CN112774033A - Method, device and system for determining detection parameters of implantable closed-loop system - Google Patents
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Abstract
One or more embodiments of the present specification disclose a method, an apparatus, and a system for determining a detection parameter of an implanted closed-loop system, including: respectively selecting a preset number of positive signals and a preset number of negative signals from historical bioelectricity signals, and respectively fitting the selected preset number of positive signals and the selected preset number of negative signals based on detection algorithms in an algorithm set stored in the MCU to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm; integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm; and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and finely adjusting the detection parameters corresponding to the selected detection algorithm and then sending the detection parameters to the MCU. Therefore, the difficulty of debugging detection parameters by a doctor user is reduced, and the detection accuracy and the diagnosis and treatment effect are improved.
Description
Technical Field
The present invention relates to the technical field of medical devices, and in particular, to a method, an apparatus, and a system for determining a detection parameter of an implantable closed-loop system.
Background
Implantable Medical Devices (IMDs) typically have a function of detecting bioelectrical signals (e.g., cardiac or electrical signals). The IMD may analyze the acquired bioelectrical signals to improve or enhance therapy performance.
At present, when the acquired bioelectric signals are detected and analyzed, generally, a doctor user can preset detection parameters according to empirical data, and sends the detection parameters to an MCU of the implantable device to be used as a detection standard, and a corresponding detection algorithm is selected to analyze the detection standard. However, considering that the bioelectric signals collected at every moment may change, the matched detection parameters and the parameter values of the detection parameters (and the detection algorithms corresponding to the detection parameters) are different from each other for the current physiological characteristics reflected by the bioelectric signals collected at every stage, so that the bioelectric signals are easily analyzed by using the unmatched detection parameters and ranges, and the detection and analysis accuracy is reduced; moreover, it increases the difficulty for the user to set up and debug.
Disclosure of Invention
One or more embodiments of the present disclosure provide a method, an apparatus, and a system for determining a detection parameter of an implantable closed-loop system, so as to reduce a difficulty in setting and debugging the detection parameter, improve accuracy of detecting and analyzing a bioelectric signal, and further improve diagnosis efficiency.
To solve the above technical problem, one or more embodiments of the present specification are implemented as follows:
in a first aspect, a method for determining a detection parameter of an implanted closed-loop system is provided, including:
respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear;
based on detection algorithms in an algorithm set stored in the MCU, fitting the selected positive signals with the preset number and the negative signals with the preset number respectively to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm;
and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment.
In a second aspect, an apparatus for determining an implantable closed-loop system detection parameter is provided, including:
the selection module is used for respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear;
the fitting module is used for respectively fitting the positive signals with the preset number and the negative signals with the preset number based on the detection algorithms in the algorithm set stored in the MCU to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
the integration module is used for integrating the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm;
and the determining module is used for combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, finely adjusting the detection parameters corresponding to the selected detection algorithm and then sending the detection parameters to the MCU.
In a third aspect, an implantable closed-loop system is provided, comprising: and the device for confirming the detection parameters of the implanted closed-loop system.
According to the technical scheme provided by one or more embodiments of the specification, a preset number of positive signals and a preset number of negative signals are respectively selected from historical bioelectricity signals, and the selected preset number of positive signals and the preset number of negative signals are respectively subjected to fitting processing based on detection algorithms in an algorithm set stored in an MCU (microprogrammed control Unit), so that a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm are obtained; integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm; and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. Therefore, the difficulty of debugging detection parameters by a doctor user can be reduced, the detection accuracy is improved, and the diagnosis and treatment effect is improved.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, reference will now be made briefly to the attached drawings, which are needed in the description of one or more embodiments or prior art, and it should be apparent that the drawings in the description below are only some of the embodiments described in the specification, and that other drawings may be obtained by those skilled in the art without inventive exercise.
Fig. 1 is a schematic diagram illustrating steps of a method for determining a detection parameter of an implantable closed-loop system according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of the principle provided by the embodiment of the present specification to determine the detection parameter set by taking a band-pass algorithm as an example in an energy and frequency algorithm.
Fig. 3 is a schematic structural diagram of an apparatus for determining a detection parameter of an implantable closed-loop system according to an embodiment of the present disclosure.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification.
Detailed Description
In order to make the technical solutions in the present specification better understood, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the accompanying drawings in one or more embodiments of the present specification, and it is obvious that the one or more embodiments described are only a part of the embodiments of the present specification, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
Example one
Referring to fig. 1, a schematic step diagram of a method for determining a detection parameter of an implantable closed-loop system provided for the embodiments of the present disclosure is shown, it should be understood that the method is applied in an apparatus for determining a detection parameter of an implantable device, where the apparatus may be a software module or a hardware module that performs message interaction with the implantable device, for example, as an upper computer that performs interaction with the implantable device outside an organism.
The method for determining the detection parameters of the implanted closed-loop system can comprise the following steps:
step 102: respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear.
In the embodiment of the present specification, the historical bioelectrical signal may be a bioelectrical signal of a patient in which the current target implantable device is implanted during any period of time in the past, wherein the patient releases an abnormal bioelectrical signal and a normal bioelectrical signal during the period of time, that is, during the period of time, the patient has a disease.
Selecting some positive signals (representing abnormal morbidity of the patient) which are expected to be detected and some negative signals (representing normal of the patient) which are expected not to be detected according to the acquired historical bioelectrical signals; meanwhile, a continuously stable background interval may be selected. Wherein the number of positive and negative signals selected can be set empirically. Taking 10 as an example, 10 positive signals Sp1-Sp10 and 10 negative signals Sn1-Sn10 are selected from the 10 positive signals.
It should be understood that in the embodiments of the present specification, the bioelectric signal may include: EEG signal, deep electrophysiological signal of brain, bioelectric signal of cerebral cortex, or central nerve signal.
The detection target may refer to a lesion site of an organism (i.e., a patient). For example, the cortex, or the deep brain.
Step 104: and respectively performing fitting treatment on the positive signals with the preset number and the negative signals with the preset number based on the detection algorithms in the algorithm set stored in the MCU to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm.
Optionally, in an embodiment of this specification, a detection algorithm in the algorithm set stored in the MCU at least includes:
and any two of a linear length algorithm, an area algorithm and a band-pass algorithm in the frequency algorithm are combined according to the logical relation of AND, OR and NOT to obtain the algorithm.
Algorithms currently used in the detection processing of bioelectric signals include: a band-pass algorithm in an energy line length algorithm, an energy area algorithm and a frequency algorithm; it should be understood that the frequency algorithm includes a band-pass algorithm, which can be extended to a low-frequency algorithm and a high-frequency algorithm; the band-pass algorithms described later refer to band-pass algorithms in frequency algorithms, and when the band-pass algorithms are further extended and analyzed, the low-frequency algorithms and the high-frequency algorithms can be considered to be respectively combined with the logic of the energy algorithms. Thus, here the set of algorithms stored in the MCU includes: the energy line length algorithm, the energy area algorithm, the band-pass algorithm, the energy line length algorithm and the energy area algorithm, the energy line length algorithm and the band-pass algorithm, the energy area algorithm and the band-pass algorithm, the energy line length algorithm or the energy area algorithm, the energy line length algorithm or the band-pass algorithm, and the energy area algorithm or the band-pass algorithm.
Correspondingly, each detection algorithm corresponds to a corresponding detection parameter set, and the following contents can be specifically referred to:
line length algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold value [ -100, 1000 ]%.
Area algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold value [ -100, 1000 ]%.
And (3) band-pass algorithm: minimum frequency [1, 100] (Hz); maximum frequency [1, 200] (Hz); amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0.0, 100 ]%.
And (3) low-frequency algorithm: amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0, 100 ]%.
High-frequency algorithm: amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0, 100 ]%.
Line length and area algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold 1[ -100, 1000 ]%; the threshold value is 2 < -100, 1000 >%.
Line length or area algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold 1[ -100, 1000 ]%; the threshold value is 2 < -100, 1000 >%.
Line length and bandpass algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold [ -100, 1000 ]%; minimum frequency [1, 100] (Hz); maximum frequency [1, 200] (Hz); amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0.0, 100 ]%.
Line length or bandpass algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold [ -100, 1000 ]%; minimum frequency [1, 100] (Hz); maximum frequency [1, 200] (Hz); amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0.0, 100 ]%.
Area and band pass algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold [ -100, 1000 ]%; minimum frequency [1, 100] (Hz); maximum frequency [1, 200] (Hz); amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0.0, 100 ]%.
Area or band pass algorithm: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold [ -100, 1000 ]%; minimum frequency [1, 100] (Hz); maximum frequency [1, 200] (Hz); amplitude [0.0, 100] (%); duration [0.1, 10.0 ](s); noise [0.0, 100 ]%.
And the detection parameter set corresponding to each detection algorithm respectively gives the value range of each detection parameter. It should be understood that the value ranges are merely illustrative and may be used as initialization parameter ranges.
For any positive signal Sp and negative signal Sn, any detection algorithm D can be used to obtain a corresponding parameter Pp ═ SpXD; pn ═ Sn X D. Pp or Pn obtained by any one of Sp and Sn after D action is not unique, so Pp and Pn are two-dimensional parameters.
Referring to fig. 2, when the detection algorithms are a band-pass algorithm and an energy algorithm, based on the detection algorithms in the algorithm set stored in the MCU, fitting the selected positive signals of the preset number and the negative signals of the preset number respectively to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm, specifically including:
for positive signals, as shown in the left diagram of fig. 2, the following contents are respectively compared with the preset reference in sequence:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation; and fitting and calculating according to the comparison result to obtain a positive detection parameter set determined by the positive signal by using a band-pass algorithm and an energy algorithm, wherein each detection parameter in the positive detection parameter set corresponds to a parameter value range determined based on the fitting of the current algorithm. In other words, first, the positive signals Sp1-Sp10 are compared with preset criteria (the criteria should be related to single energy), respectively, whether there is a single energy increase, and a first set of detection parameters is determined according to the comparison result: pp1_1, Pp1_2, Pp1_3, Pp1_4 … … Pp1_ x. Then, comparing the positive signal Sp1-Sp10 with a preset reference (the reference at this time should be related to the overall energy), determining whether the overall energy is changed, and determining a second group of detection parameters according to the comparison result: pp2_1, Pp2_2, Pp2_3, Pp2_4 … … Pp2_ y. Then, comparing the positive signal Sp1-Sp10 with a preset reference (the reference at this time should be related to the fluctuation amplitude), judging whether fluctuation amplitude changes exist, and determining a third detection parameter according to the comparison result: pp3_1, Pp3_2, Pp3_3, Pp3_4 … … Pp3_ z. Thus, the energy can be determined to be combined with the positive detection parameter determined by the band pass algorithm to be { Pp1_1, Pp1_2, Pp1_3, Pp1_4, Pp1_5, Pp1_6, Pp1_7, Pp1_8, Pp1_9, Pp1_ x, Pp2_1 … … Pp2_ y, Pp3_1 … … Pp3_ z }. Each of Pp1_ x, Pp2_ y, and Pp3_ z (where x, y, and z respectively represent the number of detection parameters determined based on different detection algorithms) has a specific value range.
For negative signals, referring to the right diagram of fig. 2, the following contents are respectively compared with a preset reference in sequence:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation; and performing fitting calculation according to the comparison result to obtain negative detection parameters of the negative signals determined by using a band-pass algorithm and an energy algorithm, wherein each detection parameter in the negative detection parameter set corresponds to a parameter value range determined based on the fitting of the current detection algorithm. Similar to the comparison of the positive signals, the sets of negative detection parameters determined by the energy and band pass algorithm may be determined as { Pn1_1, Pn1_2, Pn1_3, Pn1_4, Pn1_5, Pn1_6, Pn1_7, Pn1_8, Pn1_9, Pn1_ x, Pn2_1 … … P n2_ y, Pn3_1 … … Pn3_ z }. Each Pn1_ x, Pn2_ y, and Pn3_ z (where x, y, and z respectively represent the number of detection parameters determined based on different detection algorithms) has a specific value range.
It should be understood that in the embodiment of the present specification, all detection algorithms in the MCU are to be fitted with their corresponding detection parameter sets. Thus, each detection algorithm can determine 1 positive detection parameter set and 1 negative detection parameter set, and further, 9 detection algorithms can determine 9 positive detection parameter sets and 9 negative detection parameter sets in total, and 18 detection parameter sets in total.
Step 106: and integrating the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to preset constraint conditions to obtain the detection parameter set corresponding to each detection algorithm.
Optionally, step 106 is specifically executed as: according to preset constraint conditions, respectively integrating the ranges of the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm to obtain the detection parameter set corresponding to each detection algorithm, wherein each detection parameter in the detection parameter set corresponds to the integrated parameter value range.
Further, each detection parameter is provided with an initial parameter range; therefore, according to the preset constraint condition, the ranges of the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm are respectively integrated to obtain the detection parameter set corresponding to each detection algorithm, which may specifically include:
aiming at the detection parameters which belong to the same type and correspond to any detection algorithm:
when the range of the positive detection parameters corresponding to the detection algorithm is not smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters of the same type corresponding to the detection algorithm are as follows: a part of the parameter value range of the positive detection parameter, which is overlapped with the value range outside the parameter value range of the negative detection parameter in the initial parameter range;
when the range of the positive detection parameters corresponding to the detection algorithm is smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters belonging to the same type and corresponding to the detection algorithm are as follows: range of positive detection parameters.
Taking the line length algorithm as an example:
the detection parameters corresponding to the line length algorithm comprise: detection interval [0.01, 10 ](s); background interval [1, 500 ]; interval [1, 500 ]; threshold value [ -100, 1000 ]%. Then, in turn:
detection interval: the detection range X1 (positive) determined by the linear length algorithm and the detection range X1 (positive) determined by the linear length algorithmDetection interval range X2 (negative); if the range of the X1 is not smaller than the range of the X2, the value range of the detection interval determined by the linear length algorithm is determined as follows:
if the range of X1 is smaller than the range of X2, then the value range of the detection interval determined by the linear length algorithm is determined as follows: x1.
Background interval: the background interval range Y1 (positive) determined by the linear length algorithm and the background interval range Y2 (negative) determined by the linear length algorithm; if the range of Y1 is not less than the range of Y2, then the value range of the background interval determined by the line length determining algorithm is:
if the range of Y1 is smaller than the range of Y2, then the value range of the background interval determined by the line length algorithm is determined as follows: and Y1.
Interval: interval range M1 (positive) determined by the line length algorithm and interval range M2 (negative) determined by the line length algorithm; if the range of M1 is not less than the range of M2, the value range of the interval determined by the line length determining algorithm is:
if the range of M1 is smaller than the range of M2, the value range of the interval determined by the line length determining algorithm is: m1.
Threshold value: a threshold range N1 (positive) determined by a linear length algorithm and a threshold range N2 (negative) determined by the linear length algorithm; if the range of N1 is not less than the range of N2, then the value range of the threshold determined by the line length algorithm is determined as follows:
if the range of N1 is smaller than the range of N2, the value range of the threshold determined by the linear length algorithm is determined as follows: n1.
Further, the value ranges of the detection parameters contained in the area algorithm, the band-pass algorithm and the other 8 algorithms are calculated according to the above method.
Step 108: and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment.
And aiming at each detection algorithm, respectively combining the detection parameters contained in the detection algorithms to obtain a detection parameter set.
Still take the line length algorithm as an example:
suppose that the detection interval X1 of the linear length algorithm and the background interval are determined
Interval of time
Threshold N1. This is the final combined detection parameter set of the line length algorithm. The same may determine the set of detection parameters for other detection algorithms.
Optionally, the fine-tuning the detection parameters corresponding to the selected detection algorithm and then sending the detection parameters to the MCU specifically includes:
fine-tuning parameter value ranges of all detection parameters corresponding to the selected detection algorithm according to an adjustment scheme input by a user and determined based on the current effect; and sending all the fine-tuned detection parameters and parameter value ranges thereof to the MCU. During specific implementation, a more reliable detection parameter range can be obtained according to fine adjustment of a doctor user on the detection parameter range, and then the detection accuracy is favorably improved.
According to the technical scheme, a preset number of positive signals and a preset number of negative signals are respectively selected from historical bioelectricity signals, and fitting processing is respectively carried out on the selected preset number of positive signals and the preset number of negative signals based on detection algorithms in an algorithm set stored in an MCU (microprogrammed control unit), so that a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm are obtained; integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm; and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. Therefore, the difficulty of debugging detection parameters by a doctor user can be reduced, the detection accuracy is improved, and the diagnosis and treatment effect is improved.
Example two
Referring to fig. 3, an apparatus 300 for determining an implantable closed-loop system detection parameter provided for an embodiment of the present disclosure may include:
a selecting module 302, configured to select a preset number of positive signals and negative signals from historical bioelectrical signals, respectively, where the positive signals indicate that a detection target of an organism is abnormal at a current moment of occurrence of the bioelectrical signal, and the negative signals indicate that the detection target of the organism is normal at the current moment of occurrence of the bioelectrical signal;
the fitting module 304 is configured to perform fitting processing on the selected positive signals with the preset number and the negative signals with the preset number respectively based on detection algorithms in an algorithm set stored in the MCU, so as to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
an integration module 306, configured to integrate the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition, so as to obtain a detection parameter set corresponding to each detection algorithm;
and the determining module 308 is configured to combine the detection parameter sets of all the detection algorithms, select a matched detection algorithm based on an input user instruction, and send the detection parameter corresponding to the selected detection algorithm to the MCU after fine tuning.
Optionally, as an embodiment, the detection algorithm in the algorithm set stored in the MCU at least includes:
and any two of a linear length algorithm, an area algorithm and a band-pass algorithm in the frequency algorithm are combined according to the logical relation of AND, OR and NOT to obtain the algorithm.
In a specific implementation manner of the embodiment of the present specification, when the detection algorithm is a band-pass algorithm and an energy algorithm, the fitting module is specifically configured to:
and aiming at the positive signals, respectively comparing the following contents in sequence relative to a preset reference:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation;
fitting and calculating according to the comparison result to obtain a positive detection parameter set of the positive signal determined by using a band-pass algorithm and an energy algorithm;
aiming at the negative signals, the following contents are respectively compared with a preset reference in sequence:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation;
and fitting and calculating according to the comparison result to obtain negative detection parameters of the negative signals determined by using a band-pass algorithm and an energy algorithm.
In a specific implementation manner of the embodiment of the present specification, the integration module is specifically configured to:
and respectively integrating the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain the detection parameter set corresponding to each detection algorithm, wherein each detection parameter in the detection parameter set corresponds to the integrated parameter range.
In a specific implementation manner of the embodiments of the present description, each detection parameter is provided with an initial parameter range;
the integration module is specifically configured to, when the detection parameter sets corresponding to each detection algorithm are obtained by respectively integrating the ranges of the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to preset constraint conditions, perform:
aiming at the detection parameters which belong to the same type and correspond to any detection algorithm:
when the range of the positive detection parameters corresponding to the detection algorithm is not smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters of the same type corresponding to the detection algorithm are as follows: a part of the parameter value range of the positive detection parameter, which is overlapped with the value range outside the parameter value range of the negative detection parameter in the initial parameter range;
when the range of the positive detection parameters corresponding to the detection algorithm is smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters belonging to the same type and corresponding to the detection algorithm are as follows: range of positive detection parameters.
In a specific implementation manner of the embodiment of this specification, when the determining module sends the detection parameter corresponding to the selected detection algorithm to the MCU after fine tuning, the determining module is specifically configured to:
fine-tuning parameter value ranges of all detection parameters corresponding to the selected detection algorithm according to an adjustment scheme input by a user and determined based on the current effect;
and sending all the fine-tuned detection parameters and parameter value ranges thereof to the MCU.
According to the technical scheme, a preset number of positive signals and a preset number of negative signals are respectively selected from historical bioelectricity signals, and fitting processing is respectively carried out on the selected preset number of positive signals and the preset number of negative signals based on detection algorithms in an algorithm set stored in an MCU (microprogrammed control unit), so that a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm are obtained; integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm; and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. Therefore, the difficulty of debugging detection parameters by a doctor user can be reduced, the detection accuracy is improved, and the diagnosis and treatment effect is improved.
EXAMPLE III
The embodiment of the present specification further provides an implantable closed-loop system, which includes the apparatus for determining the parameter of the implantable closed-loop system described in the second embodiment. In addition, other existing device structures, such as an upper computer, an existing module of the implantable device, etc., are also included, and are not described herein.
Example four
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. Referring to fig. 4, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form a device for determining the detection parameters of the implantable equipment on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear;
based on detection algorithms in an algorithm set stored in the MCU, fitting the selected positive signals with the preset number and the negative signals with the preset number respectively to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm;
and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. The method performed by the apparatus according to the embodiment shown in fig. 1 of the present specification may be implemented in or by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The methods, steps, and logic blocks disclosed in one or more embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in hardware, in a software module executed by a hardware decoding processor, or in a combination of the hardware and software modules executed by a hardware decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may also execute the method of fig. 1 and implement the functions of the corresponding apparatus in the embodiment shown in fig. 1, which are not described herein again in this specification.
Of course, besides the software implementation, the electronic device of the embodiment of the present disclosure does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
According to the technical scheme, a preset number of positive signals and a preset number of negative signals are respectively selected from historical bioelectricity signals, and fitting processing is respectively carried out on the selected preset number of positive signals and the preset number of negative signals based on detection algorithms in an algorithm set stored in an MCU (microprogrammed control unit), so that a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm are obtained; integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm; and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. Therefore, the difficulty of debugging detection parameters by a doctor user can be reduced, the detection accuracy is improved, and the diagnosis and treatment effect is improved.
EXAMPLE five
Embodiments of the present specification also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, are capable of causing the portable electronic device to perform the method of the embodiment shown in fig. 1, and in particular for performing the method of:
respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear;
based on detection algorithms in an algorithm set stored in the MCU, fitting the selected positive signals with the preset number and the negative signals with the preset number respectively to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm;
and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. According to the technical scheme, a preset number of positive signals and a preset number of negative signals are respectively selected from historical bioelectricity signals, and fitting processing is respectively carried out on the selected preset number of positive signals and the preset number of negative signals based on detection algorithms in an algorithm set stored in an MCU (microprogrammed control unit), so that a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm are obtained; integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm; and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment. Therefore, the difficulty of debugging detection parameters by a doctor user can be reduced, the detection accuracy is improved, and the diagnosis and treatment effect is improved.
In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present specification shall be included in the protection scope of the present specification.
The system, apparatus, module or unit illustrated in one or more of the above embodiments may be implemented by a computer chip or an entity, or by an article of manufacture with a certain functionality. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, 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, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, 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 Discs (DVD) or other optical storage, magnetic cassettes, 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, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Claims (13)
1. A method of determining a sensed parameter of an implanted closed loop system, comprising:
respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear;
based on detection algorithms in an algorithm set stored in the MCU, fitting the selected positive signals with the preset number and the negative signals with the preset number respectively to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
integrating a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm;
and combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, and sending the detection parameters corresponding to the selected detection algorithm to the MCU after fine adjustment.
2. The method of claim 1, wherein the detection algorithms of the set of algorithms stored in the MCU include at least:
and any two of a linear length algorithm, an area algorithm and a band-pass algorithm in the frequency algorithm are combined according to the logical relation of AND, OR and NOT to obtain the algorithm.
3. The method of claim 1, wherein when the detection algorithms are a band-pass algorithm and an energy algorithm in a frequency algorithm, based on the detection algorithms in the algorithm set stored in the MCU, the fitting process is performed on the positive signals of a preset number and the negative signals of a preset number respectively to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm, and specifically includes:
and aiming at the positive signals, respectively comparing the following contents in sequence relative to a preset reference:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation;
fitting and calculating according to the comparison result to obtain a positive detection parameter set determined by a band-pass algorithm and an energy algorithm in a positive signal use frequency algorithm;
aiming at the negative signals, the following contents are respectively compared with a preset reference in sequence:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation;
and fitting and calculating according to the comparison result to obtain negative detection parameters determined by a band-pass algorithm and an energy algorithm in the negative signal use frequency algorithm.
4. The method of claim 1, wherein the step of integrating the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain the detection parameter set corresponding to each detection algorithm specifically comprises:
and respectively integrating the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain the detection parameter set corresponding to each detection algorithm, wherein each detection parameter in the detection parameter set corresponds to the integrated parameter range.
5. The method of claim 4, wherein each sensed parameter is provided with an initial parameter range;
according to preset constraint conditions, respectively integrating the ranges of the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm to obtain the detection parameter set corresponding to each detection algorithm, and specifically comprising the following steps:
aiming at the detection parameters which belong to the same type and correspond to any detection algorithm:
when the range of the positive detection parameters corresponding to the detection algorithm is not smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters of the same type corresponding to the detection algorithm are as follows: a part of the parameter value range of the positive detection parameter, which is overlapped with the value range outside the parameter value range of the negative detection parameter in the initial parameter range;
when the range of the positive detection parameters corresponding to the detection algorithm is smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters belonging to the same type and corresponding to the detection algorithm are as follows: the parameter value range of the positive detection parameter.
6. The method according to claim 4, wherein the sending the fine-tuned detection parameters corresponding to the selected detection algorithm to the MCU specifically comprises:
fine-tuning parameter value ranges of all detection parameters corresponding to the selected detection algorithm according to an adjustment scheme input by a user and determined based on the current effect;
and sending all the fine-tuned detection parameters and parameter value ranges thereof to the MCU.
7. An apparatus for determining a sensed parameter of an implanted closed loop system, comprising:
the selection module is used for respectively selecting a preset number of positive signals and negative signals from historical bioelectricity signals, wherein the positive signals represent that the detection target of the organism is abnormal at the moment when the current bioelectricity signals appear, and the negative signals represent that the detection target of the organism is normal when the current bioelectricity signals appear;
the fitting module is used for respectively fitting the positive signals with the preset number and the negative signals with the preset number based on the detection algorithms in the algorithm set stored in the MCU to obtain a positive detection parameter set and a negative detection parameter set corresponding to each detection algorithm;
the integration module is used for integrating the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain a detection parameter set corresponding to each detection algorithm;
and the determining module is used for combining the detection parameter sets of all the detection algorithms, selecting a matched detection algorithm based on an input user instruction, finely adjusting the detection parameters corresponding to the selected detection algorithm and then sending the detection parameters to the MCU.
8. The apparatus of claim 7, wherein a detection algorithm of the set of algorithms stored in the MCU comprises at least:
and any two of a linear length algorithm, an area algorithm and a band-pass algorithm in the frequency algorithm are combined according to the logical relation of AND, OR and NOT to obtain the algorithm.
9. The apparatus according to claim 7, wherein when the detection algorithm is a band-pass algorithm and an energy algorithm in a frequency algorithm, the fitting module is specifically configured to:
and aiming at the positive signals, respectively comparing the following contents in sequence relative to a preset reference:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation;
fitting and calculating according to the comparison result to obtain a positive detection parameter set determined by a band-pass algorithm and an energy algorithm in a positive signal use frequency algorithm;
aiming at the negative signals, the following contents are respectively compared with a preset reference in sequence:
whether there is a single frequency energy rise; whether there is an overall energy change; whether there is a fluctuation amplitude variation;
and fitting and calculating according to the comparison result to obtain negative detection parameters determined by a band-pass algorithm and an energy algorithm in the negative signal use frequency algorithm.
10. The apparatus of claim 7, wherein the integration module is specifically configured to:
and respectively integrating the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to a preset constraint condition to obtain the detection parameter set corresponding to each detection algorithm, wherein each detection parameter in the detection parameter set corresponds to the integrated parameter range.
11. The apparatus of claim 10, wherein each sensed parameter is provided with an initial parameter range;
the integration module is specifically configured to, when the detection parameter sets corresponding to each detection algorithm are obtained by respectively integrating the ranges of the detection parameters of the same type in the positive detection parameter set and the negative detection parameter set corresponding to each detection algorithm according to preset constraint conditions, perform:
aiming at the detection parameters which belong to the same type and correspond to any detection algorithm:
when the range of the positive detection parameters corresponding to the detection algorithm is not smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters of the same type corresponding to the detection algorithm are as follows: a part of the parameter value range of the positive detection parameter, which is overlapped with the value range outside the parameter value range of the negative detection parameter in the initial parameter range;
when the range of the positive detection parameters corresponding to the detection algorithm is smaller than the range of the negative detection parameters, determining that the value ranges of the detection parameters belonging to the same type and corresponding to the detection algorithm are as follows: range of positive detection parameters.
12. The apparatus of claim 10, wherein the determining module, when sending the fine-tuned detection parameter corresponding to the selected detection algorithm to the MCU, is specifically configured to:
fine-tuning parameter value ranges of all detection parameters corresponding to the selected detection algorithm according to an adjustment scheme input by a user and determined based on the current effect;
and sending all the fine-tuned detection parameters and parameter value ranges thereof to the MCU.
13. An implantable closed loop system, comprising: the apparatus for determining implantable closed-loop system sensing parameters of any one of claims 7-12.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090228057A1 (en) * | 2008-03-07 | 2009-09-10 | Cameron Health, Inc. | Accurate Cardiac Event Detection in an Implantable Cardiac Stimulus Device |
| CN101828917A (en) * | 2010-05-07 | 2010-09-15 | 深圳大学 | Method and system for extracting electrocardiosignal characteristic |
| US20120197323A1 (en) * | 2011-02-02 | 2012-08-02 | Efdal Elferri | Respiratory parameters for arrhythmia detection and therapy |
| US8521269B1 (en) * | 2012-06-27 | 2013-08-27 | Medtronic, Inc. | Determining tachyarrhythmia detection parameters based on prior detected episodes |
| US20140276181A1 (en) * | 2012-11-27 | 2014-09-18 | Neuropace, Inc. | Methods and Systems for Automatically Identifying Detection Parameters for an Implantable Medical Device |
| WO2016006977A1 (en) * | 2014-07-11 | 2016-01-14 | Encored Technologies, Inc. | Apparatus, server, system and method for energy measuring |
| US20160228705A1 (en) * | 2015-02-10 | 2016-08-11 | Neuropace, Inc. | Seizure onset classification and stimulation parameter selection |
| US20190223782A1 (en) * | 2018-01-25 | 2019-07-25 | Cardiac Pacemakers, Inc. | Systems and methods for dynamic respiration sensing |
| CN110292378A (en) * | 2019-07-02 | 2019-10-01 | 燕山大学 | Depression remote rehabilitation system based on the monitoring of E.E.G closed loop |
| CN111329481A (en) * | 2020-03-03 | 2020-06-26 | 中国科学院深圳先进技术研究院 | Physiological parameter determination method, physiological parameter determination device, physiological parameter detection equipment and medium |
| US20200237244A1 (en) * | 2019-01-28 | 2020-07-30 | Biotronik Se & Co. Kg | Signal replay for selection of optimal detection settings |
-
2021
- 2021-02-05 CN CN202110164477.3A patent/CN112774033A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090228057A1 (en) * | 2008-03-07 | 2009-09-10 | Cameron Health, Inc. | Accurate Cardiac Event Detection in an Implantable Cardiac Stimulus Device |
| CN101828917A (en) * | 2010-05-07 | 2010-09-15 | 深圳大学 | Method and system for extracting electrocardiosignal characteristic |
| US20120197323A1 (en) * | 2011-02-02 | 2012-08-02 | Efdal Elferri | Respiratory parameters for arrhythmia detection and therapy |
| US8521269B1 (en) * | 2012-06-27 | 2013-08-27 | Medtronic, Inc. | Determining tachyarrhythmia detection parameters based on prior detected episodes |
| US20140276181A1 (en) * | 2012-11-27 | 2014-09-18 | Neuropace, Inc. | Methods and Systems for Automatically Identifying Detection Parameters for an Implantable Medical Device |
| WO2016006977A1 (en) * | 2014-07-11 | 2016-01-14 | Encored Technologies, Inc. | Apparatus, server, system and method for energy measuring |
| US20160228705A1 (en) * | 2015-02-10 | 2016-08-11 | Neuropace, Inc. | Seizure onset classification and stimulation parameter selection |
| US20190223782A1 (en) * | 2018-01-25 | 2019-07-25 | Cardiac Pacemakers, Inc. | Systems and methods for dynamic respiration sensing |
| US20200237244A1 (en) * | 2019-01-28 | 2020-07-30 | Biotronik Se & Co. Kg | Signal replay for selection of optimal detection settings |
| CN110292378A (en) * | 2019-07-02 | 2019-10-01 | 燕山大学 | Depression remote rehabilitation system based on the monitoring of E.E.G closed loop |
| CN111329481A (en) * | 2020-03-03 | 2020-06-26 | 中国科学院深圳先进技术研究院 | Physiological parameter determination method, physiological parameter determination device, physiological parameter detection equipment and medium |
Non-Patent Citations (2)
| Title |
|---|
| SCOTT STANSLASKI, ET.AL: "Design and Validation of a Fully Implantable, Chronic, Closed-Loop Neuromodulation Device With Concurrent Sensing and Stimulation", IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 20, no. 4, 23 July 2012 (2012-07-23), pages 410 - 421, XP011453087, DOI: 10.1109/TNSRE.2012.2183617 * |
| 谢翔, 张春, 王志华: "生物医学中的植入式电子系统的现状与发展", 电子学报, no. 03, 25 March 2004 (2004-03-25), pages 462 - 467 * |
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