CN110729056B - Remote epileptic seizure intelligent monitoring system based on distributed pressure sensor - Google Patents
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Abstract
The invention provides a remote epileptic seizure intelligent monitoring system based on a distributed pressure sensor, which comprises a server end and a client end which are in communication connection with each other, 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 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 mode 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 so as to realize the real-time remote monitoring of epileptic seizure; the EEG signal is replaced by the pressure signal, remote intelligent epileptic seizure monitoring is realized based on the distributed pressure sensor, the data driving model and the investigation operation of the database, potential abnormality is alarmed, and misdiagnosis or missed detection is avoided as much 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 epileptic seizure monitoring is a key technology for epileptic diagnosis in clinical medicine, and the result directly affects the timely diagnosis and subsequent treatment of epileptic patients.
The inventors of the present disclosure found that current monitoring of epilepsy in clinical medicine relies on macroscopic and subjective decisions of medical personnel on electroencephalograms (electroencephalography, EEG), which would bring about the following limitations: (1) The monitoring data of the long-time electroencephalogram is huge in record, and medical workers need to traverse all electroencephalogram records by naked eyes through manually searching for abnormal changes of the electroencephalogram, so that the medical workers are required to spend a great deal of time and effort to finish the process, and extremely large detection delay is necessarily caused, so that the clinical requirements of real-time detection of the changes of the electroencephalogram cannot be met; (2) The experience decision of different medical staff has a certain subjective difference and lacks objective and unified decision criteria, which causes the difference of detection results; (3) EEG signals for diagnosis have the characteristics of high dimension, low signal-to-noise ratio, and EEG signals are non-stationary random signals without anaplerosis, which present great difficulties in signal processing, modeling, analysis, and diagnosis of conditions.
Disclosure of Invention
In order to solve the defects of the prior art, the disclosure provides a remote epileptic seizure intelligent monitoring system based on a distributed pressure sensor, which replaces EEG signals with pressure signals, realizes remote epileptic seizure intelligent monitoring based on the distributed pressure sensor, a data driving model and the investigation operation of a database, alarms potential abnormality, and avoids misdiagnosis or missed detection as much as possible while improving the working efficiency of medical staff.
In order to achieve the above purpose, the present disclosure adopts the following technical scheme:
The remote epileptic seizure intelligent monitoring system based on the distributed pressure sensor comprises at least one server end and at least one client end, 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 arranged on the substrate in an array manner, and the pressure sensors are used for transmitting pressure state data to the first data terminal in real time;
The client is provided with a second data terminal which is in communication connection with the first data terminal, the second data terminal invokes pressure state data in the first data terminal in real time, and a matrix is constructed according to the arrangement mode of the pressure sensors on the substrate, so that the sensor positions and matrix elements form a one-to-one correspondence, and a stress distribution diagram is drawn on the basis of the constructed matrix to monitor the posture and stress distribution state of a patient in real time, thereby realizing real-time remote monitoring of epileptic seizures.
As some possible implementation manners, the first data terminal sequences the collected pressure state data into a binary format and inserts the binary format into a relational database, and the second data terminal circularly reads the data which is newly recorded in the database and inversely sequences the data into a numerical value type, and configures the inversely-sequenced data to a stress distribution diagram of a matrix in real time
As a further limitation, the first data terminal takes 1/100 resolution time as a primary key, sequences the collected pressure state data into a binary format and inserts the binary format into a relational database MySQL.
As some possible implementations, when the second data terminal draws the stress map, each element of the matrix is mapped into a different color block according to the magnitude of the value of the element, and refreshing is performed at a frequency of 0.01 s.
As some possible implementation manners, the substrate is a square substrate made of PVC material, n×n pressure sensors are uniformly distributed on the substrate, the pressure sensors convert pressure conversion into resistance change, the resistance change is converted into voltage change through a voltage conversion module, and synchronous acquisition of voltage change data is performed in a multithreading manner through a multichannel data acquisition card and transmitted to the first data terminal in real time.
As some possible implementation manners, the second data terminal builds a data description model or a prediction model on the acquired multidimensional signals, performs comparison with the signals actually observed at the current moment based on the built mathematical model to complete quantification of the anomaly score, and adopts a hypothesis testing method based on gaussian distribution to pre-judge the current moment patient state and send out alarm information on anomalies, and analyzes the patient state according to the alarm information and the stress distribution diagram.
As a further limitation, the data description model or the prediction model is built after the multidimensional data are fused from the data layer or the feature layer by adopting an information fusion method.
As a further limitation, for the data description model, the distance between the model in which the data is observed in real time and the model at the previous time is taken as the anomaly score at the current time.
As a further limitation, for the data prediction model, the absolute residual of the predicted value and the actual observed value at the current time output by the model is taken as the anomaly score at the current time.
As a further limitation, a hypothesis test based on a gaussian distribution is used to determine the state of the patient at the current moment, in particular:
wherein H 0 indicates that the abnormal state of the patient possibly exists at the moment and early warning needs to be sent, H 1 indicates that the state of the patient is stable at the moment, For the sample mean of the historical anomaly score sequence { s 1,s2,…,st-1 }, σ t-1 is the standard deviation of the sequence { s 1,s2,…,st-1 }.
As a further limitation, the omission factor is reduced by reducing the confidence interval range;
Further, n is 3.
Compared with the prior art, the beneficial effects of the present disclosure are:
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 disclosed by the disclosure realizes remote intelligent epileptic seizure monitoring based on the distributed pressure sensor, the data driving model and the additional checking operation of the database, alarms potential abnormality, and avoids misdiagnosis or missed detection as much as possible while improving the working efficiency of medical staff.
3. The system disclosed by the disclosure is used for completing quantification of the abnormal score based on comparison of the established mathematical model and the actual observed signal at the current moment, adopting a Gaussian distribution-based hypothesis test method to pre-judge the state of the patient at the current moment and give an alarm on the abnormality, and finally evaluating the state of the patient by combining a stress distribution diagram, so that errors caused by visual observation of medical staff only can be avoided, and the effect of monitoring the occurrence of epileptic patients is greatly improved.
4. The first data terminal takes time with 1/100 resolution as a main key, sequences the collected pressure state data into a binary format and inserts the binary format into a relational database MySQL, and can achieve real-time updating of a stress distribution diagram because the resolution is smaller than the persistence time of vision by 1/24 s; meanwhile, each element of the matrix is mapped to different colors according to the value of the element to complete drawing of stress diagrams, so that medical staff can intuitively and real-time check stress distribution conditions and change conditions.
Drawings
Fig. 1 is a schematic structural diagram of a remote seizure intelligent 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 according to 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 illustrative and is intended to provide further explanation of the present 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 exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
Example 1:
As shown in fig. 1, embodiment 1 of the present disclosure provides a remote epileptic seizure intelligent monitoring system based on a distributed pressure sensor, which includes a server side and a client side, wherein the server side 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 a pressure sensor which is arranged by taking PVC as a substrate, a discharge sensor is 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 is changed due to the fact that the pressure acting on the sensor is changed, then the change of the resistance is converted into the change of voltage through a voltage conversion module and is input into a certain channel in a data acquisition card, and finally acquired data are transmitted to a data terminal in a ward through USB data.
Taking time with resolution of 1/100 (less than the persistence time of vision of 1/24 seconds) as a main key in a first data terminal in a server side (ward), serializing the acquired pressure state data into a binary format and inserting the binary format into a relational database MySQL through an Insert operation, and circularly reading the latest recorded data in the database through a Select operation by a second data terminal in a client side (monitoring station) to complete subsequent remote analysis.
The second data terminal inversely sequences the latest data read from the database into a numerical value type, and reconstructs the data 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 matrix elements form a one-to-one correspondence.
Then, on one hand, drawing a stress distribution diagram based on the established matrix to monitor the posture and stress distribution state of the patient in real time, and on the other hand, directly constructing data description or predictive modeling on the multidimensional signals, or firstly fusing multidimensional pressure state data by adopting an information fusion method and then establishing a model.
The second data terminal is used for comparing the established mathematical model with the actual observed signal at the current moment to complete the quantification of the abnormal score, and adopting a Gaussian distribution-based hypothesis testing method to pre-judge the state of the patient at the current moment and give an alarm on the abnormality, and medical staff can combine the stress distribution diagram to finally evaluate the state of the patient, and the method comprises the following specific steps:
(1) Real-time display of stress distribution diagram: the corresponding relation between each sensor and the data acquisition card channel is calibrated, the read data are constructed into a matrix corresponding to the sensor positions one by one, each element of the matrix is mapped to different colors such as blue, green and yellow according to the values of the matrix, the stress diagram is drawn, refreshing is carried out at the frequency of 0.01s, and the stress distribution diagram is shown in figure 3.
(2) Modeling data: the data description model G t can be directly constructed on the multidimensional signal collected at the time t, or a data prediction model f, i.e., x 't＝f(xt-1, is established, wherein x is an observed value and x' is a predicted value.
The data description model or the prediction model can be built after the multidimensional data are fused from the data layer or the feature layer by adopting an information fusion method.
(3) Anomaly score quantification: for the data description model, the distance between the model G t in which data is observed in real time and the model G t-1 at the previous time may be taken as the anomaly score s t at the time t, that is, s t＝|Gt-Gt-1|d, where |·| d is a distance metric index.
For the data prediction model, the absolute residual error of the predicted value at the time t and the actual observed value output by the model can be used as an anomaly score s t at the moment, namely s t＝|x't-xt|＝|f(xt-1)-xt I;
(4) Abnormality early warning: using a hypothesis test based on a Gaussian distribution to determine the state of the patient at the current time:
wherein H 0 indicates that an abnormality may exist in the patient's state at this point in time, an early warning is required, H 1 indicates that the patient's state is stable at this point in time, Is the sample mean of the historical anomaly score sequence { s 1,s2,…,st-1 }, σ t-1 is the sequence
Standard deviation of { s 1,s2,…,st-1 }. In addition, in order to reduce the detection omission as much as possible, the confidence interval range n×σ (n > 0, where n=3 is set by default) may be further narrowed.
Based on the points in time provided by the monitoring system at which anomalies may exist, the healthcare worker may integrate stress profiles, clinical experience, and expertise to make a final assessment of status of an epileptic patient.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (3)
1. The remote epileptic seizure intelligent monitoring system based on the distributed pressure sensor 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 arranged on the substrate in an array manner, and the pressure sensors are used for transmitting pressure state data to the first data terminal in real time;
The first data terminal takes time with resolution of 1/100 seconds and less than the persistence time of vision of 1/24 seconds as a main key, the collected pressure state data are serialized into a binary format and are inserted into a relational database MySQL through Insert operation, a second data terminal which is in communication connection with the first data terminal is arranged in the client, the second data terminal circularly reads the latest recorded data in the database through Select operation and is reversely serialized into a numerical value type, and the reversely serialized data are configured on a stress distribution diagram of a matrix in real time, so that the real-time update of the stress distribution diagram is achieved;
When the second data terminal draws a stress diagram, mapping each element of the matrix into different color blocks according to the numerical value of each element, and refreshing the color blocks at the frequency of 0.01 s;
The second data terminal is used for acquiring pressure state data in the first data terminal in real time, constructing a matrix according to the arrangement mode of the pressure sensors on the substrate, enabling the positions of the sensors to form a one-to-one correspondence with matrix elements, drawing a stress distribution diagram based on the constructed matrix to monitor the posture and stress distribution state of a patient in real time, directly constructing a data driving model on multidimensional signals, or firstly fusing multidimensional pressure state data by adopting an information fusion method, and then establishing a data driving model, wherein the data driving model is a data description model or a data prediction model; the real-time remote monitoring of epileptic seizures is realized based on the distributed pressure sensor, the data driving model and the investigation operation of the database;
The second data terminal builds a data driving model for the acquired multidimensional signals, performs comparison with the signals actually observed at the current moment based on the built data driving model to complete quantification of abnormal scores, and adopts a hypothesis testing method based on Gaussian distribution to pre-judge the current moment patient state and send out alarm information for the abnormality, and analyzes the patient state according to the alarm information and the stress distribution diagram;
For the data description model, taking the distance between the model with the data observed in real time and the model at the last moment as the anomaly score at the current moment; for a data prediction model, taking absolute residual errors of a predicted value and an actual observed value of the current moment output by the model as an abnormal score of the current moment; the state of the patient at the current moment is judged by adopting hypothesis test based on Gaussian distribution, which is specifically as follows:
wherein H 0 indicates that the abnormal state of the patient possibly exists at the moment and early warning needs to be sent, H 1 indicates that the state of the patient is stable at the moment, For historical anomaly score sequence/>Sample mean value of/>For sequence/>Standard deviation of/>Expressed as standard deviation of the sequence { s 1,s2,...,st }, s t is the anomaly score at time t, n=3.
2. The remote epileptic seizure intelligent monitoring system based on the distributed pressure sensor as set forth in claim 1, wherein the substrate is a square substrate made of 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, synchronously collect voltage change data in a multithreading manner through a multichannel data collection card and transmit the data to the first data terminal in real time.
3. The intelligent remote epileptic seizure monitoring system based on a distributed pressure sensor as claimed in claim 1, wherein the data description model or the data prediction model is built after the multidimensional data are fused from the data layer or the feature layer by adopting an information fusion method.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103728056A (en) * | 2012-10-12 | 2014-04-16 | 美思科技股份有限公司 | Pressure-sensing device with biplanar sensor array |
| CN205581901U (en) * | 2016-03-29 | 2016-09-14 | 华东师范大学 | Epileptic attack monitored control system based on range data |
| CN109247935A (en) * | 2018-10-31 | 2019-01-22 | 山东大学 | A kind of During Night Time parahypnosis condition monitoring system and method |
| CN110196131A (en) * | 2019-05-30 | 2019-09-03 | 中国铁道科学研究院集团有限公司 | Stress test method, system and device |
| CN110378392A (en) * | 2019-06-26 | 2019-10-25 | 华东师范大学 | A kind of indoor geriatric state's monitoring method based on LSTM-AE |
| CN110403573A (en) * | 2019-07-19 | 2019-11-05 | 深圳大学 | The automatic early warning method and system at position are easily sent out in a kind of human body pressure sore |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6975962B2 (en) * | 2001-06-11 | 2005-12-13 | Smartsignal Corporation | Residual signal alert generation for condition monitoring using approximated SPRT distribution |
| US8011041B2 (en) * | 2007-09-19 | 2011-09-06 | Persimmon Scientific, Inc. | Devices for prevention of pressure ulcers |
| US20180325409A1 (en) * | 2013-12-12 | 2018-11-15 | Brain Sentinel, Inc. | Data-Integrated Interface and Methods of Reviewing Electromyography and Audio Data |
-
2019
- 2019-11-21 CN CN201911148336.1A patent/CN110729056B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103728056A (en) * | 2012-10-12 | 2014-04-16 | 美思科技股份有限公司 | Pressure-sensing device with biplanar sensor array |
| CN205581901U (en) * | 2016-03-29 | 2016-09-14 | 华东师范大学 | Epileptic attack monitored control system based on range data |
| CN109247935A (en) * | 2018-10-31 | 2019-01-22 | 山东大学 | A kind of During Night Time parahypnosis condition monitoring system and method |
| CN110196131A (en) * | 2019-05-30 | 2019-09-03 | 中国铁道科学研究院集团有限公司 | Stress test method, system and device |
| CN110378392A (en) * | 2019-06-26 | 2019-10-25 | 华东师范大学 | A kind of indoor geriatric state's monitoring method based on LSTM-AE |
| CN110403573A (en) * | 2019-07-19 | 2019-11-05 | 深圳大学 | The automatic early warning method and system at position are easily sent out in a kind of human body pressure sore |
Non-Patent Citations (2)
| Title |
|---|
| 信息融合中多传感器测量一致性检验及其应用;王江萍 等;石油机械;19971130;第25卷(第11期);25-28页 * |
| 基于惯性传感器的老年人姿态监测装置设计;吴聪 等;生物医学工程研究;20180219;第37卷(第2期);210-214页 * |
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