating that the patient is in a stable state at the moment,
score a sequence of { s } for historical anomalies1,s2,…,st-1Mean of samples of }, σt-1Is a sequence s1,s2,…,st-1Standard deviation of.
10. The distributed pressure sensor-based remote seizure monitoring system of claim 9 wherein the miss rate is reduced by reducing the confidence interval range;
further, n is 3.
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