CN115999053A

CN115999053A - Electronic device, medical system, and computer-readable storage medium

Info

Publication number: CN115999053A
Application number: CN202211551789.0A
Authority: CN
Inventors: 章家凡; 黄宁
Original assignee: Hangzhou Nuowei Medical Technology Co ltd
Current assignee: Hangzhou Nuowei Medical Technology Co ltd
Priority date: 2022-12-05
Filing date: 2022-12-05
Publication date: 2023-04-25

Abstract

The present application provides an electronic device, a medical system and a computer readable storage medium, the electronic device comprising a memory and at least one processor, the memory storing a computer program, the at least one processor implementing the following steps when executing the computer program: s1: acquiring real-time brain electrical data of at least two channels of the patient using the sampling electrode; s2: calculating amplitude fluctuation values corresponding to the real-time electroencephalogram data of each channel and moving average difference values of the real-time electroencephalogram data of all channels; s3: detecting whether the real-time brain electrical data of the patient meets the first stimulation condition, and if so, executing step S4; s4: controlling the stimulator to deliver electrical stimulation to the patient for the first time. According to the method and the device, the electric stimulation is adaptively delivered to the patient according to the actual condition of the patient, and the condition that the stimulation is not timely or invalid is avoided.

Description

Electronic device, medical system, and computer-readable storage medium

Technical Field

The present application relates to the field of implantable device technology, and in particular, to an electronic device, a medical system, and a computer-readable storage medium.

Background

Epilepsy is a chronic disease caused by abnormal discharges resulting from highly synchronized activities of neurons, which can cause convulsions or spasticity, confusion, and sometimes even loss of consciousness, the second most common disease of the neurology department, followed by headache. Neuromodulation surgery is a common surgical procedure directed at drug-refractory epilepsy.

With the development of stereotactic technology, it is possible that electrical stimulation acts directly on a predetermined target point, and at the same time, a great deal of basic research and clinical trials support that electrical stimulation can be used as a new antiepileptic means, and electrical stimulation currently used for antiepileptic mainly includes Vagal Nerve Stimulation (VNS), cortical electrical stimulation and Deep Brain Stimulation (DBS).

However, most of the existing active implanted nerve stimulators for treating epilepsy on the market do not include the function of epilepsy detection, and only can intermittently emit electric stimulation in a fixed stimulation mode for treatment.

Based on this, the present application provides an electronic device, a medical system, and a computer-readable storage medium to solve the problems in the prior art described above.

Disclosure of Invention

It is an object of the present application to provide an electronic device, a medical system and a computer readable storage medium that adaptively deliver electrical stimulation to a patient according to the actual situation of the patient, avoiding situations where stimulation is not timely or ineffective.

The purpose of the application is realized by adopting the following technical scheme:

in a first aspect, the present application provides an electronic device for implementing control functions of a sampling electrode and a stimulator, the stimulator being disposed within a body of a patient;

the electronic device comprises a memory and at least one processor, the memory storing a computer program, the at least one processor implementing the following steps when executing the computer program:

s1: acquiring real-time brain electrical data of at least two channels of the patient using the sampling electrode;

s2: calculating amplitude fluctuation values corresponding to the real-time electroencephalogram data of each channel and moving average difference values of the real-time electroencephalogram data of all channels;

s3: detecting whether the real-time brain electrical data of the patient meets the first stimulation condition, and if so, executing step S4;

s4: controlling the stimulator to deliver electrical stimulation to the patient for the first time;

wherein the first stimulation conditions are: the amplitude fluctuation value corresponding to the real-time electroencephalogram data of at least one channel is not smaller than a preset amplitude threshold, and the moving average difference value of the real-time electroencephalogram data of all channels is not in a preset numerical range.

The beneficial effect of this technical scheme lies in: the sampling electrode is utilized to collect real-time brain electrical data of at least two channels of a patient, whether the real-time brain electrical data meets the first stimulation condition is detected, if yes, the patient is very likely to be ill, and the stimulator is controlled to deliver electrical stimulation to the patient.

Furthermore, the first stimulation conditions were: the amplitude fluctuation value corresponding to the real-time electroencephalogram data of at least one channel is not smaller than a preset amplitude threshold, the moving average difference value of the real-time electroencephalogram data of all channels is not in a preset numerical range, the two sub-conditions must be met at the same time to determine the morbidity of a patient and provide electric stimulation for the first time, the probability of misjudgment can be greatly reduced by the aid of the strict judgment mode, the electric stimulation is timely and accurately provided, and the life safety of the patient is guaranteed.

In some optional embodiments, the step S4 further includes:

continuing to perform step S1 during the delivery of electrical stimulation to the patient;

The at least one processor, when executing the computer program, further performs the steps of:

s5: detecting whether the real-time brain data of the patient meets the continuous stimulation condition, and if the real-time brain data of the patient meets the continuous stimulation condition, executing the step S6;

s6: controlling the stimulator to continue delivering electrical stimulation to the patient and continuing to perform step S1;

wherein the continued stimulation conditions include one or more of:

amplitude fluctuation values corresponding to the real-time electroencephalogram data of at least one channel are not smaller than the preset amplitude threshold value;

and the moving average difference value of the real-time brain data of all channels is not in the preset numerical value interval.

The beneficial effect of this technical scheme lies in: during the process of delivering the electrical stimulation to the patient, the work of the electroencephalogram data acquisition is not stopped (the sampling is performed while the stimulation is performed), and if the real-time electroencephalogram data of the patient meets the continuous stimulation condition, the condition that the patient is not inhibited by the first electrical stimulation (the patient is still ill) is indicated, and the electrical stimulation is continuously delivered to the patient.

In addition, the continued stimulation conditions were: the amplitude fluctuation value corresponding to the real-time electroencephalogram data of at least one channel is not smaller than the preset amplitude threshold value, and/or the moving average difference value of the real-time electroencephalogram data of all channels is not in the preset numerical value interval, that is, if one of the two sub-conditions is met, the patient is regarded as still ill (the continuous stimulation condition is not harsh), and the standard of the continuous stimulation condition is relaxed on the premise that the patient has met the first stimulation condition, so that the electric stimulation can be more suitable for the real state of the patient.

In some alternative embodiments, step S5 further comprises:

and if the real-time brain electrical data of the patient does not meet the continuous stimulation condition, controlling the stimulator to stop stimulation.

The beneficial effect of this technical scheme lies in: if the real-time brain data of the patient does not meet the continuous stimulation condition, the condition of the patient is successfully restrained by the first electric stimulation, the patient does not have the disease at present, the stimulator can be controlled to stop stimulation, and unnecessary damage to the patient is avoided.

In some alternative embodiments, the at least one processor, when executing the computer program, further performs the steps of:

s7: detecting whether the continuous stimulation duration of the stimulator is not less than a first preset duration threshold, and if not, executing step S8;

s8: the stimulator is controlled to stop stimulating, and the sampling electrode is controlled to stop collecting real-time brain electrical data;

s9: and detecting whether the stimulation stopping time period is not less than a second preset time period threshold value, and if so, continuing to execute the step S1.

The beneficial effect of this technical scheme lies in: when the continuous stimulation duration (single stimulation) of the stimulator is too long and exceeds a first preset duration threshold, the fact that the current stimulation does not have an inhibiting effect on the illness state of a patient is indicated, the current stimulation is invalid, the stimulator can be controlled to stop stimulation, the sampling electrode is controlled to stop collecting, excessive stimulation is prevented, when the stimulation stopping duration reaches a second preset duration threshold, real-time brain electrical data of the patient can be continuously collected, and the state of the patient is monitored.

In some alternative embodiments, the at least one processor, when executing the computer program, calculates the amplitude fluctuation value corresponding to the real-time electroencephalogram data of each channel in the following manner:

calculating a first difference value of the maximum value and the minimum value of the real-time electroencephalogram data of each channel in a sampling window of a first preset sampling duration, and taking the first difference value as an amplitude fluctuation value corresponding to the real-time electroencephalogram data of each channel.

The beneficial effect of this technical scheme lies in: and calculating a first difference value of the maximum value and the minimum value of the real-time electroencephalogram data of each channel in a sampling window of a first preset sampling duration, wherein the first difference value is the amplitude fluctuation value corresponding to the real-time electroencephalogram data of each channel, and the difference value calculation mode has small calculated amount and higher calculation efficiency.

In some alternative embodiments, the at least one processor, when executing the computer program, calculates a moving average difference of real-time electroencephalogram data for all channels in the following manner:

moving average is carried out on the real-time electroencephalogram data of each channel in a sampling window with a second preset sampling duration, and a plurality of moving average values corresponding to each channel are sequentially obtained;

Based on the last moving average value of each channel, the moving average difference value of the real-time brain data of all channels is calculated.

The beneficial effect of this technical scheme lies in: and the method for detecting the symptoms and performing stimulation treatment by taking the moving average difference value among the channels as the basis is not related to complex algorithms such as machine learning, deep learning and the like, is simple and effective to realize, and has high response speed.

In some alternative embodiments, the at least two channels include a first channel and a second channel;

the at least one processor, when executing the computer program, calculates a moving average difference of real-time electroencephalogram data of the first channel and the second channel in the following manner:

and calculating a second difference value between the last moving average value of the first channel and the last moving average value of the second channel, and taking the absolute value of the second difference value as the moving average difference value of the real-time brain data of the first channel and the second channel.

The beneficial effect of this technical scheme lies in: at least, only 2 channels of electroencephalogram data are required to be sampled, so that the detection and the stimulation treatment of symptoms can be performed, the hardware requirement on a sampling electrode is low, and the detection precision is high.

In some alternative embodiments, the second preset sample period is longer than the first preset sample period.

The beneficial effect of this technical scheme lies in: the second preset sampling time period is longer than the first preset sampling time period, so that the relatively abundant data volume is used as a support when moving average is carried out, and the obtained moving average difference value is more accurate.

In a second aspect, the present application provides a medical system comprising:

a stimulator disposed within the body of the patient, the stimulator for delivering electrical stimulation to the patient;

the sampling electrode is used for collecting real-time brain electrical data of at least two channels of the patient;

an electronic device as in any above.

In a third aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the functions of the electronic device of any one of the preceding claims.

Drawings

The present application is further described below with reference to the drawings and embodiments.

Fig. 1 is a schematic flow chart of a control method according to an embodiment of the present application.

Fig. 2 is a flow chart of another control method according to an embodiment of the present application.

Fig. 3 is a flow chart of another control method according to an embodiment of the present application.

Fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.

Fig. 5 is a block diagram of a medical system according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of a program product according to an embodiment of the present application.

Detailed Description

The technical solutions in the present application will be described below with reference to the drawings and the specific embodiments in the specification of the present application, and it should be noted that, on the premise of no conflict, new embodiments may be formed by any combination of the embodiments or technical features described below.

In this application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, a and b, a and c, b and c, a and b and c, wherein a, b and c can be single or multiple. It is noted that "at least one" may also be interpreted as "one (a) or more (a)".

It is also noted that, in this application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

The application field of the present application will be briefly described below.

The implanted nerve stimulation system mainly comprises a stimulator implanted in a body and a program control device outside the body. The existing nerve regulation and control technology mainly comprises the steps of implanting electrodes into specific structures (namely targets) in a body through stereotactic operation, and sending electric pulses to the targets through the electrodes by a stimulator implanted into the body of a patient, so as to regulate and control the electric activities and functions of the corresponding nerve structures and networks, thereby improving symptoms and relieving pains. The stimulator may be any one of an implantable nerve electrical stimulation device, an implantable cardiac electrical stimulation system (also called a cardiac pacemaker), an implantable drug infusion device (Implantable Drug Delivery System, abbreviated as I DDS) and a lead switching device. Examples of the implantable nerve electrical stimulation device include deep brain electrical stimulation system (Deep Brain Stimulation, abbreviated as DBS), implantable cortex stimulation system (Cortical Nerve Stimulation, abbreviated as CNS), implantable spinal cord electrical stimulation system (Spinal Cord Stimulation, abbreviated as SCS), implantable sacral nerve electrical stimulation system (Sacral Nerve Stimulation, abbreviated as SNS), implantable vagal nerve electrical stimulation system (Vagus Nerve Stimulation, abbreviated as VNS), and the like.

The stimulator may include an IPG (implantable pulse generator ) disposed within the patient to provide controllable electrical stimulation energy to tissue within the body by means of a sealed battery and electrical circuitry, and one or both controllable specific electrical stimulation energy to specific areas of tissue within the body via the implanted extension and electrode leads. The extension lead is matched with the IPG to be used as a transmission medium of the electrical stimulation signals, and the electrical stimulation signals generated by the IPG are transmitted to the electrode lead. The electrode lead releases electric stimulation energy to a specific area of the internal tissue through a plurality of electrode contacts according to the electric stimulation signals generated by the IPG; the implantable medical device is provided with one or more electrode leads on one side or two sides, a plurality of electrode contacts are arranged on the electrode leads, and the electrode contacts can be uniformly arranged or non-uniformly arranged in the circumferential direction of the electrode leads.

In some possible implementations, the stimulated in vivo tissue may be brain tissue of a patient, and the stimulated site may be a specific site of brain tissue (e.g., the vagus nerve). When the type of disease in the patient is different, the location to be stimulated will generally be different, as will the number of stimulation contacts (single or multiple sources) used, the application of one or more (single or multiple channels) specific electrical stimulation signals, and the stimulation parameter data. The type of disease to which the present application is applicable is not limited, and may be a type of disease to which Deep Brain Stimulation (DBS), spinal Cord Stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, functional electrical stimulation are applicable. Among the types of diseases that DBS may be used to treat or manage include, but are not limited to: spasticity (e.g., epilepsy), pain, migraine, psychotic disorders (e.g., major Depressive Disorder (MDD)), bipolar disorder, anxiety, post-traumatic stress disorder, depression, obsessive Compulsive Disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, movement disorders (e.g., essential tremor or parkinson's disease), huntington's disease, alzheimer's disease, drug addiction, autism, or other neurological or psychiatric disorders and impairments.

Epilepsy is a chronic disease caused by abnormal discharges resulting from highly synchronized activities of neurons, which can cause convulsions or spasticity, confusion, and sometimes even loss of consciousness, the second most common disease of the neurology department, followed by headache. Currently, most of the active implanted nerve stimulators for treating epilepsy on the market do not include the function of epilepsy detection, but only intermittently emit electric stimulation for treatment.

The research on epileptic detection is to perform offline data processing on the acquired multichannel electroencephalogram data and judge the time of epileptic occurrence, or perform online detection with a device with strong computing power based on a non-invasive wearable device.

In addition, the size, temperature rise requirements of the implantable device itself are such that the power consumption of the product cannot be too great, and too complex algorithms (machine learning, deep learning) are also difficult to apply in implantable devices.

In order to solve the problem that the existing neurostimulator cannot send out stimulation treatment according to the actual situation of an epileptic patient, a control method, electronic equipment, a medical system and a computer readable storage medium are introduced below, and the electrical stimulation is adaptively delivered to the patient according to the actual situation of the patient, so that the situation that the stimulation is not timely or invalid is avoided.

Method embodiment

Referring to fig. 1, fig. 1 is a schematic flow chart of a control method according to an embodiment of the present application.

The control method is used for realizing the control function of the sampling electrode and a stimulator, wherein the stimulator is arranged in the body of a patient, and the control method comprises the following steps:

In some embodiments, if the patient's real-time electroencephalogram data does not meet the first-time stimulation condition, no processing is done (no stimulation, acquisition of electroencephalogram data continues).

Therefore, the sampling electrode is used for collecting real-time brain electrical data of at least two channels of a patient, whether the real-time brain electrical data meet the first stimulation condition is detected, if yes, the patient is very likely to be ill, and the stimulator is controlled to deliver electrical stimulation to the patient.

In some embodiments, the amplitude fluctuation value corresponding to the real-time electroencephalogram data of at least one channel is not smaller than a preset amplitude threshold value as an amplitude fluctuation event, and the first stimulation condition is:

The duration of any amplitude fluctuation event is not less than the preset fluctuation duration, the time interval between two adjacent amplitude fluctuation events is less than the preset interval duration, and the moving average difference value of the real-time electroencephalogram data of all channels is not in the preset numerical value interval.

The preset fluctuation time length and the preset interval time length are not limited, and the preset fluctuation time length can be, for example, 1.5 seconds, 2 seconds, 2.4 seconds, 2.8 seconds, 3 seconds, 3.4 seconds and the like, and the preset interval time length can be, for example, 0.5 seconds, 1 second, 1.2 seconds, 1.5 seconds, 1.8 seconds, 2 seconds and the like.

The number of channels of the data collected by the sampling electrode is not limited in the embodiment of the present application, and may be 2, 3, 5 or 8, for example.

The preset amplitude threshold and the preset value interval are not limited in the embodiment of the present application, and the preset amplitude threshold may be, for example, 100 μv, 150 μv, 180 μv, 200 μv, 210 μv, 250 μv, and the like.

The upper limit value of the preset value interval may be, for example: 10 μv, 15 μv, 18 μv, 20 μv, 21 μv, 25 μv, etc.

The lower limit value of the preset value interval may be, for example: 5 μv, 8 μv, 10 μv, 12 μv, 16 μv, 18 μv, etc.

The corresponding stimulation parameters of the stimulator include at least one of the following: frequency (e.g., in Hz, the number of electrical stimulation pulse signals per unit time for 1 second), pulse width (duration of each pulse in mus), amplitude (generally expressed in terms of voltage, i.e., intensity of each pulse in V), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode, and cyclic stimulation mode), physician upper and lower control limits (physician adjustable range), and patient upper and lower control limits (patient autonomously adjustable range).

In some embodiments, the electrical stimulus applied by the stimulator may be a set of fixed stimuli.

The fixed stimulus, i.e. the stimulus parameters corresponding to the stimulator are fixed.

In one specific application, the stimulation parameters of the fixed stimulation are as follows: the frequency was 130Hz, the pulse width was 60. Mu.s, and the duration was 10 seconds.

In other embodiments, the stimulation parameters of the stimulator may be adjusted according to the type of illness and severity of the illness of the patient.

In one specific application, when the patient suffers from mild epilepsy, the stimulator corresponds to the stimulation parameters as follows: the frequency was 100Hz, the pulse width was 50 μs and the duration was 5 seconds.

When the patient suffers from severe parkinsonism, the corresponding stimulation parameters of the stimulator are as follows: the frequency was 200Hz, the pulse width was 80. Mu.s, and the duration was 15 seconds.

In some optional embodiments, in the step S2, calculating an amplitude fluctuation value corresponding to the real-time electroencephalogram data of each channel may include:

Therefore, a first difference value of the maximum value and the minimum value of the real-time brain electrical data of each channel in a sampling window of a first preset sampling time length is calculated, the first difference value is the amplitude fluctuation value corresponding to the real-time brain electrical data of each channel, and the calculation amount of the difference value calculation mode is small, and the calculation efficiency is high.

The first preset sampling duration is not limited, and may be 0.5 seconds, 1 second, 1.2 seconds, 1.5 seconds, 1.8 seconds, 2 seconds, and the like.

It should be noted that: the first preset sampling duration advances from the current sampling point (detection point) of the sampling electrode, so that the latest electroencephalogram data in time can be adopted, and the obtained detection result (whether the disease occurs) can be more close to the current state of the patient.

For example: the first preset sampling duration is 1 second, and the sampling window of the first preset sampling duration refers to the sampling window of the time period of the first 1 second of the current sampling point.

In a specific application, the first preset sampling time length is 1 second, and the sampling electrode acquires real-time electroencephalogram data of 3 channels (a first channel, a second channel and a third channel);

the ratio of the maximum value to the minimum value of the real-time electroencephalogram data of the first channel in the sampling window of one second before the current sampling point is 60 mu v and 100 mu v, and the amplitude fluctuation value of the first channel is 40 mu v;

the maximum value and the minimum value of the real-time brain electrical data of the second channel in a sampling window of one second before the current sampling point are 40 mu v and 700 mu v, and then the amplitude fluctuation value of the second channel is 30 mu v;

the ratio of the maximum value to the minimum value of the real-time brain electrical data of the third channel in the sampling window of one second before the current sampling point is 80 mu v and 900 mu v, and the amplitude fluctuation value of the third channel is 10 mu v.

In some optional embodiments, in the step S2, calculating a moving average difference value of the real-time electroencephalogram data of all channels may include:

Based on the last moving average value of each channel, the moving average difference value of the real-time brain data of all channels is calculated. The last moving average value is adopted in the calculation process, the data is newer in terms of time, and the detection result (whether the disease occurs or not) obtained by the method can be more close to the current state of the patient.

Moving average, i.e. using a smoothing window to move and average over the data, thereby smoothing the data and eliminating noise in the data.

For example: using a smooth window with a window size of 5, the 5 consecutive sampling points selected are: 50 μv, 60 μv, 90 μv, 80 μv and 100 μv, which are averaged and then the moving average corresponding to the center point of the current smoothing window is assigned 76 μv.

And then moving the smoothing window from front to back, so that the center point of the smoothing window traverses the real-time electroencephalogram data in the sampling window with the whole second preset sampling duration to finish moving average, and a plurality of moving averages obtained by the moving average are as follows: 76 μv, 83 μv, 71 μv, 65 μv, 73 μv, 54 μv, 23 μv, 45 μv, 82 μv, 56 μv, 67 μv (the last moving average is 67 μv).

In some embodiments, the difference value calculation may be performed on the last moving average value of any two channels to obtain a plurality of difference values, and the average value of the plurality of difference values is used as the moving average difference value of the real-time electroencephalogram data of all the channels.

Therefore, moving average is carried out on the real-time electroencephalogram data of each channel in the sampling window with the second preset sampling duration, a plurality of moving averages corresponding to each channel are sequentially obtained, and the moving average difference value of the real-time electroencephalogram data of all channels is calculated according to the last moving average value of each channel.

The second preset sampling duration is not limited in this embodiment, and may be 1.5 seconds, 2 seconds, 2.4 seconds, 2.8 seconds, 3 seconds, 3.4 seconds, and so on.

It should be noted that: similar to the first preset sampling period, the second preset sampling period advances from the current sampling point (detection point) of the sampling electrode, so that the latest electroencephalogram data in time can be adopted, and the detection result (whether the patient is ill or not) can be close to the current state of the patient.

For example: the second preset sampling duration is 2 seconds, and the sampling window of the second preset sampling duration refers to the sampling window of the time period of the first 2 seconds of the current sampling point.

the process of calculating a moving average difference value of the real-time brain electrical data of the first channel and the second channel may include:

Therefore, the brain electrical data of 2 channels at least need to be sampled, the disease can be detected and stimulated, the hardware requirement on the sampling electrode is low, and the detection accuracy is high.

In a specific application, the last moving average value obtained after the moving average is carried out on the first channel is 67 mu v, the last moving average value obtained after the moving average is carried out on the second channel is 78 mu v, the absolute value of the difference value of the last moving average value and the second moving average value is 11 mu v, and then the moving average difference value of the real-time electroencephalogram data of the first channel and the second channel is 11 mu v.

Therefore, the second preset sampling time period is longer than the first preset sampling time period, so that the relatively abundant data volume is ensured to be used as a support when moving average is carried out, and the obtained moving average difference value is more accurate.

In one specific application, the second preset sample period is 2 seconds and the first preset sample period is 1 second.

Referring to fig. 2, fig. 2 is a schematic flow chart of another control method according to an embodiment of the present application.

In some optional embodiments, the step S4 further includes:

the method may further comprise:

wherein the continued stimulation conditions include one or more of:

Thus, during the process of delivering the electrical stimulation to the patient, the work of the electroencephalogram data acquisition is not stopped (the sampling is performed while the stimulation is being performed), and if the real-time electroencephalogram data of the patient meets the continuous stimulation condition, the condition of the patient is not inhibited by the first electrical stimulation (the patient is still ill), and the electrical stimulation is continuously delivered to the patient.

In other alternative embodiments, during the delivery of electrical stimulation to the patient, no brain electrical data is acquired, but step S1 is performed after the end of the sampling mask period.

This is because the system does not use a method of collecting the brain electrical data while stimulating (i.e., the stimulation refractory period) because it is considered that the electrical stimulation is delivered to the patient, which causes some disturbance to the collected brain electrical data.

In some embodiments, the sampling mask duration may be determined based on the relative distance of the sampling electrode from the stimulation electrode of the stimulator. The farther the distance between the two is, the smaller the influence degree of the stimulus on data acquisition is, and the smaller the sampling shielding duration can be set.

In one particular application, the sampling mask duration may be, for example, 0.5 seconds, 0.8 seconds, 1 second, 1.2 seconds, 1.3 seconds, 1.4 seconds, etc.

In some alternative embodiments, step S5 further comprises:

Therefore, if the real-time brain data of the patient does not meet the continuous stimulation condition, the condition of the patient is successfully restrained by the first electric stimulation, the patient does not have the disease at present, the stimulator can be controlled to stop stimulation, and unnecessary damage to the patient is avoided.

Referring to fig. 3, fig. 3 is a schematic flow chart of another control method according to an embodiment of the present application.

In some alternative embodiments, the method may further comprise:

Therefore, when the continuous stimulation duration (single stimulation) of the stimulator is too long and exceeds a first preset duration threshold, the fact that the current stimulation does not have an inhibiting effect on the illness state of a patient is indicated, the current stimulation is invalid, the stimulator can be controlled to stop stimulation, the sampling electrode is controlled to stop collecting, excessive stimulation is prevented, when the stimulation stopping duration reaches a second preset duration threshold, real-time brain electrical data of the patient can be continuously collected, and the state of the patient is monitored.

The first preset duration threshold and the second preset duration threshold are not limited, and the first preset duration threshold may be, for example, 15 seconds, 18 seconds, 20 seconds, 22 seconds, 25 seconds, 28 seconds, and the like; the second preset duration threshold may be, for example, 5 seconds, 8 seconds, 10 seconds, 12 seconds, 15 seconds, 18 seconds, etc.

The embodiment of the application also provides a method for detecting and stimulating and treating epilepsy, which requires sampling of brain electrical signals of at least 2 channels (a channel one and a channel two), and comprises the following specific processes:

1. Calculating channel-amplitude fluctuation characteristics (denoted by feat_ch1_ff)

The calculation method is as follows: amplitude fluctuations (amplitude fluctuations, i.e., the maximum value minus the minimum value of the window data) within a time window of 1 second before the current detection point of the data of channel one are calculated.

2. Calculating the two-amplitude fluctuation characteristic (indicated by the feat_ch2_ff)

The calculation method is as follows: amplitude fluctuations (amplitude fluctuations, i.e., the maximum value minus the minimum value of the window data) within a time window of 1 second before the current detection point of the data of channel two are calculated.

3. Calculating the inter-channel moving average difference characteristic (denoted by feat ma diff)

The calculation method is as follows: firstly, carrying out moving average on data in a time window of 2 seconds before a current detection point of a first channel;

then, moving average is carried out on the data in the time window of 2 seconds before the current detection point of the second channel;

and subtracting the final data value of the second channel after the movement smoothing by using the final data value of the first channel after the movement averaging, and finally taking an absolute value of the difference value to obtain an inter-channel movement average difference value.

4. When any one of the channel one amplitude fluctuation feature (feat_ch1_ff) and the channel two amplitude fluctuation feature (feat_ch2_ff) exceeds a set threshold, an amplitude fluctuation event occurs;

5. Detecting whether the time of the amplitude fluctuation event exceeds 2 seconds and the time interval of any 2 events before and after the occurrence is less than 1 second, detecting whether the moving average difference characteristic (feat_ma_diff) between channels is greater than a set upper limit threshold or less than a set lower limit threshold, if so, judging that the patient suffers from epilepsy, and giving a group of fixed stimulation;

6. continuing to detect whether an amplitude fluctuation event occurs or whether a moving average difference characteristic (feat_ma_diff) between channels is larger than a set upper limit threshold or smaller than a set lower limit threshold (one of two conditions is met), judging that the patient continues to generate epilepsy, and continuing to apply a group of fixed stimuli;

7. the continuous stimulus duration exceeds 20 seconds, the current stimulus is judged to be invalid, the stimulus shielding time is 10 seconds (namely, the detection is not carried out again after the next 10 seconds, the stimulus is not given any more), and after the shielding time of 10 seconds, the detection is continuously started.

The method realizes the functions of detecting and stimulating the treatment of epilepsy by calculating the amplitude fluctuation characteristic (amplitude fluctuation, namely the maximum value minus the minimum value of window data) and the inter-channel moving average difference characteristic, and does not relate to complex algorithms such as machine learning, deep learning and the like, and has simple and effective realization and high response speed; and moreover, only 2 channels of electroencephalogram data are required to be sampled, so that epileptic detection and stimulation can be performed, the hardware requirement is low, and the detection accuracy is high.

Device embodiment

The embodiment of the application also provides an electronic device, and a specific implementation manner of the electronic device is consistent with the implementation manner and the achieved technical effect described in the embodiment of the method, and part of the detailed description is omitted.

The electronic equipment is used for realizing the control function of the sampling electrode and the stimulator, and the stimulator is arranged in the body of a patient;

In some embodiments, the electronic device is integrated with the stimulator.

In some optional embodiments, the amplitude fluctuation value corresponding to the real-time electroencephalogram data of at least one channel is not smaller than a preset amplitude threshold value as an amplitude fluctuation event, and the first stimulation condition is that:

In some optional embodiments, the step S4 further includes:

wherein the continued stimulation conditions include one or more of:

In some alternative embodiments, step S5 further comprises:

Referring to fig. 4, fig. 4 is a block diagram of an electronic device according to an embodiment of the present application. The electronic device may include, for example, at least one memory 210, at least one processor 220, and a bus 230 connecting the different platform systems.

Memory 210 may include readable media in the form of volatile memory, such as Random Access Memory (RAM) 211 and/or cache memory 212, and may further include Read Only Memory (ROM) 213.

The memory 210 further stores a computer program, and the computer program may be executed by the processor 220, so that the processor 220 implements the steps of the control method, and a specific implementation manner of the computer program is consistent with the implementation manner and the achieved technical effect described in the method embodiment, and some of the details are not repeated.

Memory 210 may also include utility 214 having at least one program module 215, such program modules 215 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Accordingly, the processor 220 may execute the computer programs described above, and may execute the utility 214.

The processor 220 may employ one or more application specific integrated circuits (ASICs, application Specific Integrated Circuit), DSPs, programmable logic devices (PLD, programmableLogic devices), complex programmable logic devices (CPLDs, complex Programmable Logic Device), field programmable gate arrays (FPGAs, fields-Programmable Gate Array), or other electronic components.

Bus 230 may be a local bus representing one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or any of a variety of bus architectures.

The electronic device may also communicate with one or more external devices 240, such as a keyboard, pointing device, bluetooth device, etc., as well as with one or more devices capable of interacting with the electronic device, and/or with any device (e.g., router, modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may occur through input-output interface 250. Also, the electronic device may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 260. Network adapter 260 may communicate with other modules of the electronic device via bus 230. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with an electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, data backup storage platforms, and the like.

System embodiment

Referring to fig. 5, fig. 5 is a block diagram of a medical system according to an embodiment of the present application.

The specific implementation manner of the medical system 100 is consistent with the implementation manner and the achieved technical effects described in the above method embodiments, and some of the details are not repeated.

The medical system 100 includes:

a stimulator 300, the stimulator 300 being disposed within the patient's body, the stimulator 300 for delivering electrical stimulation to the patient;

a sampling electrode 400 for acquiring real-time brain electrical data of at least two channels of the patient;

any of the above electronic devices 200.

Media embodiment

The present application further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where a specific implementation manner of the computer program is consistent with an implementation manner and an achieved technical effect recorded in an embodiment of the method, and some contents are not repeated.

Referring to fig. 6, fig. 6 shows a schematic structural diagram of a program product for implementing the control method provided in the present application. The program product may take the form of a portable compact disc read-only memory (CD-ROM) and comprises program code and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this application, the readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The present application is directed to functional enhancement and use elements, which are emphasized by the patent laws, such as the description and drawings, of the present application, but are not limited to the preferred embodiments of the present application, and therefore, all equivalents and modifications, equivalents, and modifications, etc. of the structures, devices, features, etc. of the present application are included in the scope of the present application.

Claims

1. An electronic device for implementing control functions of a sampling electrode and a stimulator, the stimulator being disposed within a patient;

2. The electronic device according to claim 1, wherein an amplitude fluctuation value corresponding to the real-time electroencephalogram data of at least one channel is not smaller than a preset amplitude threshold value as an amplitude fluctuation event, and the first stimulus condition is:

3. The electronic device of claim 1, wherein the step S4 further comprises:

wherein the continued stimulation conditions include one or more of:

4. The electronic device of claim 3, wherein step S5 further comprises:

5. The electronic device of claim 3, wherein the at least one processor when executing the computer program further performs the steps of:

6. The electronic device of any of claims 1-5, wherein the at least one processor, when executing the computer program, calculates the amplitude fluctuation value corresponding to the real-time electroencephalogram data for each channel by:

7. The electronic device of claim 6, wherein the at least one processor, when executing the computer program, calculates a moving average difference of real-time electroencephalogram data for all channels by:

8. The electronic device of claim 7, wherein the at least two channels comprise a first channel and a second channel;

9. A medical system, the medical system comprising:

the electronic device of any one of claims 1-8.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the functionality of the electronic device of any of claims 1-8.