WO2024041496A1 - 充电提醒装置、植入式神经刺激系统及存储介质 - Google Patents
充电提醒装置、植入式神经刺激系统及存储介质 Download PDFInfo
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Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
- A61N1/36125—Details of circuitry or electric components
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
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- A61N1/372—Arrangements in connection with the implantation of stimulators
- A61N1/378—Electrical supply
- A61N1/3787—Electrical supply from an external energy source
Definitions
- This application relates to the technical fields of implantable devices, remote programming and deep learning, such as charging reminder devices, implantable neurostimulation systems and storage media.
- the stimulator is implanted in the patient's body and delivers electrical stimulation to the target point through stimulation contacts on the electrode leads, thereby improving the patient's symptoms and relieving pain. Since the stimulator is inside the patient's body, it cannot be seen or touched. Unlike smart devices outside the body such as mobile phones, which can display real-time battery power, the user can know when to charge.
- the patient's symptoms cannot be effectively controlled and the patient's pain cannot be relieved. More importantly, if the patient finally makes an appointment for "remote program control" (registered in advance), When the doctor is preparing to carry out remote programming, he discovers that the patient's stimulator is out of power. This may cause the patient to miss the opportunity for treatment and have to spend time and energy to make a new appointment and wait for the doctor to have a suitable time to carry out programming. This shows that in advance It is essential to remind the patient to charge the stimulator.
- Patent CN108174034A discloses a system for real-time monitoring of a sacral neuromodulation device using an Application (APP), including a sacral neuromodulation device, a mobile terminal and a server; the sacral neuromodulation device is equipped with a built-in APP for sending and receiving information, and a mobile terminal
- An external APP is set up inside to send and receive information.
- the built-in APP is used to send built-in battery power information to the external APP via the server.
- the external APP is used to send charging requests to the external controller via the server.
- the external controller is used to control the external controller. Set up the charging device to charge the built-in charging device.
- This application provides a charging reminder device, an implantable neurostimulation system and a storage medium, which can remind the user of charging in advance without the need for frequent data interaction between the stimulator and the external communication device.
- the present application provides a charging reminder device.
- the charging reminder device includes a controller.
- the controller interacts with data respectively with a stimulator and an extracorporeal communication device.
- the stimulator is implanted in the patient's body.
- the stimulator is provided with at least one electrode lead and delivers electrical stimulation to the patient using electrode contacts of the at least one electrode lead, and the controller is configured to:
- configuration information of the stimulator including at least one of the following: the number of electrode leads, the number of electrode contacts used by each electrode lead, and the amplitude, pulse width and frequency of the electrical stimulation signal;
- a charging reminder strategy of the extracorporeal communication device is obtained to remind the patient to charge the stimulator, where the charging reminder strategy includes reminder frequency and/or reminder content.
- the external communication device includes a programmable device and/or an external charger
- the controller is configured to obtain the current power of the stimulator in the following manner:
- the program-controlled device When the program-controlled device establishes a program-controlled connection with the stimulator, use the program-controlled device to obtain the current power of the stimulator, or,
- the external charger When the external charger establishes a communication connection with the stimulator, the external charger is used to obtain the current power of the stimulator.
- the controller is configured to obtain the power consumption speed of the stimulator in the following manner:
- the training process of the power consumption prediction model includes:
- the first training set including a plurality of first training data, each first training data including configuration information of a sample stimulator and annotation data of the power consumption speed of the sample stimulator;
- the trained first deep learning model is used as the power consumption prediction model; if the preset first training end condition is not met, the next first training data is used to continue training the The first deep learning model.
- the electrode contacts of the electrode leads are also configured to collect the patient's brain electrical signals, and the controller is further configured to:
- the charging reminder strategy of the extracorporeal communication device is updated.
- the controller is configured to obtain the reference configuration information corresponding to the EEG signal in the following manner:
- the training process of the reference configuration model includes:
- the second training set includes a plurality of second training data, each second training data includes a sample EEG signal and annotation data of reference configuration information corresponding to the sample EEG signal;
- the configuration information of the stimulator includes the stimulation time period and the amplitude, pulse width and frequency of the electrical stimulation signal.
- the controller is configured to obtain the charging reminder policy of the external communication device in the following manner:
- the form of the corresponding relationship includes a corresponding relationship table and/or a corresponding relationship diagram
- the charging reminder strategy corresponding to the charging time range in which the charging time is located is found in the corresponding relationship, and is used as the charging reminder strategy of the external communication device.
- the patient's disease type includes one or more of epilepsy, tremor, Parkinson's disease, depression, obsessive-compulsive disorder, Alzheimer's disease and drug addiction.
- This application also provides a charging reminder method, which method is applied to an implanted neurostimulation system.
- the implanted neurostimulation system includes a stimulator and an extracorporeal communication device.
- the stimulator is implanted in the patient's body.
- the stimulator is provided with at least one electrode lead and uses electrode contacts of the electrode lead to deliver electrical stimulation to the patient, and the method includes:
- configuration information of the stimulator including at least one of the following: the number of electrode leads, the number of electrode contacts used by each electrode lead, and the amplitude, pulse width and frequency of the electrical stimulation signal;
- a charging reminder strategy of the extracorporeal communication device is obtained to remind the patient to charge the stimulator, where the charging reminder strategy includes reminder frequency and/or reminder content.
- the external communication device includes a programmable device and/or an external charger
- the obtaining the current power of the stimulator includes:
- the program-controlled device When the program-controlled device establishes a program-controlled connection with the stimulator, use the program-controlled device to obtain the current power of the stimulator, or,
- the external charger When the external charger establishes a communication connection with the stimulator, the external charger is used to obtain the current power of the stimulator.
- obtaining the power consumption speed of the stimulator includes:
- the training process of the power consumption prediction model includes:
- the first training set includes a plurality of first training data.
- Each first training data includes configuration information of a sample stimulator and a label number of the power consumption speed of the sample stimulator. according to;
- the preset first training end condition Detect whether the preset first training end condition is met; if the preset first training end condition is met, use the trained first deep learning model as the power consumption prediction model; if the preset first training end condition is not met; In the case of the preset first training end condition, the next first training data is used to continue training the first deep learning model.
- the electrode contacts of the electrode leads are also configured to collect the patient's brain electrical signals, and the method further includes:
- the charging reminder strategy of the extracorporeal communication device is updated.
- obtaining the reference configuration information corresponding to the EEG signal includes:
- the training process of the reference configuration model includes:
- the second training set includes a plurality of second training data, each second training data includes a sample EEG signal and annotation data of reference configuration information corresponding to the sample EEG signal;
- the configuration information of the stimulator includes the stimulation time period and the amplitude, pulse width and frequency of the electrical stimulation signal.
- the obtaining the charging reminder strategy of the external communication device includes:
- the form of the corresponding relationship includes a corresponding relationship table and/or a corresponding relationship diagram
- the charging reminder strategy corresponding to the charging time range in which the charging time is located is found in the corresponding relationship, and is used as the charging reminder strategy of the external communication device.
- the patient's disease type includes one or more of epilepsy, tremor, Parkinson's disease, depression, obsessive-compulsive disorder, Alzheimer's disease and drug addiction.
- This application also provides an implantable neurostimulation system, which includes:
- a stimulator implanted in a patient the stimulator being provided with at least one electrode lead and utilizing electrode contacts of the electrode lead to deliver electrical stimulation to the patient;
- An extracorporeal communication device the extracorporeal communication device is arranged outside the body of the patient, and the extracorporeal communication device is configured to remind the patient to charge the stimulator;
- the external communication device and the charging reminder device are integrated.
- This application also provides a computer-readable storage medium that stores a computer program.
- the computer program When executed by a processor, it implements the functions of any of the above controllers.
- Figure 1 is a structural block diagram of an implantable neurostimulation system provided by an embodiment of the present application.
- Figure 2 is a structural block diagram of a stimulator provided by an embodiment of the present application.
- FIG. 3 is a schematic flowchart of a charging reminder method provided by an embodiment of the present application.
- Figure 4 is a structural block diagram of a controller provided by an embodiment of the present application.
- Figure 5 is a schematic structural diagram of a program product provided by an embodiment of the present application.
- At least one refers to one or more, and “multiple” refers to two or more.
- “And/or” describes the association of associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A exists alone, A and B exist simultaneously, and B exists alone, where A, B can be singular or plural.
- the character “/” generally indicates that the related objects are in an “or” relationship.
- “At least one of the following” or similar expressions thereof refers to any combination of these items, including any combination of a single item (items) or a plurality of items (items).
- At least one of a, b or c can mean: a, b, c, a and b, a and c, b and c, a and b and c, where a, b and c can It can be single or multiple. It is worth noting that "at least one item (item)” can also be interpreted as “one item (item) or multiple items (item)”.
- An implantable neurostimulation system (an implantable medical system) mainly includes a stimulator implanted in the patient's body and a program-controlled device installed outside the patient's body.
- Existing neuromodulation technology mainly implants electrodes into specific structures (i.e. target points) in the body through stereotaxic surgery, and the stimulator implanted in the patient's body sends electrical pulses to the target point through the electrodes to regulate the corresponding neural structures and networks. electrical activity and its functions, thereby improving symptoms and relieving pain.
- the stimulator can be an implantable nerve electrical stimulation device, an implantable cardiac electrical stimulation system (also known as a pacemaker), an implantable drug delivery device (Implantable Drug Delivery System, IDDS) and a lead adapter device any of them.
- Implantable neuroelectric stimulation devices include, for example, Deep Brain Stimulation (DBS) systems, Implantable Cortical Nerve Stimulation (CNS) systems, and Implantable Spinal Cord Stimulation (SCS) systems. system, implantable sacral nerve stimulation (Sacral Nerve Stimulation, SNS) system, and implantable vagus nerve stimulation (Vagus Nerve Stimulation, VNS) system, etc.
- DBS Deep Brain Stimulation
- CNS Implantable Cortical Nerve Stimulation
- SNS Implantable Spinal Cord Stimulation
- VNS vagus Nerve Stimulation
- the stimulator can include an Implantable Pulse Generator (IPG), extension wires, and electrode wires.
- IPG Implantable Pulse Generator
- the IPG is placed in the patient's body and responds to program-controlled instructions sent by the program-controlled device. It relies on sealed batteries and circuits to provide controllable energy to tissues in the body.
- the electrical stimulation energy is delivered through implanted extension wires and electrode leads to deliver one or two controllable specific electrical stimulations to specific areas of tissue in the body.
- the extension lead is used in conjunction with the IPG as a transmission medium for electrical stimulation signals to transmit the electrical stimulation signals generated by the IPG to the electrode leads.
- the electrode leads pass through multiple electrode contacts to deliver specific areas of tissue in the body. Send electrical stimulation.
- the stimulator is provided with one or more electrode leads on one or both sides, and multiple electrode contacts are provided on the electrode leads.
- the electrode contacts can be arranged evenly or non-uniformly in the circumferential direction of the electrode leads. As an example, the electrode contacts may be arranged in an array of 4 rows and 3 columns (12 electrode contacts in total) in the circumferential direction of the electrode lead.
- the electrode contacts may include stimulation electrode contacts and/or collection electrode contacts.
- the electrode contacts may be in the shape of, for example, a sheet, a ring, a dot, or the like.
- the stimulated body tissue may be the patient's brain tissue, and the stimulated site may be a specific part of the brain tissue.
- the stimulated parts are generally different, the number of stimulation contacts used (single source or multiple sources), one or more channels (single channel or multi-channel) specific electrical stimulation signals
- the application and stimulation parameter data are also different.
- the embodiments of this application do not limit the applicable disease types, which may be the disease types applicable to deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation.
- DBS diseases that DBS can be used to treat or manage
- spastic diseases such as epilepsy
- pain migraine
- mental illness such as major depressive disorder (Major Depressive Disorder, MDD)
- bipolar disorder such as major depressive disorder (Major Depressive Disorder, MDD)
- anxiety disorder e.g., post-traumatic stress disorder
- mild depression e.g., depression
- behavioral disorders e.sive-compulsive disorder
- mood disorders e.g., depression
- memory disorders e.sive-compulsive disorder
- movement disorders such as essential tremor or Parkinson's disease
- Huntington's disease Alzheimer's disease, drug addiction, autism, or other neurological or psychiatric diseases and impairments.
- the program-controlled device when the program-controlled device and the stimulator establish a program-controlled connection, can be used to adjust the stimulation parameters of the stimulator (different stimulation parameters correspond to different electrical stimulation signals), and the stimulator can also be used to sense the deep brain of the patient.
- the electrophysiological activity can be used to collect electrophysiological signals, and the stimulation parameters of the stimulator can be continuously adjusted through the collected electrophysiological signals.
- the stimulation parameters may include at least one of the following: stimulation contact identification (for example, it can be 2# electrode contact and 3# electrode contact), frequency (for example, the number of electrical stimulation pulse signals within 1 second per unit time, the unit is Hz) , pulse width (the duration of each pulse, in ⁇ s), amplitude (generally expressed in voltage, that is, the intensity of each pulse, in V), timing (for example, it can be continuous or burst, and burst refers to Discontinuous sequential behavior composed of multiple processes), stimulation mode (including one or more of current mode, voltage mode, timing stimulation mode and cyclic stimulation mode), doctor control upper and lower limits (the range that the doctor can adjust) and patient control upper and lower limits (the range that the patient can adjust independently).
- stimulation contact identification for example, it can be 2# electrode contact and 3# electrode contact
- frequency for example, the number of electrical stimulation pulse signals within 1 second per unit time, the unit is Hz
- pulse width the duration of each pulse, in ⁇ s
- amplitude generally expressed in voltage, that is,
- At least one stimulation parameter of the stimulator can be adjusted in current mode or voltage mode.
- the program-controlled equipment may be a doctor-programmed equipment (that is, a program-controlled equipment used by a doctor) or a patient-programmed equipment (that is, a program-controlled equipment used by a patient).
- the doctor's program-controlled equipment may be, for example, a tablet computer, a notebook computer, a desktop computer, a mobile phone, or other intelligent terminal equipment equipped with program-controlled software.
- Patient program-controlled equipment can be, for example, tablet computers, laptop computers, desktop computers, mobile phones and other smart terminals equipped with program-controlled software.
- Terminal equipment, patient program-controlled equipment can also be other electronic equipment with program-controlled functions (such as chargers and data acquisition equipment with program-controlled functions).
- the embodiments of this application do not limit the data interaction between the doctor's program-controlled equipment and the stimulator.
- the doctor remotely programs the device
- the doctor's program-controlled equipment can interact with the stimulator through the server and the patient's program-controlled equipment.
- the doctor performs face-to-face programming with the patient offline
- the doctor's program-controlled equipment can interact with the stimulator through the patient's program-controlled equipment, and the doctor's program-controlled equipment can also directly interact with the stimulator.
- the patient programming device may include a host (in communication with the server) and a slave (in communication with the stimulator), the host and slave being communicatively connected.
- the doctor's program-controlled equipment can pass the third-generation mobile communication technology/the fourth generation mobile communication technology/the fifth generation mobile communication technology (3rd-Generation/the 4th Generation mobile communication technology/the 5th Generation mobile communication technology, 3G/4G/ 5G) network interacts with the server.
- the server can interact with the host through the 3G/4G/5G network.
- the host can interact with the host through the Bluetooth protocol/Wireless Fidelity (WIFI) protocol/Universal Serial Bus (Universal Serial Bus). USB) protocol for data interaction with the slave machine.
- WIFI Bluetooth protocol/Wireless Fidelity
- USB Universal Serial Bus
- the slave machine can interact with the stimulator through the 401MHz-406MHz working frequency band/2.4GHz-2.48GHz working frequency band.
- the doctor's program-controlled equipment can interact with the stimulator through the 401MHz-406MHz working frequency band/2.4GHz-2.48GHz
- the working frequency band directly interacts with the stimulator.
- the handset when the handset communicates with the IPG in the patient's body, the current power of the IPG can be obtained, and the IPG power information is recorded into the handset.
- the handset integrates a communication module (3G module/4G module/5G module) , through this communication module, it can communicate with the server, and use the server to remind the user to charge the IPG.
- Figure 1 shows a structural block diagram of an implantable nerve stimulation system provided by an embodiment of the present application.
- the implantable neurostimulation system includes:
- the stimulator 10 is implanted in the patient's body, the stimulator 10 is provided with at least one electrode lead and uses the electrode contacts of the electrode lead to deliver electrical stimulation to the patient;
- Extracorporeal communication device 20 which is arranged outside the body of the patient, and is configured to remind the patient to charge the stimulator 10;
- Charging reminder device 30 includes a controller 200 configured to implement a charging reminder method.
- Figure 2 is a structural block diagram of a stimulator 10 provided by an embodiment of the present application.
- the stimulator 10 includes an implantable pulse generator (IPG) 11 , at least one electrode lead 12 and at least one extension lead 13 .
- IPG implantable pulse generator
- At least one extension lead 13 is connected to at least one electrode lead 12 in a one-to-one correspondence.
- Each extension lead 13 is disposed between the implantable pulse generator 11 and the corresponding electrode lead 12 and is configured to implement the implantable pulse generator. 11 and the corresponding electrode wire 12.
- the extracorporeal communication device 20 may include, for example, one or more of a tablet computer, a laptop computer, a desktop computer, a mobile phone, and a smart wearable device.
- the external communication device 20 can be integrated with the charging reminder device 30 .
- the charging reminder method will be explained below.
- Figure 3 is a schematic flowchart of a charging reminder method provided by an embodiment of the present application.
- the method is applied to an implantable neurostimulation system, which includes a stimulator and an extracorporeal communication device.
- the stimulator is implanted in a patient's body.
- the stimulator is provided with at least one electrode lead and utilizes Electrode contacts of an electrode lead deliver electrical stimulation to the patient, the method comprising:
- Step S101 Obtain the configuration information of the stimulator.
- the configuration information includes at least one of the following: the number of electrode leads, the number of electrode contacts used by each electrode lead, and the amplitude, pulse width and frequency of the electrical stimulation signal. .
- Step S102 Obtain the power consumption speed of the stimulator based on the configuration information of the stimulator.
- Step S103 Use the extracorporeal communication device to obtain the current power of the stimulator, and predict the corresponding charging time when the power of the stimulator reaches the preset power threshold based on the current power of the stimulator and the power consumption rate. time.
- Step S104 Based on the charging time, obtain the charging reminder strategy of the extracorporeal communication device to remind the patient to charge the stimulator.
- the charging reminder strategy includes reminder frequency and/or reminder content.
- the power consumption speed of the stimulator is evaluated based on the configuration information of the stimulator, and the charging time corresponding to when the power of the stimulator reaches the preset power threshold is predicted based on the current power and power consumption speed of the stimulator (according to the estimated power consumption). speed, when the battery reaches the critical point of charging), and according to the charging time, obtain the charging reminder strategy of the external communication device to remind the patient to charge the stimulator.
- the external communication device In this case, you only need to use the external communication device to obtain the power of the stimulator once in advance, and you can predict how long it will take for the stimulator to be charged. There is no need to obtain the power of the stimulator in real time. In other words, the stimulator does not need to communicate frequently with the outside of the body.
- the device interacts with data, reducing the computational burden of the stimulator, and can remind the user to charge in advance (multiple times) to prevent patients from forgetting to charge the stimulator.
- the stimulator includes an IPG, an extension lead, and an electrode lead, wherein the IPG responds to programming instructions sent by the programming device and relies on a sealed battery and circuitry to provide controllable stimulation to tissues in the body. Electrical stimulation energy delivers one or two controllable specific electrical stimulations to specific areas of tissue in the body through implanted extension wires and electrode leads. The power of the stimulator is also the power of the IPG.
- the configuration information of the stimulator is used to indicate at least one stimulation parameter of the stimulator.
- the configuration of the stimulator is also related to the performance of the battery itself. That is to say, the configuration information of the stimulator may also include the remaining service life of the battery, performance parameters, etc.
- the power consumption rate can be expressed as the percentage of power consumption per day, the percentage of power consumption per hour, or the percentage of power consumption per minute, for example, 10% power consumption per day and 1% power consumption per hour.
- the embodiment of the present application does not limit the preset power threshold.
- the preset power threshold may be, for example, 5%, 10% or 15%.
- the extracorporeal communication device may be provided with a display screen and/or an audio playback device, and the method may further include:
- the audio playback device is used to play voice information every preset time period.
- the content of the voice information can be the predicted remaining power.
- the preset time period is, for example, 4 hours, 8 hours, or 1 day.
- the voice information is, for example, "Stimulator battery level.” It is estimated that 20% is left, please charge as soon as possible.”
- the charging reminder range corresponding to the current power level can be configured (the charging reminder policy is set only based on the current power level), and different reminders are provided according to different ranges. For example: if the current battery level is between 70% and 100%, the user will be reminded to charge after five days; if the current battery level is between 50% and 70%, the user will be reminded to charge after three days; if the current battery level is below 50%, the user will be reminded to charge after one day. This can effectively ensure that the stimulator is out of power because the patient forgets to charge it, thus avoiding any impact on the patient's health and life.
- the charging time can be predicted based on the current power and configuration information of the stimulator, and the charging reminder strategy can be set based on the charging time (the charging reminder strategy is set based on both the current power and configuration information). In this way, the prediction result More accurate.
- patient Xiao Wang’s stimulator configuration information is as follows: 2 electrode leads, each electrode lead uses 2 electrode contacts to deliver electrical stimulation, the amplitude of the electrical stimulation signal is 0.3V, the pulse width is 60us, and the frequency is 130Hz.
- the estimated daily power consumption is 10%, the current power of the stimulator (8 a.m.) is 70%, and the preset power threshold is 20%, then the charging time is 8 a.m. five days later.
- the charging reminder strategy can be set by the patient himself. For older and forgetful patients, it can be set Higher reminder frequency (several times a day), for patients with better memory, you can set a lower reminder frequency (only remind when the battery is low, and do not remind when the battery is sufficient).
- the charging reminder strategy can be: 1 reminder on the day, the reminder content is: please charge after 5 days; 2 reminders on the next day (one in the morning and one in the evening), the reminder content is: please charge after 4 days; on the third day Reminder 2 times (once in the morning and once in the evening), the reminder content is: please charge after 3 days; reminder 3 times on the fourth day (once in the morning, noon and evening), the reminder content is: please charge after 2 days; It reminds you 4 times in five days.
- the reminder content is: Please charge after 1 day. Such repeated reminders in advance can deepen the patient's memory about charging.
- the charging reminder strategy can also be: no reminder for the first 4 days, and then remind the user on the fifth day. This way, the patient can be reminded only when the battery is low, and no reminder is given when the battery is sufficient, thus preventing patients from being reminded frequently. , causing resentment in patients.
- the external communication device includes a programmable device and/or an external charger
- the obtaining the current power of the stimulator may include:
- the program-controlled device When the program-controlled device establishes a program-controlled connection with the stimulator, use the program-controlled device to obtain the current power of the stimulator, or,
- the external charger When the external charger establishes a communication connection with the stimulator, the external charger is used to obtain the current power of the stimulator.
- the extracorporeal communication device can be a programmable device or an extracorporeal charger.
- the programmable device When the programmable device establishes a programmable connection with the stimulator to program the stimulator, it can obtain the power of the stimulator while programming; when the extracorporeal charger is connected to the stimulator When the device establishes a communication connection, you can use the external charger to obtain the power of the stimulator.
- the external charger can charge the stimulator (that is, charge the IPG), and can also provide the function of data interaction with the stimulator to obtain the current power of the stimulator.
- obtaining the power consumption speed of the stimulator may include:
- the training process of the power consumption prediction model includes:
- the first training set including a plurality of first training data, each first training data including configuration information of a sample stimulator and annotation data of the power consumption speed of the sample stimulator;
- the preset first deep learning model can be obtained.
- the learning and tuning of the model establishes the functional relationship from input to output. Although the functional relationship between input and output cannot be found 100%, it can be as close as possible to the realistic correlation relationship.
- the power consumption prediction model obtained from this training The power consumption rate of the stimulator can be predicted based on the configuration information of the stimulator, and the prediction results are highly accurate and reliable.
- the embodiments of the present application can use the above training process to train the power consumption prediction model. In other implementations, the embodiments of the present application can use a pre-trained power consumption prediction model.
- the embodiment of the present application does not limit the method of obtaining the annotated data.
- manual annotation, automatic annotation or semi-automatic annotation may be used.
- the embodiment of the present application does not limit the training process of the power consumption prediction model.
- the training method of the above-mentioned supervised learning may be used, or the training method of semi-supervised learning may be used, or the training method of unsupervised learning may be used.
- the embodiment of the present application does not limit the preset first training end condition, which may be, for example, that the number of training times reaches a preset number of times (the preset number of times is, for example, 1 time, 3 times, 10 times, 100 times, 1000 times, 10000 times, etc. ), or it can be that all the training data in the first training set have completed one or more trainings, or it can be that the total loss value obtained in this training is not greater than the preset loss value.
- the preset first training end condition may be, for example, that the number of training times reaches a preset number of times (the preset number of times is, for example, 1 time, 3 times, 10 times, 100 times, 1000 times, 10000 times, etc. ), or it can be that all the training data in the first training set have completed one or more trainings, or it can be that the total loss value obtained in this training is not greater than the preset loss value.
- the electrode contacts of the electrode leads are also configured to collect the patient's brain electrical signals, and the method may further include:
- the charging reminder strategy of the extracorporeal communication device is updated.
- the patient's condition is generally not fixed.
- the configuration information of the stimulator also needs to be adjusted to suit the patient's physical condition.
- the power consumption needs to be re-evaluated.
- the stimulation contacts of the electrode leads can be used to collect the patient's EEG signal.
- the EEG signal can reflect the severity of the patient's condition.
- the corresponding reference configuration information is set according to the EEG signal.
- the configuration information of the stimulator is updated according to the reference configuration information, so that the updated configuration information of the stimulator is used to re-evaluate the power consumption speed, and then the charging reminder strategy of the external communication device is updated.
- the configuration The information can be updated based on the patient's condition, and the charging reminder strategy can be adaptively adjusted as the configuration information is updated to avoid false reminders.
- Artificial intelligence algorithms such as machine learning models, deep learning models or reinforcement learning models can be used to analyze and process EEG signals, thereby obtaining reference configuration information, and update the current configuration information of the stimulator (electrode leads and contacts) based on the reference configuration information.
- the number of points generally remains unchanged, and the amplitude, frequency, and pulse width of the electrical stimulation signal are generally adjusted) to make the updated configuration information more suitable for the patient's current condition.
- a similarity detection model can be used to detect the similarity between the reference configuration information and the configuration information of the stimulator. When the similarity between the two is less than a preset similarity threshold, it is determined that the reference configuration information is the same as the stimulator. The configuration information does not match.
- obtaining the reference configuration information corresponding to the EEG signal may include:
- the training process of the reference configuration model includes:
- the second training set includes a plurality of second training data, each second training data includes a sample EEG signal and annotation data of reference configuration information corresponding to the sample EEG signal;
- a preset second deep learning model can be obtained.
- the learning and tuning of the model establishes the functional relationship from input to output. Although the functional relationship between input and output cannot be found 100%, it can be as close as possible to the realistic correlation relationship.
- the reference configuration model obtained by training can The corresponding reference configuration information is obtained based on the patient's EEG signal, and the calculation results are highly accurate and reliable.
- the embodiments of the present application can use the above training process to train the reference configuration model. In other implementations, the embodiments of the present application can use a pre-trained reference configuration model.
- the embodiment of the present application does not limit the method of obtaining the annotated data.
- manual annotation, automatic annotation or semi-automatic annotation may be used.
- the embodiment of the present application does not limit the training process of the reference configuration model.
- the training method of the above-mentioned supervised learning can be used, or the training method of semi-supervised learning can be used, or the training method of unsupervised learning can be used.
- the embodiment of the present application does not limit the preset second training end condition, which may be, for example, that the number of training times reaches a preset number of times (the preset number of times is, for example, 1 time, 3 times, 10 times, 100 times, 1000 times, 10000 times, etc. ), or it can be that the training data in the second training set have completed one or more trainings, or it can be that the total loss value obtained in this training is not greater than the preset loss value.
- the configuration information of the stimulator includes the stimulation time period and the amplitude, pulse width and frequency of the electrical stimulation signal.
- the configuration information of the stimulator can also include the stimulation time period.
- the patient's daily stimulation time period is fixed, for example, from 8 a.m. to 10 p.m., the stimulation time The longer the segment, the higher the daily power consumption.
- the obtaining the charging reminder strategy of the external communication device may include:
- the form of the corresponding relationship includes a corresponding relationship table and/or a corresponding relationship diagram
- the charging reminder strategy corresponding to the charging time range in which the charging time is located is found in the corresponding relationship, and is used as the charging reminder strategy of the external communication device.
- a corresponding relationship (represented by a diagram or table) can be established in advance between the charging time range and the charging reminder strategy.
- the predicted charging time can be directly searched in the corresponding relationship.
- the corresponding charging reminder strategy has a small amount of calculation and high calculation efficiency.
- the patient's disease type includes one or more of epilepsy, tremor, Parkinson's disease, depression, obsessive-compulsive disorder, Alzheimer's disease and drug addiction.
- the external charger is suitable for patients with many different disease types and has a wide range of applications.
- the charging reminder device includes a controller 200. Its implementation is consistent with the implementation described in the above-mentioned method implementation and the technical effects achieved, and part of the content will not be described again.
- the controller 200 performs data interaction with a stimulator and an extracorporeal communication device respectively.
- the stimulator is implanted in the patient's body.
- the stimulator is provided with at least one electrode lead and uses electrode contacts of the electrode lead to deliver data to the patient.
- the controller 200 is configured to:
- configuration information of the stimulator including at least one of the following: the number of electrode leads, the number of electrode contacts used by each electrode lead, and the amplitude, pulse width and frequency of the electrical stimulation signal;
- a charging reminder strategy of the extracorporeal communication device is obtained to remind the patient to charge the stimulator, where the charging reminder strategy includes reminder frequency and/or reminder content.
- the external communication device includes a programmable device and/or an external charger
- the controller 200 is configured to obtain the current power of the stimulator in the following manner:
- the program-controlled device When the program-controlled device establishes a program-controlled connection with the stimulator, use the program-controlled device to obtain the current power of the stimulator, or,
- the external charger When the external charger establishes a communication connection with the stimulator, the external charger is used to obtain the current power of the stimulator.
- the controller 200 is configured to obtain the power consumption speed of the stimulator in the following manner:
- the training process of the power consumption prediction model includes:
- the first training set includes a plurality of first training data, each first training data
- the data includes configuration information of a sample stimulator and annotation data of the power consumption speed of the sample stimulator;
- the electrode contacts of the electrode leads are also configured to collect the patient's EEG signals, and the controller 200 is further configured to:
- the charging reminder strategy of the extracorporeal communication device is updated.
- the controller 200 is configured to obtain the reference configuration information corresponding to the EEG signal in the following manner:
- the training process of the reference configuration model includes:
- the second training set includes a plurality of second training data, each second training data includes a sample EEG signal and annotation data of reference configuration information corresponding to the sample EEG signal;
- the configuration information of the stimulator includes the stimulation time period and the amplitude, pulse width and frequency of the electrical stimulation signal.
- the controller 200 is configured to obtain the charging reminder policy of the external communication device in the following manner:
- the form of the corresponding relationship includes a corresponding relationship table and/or a corresponding relationship diagram
- the charging reminder strategy corresponding to the charging time range in which the charging time is located is found in the corresponding relationship, and is used as the charging reminder strategy of the external communication device.
- the patient's disease type includes one or more of epilepsy, tremor, Parkinson's disease, depression, obsessive-compulsive disorder, Alzheimer's disease and drug addiction.
- Figure 4 is a structural block diagram of a controller 200 provided by an embodiment of the present application.
- the controller 200 may include, for example, at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.
- the memory 210 may include readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 211 and/or cache memory 212, and may also include read-only memory (Read-Only Memory, ROM) 213 .
- RAM Random Access Memory
- ROM Read-Only Memory
- the memory 210 also stores a computer program, and the computer program can be executed by the processor 220, so that the processor 220 realizes any of the above functions of the controller 200, and its implementation is the same as the implementation described in the above method implementation. The technical effects are consistent, and some contents will not be repeated.
- Memory 210 may also include a utility 214 having at least one program module 215 including: an operating system, one or more application programs, other program modules, and program data, in each or a combination of these examples. May include implementation of network environment.
- the processor 220 can execute the above-mentioned computer program, and can execute the utility tool 214.
- the processor 220 may use one or more application specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processor (Digital Signal Processor, DSP), programmable logic device (Programmable Logic Device, PLD), complex programmable logic device (Complex Programmable Logic Device, CPLD), Field-Programmable Gate Array (FPGA) or other electronic components.
- ASIC Application Specific Integrated Circuit
- DSP Digital Signal Processor
- PLD programmable logic device
- PLD complex programmable logic device
- CPLD Complex Programmable Logic Device
- FPGA Field-Programmable Gate Array
- Bus 230 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics accelerated port, a processor, or any other bus structure using a variety of bus structures. Bus structure of local bus.
- the controller 200 may also communicate with one or more external devices 240, such as a keyboard, a pointing device, a Bluetooth device, etc., and may also communicate with one or more devices capable of interacting with the controller 200, and/or with a device that enables the controller 200 to 200 is any device capable of communicating with one or more other computing devices (eg, router, modem, etc.). This communication may occur through the input/output interface 250.
- the controller 200 can also communicate with one or more networks (such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and/or a public network, such as the Internet) through the network adapter 260.
- Network adapter 260 may communicate with other modules of controller 200 via bus 230.
- controller 200 may be used in conjunction with the controller 200, including: microcode, device drivers, redundant processors, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage platforms.
- microcode device drivers
- redundant processors redundant processors
- external disk drive arrays disk arrays (Redundant Arrays of Independent Disks, RAID) systems
- tape drives tape drives
- data backup storage platforms data backup storage platforms.
- This application also provides a computer-readable storage medium, which stores a computer program.
- the computer program When the computer program is executed by a processor, the computer program realizes the functions of any of the above controllers or implements the above charging reminder method.
- the steps and their implementation are consistent with the implementation described in the implementation of the controller and the technical effects achieved, and part of the content will not be described again.
- Figure 5 shows a schematic structural diagram of a program product for implementing a charging reminder method provided by this application.
- the program product can take the form of a portable Compact Disc Read Only Memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer.
- CD-ROM Compact Disc Read Only Memory
- the program product of the present application is not limited thereto.
- the readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.
- the Program Product may take the form of one or more readable media in any combination.
- the readable medium may be a readable signal medium or a readable storage medium.
- the readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof.
- Readable storage media include: electrical connection with one or more wires, portable disk, hard disk, RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), flash memory, optical fiber, portable CD - ROM, optical storage device, magnetic storage device, or any suitable combination of the above.
- the storage medium may be a non-transitory storage medium.
- a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave carrying the readable program code therein. Such propagated data signals may take many forms, including electromagnetic signals, optical signals, or any suitable combination of the above.
- a readable storage medium may also be any readable medium that can transmit, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device.
- Program code contained on a readable storage medium may be stored in any Appropriate media transmission, including wireless, wired, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
- the program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also includes conventional procedural programming languages. Such as C language or similar programming language.
- 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 and partly on a remote computing device, or entirely on the remote computing device or server execute on.
- the remote computing device may be connected to the user computing device through any kind of network, including a LAN or WAN, or may be connected to an external computing device (such as through the Internet using an Internet service provider) .
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Abstract
一种充电提醒装置（30）、植入式神经刺激系统及存储介质，充电提醒装置（30）包括控制器（200），控制器（200）被配置成：获取刺激器（10）的配置信息；基于刺激器（10）的配置信息，获取刺激器（10）的耗电速度；利用体外通信设备（20）获取刺激器（10）的当前电量，基于刺激器（10）的当前电量和耗电速度，预测刺激器（10）的电量达到预设电量阈值时对应的应充电时刻；基于应充电时刻，获取体外通信设备（20）的充电提醒策略，以提醒患者对刺激器（10）进行充电，充电提醒策略包括提醒频率和/或提醒内容。
Description
本申请要求在2022年08月22日提交中国专利局、申请号为202211006766.1的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
本申请涉及植入式器械、远程程控和深度学习的技术领域，例如涉及充电提醒装置、植入式神经刺激系统及存储介质。
刺激器植入于患者体内，通过电极导线上的刺激触点向靶点递送电刺激，从而改善患者症状、以及缓解病痛。由于刺激器在患者体内，看不见摸不着，不像手机等体外智能设备可以显示实时电量，让用户明确什么时候该充电。
一旦患者体内的刺激器未及时充电，电量耗尽，会导致患者的病症无法得到有效控制，无法缓解患者的病痛，更重要的是，如果患者好不容易(提前挂号)预约了“远程程控”，等医生准备进行远程程控时，却发现患者的刺激器没电了，可能会使患者错失治病的机会，又得耗费时间和精力重新预约，等待医生有合适的时间进行程控，由此可见提前提醒患者对刺激器充电是非常必要的。
专利CN108174034A公开了一种采用应用程序(Application，APP)实时监控骶神经调节装置的系统，包括骶神经调节装置、移动终端及服务器；骶神经调节装置内设置用于收发信息的内置APP，移动终端内设置用于收发信息的外置APP，内置APP用于经服务器向外置APP发送内置电池电量信息，外置APP用于经服务器向外置控制器发送充电请求，利用外置控制器控制外置充电装置向内置充电装置充电。这种方式虽然可以通过外部设备实时地去监测骶神经调节装置内置电池的电量，但是要求骶神经调节装置频繁地与外部设备进行交互，大大加重骶神经调节装置的运算负担，甚至会干扰到正常的电刺激功能。
发明内容
本申请提供充电提醒装置、植入式神经刺激系统及存储介质，在刺激器无需频繁地与体外通信设备进行数据交互的情况下，提前对用户进行充电提醒。
本申请提供了一种充电提醒装置，所述充电提醒装置包括控制器，所述控制器分别与刺激器和体外通信设备进行数据交互，所述刺激器植入于患者体内，
所述刺激器设置有至少一个电极导线并利用所述至少一个电极导线的电极触点向所述患者递送电刺激，所述控制器被配置成：
获取所述刺激器的配置信息，所述配置信息包括以下至少一种：电极导线的数量、每个电极导线使用的电极触点的数量以及电刺激信号的幅值、脉宽和频率；
基于所述刺激器的配置信息，获取所述刺激器的耗电速度；
利用所述体外通信设备获取所述刺激器的当前电量，基于所述刺激器的当前电量和所述耗电速度，预测所述刺激器的电量达到预设电量阈值时对应的应充电时刻；
基于所述应充电时刻，获取所述体外通信设备的充电提醒策略，以提醒所述患者对所述刺激器进行充电，所述充电提醒策略包括提醒频率和/或提醒内容。
在一些可选的实施例中，所述体外通信设备包括程控设备和/或体外充电器；
所述控制器被配置成采用以下方式获取所述刺激器的当前电量：
当所述程控设备与所述刺激器建立程控连接时，利用所述程控设备获取所述刺激器的当前电量，或者，
当所述体外充电器与所述刺激器建立通信连接时，利用所述体外充电器获取所述刺激器的当前电量。
在一些可选的实施例中，所述控制器被配置成采用以下方式获取所述刺激器的耗电速度：
将所述刺激器的配置信息输入至耗电预测模型，以得到所述刺激器的耗电速度；
其中，所述耗电预测模型的训练过程包括：
获取第一训练集，所述第一训练集包括多个第一训练数据，每个第一训练数据包括一个样本刺激器的配置信息以及所述样本刺激器的耗电速度的标注数据；
针对所述第一训练集中的每个第一训练数据，执行以下处理：
将所述第一训练数据中的样本刺激器的配置信息输入至预设的第一深度学习模型，以得到所述样本刺激器的耗电速度的预测数据；
基于所述样本刺激器的耗电速度的预测数据和标注数据，对所述第一深度学习模型的模型参数进行更新；
检测是否满足预设的第一训练结束条件；在满足所述预设的第一训练结束
条件的情况下，将训练出的第一深度学习模型作为所述耗电预测模型；在不满足所述预设的第一训练结束条件的情况下，利用下一个第一训练数据继续训练所述第一深度学习模型。
在一些可选的实施例中，电极导线的电极触点还被配置成采集所述患者的脑电信号，所述控制器还被配置成：
利用所述电极触点采集所述患者的脑电信号；
获取所述脑电信号对应的参考配置信息，当所述参考配置信息与所述刺激器的配置信息不匹配时，利用所述参考配置信息更新所述刺激器的配置信息；
基于更新后的刺激器的配置信息，对所述体外通信设备的充电提醒策略进行更新。
在一些可选的实施例中，所述控制器被配置成采用以下方式获取所述脑电信号对应的参考配置信息：
将所述患者的脑电信号输入至参考配置模型，以得到所述脑电信号对应的参考配置信息；
其中，所述参考配置模型的训练过程包括：
获取第二训练集，所述第二训练集包括多个第二训练数据，每个第二训练数据包括一个样本脑电信号以及所述样本脑电信号对应的参考配置信息的标注数据；
针对所述第二训练集中的每个第二训练数据，执行以下处理：
将所述第二训练数据中的样本脑电信号输入至预设的第二深度学习模型，以得到所述样本脑电信号对应的参考配置信息的预测数据；
基于所述样本脑电信号对应的参考配置信息的预测数据和标注数据，对所述第二深度学习模型的模型参数进行更新；
检测是否满足预设的第二训练结束条件；在满足所述预设的第二训练结束条件的情况下，将训练出的第二深度学习模型作为所述参考配置模型；在不满足所述预设的第二训练结束条件的情况下，利用下一个第二训练数据继续训练所述第二深度学习模型。
在一些可选的实施例中，所述刺激器的配置信息包括刺激时间段以及电刺激信号的幅值、脉宽和频率。
在一些可选的实施例中，所述控制器被配置成采用以下方式获取所述体外通信设备的充电提醒策略：
获取应充电时刻范围与充电提醒策略的对应关系，所述对应关系的形式包括对应关系表和/或对应关系图；
在所述对应关系中查找所述应充电时刻所处的应充电时刻范围对应的充电提醒策略，作为所述体外通信设备的充电提醒策略。
在一些可选的实施例中，所述患者的疾病类型包括癫痫、震颤、帕金森病、抑郁症、强迫症、阿尔茨海默症和药物成瘾症中的一个或多个。
本申请还提供了一种充电提醒方法，所述方法应用于植入式神经刺激系统，所述植入式神经刺激系统包括刺激器和体外通信设备，所述刺激器植入于患者体内，所述刺激器设置有至少一个电极导线并利用电极导线的电极触点向所述患者递送电刺激，所述方法包括：
获取所述刺激器的配置信息，所述配置信息包括以下至少一种：电极导线的数量、每个电极导线使用的电极触点的数量以及电刺激信号的幅值、脉宽和频率；
基于所述刺激器的配置信息，获取所述刺激器的耗电速度；
利用所述体外通信设备获取所述刺激器的当前电量，基于所述刺激器的当前电量和所述耗电速度，预测所述刺激器的电量达到预设电量阈值时对应的应充电时刻；
基于所述应充电时刻，获取所述体外通信设备的充电提醒策略，以提醒所述患者对所述刺激器进行充电，所述充电提醒策略包括提醒频率和/或提醒内容。
在一些可选的实施例中，所述体外通信设备包括程控设备和/或体外充电器；
所述获取所述刺激器的当前电量包括：
当所述程控设备与所述刺激器建立程控连接时，利用所述程控设备获取所述刺激器的当前电量，或者，
当所述体外充电器与所述刺激器建立通信连接时，利用所述体外充电器获取所述刺激器的当前电量。
在一些可选的实施例中，所述获取所述刺激器的耗电速度包括：
将所述刺激器的配置信息输入至耗电预测模型，以得到所述刺激器的耗电速度；
其中，所述耗电预测模型的训练过程包括：
获取第一训练集，所述第一训练集包括多个第一训练数据，每个第一训练数据包括一个样本刺激器的配置信息以及所述样本刺激器的耗电速度的标注数
据；
针对所述第一训练集中的每个第一训练数据，执行以下处理：
将所述第一训练数据中的样本刺激器的配置信息输入至预设的第一深度学习模型，以得到所述样本刺激器的耗电速度的预测数据；
基于所述样本刺激器的耗电速度的预测数据和标注数据，对所述第一深度学习模型的模型参数进行更新；
检测是否满足预设的第一训练结束条件；在满足所述预设的第一训练结束条件的情况下，将训练出的第一深度学习模型作为所述耗电预测模型；在不满足所述预设的第一训练结束条件的情况下，利用下一个第一训练数据继续训练所述第一深度学习模型。
在一些可选的实施例中，电极导线的电极触点还被配置成采集所述患者的脑电信号，所述方法还包括：
利用所述电极触点采集所述患者的脑电信号；
获取所述脑电信号对应的参考配置信息，当所述参考配置信息与所述刺激器的配置信息不匹配时，利用所述参考配置信息更新所述刺激器的配置信息；
基于更新后的刺激器的配置信息，对所述体外通信设备的充电提醒策略进行更新。
在一些可选的实施例中，所述获取所述脑电信号对应的参考配置信息包括：
将所述患者的脑电信号输入至参考配置模型，以得到所述脑电信号对应的参考配置信息；
其中，所述参考配置模型的训练过程包括：
获取第二训练集，所述第二训练集包括多个第二训练数据，每个第二训练数据包括一个样本脑电信号以及所述样本脑电信号对应的参考配置信息的标注数据；
针对所述第二训练集中的每个第二训练数据，执行以下处理：
将所述第二训练数据中的样本脑电信号输入至预设的第二深度学习模型，以得到所述样本脑电信号对应的参考配置信息的预测数据；
基于所述样本脑电信号对应的参考配置信息的预测数据和标注数据，对所述第二深度学习模型的模型参数进行更新；
检测是否满足预设的第二训练结束条件；在满足所述预设的第二训练结束条件的情况下，将训练出的第二深度学习模型作为所述参考配置模型；在不满
足所述预设的第二训练结束条件的情况下，利用下一个第二训练数据继续训练所述第二深度学习模型。
在一些可选的实施例中，所述刺激器的配置信息包括刺激时间段以及电刺激信号的幅值、脉宽和频率。
在一些可选的实施例中，所述获取所述体外通信设备的充电提醒策略包括：
获取应充电时刻范围与充电提醒策略的对应关系，所述对应关系的形式包括对应关系表和/或对应关系图；
在所述对应关系中查找所述应充电时刻所处的应充电时刻范围对应的充电提醒策略，作为所述体外通信设备的充电提醒策略。
在一些可选的实施例中，所述患者的疾病类型包括癫痫、震颤、帕金森病、抑郁症、强迫症、阿尔茨海默症和药物成瘾症中的一个或多个。
本申请还提供了一种植入式神经刺激系统，所述植入式神经刺激系统包括：
刺激器，所述刺激器植入于患者体内，所述刺激器设置有至少一个电极导线并利用电极导线的电极触点向所述患者递送电刺激；
体外通信设备，所述体外通信设备设置于所述患者的体外，所述体外通信设备被配置成提醒所述患者对所述刺激器进行充电；
上述任一项充电提醒装置。
在一些可选的实施例中，所述体外通信设备与所述充电提醒装置集成为一体。
本申请还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述任一项控制器的功能。
图1是本申请实施例提供的一种植入式神经刺激系统的结构框图。
图2是本申请实施例提供的一种刺激器的结构框图。
图3是本申请实施例提供的一种充电提醒方法的流程示意图。
图4是本申请实施例提供的一种控制器的结构框图。
图5是本申请实施例提供的一种程序产品的结构示意图。
下面，结合附图以及实施方式，对本申请进行描述，在不相冲突的前提下，
以下描述的多个实施例之间或多个技术特征之间可以任意组合形成新的实施例。
在本申请实施例中，“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B的情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，a和b，a和c，b和c，a和b和c，其中a、b和c可以是单个，也可以是多个。值得注意的是，“至少一项(个)”还可以解释成“一项(个)或多项(个)”。
本申请实施例中，“示例性的”或者“例如”等词用于表示作例子、例证或说明。本申请实施例中被描述为“示例性的”或者“例如”的任何实施方式或设计方案不应被解释为比其他实施方式或设计方更具优势。确切而言，使用“示例性的”或者“例如”等词旨在以具体方式呈现相关概念。
下面，对本申请实施例的其中一个应用领域(即植入式器械)进行简单说明。
植入式神经刺激系统(一种植入式医疗系统)主要包括植入患者体内的刺激器以及设置于患者体外的程控设备。现有的神经调控技术主要是通过立体定向手术在体内特定结构(即靶点)植入电极，并由植入患者体内的刺激器经电极向靶点发放电脉冲，调控相应神经结构和网络的电活动及其功能，从而改善症状、并缓解病痛。其中，刺激器可以是植入式神经电刺激装置、植入式心脏电刺激系统(又称心脏起搏器)、植入式药物输注装置(Implantable Drug Delivery System，IDDS)和导线转接装置中的任意一种。植入式神经电刺激装置例如是脑深部电刺激(Deep Brain Stimulation，DBS)系统、植入式脑皮层刺激(Cortical Nerve Stimulation，CNS)系统、植入式脊髓电刺激(Spinal Cord Stimulation，SCS)系统、植入式骶神经电刺激(Sacral Nerve Stimulation，SNS)系统、以及植入式迷走神经电刺激(Vagus Nerve Stimulation，VNS)系统等。
刺激器可以包括植入式脉冲发生器(Implantable Pulse Generator，IPG)、延伸导线和电极导线，IPG设置于患者体内，响应于程控设备发送的程控指令，依靠密封电池和电路向体内组织提供可控制的电刺激能量，通过植入的延伸导线和电极导线，为体内组织的特定区域递送一路或两路可控制的特定电刺激。延伸导线配合IPG使用，作为电刺激信号的传递媒体，将IPG产生的电刺激信号，传递给电极导线。电极导线通过多个电极触点，向体内组织的特定区域递
送电刺激。刺激器设置有单侧或双侧的一路或多路电极导线，电极导线上设置有多个电极触点，电极触点可以均匀排列或者非均匀排列在电极导线的周向上。作为一个示例，电极触点可以以4行3列的阵列(共计12个电极触点)排列在电极导线的周向上。电极触点可以包括刺激电极触点和/或采集电极触点。电极触点例如可以采用片状、环状、点状等形状。
在一些可能的实施方式中，受刺激的体内组织可以是患者的脑组织，受刺激的部位可以是脑组织的特定部位。当患者的疾病类型不同时，受刺激的部位一般来说是不同的，所使用的刺激触点(单源或多源)的数量、一路或多路(单通道或多通道)特定电刺激信号的运用以及刺激参数数据也是不同的。本申请实施例对适用的疾病类型不做限定，其可以是脑深部刺激(DBS)、脊髓刺激(SCS)、骨盆刺激、胃刺激、外周神经刺激、功能性电刺激所适用的疾病类型。其中，DBS可以用于治疗或管理的疾病类型包括：痉挛疾病(例如，癫痫)、疼痛、偏头痛、精神疾病(例如，重度抑郁症(Major Depressive Disorder，MDD))、躁郁症、焦虑症、创伤后压力心理障碍症、轻郁症、强迫症(Obsessive-Compulsive Disorder，OCD)、行为障碍、情绪障碍、记忆障碍、心理状态障碍、移动障碍(例如，特发性震颤或帕金森氏病)、亨廷顿病、阿尔茨海默症、药物成瘾症、孤独症或其他神经学或精神科疾病和损害。
本申请实施例中，程控设备和刺激器建立程控连接时，可以利用程控设备调整刺激器的刺激参数(不同的刺激参数所对应的电刺激信号不同)，也可以通过刺激器感测患者脑深部的电生理活动以采集得到电生理信号，并可以通过所采集到的电生理信号来继续调节刺激器的刺激参数。
刺激参数可以包括以下至少一种：刺激触点标识(例如可以是2#电极触点和3#电极触点)、频率(例如是单位时间1s内的电刺激脉冲信号个数，单位为Hz)、脉宽(每个脉冲的持续时间，单位为μs)、幅值(一般用电压表述，即每个脉冲的强度，单位为V)、时序(例如可以是连续或者簇发，簇发是指多个过程组成的不连续的时序行为)、刺激模式(包括电流模式、电压模式、定时刺激模式和循环刺激模式中的一种或多种)、医生控制上限及下限(医生可调节的范围)和患者控制上限及下限(患者可自主调节的范围)。
在一个应用场景中，可以在电流模式或者电压模式下对刺激器的至少一种刺激参数进行调节。
程控设备可以是医生程控设备(即医生使用的程控设备)或者患者程控设备(即患者使用的程控设备)。医生程控设备例如可以是搭载有程控软件的平板电脑、笔记本电脑、台式计算机、手机等智能终端设备。患者程控设备例如可以是搭载有程控软件的平板电脑、笔记本电脑、台式计算机、手机等智能终
端设备，患者程控设备还可以是其他具有程控功能的电子设备(例如是具有程控功能的充电器、数据采集设备)。
本申请实施例对医生程控设备和刺激器的数据交互不进行限制，当医生远程程控时，医生程控设备可以通过服务器、患者程控设备与刺激器进行数据交互。当医生线下和患者面对面进行程控时，医生程控设备可以通过患者程控设备与刺激器进行数据交互，医生程控设备还可以直接与刺激器进行数据交互。
在一些可选的实施方式中，患者程控设备可以包括(与服务器通信的)主机和(与刺激器通信的)子机，主机和子机可通信的连接。其中，医生程控设备可以通过第三代移动通信技术/第四代移动通信技术/第五代移动通信技术(3rd-Generation/the 4th Generation mobile communication technology/the 5th Generation mobile communication technology，3G/4G/5G)网络与服务器进行数据交互，服务器可以通过3G/4G/5G网络与主机进行数据交互，主机可以通过蓝牙协议/无线保真(Wireless Fidelity，WIFI)协议/通用串行总线(Universal Serial Bus，USB)协议与子机进行数据交互，子机可以通过401MHz-406MHz工作频段/2.4GHz-2.48GHz工作频段与刺激器进行数据交互，医生程控设备可以通过401MHz-406MHz工作频段/2.4GHz-2.48GHz工作频段与刺激器直接进行数据交互。
在一应用中，当子机和患者体内的IPG通信时可以获取到IPG的当前电量，并将IPG电量信息记录到子机内，同时子机集成通信模块(3G模块/4G模块/5G模块)，通过该通信模块可以和服务器进行通信，利用服务器提醒用户对IPG进行充电。
参见图1，图1示出了本申请实施例提供的一种植入式神经刺激系统的结构框图。
所述植入式神经刺激系统包括：
刺激器10，所述刺激器10植入于患者体内，所述刺激器10设置有至少一个电极导线并利用电极导线的电极触点向所述患者递送电刺激；
体外通信设备20，所述体外通信设备20设置于所述患者的体外，所述体外通信设备20被配置成提醒所述患者对所述刺激器10进行充电；
充电提醒装置30，所述充电提醒装置30包括控制器200，所述控制器200被配置成实现充电提醒方法。
参见图2，图2是本申请实施例提供的一种刺激器10的结构框图。
所述刺激器10包括植入式脉冲发生器(IPG)11、至少一个电极导线12和至少一个延伸导线13。
至少一个延伸导线13与至少一个电极导线12一一对应地连接，每个延伸导线13设置于植入式脉冲发生器11与对应的电极导线12之间，被配置成实现植入式脉冲发生器11与对应的电极导线12之间的数据传输功能。
体外通信设备20例如可以包括平板电脑、笔记本电脑、台式机、手机和智能穿戴设备中的一种或多种。
在一些可选的实施例中，所述体外通信设备20可以与所述充电提醒装置30集成为一体。
下文将对充电提醒方法进行说明。
参见图3，图3是本申请实施例提供的一种充电提醒方法的流程示意图。
所述方法应用于植入式神经刺激系统，所述植入式神经刺激系统包括刺激器和体外通信设备，所述刺激器植入于患者体内，所述刺激器设置有至少一个电极导线并利用电极导线的电极触点向所述患者递送电刺激，所述方法包括：
步骤S101、获取所述刺激器的配置信息，所述配置信息包括以下至少一种：电极导线的数量、每个电极导线使用的电极触点的数量以及电刺激信号的幅值、脉宽和频率。
步骤S102、基于所述刺激器的配置信息，获取所述刺激器的耗电速度。
步骤S103、利用所述体外通信设备获取所述刺激器的当前电量，基于所述刺激器的当前电量和所述耗电速度，预测所述刺激器的电量达到预设电量阈值时对应的应充电时刻。
步骤S104、基于所述应充电时刻，获取所述体外通信设备的充电提醒策略，以提醒所述患者对所述刺激器进行充电，所述充电提醒策略包括提醒频率和/或提醒内容。
由此，根据刺激器的配置信息评估刺激器的耗电速度，根据刺激器的当前电量和耗电速度，预测刺激器电量达到预设电量阈值时对应的应充电时刻(按预估的耗电速度，电量什么时候到充电的临界点)，根据应充电时刻，获取体外通信设备的充电提醒策略，以提醒患者对刺激器进行充电。
这样的话，只需利用体外通信设备提前获取一次刺激器的电量，就能预测刺激器还有多久应该充电，不用实时去获取刺激器的电量，换而言之，刺激器无需频繁地与体外通信设备进行数据交互，减轻了刺激器的运算负担，并且可以提前(多次)对用户进行充电提醒，避免患者忘记对刺激器进行充电。
在一些实施方式中，刺激器包括IPG、延伸导线和电极导线，其中，IPG响应于程控设备发送的程控指令，依靠密封电池和电路向体内组织提供可控制的
电刺激能量，通过植入的延伸导线和电极导线，为体内组织的特定区域递送一路或两路可控制的特定电刺激。刺激器的电量也即IPG的电量。
在一些实施方式中，刺激器的配置信息用于指示刺激器的至少一项刺激参数。
在一些实施方式中，刺激器的配置情况还与电池本身的性能有关，也就是说，刺激器的配置信息还可以包括电池的剩余使用寿命、性能参数等。
耗电速度可以用每天耗电百分比、每小时耗电百分比或者每分钟耗电百分比表示，例如每天耗电10％、每小时耗电1％。
本申请实施例对预设电量阈值不作限定，预设电量阈值例如可以是5％、10％或者15％。
在一些实施方式中，所述体外通信设备可以设置有显示屏和/或音频播放装置，所述方法还可以包括：
基于所述刺激器的当前电量和所述耗电速度，实时预测所述刺激器的剩余电量；
利用显示屏实时显示所预测的剩余电量；和/或，
利用所述音频播放装置每隔预设时长播放语音信息，语音信息的内容可以是所预测的剩余电量，预设时长例如是4小时、8小时或者1天，语音信息例如为，“刺激器电量预计还剩20％，请尽快充电”。
在一些实施方式中，可以配置当前电量对应的充电提醒范围(仅根据当前电量设置充电提醒策略)，根据不同的范围进行不同的提醒。例如：当前电量如果在70％～100％，五天后提醒用户充电；当前电量如果在50％～70％，三天后提醒用户充电；当前电量如果低于50％，一天后提醒用户充电。这样可以有效保证因为患者忘记充电导致刺激器没有电，避免影响患者健康和生活。
在另一些实施方式中，可以基于刺激器的当前电量和配置信息预测应充电时刻，根据应充电时刻设置充电提醒策略(根据当前电量和配置信息两方面设置充电提醒策略)，这种方式预测结果较为准确。
在一应用中，患者小王的刺激器配置信息如下：2个电极导线，每个电极导线使用2个电极触点递送电刺激，电刺激信号的幅值为0.3V，脉宽为60us，频率为130Hz。
预估每天的耗电量为10％，刺激器的当前电量(上午8点)为70％，预设电量阈值为20％，则应充电时刻是5天后的上午八点。
充电提醒策略可以由患者自己设置，对于年纪大、健忘的患者，可以设置
较高的提醒频率(一天好几次)，对于记忆力较好的患者，可以设置较低的提醒频率(只在电量不足的时候进行提醒，电量充足的时候不提醒)。
充电提醒策略可以是：当天提醒1次，提醒内容为：请于5天后进行充电；第二天提醒2次(早、晚分别一次)，提醒内容为：请于4天后进行充电；第三天提醒2次(早、晚分别一次)，提醒内容为：请于3天后进行充电；第四天提醒3次(早、中、晚分别一次)，提醒内容为：请于2天后进行充电；第五天提醒4次，提醒内容为：请于1天后进行充电。这样重复多次的提前提醒，可以加深患者对于充电这件事情的记忆。
或者，充电提醒策略也可以是：前4天不提醒，到了第五天再对用户进行提醒，这样可以只在电量不足的时候对患者进行提醒，电量充足的时候不提醒，避免患者频繁被提醒，引起患者的反感。
在一些可选的实施例中，所述体外通信设备包括程控设备和/或体外充电器；
所述获取所述刺激器的当前电量可以包括：
当所述程控设备与所述刺激器建立程控连接时，利用所述程控设备获取所述刺激器的当前电量，或者，
当所述体外充电器与所述刺激器建立通信连接时，利用所述体外充电器获取所述刺激器的当前电量。
由此，体外通信设备可以是程控设备或者体外充电器，当程控设备与刺激器建立程控连接以对刺激器进行程控时，可以在程控的同时顺便获取刺激器的电量；当体外充电器与刺激器建立通信连接时，可以利用体外充电器顺便获取刺激器的电量。
体外充电器可以对刺激器进行充电(也就是对IPG进行充电)，还可以提供与刺激器进行数据交互的功能，从而获取刺激器的当前电量。
在一些可选的实施例中，所述获取所述刺激器的耗电速度可以包括：
将所述刺激器的配置信息输入至耗电预测模型，以得到所述刺激器的耗电速度；
其中，所述耗电预测模型的训练过程包括：
获取第一训练集，所述第一训练集包括多个第一训练数据，每个第一训练数据包括一个样本刺激器的配置信息以及所述样本刺激器的耗电速度的标注数据；
针对所述第一训练集中的每个第一训练数据，执行以下处理：
将所述第一训练数据中的样本刺激器的配置信息输入至预设的第一深度学
习模型，以得到所述样本刺激器的耗电速度的预测数据；
基于所述样本刺激器的耗电速度的预测数据和标注数据，对所述第一深度学习模型的模型参数进行更新；
检测是否满足预设的第一训练结束条件；如果是，则将训练出的第一深度学习模型作为所述耗电预测模型；如果否，则利用下一个第一训练数据继续训练所述第一深度学习模型。
由此，通过设计，建立适量的神经元计算节点和多层运算层次结构，选择合适的输入层和输出层，就可以得到预设的第一深度学习模型，通过该预设的第一深度学习模型的学习和调优，建立起从输入到输出的函数关系，虽然不能100％找到输入与输出的函数关系，但是可以尽可能地逼近现实的关联关系，由此训练得到的耗电预测模型，可以基于刺激器的配置信息预测刺激器的耗电速度，且预测结果准确性高、可靠性高。
在一些实施方式中，本申请实施例可以采用上述训练过程训练得到耗电预测模型，在另一些实施方式中，本申请实施例可以采用预先训练好的耗电预测模型。
本申请实施例对标注数据的获取方式不作限定，例如可以采用人工标注的方式，也可以采用自动标注或者半自动标注的方式。
本申请实施例对耗电预测模型的训练过程不作限定，其例如可以采用上述监督学习的训练方式，或者可以采用半监督学习的训练方式，或者可以采用无监督学习的训练方式。
本申请实施例对预设的第一训练结束条件不作限定，其例如可以是训练次数达到预设次数(预设次数例如是1次、3次、10次、100次、1000次、10000次等)，或者可以是第一训练集中的训练数据都完成一次或多次训练，或者可以是本次训练得到的总损失值不大于预设损失值。
在一些可选的实施例中，电极导线的电极触点还被配置成采集所述患者的脑电信号，所述方法还可以包括：
利用所述电极触点采集所述患者的脑电信号；
获取所述脑电信号对应的参考配置信息，当所述参考配置信息与所述刺激器的配置信息不匹配时，利用所述参考配置信息更新所述刺激器的配置信息；
基于更新后的刺激器的配置信息，对所述体外通信设备的充电提醒策略进行更新。
由此，患者的病情一般不是固定不变的，当患者的病情变化时，相应地，
刺激器的配置信息也需要进行调整以适应患者的身体状况，这个时候需要重新对耗电情况进行评估。
可以利用电极导线的刺激触点采集患者的脑电信号，脑电信号可以反映患者病情的严重程度，根据脑电信号设置相应的参考配置信息，当参考配置信息与刺激器的当前的配置信息相差很大，不匹配时，根据参考配置信息更新刺激器的配置信息，从而利用更新后的刺激器的配置信息重新评估耗电速度，进而对体外通信设备的充电提醒策略进行更新，这样的话，配置信息可以基于患者的病情更新，充电提醒策略可以随着配置信息的更新而自适应调节，避免错误提醒的情况。
可以采用机器学习模型、深度学习模型或者强化学习模型等人工智能算法对脑电信号进行分析处理，由此得到参考配置信息，根据参考配置信息对刺激器的当前配置信息进行更新(电极导线及触点数量一般不变，一般会对电刺激信号的幅值、频率、脉宽进行调整)，使得更新后的配置信息更适用于患者当前阶段的病情。
在一些实施方式中，可以利用相似度检测模型检测参考配置信息与刺激器的配置信息的相似度，当二者相似度小于预设相似度阈值时，确定所述参考配置信息与所述刺激器的配置信息不匹配。
在一些可选的实施例中，所述获取所述脑电信号对应的参考配置信息可以包括：
将所述患者的脑电信号输入至参考配置模型，以得到所述脑电信号对应的参考配置信息；
其中，所述参考配置模型的训练过程包括：
获取第二训练集，所述第二训练集包括多个第二训练数据，每个第二训练数据包括一个样本脑电信号以及所述样本脑电信号对应的参考配置信息的标注数据；
针对所述第二训练集中的每个第二训练数据，执行以下处理：
将所述第二训练数据中的样本脑电信号输入至预设的第二深度学习模型，以得到所述样本脑电信号对应的参考配置信息的预测数据；
基于所述样本脑电信号对应的参考配置信息的预测数据和标注数据，对所述第二深度学习模型的模型参数进行更新；
检测是否满足预设的第二训练结束条件；如果是，则将训练出的第二深度学习模型作为所述参考配置模型；如果否，则利用下一个第二训练数据继续训
练所述第二深度学习模型。
由此，通过设计，建立适量的神经元计算节点和多层运算层次结构，选择合适的输入层和输出层，就可以得到预设的第二深度学习模型，通过该预设的第二深度学习模型的学习和调优，建立起从输入到输出的函数关系，虽然不能100％找到输入与输出的函数关系，但是可以尽可能地逼近现实的关联关系，由此训练得到的参考配置模型，可以基于患者的脑电信号获取对应的参考配置信息，且计算结果准确性高、可靠性高。
在一些实施方式中，本申请实施例可以采用上述训练过程训练得到参考配置模型，在另一些实施方式中，本申请实施例可以采用预先训练好的参考配置模型。
本申请实施例对标注数据的获取方式不作限定，例如可以采用人工标注的方式，也可以采用自动标注或者半自动标注的方式。
本申请实施例对参考配置模型的训练过程不作限定，其例如可以采用上述监督学习的训练方式，或者可以采用半监督学习的训练方式，或者可以采用无监督学习的训练方式。
本申请实施例对预设的第二训练结束条件不作限定，其例如可以是训练次数达到预设次数(预设次数例如是1次、3次、10次、100次、1000次、10000次等)，或者可以是第二训练集中的训练数据都完成一次或多次训练，或者可以是本次训练得到的总损失值不大于预设损失值。
在一些可选的实施例中，所述刺激器的配置信息包括刺激时间段以及电刺激信号的幅值、脉宽和频率。
由此，刺激器的配置信息除了幅值、脉宽和频率，还可以包括刺激时间段，一般而言，患者每天的刺激时间段是固定的，例如是早上8点到晚上10点，刺激时间段越长，每天的耗电量越高。
在一些可选的实施例中，所述获取所述体外通信设备的充电提醒策略可以包括：
获取应充电时刻范围与充电提醒策略的对应关系，所述对应关系的形式包括对应关系表和/或对应关系图；
在所述对应关系中查找所述应充电时刻所处的应充电时刻范围对应的充电提醒策略，作为所述体外通信设备的充电提醒策略。
由此，应充电时刻范围可以与充电提醒策略预先建立起对应关系(用图或者表来表示)，这样的话，就可以根据直接在对应关系中查找预测的应充电时
刻对应的充电提醒策略，计算量较小，计算效率高。
在一些可选的实施例中，所述患者的疾病类型包括癫痫、震颤、帕金森病、抑郁症、强迫症、阿尔茨海默症和药物成瘾症中的一个或多个。
由此，体外充电器适用于多种不同疾病类型的患者，适用范围较广。
本申请还提供了一种充电提醒装置，所述充电提醒装置包括控制器200，其实现方式与上述方法实施方式中记载的实施方式、所达到的技术效果一致，部分内容不再赘述。
所述控制器200分别与刺激器和体外通信设备进行数据交互，所述刺激器植入于患者体内，所述刺激器设置有至少一个电极导线并利用电极导线的电极触点向所述患者递送电刺激，所述控制器200被配置成：
获取所述刺激器的配置信息，所述配置信息包括以下至少一种：电极导线的数量、每个电极导线使用的电极触点的数量以及电刺激信号的幅值、脉宽和频率；
基于所述刺激器的配置信息，获取所述刺激器的耗电速度；
利用所述体外通信设备获取所述刺激器的当前电量，基于所述刺激器的当前电量和所述耗电速度，预测所述刺激器的电量达到预设电量阈值时对应的应充电时刻；
基于所述应充电时刻，获取所述体外通信设备的充电提醒策略，以提醒所述患者对所述刺激器进行充电，所述充电提醒策略包括提醒频率和/或提醒内容。
在一些可选的实施例中，所述体外通信设备包括程控设备和/或体外充电器；
所述控制器200被配置成采用以下方式获取所述刺激器的当前电量：
当所述程控设备与所述刺激器建立程控连接时，利用所述程控设备获取所述刺激器的当前电量，或者，
当所述体外充电器与所述刺激器建立通信连接时，利用所述体外充电器获取所述刺激器的当前电量。
在一些可选的实施例中，所述控制器200被配置成采用以下方式获取所述刺激器的耗电速度：
将所述刺激器的配置信息输入至耗电预测模型，以得到所述刺激器的耗电速度；
其中，所述耗电预测模型的训练过程包括：
获取第一训练集，所述第一训练集包括多个第一训练数据，每个第一训练
数据包括一个样本刺激器的配置信息以及所述样本刺激器的耗电速度的标注数据；
针对所述第一训练集中的每个第一训练数据，执行以下处理：
将所述第一训练数据中的样本刺激器的配置信息输入至预设的第一深度学习模型，以得到所述样本刺激器的耗电速度的预测数据；
基于所述样本刺激器的耗电速度的预测数据和标注数据，对所述第一深度学习模型的模型参数进行更新；
检测是否满足预设的第一训练结束条件；如果是，则将训练出的第一深度学习模型作为所述耗电预测模型；如果否，则利用下一个第一训练数据继续训练所述第一深度学习模型。
在一些可选的实施例中，电极导线的电极触点还被配置成采集所述患者的脑电信号，所述控制器200还被配置成：
利用所述电极触点采集所述患者的脑电信号；
获取所述脑电信号对应的参考配置信息，当所述参考配置信息与所述刺激器的配置信息不匹配时，利用所述参考配置信息更新所述刺激器的配置信息；
基于更新后的刺激器的配置信息，对所述体外通信设备的充电提醒策略进行更新。
在一些可选的实施例中，所述控制器200被配置成采用以下方式获取所述脑电信号对应的参考配置信息：
将所述患者的脑电信号输入至参考配置模型，以得到所述脑电信号对应的参考配置信息；
其中，所述参考配置模型的训练过程包括：
获取第二训练集，所述第二训练集包括多个第二训练数据，每个第二训练数据包括一个样本脑电信号以及所述样本脑电信号对应的参考配置信息的标注数据；
针对所述第二训练集中的每个第二训练数据，执行以下处理：
将所述第二训练数据中的样本脑电信号输入至预设的第二深度学习模型，以得到所述样本脑电信号对应的参考配置信息的预测数据；
基于所述样本脑电信号对应的参考配置信息的预测数据和标注数据，对所述第二深度学习模型的模型参数进行更新；
检测是否满足预设的第二训练结束条件；如果是，则将训练出的第二深度
学习模型作为所述参考配置模型；如果否，则利用下一个第二训练数据继续训练所述第二深度学习模型。
在一些可选的实施例中，所述刺激器的配置信息包括刺激时间段以及电刺激信号的幅值、脉宽和频率。
在一些可选的实施例中，所述控制器200被配置成采用以下方式获取所述体外通信设备的充电提醒策略：
获取应充电时刻范围与充电提醒策略的对应关系，所述对应关系的形式包括对应关系表和/或对应关系图；
在所述对应关系中查找所述应充电时刻所处的应充电时刻范围对应的充电提醒策略，作为所述体外通信设备的充电提醒策略。
在一些可选的实施例中，所述患者的疾病类型包括癫痫、震颤、帕金森病、抑郁症、强迫症、阿尔茨海默症和药物成瘾症中的一个或多个。
参见图4，图4是本申请实施例提供的一种控制器200的结构框图。
控制器200例如可以包括至少一个存储器210、至少一个处理器220以及连接不同平台系统的总线230。
存储器210可以包括易失性存储器形式的可读介质，例如随机存取存储器(Random Access Memory，RAM)211和/或高速缓存存储器212，还可以包括只读存储器(Read-Only Memory，ROM)213。
其中，存储器210还存储有计算机程序，计算机程序可以被处理器220执行，使得处理器220实现上述任一项控制器200的功能，其实现方式与上述方法实施方式中记载的实施方式、所达到的技术效果一致，部分内容不再赘述。
存储器210还可以包括具有至少一个程序模块215的实用工具214，这样的程序模块215包括：操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例的每一个或一种组合中可能包括网络环境的实现。
相应的，处理器220可以执行上述计算机程序，以及可以执行实用工具214。
处理器220可以采用一个或多个应用专用集成电路(Application Specific Integrated Circuit，ASIC)、数字信号处理器(Digital Signal Processor，DSP)、可编程逻辑器件(ProgrammableLogic Device，PLD)、复杂可编程逻辑器件(Complex Programmable Logic Device，CPLD)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或其他电子元件。
总线230可以为表示几类总线结构的一种或多种，包括存储器总线或者存储器控制器、外围总线、图形加速端口、处理器或者使用多种总线结构的任意
总线结构的局域总线。
控制器200也可以与一个或多个外部设备240例如键盘、指向设备、蓝牙设备等通信，还可与一个或者多个能够与该控制器200交互的设备通信，和/或与使得该控制器200能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等)通信。这种通信可以通过输入输出接口250进行。并且，控制器200还可以通过网络适配器260与一个或者多个网络(例如局域网(Local Area Network，LAN)，广域网(Wide Area Network，WAN)和/或公共网络，例如因特网)通信。网络适配器260可以通过总线230与控制器200的其它模块通信。尽管图中未示出，可以结合控制器200使用其它硬件和/或软件模块，包括：微代码、设备驱动器、冗余处理器、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks，RAID)系统、磁带驱动器以及数据备份存储平台等。
本申请还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述任一项控制器的功能或者实现上述充电提醒方法的步骤，其实现方式与上述控制器的实施方式中记载的实施方式、所达到的技术效果一致，部分内容不再赘述。
参见图5，图5示出了本申请提供的一种用于实现充电提醒方法的程序产品的结构示意图。程序产品可以采用便携式紧凑盘只读存储器(Compact Disc Read Only Memory，CD-ROM)并包括程序代码，并可以在终端设备，例如个人电脑上运行。然而，本申请的程序产品不限于此，在本申请实施例中，可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。可读存储介质包括：具有一个或多个导线的电连接、便携式盘、硬盘、RAM、ROM、可擦式可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM)、闪存、光纤、便携式CD-ROM、光存储器件、磁存储器件、或者上述的任意合适的组合。存储介质可以是非暂态(non-transitory)存储介质。
计算机可读存储介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了可读程序代码。这种传播的数据信号可以采用多种形式，包括电磁信号、光信号或上述的任意合适的组合。可读存储介质还可以是任何可读介质，该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。可读存储介质上包含的程序代码可以用任何
适当的介质传输，包括无线、有线、光缆、射频(Radio Frequency，RF)等，或者上述的任意合适的组合。可以以一种或多种程序设计语言的任意组合来编写用于执行本申请操作的程序代码，程序设计语言包括面向对象的程序设计语言诸如Java、C++等，还包括常规的过程式程序设计语言诸如C语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括LAN或WAN，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。
Claims (10)
- 一种充电提醒装置，包括控制器，所述控制器与刺激器和体外通信设备分别进行数据交互，所述刺激器植入于患者体内，所述刺激器设置有至少一个电极导线并利用所述至少一个电极导线的电极触点向所述患者递送电刺激，所述控制器被配置成：获取所述刺激器的配置信息，所述配置信息包括以下至少一种：电极导线的数量、每个电极导线使用的电极触点的数量以及电刺激信号的幅值、脉宽和频率；基于所述刺激器的配置信息，获取所述刺激器的耗电速度；利用所述体外通信设备获取所述刺激器的当前电量，基于所述刺激器的当前电量和所述耗电速度，预测所述刺激器的电量达到预设电量阈值的情况下对应的应充电时刻；基于所述应充电时刻，获取所述体外通信设备的充电提醒策略，以提醒所述患者对所述刺激器进行充电，所述充电提醒策略包括以下至少之一：提醒频率、提醒内容。
- 根据权利要求1所述的充电提醒装置，其中，所述体外通信设备包括以下至少之一：程控设备、体外充电器；所述控制器被配置成采用以下方式获取所述刺激器的当前电量：在所述程控设备与所述刺激器建立程控连接的情况下，利用所述程控设备获取所述刺激器的当前电量，或者，在所述体外充电器与所述刺激器建立通信连接的情况下，利用所述体外充电器获取所述刺激器的当前电量。
- 根据权利要求1所述的充电提醒装置，其中，所述控制器被配置成采用以下方式获取所述刺激器的耗电速度：将所述刺激器的配置信息输入至耗电预测模型，以得到所述刺激器的耗电速度；其中，所述耗电预测模型的训练过程包括：获取第一训练集，所述第一训练集包括多个第一训练数据，每个第一训练数据包括一个样本刺激器的配置信息以及所述样本刺激器的耗电速度的标注数据；针对所述第一训练集中的每个第一训练数据，执行以下处理：将所述第一训练数据中的样本刺激器的配置信息输入至预设的第一深度学 习模型，以得到所述样本刺激器的耗电速度的预测数据；基于所述样本刺激器的耗电速度的预测数据和标注数据，对所述第一深度学习模型的模型参数进行更新；检测是否满足预设的第一训练结束条件；在满足所述预设的第一训练结束条件的情况下，将训练出的第一深度学习模型作为所述耗电预测模型；在不满足所述预设的第一训练结束条件的情况下，利用下一个第一训练数据继续训练所述第一深度学习模型。
- 根据权利要求1所述的充电提醒装置，其中，所述至少一个电极导线的电极触点还被配置成采集所述患者的脑电信号，所述控制器还被配置成：利用所述电极触点采集所述患者的脑电信号；获取所述脑电信号对应的参考配置信息，在所述参考配置信息与所述刺激器的配置信息不匹配的情况下，利用所述参考配置信息更新所述刺激器的配置信息；基于更新后的刺激器的配置信息，对所述体外通信设备的充电提醒策略进行更新。
- 根据权利要求4所述的充电提醒装置，其中，所述控制器被配置成采用以下方式获取所述脑电信号对应的参考配置信息：将所述患者的脑电信号输入至参考配置模型，以得到所述脑电信号对应的参考配置信息；其中，所述参考配置模型的训练过程包括：获取第二训练集，所述第二训练集包括多个第二训练数据，每个第二训练数据包括一个样本脑电信号以及所述样本脑电信号对应的参考配置信息的标注数据；针对所述第二训练集中的每个第二训练数据，执行以下处理：将所述第二训练数据中的样本脑电信号输入至预设的第二深度学习模型，以得到所述样本脑电信号对应的参考配置信息的预测数据；基于所述样本脑电信号对应的参考配置信息的预测数据和标注数据，对所述第二深度学习模型的模型参数进行更新；检测是否满足预设的第二训练结束条件；在满足所述预设的第二训练结束条件的情况下，将训练出的第二深度学习模型作为所述参考配置模型；在不满足所述预设的第二训练结束条件的情况下，利用下一个第二训练数据继续训练所述第二深度学习模型。
- 根据权利要求1所述的充电提醒装置，其中，所述刺激器的配置信息包括刺激时间段以及电刺激信号的幅值、脉宽和频率。
- 根据权利要求1所述的充电提醒装置，其中，所述控制器被配置成采用以下方式获取所述体外通信设备的充电提醒策略：获取应充电时刻范围与所述充电提醒策略的对应关系，所述对应关系的形式包括以下至少之一：对应关系表、对应关系图；在所述对应关系中查找所述应充电时刻所处的应充电时刻范围对应的充电提醒策略，作为所述体外通信设备的充电提醒策略。
- 根据权利要求1所述的充电提醒装置，其中，所述患者的疾病类型包括以下至少之一：癫痫、震颤、帕金森病、抑郁症、强迫症、阿尔茨海默症和药物成瘾症。
- 一种植入式神经刺激系统，包括：刺激器，所述刺激器植入于患者体内，所述刺激器设置有至少一个电极导线并利用所述至少一个电极导线的电极触点向所述患者递送电刺激；体外通信设备，所述体外通信设备设置于所述患者的体外，所述体外通信设备被配置成提醒所述患者对所述刺激器进行充电；权利要求1至8中任一项所述的充电提醒装置。
- 一种计算机可读存储介质，其中，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至8中任一项所述控制器的功能。
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