WO2022166683A1

WO2022166683A1 - Method and device for automatically detecting epileptic seizure by implantable closed-loop system on basis of area algorithm

Info

Publication number: WO2022166683A1
Application number: PCT/CN2022/073769
Authority: WO
Inventors: 曹鹏; 陈新蕾; 郑开
Original assignee: 杭州诺为医疗技术有限公司
Priority date: 2021-02-05
Filing date: 2022-01-25
Publication date: 2022-08-11
Also published as: CN112972891A

Abstract

One or more embodiments of the present description disclose a method and device for automatically detecting an epileptic seizure by an implantable closed-loop system on the basis of an area algorithm. The method comprises: acquiring and detecting bioelectrical signals in real time by means of a hardware algorithm; and then comparing the ratio of the average area of detection windows to the average area of background windows in each detection period with a preset threshold, to determine whether there is an epileptic seizure, and trigger stimulation treatment by means of an identification signal. Thus, low-power-consumption real-time detection is implemented, and the integration degree and the detection accuracy and convenience are improved.

Description

Method and device for automatic detection of epilepsy in implantable closed-loop system based on area algorithm

technical field

This document relates to the technical field of medical devices, in particular to a method and device for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm.

Background technique

Epilepsy, commonly known as "epilepsy", is a chronic neurological disease caused by abnormal discharges formed by the highly synchronized activity of neurons. The pathogenesis of epilepsy is complex, with many pathogenic factors, great harm to health and difficult to cure. Therefore, the study of epilepsy detection technology is an important topic in neuromedicine.

At present, the acquisition of epilepsy EEG is mostly through the EEG machine, which analyzes and processes the collected EEG data offline and determines the moment of epilepsy. Responses. The method of transplanting the software algorithm program to the microcontroller chip MCU to detect epilepsy can achieve real-time performance, but it needs to run the MCU all the time, and consumes a lot of power; at the same time, too complex algorithms are difficult to apply to implantable devices, and the integration is not high.

In conclusion, there is an urgent need to find a new epilepsy detection scheme.

SUMMARY OF THE INVENTION

The purpose of one or more embodiments of this specification is to provide a method and device for an implantable closed-loop system to automatically detect epilepsy based on an area algorithm, and to flexibly configure an integrated implantable device with a hardware algorithm to perform low-power real-time detection of epilepsy, Improve integration and detection convenience.

To solve the above technical problems, one or more embodiments of the present specification are implemented as follows:

In the first aspect, a method for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm is proposed, which is applied to an epilepsy detection device composed of a chip, and the method includes:

The detection parameters are configured through the SPI serial peripheral interface, wherein the detection parameters at least include: the detection window size, the interval window size and the background window size in one detection period;

Collect bioelectrical signals in real time from the organism implanted in the detection chip, and store them in the memory;

Calculate the absolute value of the difference between the amplitude of the bioelectrical signal and the DC offset respectively according to the time sequence, and send it to the accumulator;

Based on the value in the accumulator, respectively determine the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window;

Determine whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold;

When the judgment result is greater than, output an identification signal representing epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.

In the second aspect, an implantable closed-loop system for automatically detecting epilepsy based on an area algorithm is proposed, including:

The configuration module configures detection parameters through the SPI serial peripheral interface, wherein the detection parameters at least include: detection window size, interval window size and background window size in one detection period;

The acquisition and storage module collects bioelectrical signals in real time from the organism implanted in the detection chip, and stores them in the memory;

The calculation module calculates the absolute value of the difference between the amplitude of the bioelectrical signal and the DC offset respectively according to the time sequence, and sends it to the accumulator;

The determination module, based on the value in the accumulator, respectively determines the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window;

a detection module, for judging whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold;

The treatment module, when the judgment result is greater than, outputs an identification signal representing an epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.

In a third aspect, a device for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm is proposed, which at least includes: a hardware algorithm chip for executing the above method and other functional modules; wherein, the hardware algorithm chip includes: a serial peripheral interface , memory, accumulator, counter; where,

The serial peripheral interface is used to configure detection parameters and transmit bioelectrical signals; wherein, the detection parameters at least include: detection window size, interval window size and background window size in one detection cycle;

The memory is used to store the real-time acquisition of bioelectric signals from the organism implanted in the detection chip, and after calculating the absolute value of the difference between the amplitude of the bioelectric signal and the DC offset according to the counter according to the time series, into the accumulator;

After determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window respectively based on the value in the accumulator, the detection enabling terminal determines that the average area of the current detection window is the same as the total area of the current detection window. Whether the ratio of the average area of the background window is greater than a preset threshold;

When the judgment result is greater than, the serial peripheral interface outputs an identification signal representing an epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.

In a fourth aspect, an electronic device is provided, comprising:

processor; and

memory arranged to store computer-executable instructions which, when executed, cause the processor to execute:

It can be seen from the technical solutions provided by one or more embodiments of the present specification that through the above technical solutions, a hardware algorithm can be used to collect and detect bioelectrical signals in real time, and then, according to the average area of the detection window in each detection period and the difference between the background window. The comparison between the ratio between the average areas and the preset threshold value determines whether the epileptic seizure is triggered by the identification signal for stimulation treatment. Thus, real-time detection with low power consumption improves integration, detection accuracy, and convenience.

Description of drawings

In order to more clearly illustrate one or more embodiments of the present specification or the technical solutions in the prior art, the following briefly introduces the accompanying drawings required for the description of one or more embodiments or the prior art. It is obvious that , the drawings in the following description are only some embodiments described in this specification, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

FIG. 1 is a schematic diagram of steps of a method for an implantable closed-loop system to automatically detect epilepsy based on an area algorithm according to an embodiment of the present specification.

FIG. 2 is a schematic diagram of a detection period provided by an embodiment of the present specification.

FIG. 3 is a schematic structural diagram of an apparatus for automatically detecting epilepsy based on an area algorithm in an implantable closed-loop system provided by an embodiment of the present specification.

FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present specification.

FIG. 5 is a schematic structural diagram of a device for automatically detecting epilepsy based on an area algorithm in an implantable closed-loop system of the present specification.

Detailed ways

In order to make those skilled in the art better understand the technical solutions in this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings in one or more embodiments of this specification. It is apparent that the described embodiment or embodiments are only some, but not all, embodiments of this specification. Based on one or more embodiments in this specification, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

The embodiments of this specification propose a hardware algorithm for an implantable closed-loop system to automatically detect epilepsy based on an area algorithm, which belongs to the fields of neuroscience and medical devices. The hardware algorithm can be written in Verilog hardware language or other achievable hardware language. It can accurately detect whether the bioelectrical signal is abnormal in real time, and raise the Flag warning signal when the bioelectrical signal is abnormal. At this time, the detection enable signal If it is 1, the hardware algorithm suspends detection, and the hardware algorithm performs detection when the detection enable signal is 0. The specific detection standard can refer to the following content, so that the signal data of the previous period (generally set to 30s) when the abnormal signal occurs and the next period of the abnormal signal occurrence (generally can be set through the serial peripheral interface SPI protocol) 60s) signal data is sent out, and this part of the data can be saved externally for medical research. In the whole detection process, for each round of detection, the size of the detection window, background window, interval window and threshold of the algorithm can be adjusted, which has wide applicability. And the hardware algorithm can be used as an IP core, that is, an intellectual property module, which is an IC module that has been verified, can be reused, and has a certain function. It is divided into soft IP (soft IP core), solid IP (firm IP core) and hard IP (hard IP core). Soft IP uses a certain high-level language to describe the behavior of functional blocks, but does not involve any circuits and circuit elements used to realize these behaviors, and the embodiment of the present application is soft IP. The algorithm chip is made into an algorithm chip through a semiconductor process, so as to be used in various neurotherapy devices. Compared with the software algorithm running on the MCU, the power consumption is lower and the integration level is higher.

Example 1

Referring to FIG. 1, which is a schematic diagram of the method steps of an implantable closed-loop system for automatically detecting epilepsy based on an area algorithm provided by the embodiment of the present specification, it should be understood that the method is applied to an epilepsy detection device composed of a chip, and its execution body It can be an integrated circuit of a chip, or an epilepsy detection device composed of the chip; the detection method can include the following steps:

Step 102: Configure detection parameters through the SPI serial peripheral interface, wherein the detection parameters at least include: a detection window size, an interval window size, and a background window size in one detection cycle.

It should be noted that, as shown in FIG. 2 , each round of detection scheme may include multiple detection periods, and each detection period includes a background window, an interval window and a detection window. Also, the background window may be located in the preceding time period of the detection window, and the interval window may be located between the background window and the detection window. The first detection period shown in Figure 2 is from t0-t5, where t0-t2 is the background window, t2-t4 is the interval window, and t4-t5 is the detection window; the second detection period is from t1-t6 , where t1-t3 is the background window, t3-t5 is the interval window, and t5-t6 is the detection window. The subsequent third detection period and fourth detection period are similar. It can be seen from FIG. 2 that, except for the first detection period, each detection period partially overlaps with the previous detection period, that is, the bioelectrical signal in the current detection period includes part of the bioelectrical signal of the previous detection period.

Wherein, the size of the background window may be N1 times the size of the detection window, and the size of the interval window is N2 times the size of the detection window; wherein, the N1 and the N2 are both positive integers greater than or equal to 2 . It should be understood that, in the embodiments of the present specification, the values of N1 and N2 may be equal or unequal. After the size of the detection window is determined, the size of the background window and the size of the interval window can be flexibly configured according to the requirements of the current round of detection.

Step 104: Collect bioelectrical signals in real time from the organism implanted in the detection chip, and store them in the memory.

In the embodiments of the present specification, the bioelectrical signal may specifically be an electroencephalogram signal, or a deep brain electrophysiological signal, or a cerebral cortex bioelectrical signal, or a central nervous system signal, or the like.

The memory may be a SRAM SRAM with a capacity of 2kB for storing bioelectrical signals.

Step 106: Calculate the absolute value of the difference between the amplitude of the bioelectrical signal and the DC offset according to the time sequence, and send it to the accumulator.

Wherein, the accumulator may be an accumulator that accumulates the sum of the areas in each detection window. It should be understood that the ratio of the amplitude of the bioelectrical signal to the DC offset is substantially the ratio of the area covered by the amplitude of the bioelectrical signal to the area covered by the DC offset.

Step 108: Based on the value in the accumulator, determine the area sum of the current detection window and the area sum of the background window corresponding to the current detection window, respectively.

Optionally, if the current detection window belongs to the first detection cycle of the current round of detection, then when the counter counts from 1 to N1, the value in the accumulator is written into the background window register, until the end of counting to N1, The background window register stores the area sum of the background window corresponding to the current detection window; when the counter counts to N1+N2+1, the value in the accumulator is written into the detection window register, and the detection window register stores the current detection window. The area of the window and.

Further, before counting, the method further includes: taking the detection window as a basic window unit, determining that in the background window, interval window and detection window included in the first detection period, the number of basic window units is N1+N2+1 ;Correspondingly,

When the count value of the counter is not greater than N1+N2+1 and the current window sampling ends, the value of the memory is written into the buffer from low address to high address, and the counter is incremented by 1; when the count value of the counter is less than or equal to N1 and the current When the window sampling ends, the memory is sequentially accumulated and written into the background window register, and the background window register stores the area sum of the background window in the first detection period; when the count value of the counter is equal to N1+N2+1 and the current sampling window ends , the memory writes the detection window register, and the detection window register stores the area sum of the detection window in the first detection cycle.

Optionally, if the current detection window belongs to the non-first detection cycle of the current round of detection, the value of the lowest bit is read from the buffer and written into the first temporary register for temporary storage;

The buffer is shifted to the left once, the value of the memory is written to the N1+N2+1 address, and then the value of the N1+1 bit in the buffer is read to the second temporary register for temporary storage;

Write the value in the memory into the detection window register so as to reassign the area and the current detection window;

The value of the background window register minus the value of the first temporary register, plus the value of the second temporary register, is written into the background window register as the area and assignment of the current background window.

Step 110: Determine whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold.

Wherein, the preset threshold may be a standard value obtained according to an empirical data signal, and is used to measure whether the organism implanted in the detection chip has epilepsy.

Step 112 : when the judgment result is greater than, output an identification signal indicating an epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.

In specific implementation, the identification signal may be a Flag signal. When the Flag signal is pulled high, it indicates an epileptic seizure, and stimulation treatment can be triggered; otherwise, if the epilepsy does not occur, no treatment is performed, and further detection is continued.

The epilepsy detection scheme involved in this specification will be described in detail below through specific examples. Take EEG signals as an example.

First, the algorithm signals and hardware units involved in the detection scheme are introduced.

The input ports of the hardware algorithm are: CLK1 port, CLK2 port, nRST port, MOSI port, Data_ADC port, EN_detect port; output ports include MISO port and Flag port. The communication mode of the overall scheme adopts the serial peripheral interface SPI. Among them, the SPI communication mode is to send data on the rising edge and collect data on the falling edge. The CLK1 port and the CLK2 port are respectively two master clock ports with the same phase. The nRST port is the reset signal port, the signal output 0 is reset, and the signal output 1 is normal operation. The MOSI port is an SPI communication port, and the signal direction is output from the master and input from the slave. The Data_ADC port is used to transmit 16-bit wide EEG data, and can be connected to the output of a 16-bit ADC in practical applications. The EN_detect port is the detection enable signal port. The MISO port is the SPI communication port, and the signal direction is input from the master and output from the slave. The Flag port is the epileptic seizure warning signal port. Correspondingly, each port corresponds to a corresponding signal output.

Further, define T as the number of sampling points in the detection window, configured through the SPI port, ranging from 32 to 1024. N1 is the multiple of the size of the background window and the size of the detection window, configured through the SPI port, the range is 0-255. N2 is a multiple of the size of the interval window and the size of the detection window, configured through the SPI port, the range is 0-255. LIM is the preset threshold, configured through the SPI port, in the range 110-180. CNT1 is a counter that counts the number of sampling points in the detection window. Q is the variable that counts the number of detection windows in the first calculation cycle. Sum1 is an accumulator that accumulates the sum of the areas within each detection window. Sum_background is the background window register, which is used to accumulate the sum of the area in the background window. Sum_detect is the detection window register, which is used to store the area sum of the detection window in each calculation cycle. Sum_L is the first temporary register, which is used to store the value of the lowest bit in the buffer. Sum_N is the second temporary register for storing the value of the N1+1th bit in the buffer. Sram1 is a static random access memory with a capacity of 2kB. SramS1 is a buffer, dual-port ram, used for storage of intermediate operation results in the calculation process. SramS2 is an output buffer, dual-port ram, used to buffer epilepsy EEG data output.

The detection scheme in this manual can roughly include the following parts:

1. Configure the algorithm parameters.

2. Store the EEG signal to Sram1.

3. Find the difference between the amplitude of the EEG signal and the DC offset, and take the absolute value.

4. The calculation process to detect the occurrence of epilepsy. in,

(4.1) First calculate the area sum of the length of each detection window, write the area sum of the background window into the Sum_background register, and write the area sum of the detection window into the Sum_detect register.

(4.2) Find the average area of the detection window, find the average area of the background window, judge the detection result and give the Flag signal.

5. The process data of epilepsy detection is serially output through the communication port.

The specific implementation process can refer to the following:

1. Data_ADC[15:0] EEG data is written into Sram1 memory at 200Hz frequency.

Among them, the input frequency can be set flexibly, and the 200Hz here is just an example.

2. The absolute value of the difference between the amplitude of the EEG signal and the DC offset is added to the Sum1 accumulator in turn. The CNT1[10:0] counter starts counting the accumulation times of Sum1 from 0, and takes the length of one detection window as a cycle.

3. The first calculation cycle: the register Q records the number of detection windows in the first calculation cycle, Q>=0 and Q<=N1+N2+2. When Q<=N1+N2+1 and CNT1=T (the current detection window ends, and the next detection window adjacent to it starts), the value of Sum1 is written into SramS1 from low address to high address, and Q is incremented by 1 at the same time. When Q<=N1 and CNT1=T, Sum1 is successively written to the Sum_background register, and the Sum_background register stores the area sum of the background window in the first detection period; when Q=N1+N2+1 and CNT1=T, Sum1 is written to the Sum_detect register, that is, the Sum_detect register stores the sum of the detection window area. According to the formula

Among them, Sum_detect is the sum of the area of the detection window, Td is the number of detection windows, the default is 1, Sum_background is the total area of the background windows, and T is the number of background windows. The ratio of the average area of the detection window to the average area of the background window can be obtained, and the ratio is compared with the preset threshold LIM. If the ratio is greater than the LIM, it means epileptic seizures, while the Flag signal is pulled up, otherwise, epilepsy does not occur. The preset threshold LIM may be 400%, that is, when the ratio is less than or equal to 400%, the Flag signal will not be triggered to pull up.

5. After the first detection, enter the second detection cycle.

At this point, N1+N2+1 values are stored in SramS1, the data in the lower N1 bits is the Sum1 value of the background window in the first calculation cycle, and the data in the next N2 bits is the interval window in the first calculation cycle. Sum1 value, the highest bit is the Sum1 value of the detection window. When CNT1=T, first read the lowest bit data in SramS1 to the first temporary register Sum_L for temporary storage, then SramS1 is shifted to the left once, and the value of Sum1 is written to the N1+N2+1th address, and then read Take the data of the N1+1th bit in SramS1 to the second temporary register Sum_N for temporary storage, and at the same time, the value of Sum1 is also written into the register Sum_detect. The current Sum_background stores the area sum of the background interval in the first calculation cycle. At this time, Sum_background-Sum_L+Sum_N is assigned to Sum_background, which is the background window area sum of the second calculation cycle. According to formula (1) again, the ratio of the average area of the detection interval of the second calculation period to the average area of the background window can be obtained, which is also compared with the threshold LIM. If it is greater than the LIM, it means an epileptic seizure, and the Flag signal is pulled high, and vice versa. , no epilepsy.

6. Continue the loop for the third and more detection cycles according to the logic in 5.

7. Every time an epileptic seizure is detected, when the Flag is pulled high, the EN_detect terminal can be pulled up externally, the detection is suspended, and the stimulation is performed.

At the same time, the Sram1 read enable in the hardware algorithm takes effect. By controlling the address terminal, the EEG data 30s before and 60s after the epileptic seizure can be read. Since the reading frequency of Sram1 is much lower than the SPI communication frequency, the data output by Sram1 is written into the cache SramS2. After the data is written, it is read from low to high in sequence, and serially output through the MISO port. After output, it can be stored in external Flash. , which can be used for follow-up medical research and is of great significance to neuroscience research.

Therefore, through the above technical solution, a hardware algorithm can be used to collect and detect bioelectrical signals in real time, and then, according to the comparison between the ratio between the average area of the detection window and the average area of the background window in each detection period and the preset threshold , to determine whether epileptic seizures are triggered by identifying signals for stimulation therapy. Thus, real-time detection with low power consumption improves integration, detection accuracy, and convenience.

Embodiment 2

Referring to FIG. 3 , a device 300 for automatically detecting epilepsy by an implantable closed-loop system based on an area algorithm provided in an embodiment of the present specification mainly includes:

The configuration module 302 configures detection parameters through the SPI serial peripheral interface, wherein the detection parameters at least include: detection window size, interval window size and background window size in one detection period;

The acquisition and storage module 304 collects bioelectrical signals in real time from the organism implanted in the detection chip, and stores them in the memory;

The calculation module 306 respectively calculates the absolute value of the difference between the amplitude of the bioelectrical signal and the DC offset according to the time sequence, and sends it to the accumulator;

The determination module 308, based on the value in the accumulator, respectively determines the area sum of the current detection window and the area sum of the background window corresponding to the current detection window;

The detection module 310 determines whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold;

The treatment module 312, when the judgment result is greater than, outputs an identification signal indicating an epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.

Optionally, as an embodiment, the size of the background window is N1 times the size of the detection window, and the size of the interval window is N2 times the size of the detection window; wherein, the N1 and the N2 are both. A positive integer greater than or equal to 2.

In a specific implementation manner of the embodiment of this specification, the determining module 308 is specifically configured to:

When the counter counts from 1 to N1, the value in the accumulator is written into the background window register, and the background window register stores the area sum of the background window corresponding to the current detection window until the count reaches N1;

When the counter counts to N1+N2+1, the value in the accumulator is written into the detection window register, and the detection window register stores the area sum of the current detection window.

In yet another specific implementation of the embodiment of the present specification, before counting, the determining module 308 is further configured to:

Taking the detection window as the basic window unit, determine the number of basic window units N1+N2+1 in the background window, interval window and detection window included in the first detection period; accordingly,

When the count value of the counter is not greater than N1+N2+1 and the sampling of the current window is over, the value of the memory is written into the buffer from low address to high address, and the counter is incremented by 1;

When the count value of the counter is less than or equal to N1 and the sampling of the current window ends, the memory is sequentially accumulated and written into the background window register, and the background window register stores the area sum of the background window in the first detection period;

When the count value of the counter is equal to N1+N2+1 and the current sampling window ends, the memory writes the detection window register, which stores the area sum of the detection window in the first detection cycle.

In yet another specific implementation of the embodiment of this specification, if the current detection window belongs to the non-first detection cycle of the current round of detection, the determination module is specifically used for:

Read the value of the lowest bit from the buffer, and write it into the first temporary register for temporary storage;

Write the value in the memory into the detection window register to reassign the area and the current detection window;

In yet another specific implementation of the embodiment of the present specification, when the organism is stimulated for treatment, an output module is further included, configured to acquire the bioelectrical signal in the first period before the epilepsy time and the second time after the epilepsy time. Period of bioelectrical signal and serial output through SPI.

Through the above technical solution, a hardware algorithm can be used to collect and detect bioelectrical signals in real time, and then, according to the comparison between the ratio between the average area of the detection window and the average area of the background window in each detection period and the preset threshold, determine whether to Epilepsy seizures are triggered by marker signals for stimulation therapy. Thus, real-time detection with low power consumption improves integration, detection accuracy, and convenience.

Embodiment 3

Referring to FIG. 5 , a schematic structural diagram of an implantable closed-loop system for automatically detecting epilepsy based on an area algorithm provided in an embodiment of the present specification includes at least: a hardware algorithm chip and other functional modules for executing the method in the above-mentioned first embodiment; wherein , the hardware algorithm chip includes: serial peripheral interface, memory, algorithm register, accumulator, counter; wherein,

The memories are respectively used to store the real-time acquisition of bioelectric signals from the organism implanted in the detection chip, and after calculating the absolute value of the difference between the amplitude of the bioelectric signal and the DC offset according to the counter in sequence, respectively , into the accumulator;

After determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window respectively based on the value in the accumulator, the detection enabling terminal determines that the average area of the current detection window is the same as the total area of the current detection window. Whether the ratio of the average area of the background window is greater than the preset threshold;

For other hardware unit parts, reference may be made to the content of the first embodiment, which will not be repeated here.

Embodiment 4

FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present specification. Referring to FIG. 4 , at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The memory may include memory, such as high-speed random-access memory (Random-Access Memory, RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Of course, the electronic equipment may also include hardware required for other services.

The processor, network interface and memory can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Component Interconnect Standard) bus. Industry Standard Architecture, extended industry standard structure) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one bidirectional arrow is used in FIG. 4, but it does not mean that there is only one bus or one type of bus.

memory for storing programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include memory and non-volatile memory and provide instructions and data to the processor.

The processor reads the corresponding computer program from the non-volatile memory into the memory and runs it, forming a device for automatically detecting epilepsy at the logical level. The processor executes the program stored in the memory, and is specifically used to perform the following operations:

The above-mentioned method performed by the apparatus disclosed in the embodiment shown in FIG. 1 of this specification may be applied to a processor, or implemented by a processor. A processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above-mentioned method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above-mentioned processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it can also be a digital signal processor (Digital Signal Processor, DSP), dedicated integrated Circuit (Application Specific Integrated Circuit, ASIC), Field Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in one or more embodiments of this specification can be implemented or executed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in conjunction with one or more embodiments of this specification may be directly embodied as executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.

The electronic device can also execute the method shown in FIG. 1 , and implement the functions of the corresponding apparatus in the embodiment shown in FIG. 1 , and the embodiments of this specification will not be repeated here.

Of course, in addition to software implementations, the electronic devices in the embodiments of this specification do not exclude other implementations, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subjects of the following processing procedures are not limited to each logic A unit can also be a hardware or logic device.

Through the above technical solution, a hardware algorithm can be used to collect and detect bioelectrical signals in real time, and then, according to the comparison between the ratio between the average area of the detection window and the average area of the background window in each detection period and the preset threshold, it is determined whether Epilepsy seizures are triggered by marker signals for stimulation therapy. Thus, real-time detection with low power consumption improves integration, detection accuracy, and convenience.

In a word, the above descriptions are only preferred embodiments of the present specification, and are not intended to limit the protection scope of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included within the protection scope of this specification.

The systems, devices, modules or units described in one or more of the above embodiments may be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or A combination of any of these devices.

Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology. Information may be computer readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

It should also be noted that the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a series of elements includes not only those elements, but also Other elements not expressly listed, or which are inherent to such a process, method, article of manufacture, or apparatus are also included. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article of manufacture, or device that includes the element.

Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for related parts, please refer to the partial descriptions of the method embodiments.

The foregoing describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

A method for an implantable closed-loop system to automatically detect epilepsy based on an area algorithm, characterized in that it is applied to an epilepsy detection device composed of a chip, and the method includes:

The detection parameters are configured through the SPI serial peripheral interface, wherein the detection parameters at least include: the detection window size, the interval window size and the background window size in one detection period;

Collect bioelectrical signals in real time from the organism implanted in the detection chip, and store them in the memory;

Calculate the absolute value of the ratio of the amplitude of the bioelectrical signal to the DC offset respectively according to the time sequence, and send it to the accumulator;

Based on the value in the accumulator, respectively determine the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window;

Determine whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold;

When the judgment result is greater than, output an identification signal representing epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.
The method for automatically detecting epilepsy by an implantable closed-loop system based on an area algorithm according to claim 1, wherein the size of the background window is N1 times the size of the detection window, and the size of the interval window is the size of the detection window N2 times the size; wherein, both the N1 and the N2 are positive integers greater than or equal to 2.
The method for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm according to claim 2, wherein if the current detection window belongs to the first detection cycle of the current round of detection, then based on the value in the accumulator , respectively determine the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window, specifically including:

When the counter counts from 1 to N1, the value in the accumulator is written into the background window register, and the background window register stores the area sum of the background window corresponding to the current detection window until the count reaches N1;

When the counter counts to N1+N2+1, the value in the accumulator is written into the detection window register, and the detection window register stores the area sum of the current detection window.
The method for automatically detecting epilepsy by an implantable closed-loop system based on an area algorithm according to claim 3, wherein, before counting, the method further comprises:

Taking the detection window as the basic window unit, it is determined that in the background window, interval window and detection window included in the first detection period, the number of basic window units is N1+N2+1; accordingly,

When the count value of the counter is not greater than N1+N2+1 and the sampling of the current window ends, the value of the memory is written into the buffer from low address to high address, and the counter is incremented by 1;

When the count value of the counter is less than or equal to N1 and the sampling of the current window ends, the memory is sequentially accumulated and written into the background window register, and the background window register stores the area sum of the background window in the first detection period;

When the count value of the counter is equal to N1+N2+1 and the current sampling window ends, the memory writes the detection window register, which stores the area sum of the detection window in the first detection cycle.
The method for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm according to claim 4, wherein if the current detection window belongs to a non-first detection period of the current round of detection, then based on the value obtained in the accumulator value, respectively determine the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window, specifically including:

Read the value of the lowest bit from the buffer, and write it into the first temporary register for temporary storage;

The buffer is shifted to the left once, the value of the memory is written to the N1+N2+1 address, and then the value of the N1+1 bit in the buffer is read to the second temporary register for temporary storage;

Write the value in the memory into the detection window register so as to reassign the area and the current detection window;

The value of the background window register minus the value of the first temporary register, plus the value of the second temporary register, is written into the background window register as the area and assignment of the current background window.
The method for automatically detecting epilepsy by an implantable closed-loop system based on an area algorithm according to claim 1, wherein when the organism is stimulated for treatment, the method further comprises:

Obtain the bioelectrical signal in the first period before the epileptic seizure time and the bioelectrical signal in the second period after the epileptic seizure time, and output them serially through SPI.
A device for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm, characterized in that it includes:

The configuration module configures detection parameters through the SPI serial peripheral interface, wherein the detection parameters at least include: detection window size, interval window size and background window size in one detection period;

The acquisition and storage module collects bioelectrical signals in real time from the organism implanted in the detection chip, and stores them in the memory;

The calculation module calculates the absolute value of the difference between the amplitude of the bioelectrical signal and the DC offset respectively according to the time sequence, and sends it to the accumulator;

The determination module, based on the value in the accumulator, respectively determines the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window;

a detection module, for judging whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold;

The treatment module, when the judgment result is greater than, outputs an identification signal representing an epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.
A device for automatically detecting epilepsy in an implantable closed-loop system based on an area algorithm, characterized in that it at least includes: a hardware algorithm chip and other functional modules for executing the method according to any one of the preceding claims 1-6; wherein, the hardware algorithm The chip contains: serial peripheral interface, memory, accumulator, counter; among them,

The serial peripheral interface is used to configure detection parameters and transmit bioelectrical signals; wherein, the detection parameters at least include: detection window size, interval window size and background window size in one detection cycle;

The memory is used to store the real-time acquisition of bioelectric signals from the organism implanted in the detection chip, and after calculating the absolute value of the difference between the amplitude of the bioelectric signal and the DC offset according to the counter according to the time series, into the accumulator;

After determining the area sum of the current detection window and the area sum of the background window corresponding to the current detection window respectively based on the value in the accumulator, the detection enabling terminal determines that the average area of the current detection window is the same as the total area of the current detection window. Whether the ratio of the average area of the background window is greater than a preset threshold;

When the judgment result is greater than, the serial peripheral interface outputs an identification signal representing an epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.
The device for automatically detecting epilepsy by an implantable closed-loop system based on an area algorithm according to claim 8, wherein when the organism is stimulated for treatment, the SPI is further used to obtain the first time period before the epileptic seizure moment. The bioelectrical signal and the bioelectrical signal of the second period after the epileptic seizure are outputted serially.
An electronic device comprising:

processor; and

memory arranged to store computer-executable instructions which, when executed, cause the processor to perform:

The detection parameters are configured through the SPI serial peripheral interface, wherein the detection parameters at least include: the detection window size, the interval window size and the background window size in one detection period;

Collect bioelectrical signals in real time from the organism implanted in the detection chip, and store them in the memory;

Calculate the absolute value of the difference between the amplitude of the bioelectrical signal and the DC offset respectively according to the time sequence, and send it to the accumulator;

Based on the value in the accumulator, respectively determine the area sum of the current detection window, and the area sum of the background window corresponding to the current detection window;

Determine whether the ratio of the average area of the current detection window to the average area of the background window is greater than a preset threshold;

When the judgment result is greater than, output an identification signal representing epileptic seizure, so as to trigger the treatment device to perform stimulation treatment on the living body.