WO2023241338A1 - 程控设备、程控***、电子设备和计算机可读存储介质 - Google Patents
程控设备、程控***、电子设备和计算机可读存储介质 Download PDFInfo
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Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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- A—HUMAN NECESSITIES
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- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
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- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
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- A—HUMAN NECESSITIES
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Definitions
- This application relates to the technical field of implantable medical equipment, such as program-controlled equipment, program-controlled systems, electronic equipment and computer-readable storage media.
- Patent CN114082102A discloses a closed-loop vagus nerve stimulator based on scalp EEG, including a signal acquisition module, a scalp EEG signal processing module and a vagus nerve stimulator.
- the signal acquisition module is connected to the scalp EEG signal processing module; the signal acquisition module is used to collect the scalp EEG signal; the scalp EEG signal processing module is used to process the scalp EEG signal.
- the EEG signal determines whether epilepsy has occurred and transmits the epilepsy occurrence signal to the vagus nerve stimulator; the vagus nerve stimulator is used to receive the signal and generate stimulation pulses.
- the signal acquisition module still needs to collect for a long time. Once the acquisition starts, it can only wait until the acquisition is completed. The acquisition process cannot be intervened, and the patient's life safety cannot be guaranteed.
- the purpose of this application is to provide program-controlled equipment and program-controlled systems to ensure that doctors obtain ideal collection data, give patients the opportunity to adapt to the collection process in advance, and ensure the patient's life safety.
- this application provides a program-controlled device, which is configured to:
- control the bioelectric collection device In response to the pre-acquisition operation, control the bioelectric collection device to enter the pre-acquisition mode to collect the patient's first bioelectric signal;
- the first bioelectric signal When the first bioelectric signal does not meet the preset conditions, generate first prompt information and send it to the user equipment, and/or stop pre-acquisition;
- the bioelectric collection device In response to the collection confirmation operation, the bioelectric collection device is controlled to enter a formal collection mode to collect the second bioelectric signal of the patient, wherein the collection duration of the pre-collection mode is shorter than the collection time of the formal collection mode. duration.
- the validity score of the first bioelectric signal is not less than a preset validity score threshold
- the program-controlled device is configured to detect whether the first bioelectrical signal is abnormal in the following manner:
- the first bioelectrical signal is input into an abnormality detection model to output the predicted similarity corresponding to the first bioelectrical signal; wherein, the abnormality detection model is used to compare the first bioelectrical signal with the abnormality database respectively. Compare each abnormal bioelectric signal to obtain the similarity between the first bioelectric signal and each abnormal bioelectric signal, and use the highest similarity among the multiple similarities corresponding to the first bioelectric signal as The predicted similarity corresponding to the first bioelectric signal;
- the predicted similarity is less than the preset similarity threshold, it is determined that the first bioelectric signal is not abnormal.
- the program-controlled device is further configured to:
- this application provides a program-controlled system, which includes program-controlled equipment and bioelectricity collection equipment;
- the program-controlled device is communicably connected to the bioelectricity collection device, and the program-controlled device is configured to:
- control the bioelectric collection device In response to the pre-acquisition operation, control the bioelectric collection device to enter the pre-acquisition mode to collect the patient's first bioelectric signal;
- the first bioelectric signal When the first bioelectric signal does not meet the preset conditions, generate first prompt information and send it to the user equipment, and/or stop pre-acquisition;
- the application provides an electronic device.
- the electronic device includes a memory and a processor.
- the memory stores a computer program.
- the processor executes the computer program, the following steps are implemented:
- the first bioelectric signal When the first bioelectric signal does not meet the preset conditions, generate first prompt information and send it to the user equipment, and/or stop pre-acquisition;
- the bioelectrical acquisition device is controlled to enter the pre-acquisition mode to collect the patient's The first bioelectrical signal
- the doctor is given the opportunity to re-modify the acquisition parameters and re-issue the acquisition instructions. If the pre-acquisition bioelectrical signals do not meet the standards (poor quality, abnormality), the acquisition parameters can be re-adjusted over and over again ( Pre-collection has a short collection time (compared with formal collection, trial-and-error time and cost are lower), which provides a guarantee for doctors to obtain ideal collection data. For patients, it gives them the opportunity to adapt to the collection process in advance and ensures the patient's safety. life safety.
- Figure 1 is a schematic flowchart of a control method for program-controlled equipment provided by an embodiment of the present application.
- Figure 2 is a schematic flowchart of obtaining effectiveness scores provided by an embodiment of the present application.
- Figure 3 is a schematic flowchart of detecting whether an abnormality occurs according to an embodiment of the present application.
- FIG. 5 is a structural block diagram of a program control system 300 provided by an embodiment of the present application.
- Implantable neurostimulation system mainly consists of a stimulator implanted in the body (i.e. Implantable neurostimulator, a neurostimulator) and programmable devices placed 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 for short) and a lead. Any type of switching device.
- Implantable neuroelectric stimulation devices include, for example, Deep Brain Stimulation (DBS), Cortical Nerve Stimulation (CNS), and Spinal Cord stimulation systems. Stimulation, referred to as SCS), implanted sacral nerve electrical stimulation system (Sacral Nerve Stimulation, referred to as SNS), implanted vagus nerve electrical stimulation system (V agus Nerve Stimulation, referred to as VNS), etc.
- the stimulated biological 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. This application does not limit the applicable disease types, which can 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 deep brain stimulation
- SCS spinal cord stimulation
- pelvic stimulation gastric stimulation
- peripheral nerve stimulation and functional electrical stimulation.
- DBS diseases that DBS can be used to treat or manage
- diseases include, but are not limited to: spasmodic diseases (e.g., epilepsy), pain, migraines, Mental illness (for example, major depressive disorder (MDD)), bipolar disorder, anxiety disorder, post-traumatic stress disorder, mild depression, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental status disorders, Mobility disorders (e.g., essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, drug addiction, autism, or other neurological or psychiatric diseases and impairments.
- spasmodic diseases e.g., epilepsy
- pain migraines
- Mental illness for example, major depressive disorder (MDD)
- bipolar disorder for example, major depressive disorder (MDD)
- anxiety disorder post-traumatic stress disorder
- mild depression obsessive-compulsive disorder (OCD)
- OCD obsessive-compulsive disorder
- behavioral disorders e.g
- the stimulator in this application takes a deep brain stimulator (DBS) as an example.
- DBS deep brain stimulator
- the program-controlled device can be used to adjust the stimulation parameters of the electrical stimulation signal of the stimulator, or the stimulator can sense It measures the bioelectrical activity in the deep part of the patient's brain, and can continue to adjust the stimulation parameters of the electrical stimulation signal of the stimulator through the sensed bioelectrical activity.
- the programmable device can be a doctor programmer (that is, a programmer used by doctors) or a patient programmer (that is, a programmer used by patients).
- the doctor's program controller can be, for example, a tablet computer, a laptop computer, a desktop computer, a mobile phone and other intelligent terminal devices equipped with program control software.
- the patient program controller can be, for example, a tablet computer, a notebook computer, a desktop computer, a mobile phone and other intelligent terminal devices equipped with program control software.
- the patient program controller can also be other electronic devices with program control functions (such as chargers, data sets with program control functions). collection equipment).
- Step S101 Receive the user's pre-collection operation
- Step S102 In response to the pre-acquisition operation, control the bioelectric collection device to enter the pre-acquisition mode to collect the first bioelectric signal of the patient;
- the pre-acquisition process stops immediately and subsequent formal acquisition cannot be carried out, and/or, prompt If the user's first bioelectrical signal does not meet the preset conditions, it is convenient for the user to take corresponding measures. For example, the user can reset the acquisition parameters and pre-acquire again, or stop collecting temporarily.
- the program controller switches to the program-controlled mode and the patient's bioelectrical signal can be measured another day. Electrical signals are collected; on the other hand, if the preset conditions are met, the user confirms whether to perform formal collection, and the user confirms If the patient's physical condition is OK, he can enter the formal collection mode and obtain the second bioelectrical signal through formal collection. Since the collection time corresponding to the second bioelectrical signal is relatively long, it will be very helpful for analyzing the patient's condition.
- the user equipment can be a mobile phone, tablet computer, desktop computer, laptop computer, smart wearable device, etc. used by the user.
- the bioelectricity collection equipment may include any one or more of the following: EEG collection equipment (such as electrode caps, cortical electrodes, intracranial electrodes, etc.), ECG collection equipment, and eye electricity collection.
- the first bioelectrical signal may include any one or more of the following: electroencephalogram signal, electrocardiogram signal, electrooculogram signal, and electromechanical signal.
- pre-collection can be stopped first, and then prompt information is generated and sent to the user device;
- prompt information can be generated and sent to the user device, and then the pre-collection can be stopped.
- the effectiveness score can be represented by a number, such as 60, 80 or 90. The higher the number, the higher the effectiveness score; it can also be represented by a letter, such as A, B, C or D. The higher the letter is, the higher the effectiveness score. high.
- the preset validity score threshold is, for example, 70, 80 or 85.
- FIG. 2 is a schematic flowchart of obtaining effectiveness scores provided by an embodiment of the present application.
- Step S203 Determine the validity score of the first bioelectric signal based on the number of valid sampling points.
- the effectiveness score can be determined by sampling and detecting the first bioelectric signal.
- the first bioelectric signal is discretized through sampling to obtain multiple sampling points.
- the number of effective sampling points can reflect the first bioelectric signal.
- the overall effectiveness of the electrical signal is scored. In this way, there is no need to analyze the entire first bioelectrical signal, but only a limited sampling point, which greatly reduces the amount of calculation and improves the detection efficiency.
- FIG. 3 is a schematic flowchart of detecting whether an abnormality occurs according to an embodiment of the present application.
- the anomaly detection model is used to determine the predicted similarity corresponding to the first bioelectric signal.
- the anomaly detection model can call the anomaly database, which stores a large number of abnormal bioelectric signals.
- the signal specifically, can be the bioelectrical signals corresponding to different patients at the time of onset, or the bioelectrical signals corresponding to the current patient himself at the time of onset.
- the abnormal bioelectrical signals in the abnormal database The signal may include the bioelectrical signal corresponding to the onset of Parkinson's disease.
- the first bioelectrical signal is compared with each abnormal bioelectrical signal in the abnormal database, and the abnormal bioelectrical signal with the highest similarity is combined.
- the predicted similarity can be expressed as a number or a percentage. When expressed as a number, the predicted similarity is, for example, 60, 80, or 90; when expressed as a percentage, the predicted similarity is, for example, 60%, 80%, or 90%. The higher the value, the better the predicted similarity. The higher the similarity.
- This application does not limit the preset similarity threshold, which can be 70%, 80% or 90%.
- the second training set includes a plurality of second training data, each of the second training data includes a first sample signal used for training, a second sample signal and the first sample Annotated data on the similarity between the signal and the second sample signal;
- the first sample signal and the second sample signal in the second training data are respectively input into the preset second deep learning model to obtain a prediction of the similarity between the first sample signal and the second sample signal. data;
- This 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 all 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 preset condition includes: the validity score of the first bioelectric signal is not less than a preset validity score threshold;
- the method may further include:
- acquisition parameter configuration information corresponding to the bioelectric collection device is obtained, and the bioelectric collection device is controlled to enter the pre-acquisition mode again.
- the first bioelectrical signal does not satisfy the preset condition, that is, when the first bioelectrical signal has When the effectiveness score is less than the preset effectiveness score threshold, it indicates that the signal strength of the first bioelectrical signal is relatively low and the current collection parameters are inappropriate.
- the collection parameter configuration information can be obtained based on the first bioelectrical signal to adjust the collection parameters. Pre-acquisition is performed again, and through gradual adjustments, the bioelectric signals obtained in the pre-acquisition meet the preset conditions, providing a reference for subsequent formal acquisition.
- the collection parameter configuration information is, for example: 10 bits of sampling bits and 1 Mbps data transmission rate.
- Figure 4 is a partial flow chart of a control method for program-controlled equipment provided by an embodiment of the present application.
- the first bioelectrical signal includes an electroencephalogram signal.
- the method may further include steps S106 to S107.
- Step S106 Obtain the patient's pain score based on the first bioelectric signal
- Step S107 When the pain score is not less than the preset pain score threshold, stop pre-acquisition.
- the patient's pain score can be obtained, thereby reflecting the patient's pain score.
- the patient's pain score is not less than the preset pain score threshold, it indicates that the patient is suffering unbearable pain, and the pre-acquisition can be automatically stopped to alleviate the patient's pain. This way of fully respecting the patient's individual feelings reflects Humanized care greatly reduces the patient's psychological burden during pre-collection.
- the pain score can be represented by a number, such as 60, 80 or 90. The higher the number, the higher the pain score; it can also be represented by a letter, such as A, B, C or D. The higher the letter is, the higher the pain score.
- the preset pain score threshold is, for example, 70, 80 or 85.
- the program-controlled device is further used to obtain the patient's pain score in the following ways:
- the training process of the pain prediction model includes:
- the first training set including a plurality of first training data, each of the first training data including the bioelectrical signal of a sample object and the annotation data of the pain score of the sample object;
- 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 approximate the realistic correlation as much as possible.
- the pain prediction model obtained by training can The patient's pain score is obtained based on the first bioelectric signal, and the calculation results are highly accurate and reliable.
- This 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 bioelectricity collection device is a stimulator implanted in the patient's body, and the method may further include:
- the stimulation parameter configuration information corresponding to the patient is obtained based on the second bioelectric signal, so that the bioelectric collection device applies corresponding electrical stimulation to the patient. energy.
- the bioelectrical collection equipment can have the functions of providing electrical stimulation and sensing bioelectrical signals. Since the collection time corresponding to the second bioelectrical signal is relatively long, it is very helpful for analyzing the patient's condition. According to the second bioelectrical signal, Signal, obtain stimulation parameter configuration information, and use bioelectricity acquisition equipment to provide precise electrical stimulation to patients.
- the stimulation parameter configuration information is: frequency 130Hz, pulse width 60 ⁇ s, and amplitude 2-3V.
- the embodiments of the present application also provide a program-controlled device, the specific manner of which is consistent with the implementation manner and achieved technical effects recorded in the embodiments of the above method, and part of the content will not be described again.
- the program-controlled equipment is configured to:
- control the bioelectric collection device In response to the pre-acquisition operation, control the bioelectric collection device to enter the pre-acquisition mode to collect the patient's first bioelectric signal;
- the bioelectric collection device In response to the collection confirmation operation, the bioelectric collection device is controlled to enter a formal collection mode to collect the second bioelectric signal of the patient, wherein the collection duration of the pre-collection mode is shorter than the collection time of the formal collection mode. duration.
- the preset conditions include at least one of the following:
- the validity score of the first bioelectric signal is not less than a preset validity score threshold
- the program-controlled device is configured to obtain the first raw material in the following ways: Effectiveness score of physical and electrical signals:
- a validity score of the first bioelectric signal is determined.
- the program-controlled device is configured to detect whether the first bioelectrical signal is abnormal in the following manner:
- the first bioelectrical signal is input into an abnormality detection model to output the predicted similarity corresponding to the first bioelectrical signal; wherein, the abnormality detection model is used to compare the first bioelectrical signal with the abnormality database respectively. Compare each abnormal bioelectric signal to obtain the similarity between the first bioelectric signal and each abnormal bioelectric signal, and use the highest similarity among the multiple similarities corresponding to the first bioelectric signal as The predicted similarity corresponding to the first bioelectric signal;
- the predicted similarity is less than the preset similarity threshold, it is determined that the first bioelectric signal is not abnormal.
- the preset condition includes: the validity score of the first bioelectric signal is not less than a preset validity score threshold;
- the program-controlled device is further configured to:
- the first bioelectrical signal includes an electroencephalogram signal.
- the program-controlled device is further configured to:
- the program-controlled device is further used to obtain the patient's pain score in the following ways:
- the first training set including a plurality of first training data, each of the first training data including the bioelectrical signal of a sample object and the annotation data of the pain score of the sample object;
- the bioelectricity collection device is a stimulator implanted in the patient's body
- the program-controlled device is further configured to:
- the stimulation parameter configuration information corresponding to the patient is obtained based on the second bioelectric signal, so that the bioelectric collection device applies corresponding electrical stimulation to the patient. energy.
- Figure 5 is a structural block diagram of a program control system 300 provided by an embodiment of the present application.
- the embodiment of the present application also provides a program-controlled system 300, the specific manner of which is consistent with the implementation manner and achieved technical effects recorded in the embodiments of the above method, and part of the content will not be described again.
- the program-controlled system 300 includes a program-controlled device 100 and a bioelectricity collection device 200;
- the program-controlled device 100 is communicably connected to the bioelectricity collection device 200, and the program-controlled device 100 is configured to:
- control the bioelectric collection device 200 In response to the pre-acquisition operation, control the bioelectric collection device 200 to enter the pre-acquisition mode to collect the first bioelectric signal of the patient;
- the first bioelectric signal When the first bioelectric signal does not meet the preset conditions, generate first prompt information and send it to the user equipment, and/or stop pre-acquisition;
- the bioelectric collection device 200 is controlled to enter a formal collection mode to collect the second bioelectric signal of the patient, wherein the collection duration of the pre-collection mode is shorter than that of the formal collection mode. Collection duration.
- An embodiment of the present application also provides an electronic device.
- the electronic device includes a memory and a processor.
- the memory stores a computer program.
- the processor executes the computer program, the following steps are implemented:
- control the bioelectric collection device In response to the pre-acquisition operation, control the bioelectric collection device to enter the pre-acquisition mode to collect the patient's first bioelectric signal;
- the first bioelectric signal When the first bioelectric signal does not meet the preset conditions, generate first prompt information and send it to the user equipment, and/or stop pre-acquisition;
- the bioelectric collection device In response to the collection confirmation operation, the bioelectric collection device is controlled to enter a formal collection mode to collect the second bioelectric signal of the patient, wherein the collection duration of the pre-collection mode is shorter than the collection time of the formal collection mode. duration.
- Embodiments of the present application also provide a computer-readable storage medium.
- the computer-readable storage medium stores a computer program.
- the computer program is executed by a processor, the following steps are implemented:
- control the bioelectric collection device In response to the pre-acquisition operation, control the bioelectric collection device to enter the pre-acquisition mode to collect the patient's first bioelectric signal;
- the first bioelectric signal When the first bioelectric signal does not meet the preset conditions, generate first prompt information and send it to the user equipment, and/or stop pre-acquisition;
- the bioelectric collection device In response to the collection confirmation operation, the bioelectric collection device is controlled to enter a formal collection mode to collect the second bioelectric signal of the patient, wherein the collection duration of the pre-collection mode is shorter than the collection time of the formal collection mode. duration.
- an element When an element is referred to as being “on,””on,””connectedto” or “coupled to” another element, it can be directly on, directly connected to, or directly connected to the other element. Either directly coupled to said other component, or intervening components may be present. However, when an element is referred to as being “directly on,””directly connected to” or “directly coupled to” another element, there are no intervening elements present.
- the term “connected” may refer to a physical connection, an electrical connection, etc., with or without intervening components.
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Abstract
本申请提供了一种程控设备、程控***、电子设备和计算机可读存储介质,所述程控设备被配置成:接收用户的预采集操作;响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号。本申请给予了患者提前适应采集过程的机会,保障患者的生命安全。
Description
本申请要求于2022年6月17日提交的申请号为202210689123.5的中国专利的优先权,上述中国专利通过全文引用的形式并入。
本申请涉及植入式医疗设备技术领域,例如涉及程控设备、程控***、电子设备和计算机可读存储介质。
现有的程控***中,只要下达数据采集指令,患者刺激器就会退出程控模式进入采集模式。一旦进入到采集模式,就只能等待刺激器采集结束,这是一个漫长且不可逆的过程。此时,不论采集信号是否满足要求,都不能停下来,重新设置采集参数,重新采集。并且,患者出现身体不适或者发病时,也无法退出采集,重新切换到程控模式,调整刺激参数,有较大的安全隐患。
专利CN114082102A公开了一种基于头皮脑电的闭环迷走神经刺激器,包括信号采集模块、头皮脑电信号处理模块以及迷走神经刺激器。所述信号采集模块与头皮脑电信号处理模块连接;所述信号采集模块用于采集头皮脑电信号;所述头皮脑电信号处理模块用于对头皮脑电信号进行处理,根据处理后的头皮脑电信号判断是否发生癫痫,并将癫痫发生信号传输给迷走神经刺激器;迷走神经刺激器用于接收信号后产生刺激脉冲。这种方式下,信号采集模块仍然需要进行长时间的采集,一旦开始采集了,就只能等到采集结束,采集过程不能干预,患者的生命安全得不到保障。
因此,亟需提供程控设备、程控***、电子设备和计算机可读存储介质,解决相关技术存在的问题。
发明内容
本申请的目的在于提供程控设备和程控***,为医生获取理想的采集数据提供了保障,给予了患者提前适应采集过程的机会,保障患者的生命安全。
本申请的目的采用以下技术方案实现:
第一方面,本申请提供了一种程控设备,所述程控设备被配置成:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
在一种可能的实现方式中,所述预设条件包括以下至少一种:
所述第一生物电信号的有效性评分不小于预设有效性评分阈值;
所述第一生物电信号没有发生异常。
在一种可能的实现方式中,所述程控设备被配置成采用如下方式获取所述第一生物电信号的有效性评分:
对所述第一生物电信号进行采样,得到多个采样点;
针对每个采样点,逐个计算所述采样点的幅值与预设幅值的比对值,将比对值不小于预设比对阈值的采样点作为有效采样点;
基于所述有效采样点的数量,确定所述第一生物电信号的有效性评分。
在一种可能的实现方式中,所述程控设备被配置成采用如下方式检测所述第一生物电信号是否发生异常:
将所述第一生物电信号输入异常检测模型,以输出所述第一生物电信号对应的预测相似度;其中,所述异常检测模型用于将所述第一生物电信号分别与异常数据库中的每个异常生物电信号进行比对,得到所述第一生物电信号与每个异常生物电信号的相似度,将所述第一生物电信号对应的多个相似度中最高的相似度作为所述第一生物电信号对应的预测相似度;
当所述预测相似度不小于预设相似度阈值时,确定所述第一生物电信号发生异常;
当所述预测相似度小于预设相似度阈值时,确定所述第一生物电信号没有发生异常。
在一种可能的实现方式中,所述预设条件包括:所述第一生物电信号的有效性评分不小于预设有效性评分阈值;
当所述第一生物电信号不满足所述预设条件时,所述程控设备还被配置成:
基于所述第一生物电信号,获取所述生物电采集设备对应的采集参数配置信息,控制所述生物电采集设备再次进入所述预采集模式。
在一种可能的实现方式中,所述第一生物电信号包括脑电信号,在控制所述生物电采集设备进入所述预采集模式之后,所述程控设备还被配置成:
基于所述第一生物电信号,获取所述患者的疼痛评分;
当所述疼痛评分不小于预设疼痛评分阈值时,停止预采集。
在一种可能的实现方式中,所述程控设备被进一步采用如下方式获取所述患者的疼痛评分:
将所述第一生物电信号输入疼痛预测模型,得到所述患者的疼痛评分;
其中,所述疼痛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,每个所述第一训练数据包括一个样本对象的生物电信号以及所述样本对象的疼痛评分的标注数据;
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对象的生物电信号输入预设的第一深度学习模型,得到所述样本对象的疼痛评分的预测数据;
基于所述样本对象的疼痛评分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疼痛预测模型;如果否,则利用下一个所述第一训练数据继续训练所述第一深度学习模型。
在一种可能的实现方式中,所述生物电采集设备是植入于所述患者体内的刺激器,所述程控设备还被配置成:
在利用所述生物电采集设备完成正式采集以后,基于所述第二生物电信号,
获取所述患者对应的刺激参数配置信息,以使所述生物电采集设备对所述患者施加对应的电刺激能量。
第二方面,本申请提供了一种程控***,所述程控***包括程控设备和生物电采集设备;
所述程控设备与所述生物电采集设备可通信地连接,所述程控设备被配置成:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
第三方面,本申请提供了一种电子设备,所述电子设备包括存储器和处理器,所述存储器存储有计算机程序,所述处理器执行所述计算机程序时实现如下步骤:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
第四方面,本申请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如下步骤:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者
的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
采用本申请提供的程控设备、程控***、电子设备和计算机可读存储介质,至少具有以下优点:
通过预采集,对于医生而言,给予了医生重新修改采集参数、重新下达采集指令的机会,如果预采集的生物电信号不达标(质量不好、出现异常),可以一遍遍地重新调整采集参数(预采集的采集时长短,相比于正式采集,试错时间和成本低),为医生获取理想的采集数据提供了保障,对于患者而言,给予了患者提前适应采集过程的机会,保障患者的生命安全。
下面结合附图和实施例对本申请进一步说明。
图1是本申请实施例提供的一种程控设备的控制方法的流程示意图。
图2是本申请实施例提供的一种获取有效性评分的流程示意图。
图3是本申请实施例提供的一种检测是否发生异常的流程示意图。
图4是本申请实施例提供的一种程控设备的控制方法的部分流程示意图。
图5是本申请实施例提供的一种程控***300的结构框图。
下面,结合附图以及具体实施方式,对本申请做进一步描述,需要说明的是,在不相冲突的前提下,以下描述的各实施例之间或各技术特征之间可以任意组合形成新的实施例。
下面,首先对本申请的其中应用领域(即植入式神经刺激器)进行简单说明。
植入式神经刺激***(一种神经刺激***)主要包括植入体内的刺激器(即
植入式神经刺激器,一种神经刺激器)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、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***(又称心脏起搏器)、植入式药物输注装置(Imp lantable Drug Delivery System,简称I DDS)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,简称D BS)、植入式脑皮层刺激***(Cortical Nerve Stimulation,简称CNS)、植入式脊髓电刺激***(Spinal Cord Stimulation,简称SCS)、植入式骶神经电刺激***(Sacral Nerve Stimulation,简称SNS)、植入式迷走神经电刺激***(V agus Nerve Stimulation,简称VNS)等。
刺激器可以包括IPG、延伸导线和电极导线,IPG(implantable pulse gener ator,植入式脉冲发生器)设置于患者体内,依靠密封电池和电路向生物体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,为生物体组织的特定区域提供一路或两路可控制的特定电刺激能量。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。电极导线将IPG产生的电刺激信号,通过多个电极触点,向生物体组织的特定区域释放电刺激能量;所述植入式医疗设备具有单侧或双侧的一路或多路电极导线,所述电极导线上设置有多个电极触点,所述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,所述电极触点以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。
在一些可能的方式中,受刺激的生物体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但不限于:痉挛疾病(例如,癫痫)、疼痛、偏头痛、
精神疾病(例如,重度抑郁症(MDD))、躁郁症、焦虑症、创伤后压力心理障碍症、轻郁症、强迫症(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、自闭症或其他神经学或精神科疾病和损害。当DBS用于治疗药物成瘾症患者时,可以帮助吸毒人员戒毒,提升他们的幸福感和生命质量。
本申请中的刺激器以脑深部刺激器(DBS)为例进行阐述,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电刺激信号的刺激参数,也可以通过刺激器感测患者脑深部的生物电活动,并可以通过所感测到的生物电活动来继续调节刺激器的电刺激信号的刺激参数。
电刺激信号的刺激参数可以包括频率(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触发)、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)中的一种或多种。中的任意一种或多种。在具体应用中,可以在电流模式或者电压模式下对刺激器的各刺激参数进行调节。
程控设备可以是医生程控器(即医生使用的程控器)或者患者程控器(即患者使用的程控器)。医生程控器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。患者程控器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,患者程控器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)。
本申请对医生程控器和刺激器的数据交互不进行限制,当医生远程程控时,医生程控器可以通过服务器、患者程控器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,医生程控器可以通过患者程控器与刺激器进行数据交互,医生程控器还可以直接与刺激器进行数据交互。
患者程控器可以包括(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,医生程控器可以通过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/WIFI协议/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,医生程控器可以通过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。
参见图1,图1是本申请实施例提供的一种程控设备的控制方法的流程示意图。
本申请实施例提供了一种程控设备的控制方法,所述方法包括步骤S101~S105。
步骤S101:接收用户的预采集操作;
步骤S102:响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集所述患者的第一生物电信号;
步骤S103:当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
步骤S104:当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
步骤S105:响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
由此,生物电采集设备可以设置有两种采集模式,分别为预采集模式和正式采集模式,预采集模式下,采集时长较短,例如可以是2分钟、5分钟或者10分钟,采集过程可以随时停止,而正式采集模式下,采集时长很长,例如可以是1小时、2小时或者4小时,采集过程不可以随时停止,在进入长时间的正式采集模式之前,必须先进入预采集模式,通过预采集得到第一生物电信号,判断第一生物电信号是否满足预设条件,一方面,如果不满足预设条件,预采集过程当即停止,无法进行后续的正式采集,和/或,提示用户第一生物电信号不满足预设条件,便于用户采取相应的措施,例如用户可以重新设置采集参数再次预采集,也可以暂时不采集了,程控器切换到程控模式,改天再对患者的生物电信号进行采集;另一方面,如果满足预设条件,由用户确认是否进行正式采集,用户确认
患者的身体状态没问题,就可以进入正式采集模式,通过正式采集获取第二生物电信号,由于第二生物电信号对应的采集时间比较长,对患者的病情分析会非常有帮助。
本申请通过预采集,对于医生而言,给予了医生重新修改采集参数、重新下达采集指令的机会,如果预采集的生物电信号不达标(质量不好、出现异常),可以一遍遍地重新调整采集参数(预采集的采集时长短,相比于正式采集,试错时间和成本低),为医生获取理想的采集数据提供了保障(理想的采集数据,例如是所采集的信号的波形是医生预期的波形,或者是,所采集的信号的平均幅值达到医生的预期值),对于患者而言,给予了患者提前适应采集过程的机会,保障患者的生命安全。
用户可以是患者自身,也可以是患者的医生、护士、护工、保姆、家人等照护患者的人。
本申请对用户设备不作限定,用户设备可以是用户使用的手机、平板电脑、台式计算机、笔记本电脑、智能穿戴设备等。
本申请对生物电采集设备不作限定,生物电采集设备可以包括以下任意一种或多种:脑电采集设备(例如电极帽、皮层电极、颅内电极等)、心电采集设备、眼电采集设备、肌电采集设备。相应地,第一生物电信号可以包括以下任意一种或多种:脑电信号、心电信号、眼电信号、机电信号。
在一些可能的方式中,生物电采集设备可以是所述刺激器,第一生物电信号可以包括脑电信号,生物电采集设备兼具感测脑电信号和释放电刺激能量的功能。
当不满足预设条件时,可以只停止预采集,或者,只发送第一提示信息,或者,既停止预采集,又发送第一提示信息。
第一提示信息可以采用文字、视频、语音等方式,例如可以是文本信息“第一生物电信号发生异常”、“第一生物电信号过于微弱,建议重新采集”。
在一些可能的方式中,当不满足预设条件时,可以先停止预采集,再生成提示信息并发送至用户设备;
在另一些可能的方式中,当不满足预设条件时,可以先生成提示信息并发送至用户设备,再停止预采集。
在一些可能的方式中,当满足预设条件时,可以生成第二提示信息并发送至用户设备。
第二提示信息可以采用文字、视频、语音等方式,例如可以是语音信息“第一生物电信号达标,建议开始正式采集”。
在一些可能的方式中,所述预设条件可以包括以下至少一种:
所述第一生物电信号的有效性评分不小于预设有效性评分阈值;
所述第一生物电信号没有发生异常。
由此,一方面,预设条件可以是第一生物电信号的有效性评分不小于预设有效性评分阈值,当满足预设条件时,表明第一生物电信号的信号强度比较高,后续的正式采集可以在同样的采集参数下进行,有利于对患者的病情进行分析,当不满足预设条件时,表明第一生物电信号的信号强度比较低,当前的采集参数不合适;另一方面,预设条件可以是第一生物电信号没有发生异常,当满足预设条件时,表明患者没有发病,可以进行后续的正式采集,当不满足预设条件时,表明患者有较大可能发病,采集工作暂停,需要进行电刺激治疗。
在一些可能的方式中,预设条件可以包括:所述第一生物电信号的有效性评分不小于预设有效性评分阈值,并且,所述第一生物电信号没有发生异常。
有效性评分可以用数字表示,例如60、80或者90,数值越高,有效性评分越高;也可以用字母表示,例如A、B、C或者D,字母排序越靠前,有效性评分越高。
本申请对预设有效性评分阈值不作限定,预设有效性评分阈值例如是70、80或者85。
参见图2,图2是本申请实施例提供的一种获取有效性评分的流程示意图。
在一些可能的方式中,获取所述第一生物电信号的有效性评分的过程可以包括步骤S201~S203。
步骤S201:对所述第一生物电信号进行采样,得到多个采样点;
步骤S202:针对每个采样点,逐个计算所述采样点的幅值与预设幅值的比对值,将比对值不小于预设比对阈值的采样点作为有效采样点;
步骤S203:基于所述有效采样点的数量,确定所述第一生物电信号的有效性评分。
由此,可以通过对第一生物电信号抽样检测的方式来确定有效性评分,通过采样将第一生物电信号离散化,得到多个采样点,通过有效采样点的数量,可以反映第一生物电信号整体的有效性评分,如此一来,不用对整个第一生物电信号进行分析,只需对有限的采样点进行分析,大大降低了运算量,提高了检测效率。
采样点的幅值与预设幅值的比对值可以是经过差值计算或者比值计算得到。
本申请对预设幅值和预设比对阈值不作限定,预设幅值可以是10μV、20μV或者300μV。预设比对阈值可以是1(μV)、0.2(μV)或者0.5(μV)。
在一些可能的方式中,所述基于所述有效采样点的数量,确定所述第一生物电信号的有效性评分,可以包括:
基于所述有效采样点的数量和所述多个采样点的(总)数量,确定所述第一生物电信号的有效性评分。
具体而言,有效性评分可以是有效采样点的数量与采样点的总数量的比值。
在另一些实施方式中,所述基于所述有效采样点的数量,确定所述第一生物电信号的有效性评分,可以包括:
当有效采样点的数量在100-200之间时,确定第一生物电信号的有效性评分为40;
当有效采样点的数量在200-400之间时,确定第一生物电信号的有效性评分为60;
当有效采样点的数量在400-600之间时,确定第一生物电信号的有效性评分
为80。
在另一些可选的实施例中,医生可以根据第一生物电信号的波形,估计第一生物电信号的强度,从而判断第一生物电信号是否满足预设条件。
参见图3,图3是本申请实施例提供的一种检测是否发生异常的流程示意图。
在一些可能的方式中,检测所述第一生物电信号是否发生异常的过程可以包括步骤S301~S303。
步骤S301:将所述第一生物电信号输入异常检测模型,以输出所述第一生物电信号对应的预测相似度;其中,所述异常检测模型用于将所述第一生物电信号分别与异常数据库中的每个异常生物电信号进行比对,得到所述第一生物电信号与每个异常生物电信号的相似度,将所述第一生物电信号对应的多个相似度中最高的相似度作为所述第一生物电信号对应的预测相似度;
步骤S302:当所述预测相似度不小于预设相似度阈值时,确定所述第一生物电信号发生异常;
步骤S303:当所述预测相似度小于预设相似度阈值时,确定所述第一生物电信号没有发生异常。
由此,通过将第一生物电信号输入异常检测模型,利用异常检测模型确定第一生物电信号对应的预测相似度,其中,异常检测模型可以调用异常数据库,异常数据库存储着大量的异常生物电信号,具体而言,可以是不同的病患在发病时对应的生物电信号,还可以是当前患者本人在发病时对应的生物电信号,当前患者患有帕金森时,异常数据库的异常生物电信号可以包括帕金森患者在发病时对应的生物电信号,一方面,通过将第一生物电信号分别与异常数据库中的每个异常生物电信号进行比对,结合相似度最高的那个异常生物电信号所属的疾病类型,判断患者有没有发病,另一方面,通过比较预测相似度和预设相似度阈值的大小来判断第一生物电信号是否发生异常,显然,预测相似度越高,第一生物电信号越有可能发生异常,患者越有可能发病,将第一生物电信号与异常数据库预先存储的多个异常生物电信号逐一进行比对,找出相似度最高的异常生物电信号,进一步提高了异常检测结果的准确性。
预测相似度可以用数字或者百分数表示,用数字表示时,预测相似度例如是60、80或者90;用百分数表示时,预测相似度例如是60%、80%或者90%,数值越高,预测相似度越高。
本申请对预设相似度阈值不作限定,其可以是70%、80%或者90%。
所述异常检测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,每个所述第二训练数据包括用于训练的第一样本信号、第二样本信号以及所述第一样本信号和所述第二样本信号的相似度的标注数据;
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第一样本信号和第二样本信号分别输入预设的第二深度学习模型,得到所述第一样本信号和所述第二样本信号的相似度的预测数据;
基于所述第一样本信号和所述第二样本信号的相似度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异常检测模型;如果否,则利用下一个所述第二训练数据继续训练所述第一深度学习模型。
本申请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。
在一些可能的方式中,所述预设条件包括:所述第一生物电信号的有效性评分不小于预设有效性评分阈值;
当所述第一生物电信号不满足所述预设条件时,所述方法还可以包括:
基于所述第一生物电信号,获取所述生物电采集设备对应的采集参数配置信息,控制所述生物电采集设备再次进入所述预采集模式。
由此,第一生物电信号不满足预设条件,也就是说,当第一生物电信号的有
效性评分小于预设有效性评分阈值时,表明第一生物电信号的信号强度比较低,当前的采集参数不合适,可以根据第一生物电信号,获取采集参数配置信息,从而调整采集参数,再次进行预采集,通过逐步调整,以使预采集得到的生物电信号满足预设条件,为后续的正式采集提供参考依据。
本申请对采集参数配置信息不作限定,采集参数配置信息例如是:采样位数10bits、数据传输速率1Mbps。
参见图4,图4是本申请实施例提供的一种程控设备的控制方法的部分流程示意图。
在一些可能的方式中,所述第一生物电信号包括脑电信号,在控制所述生物电采集设备进入所述预采集模式之后,所述方法还可以包括步骤S106~S107。
步骤S106:基于所述第一生物电信号,获取所述患者的疼痛评分;
步骤S107:当所述疼痛评分不小于预设疼痛评分阈值时,停止预采集。
由此,在采集生物电信号的过程中,还要考虑到患者的身体状况能不能承受,通过预采集得到的第一生物电信号(脑电信号),可以获取患者的疼痛评分,从而反映患者的疼痛程度,当患者的疼痛评分不小于预设疼痛评分阈值时,表明患者正在承受难以忍受的痛苦,可以自动停止预采集,减轻患者的痛苦,这种充分尊重患者个体感受的方式,体现了人性化关怀,大大减轻了患者在预采集时的心理负担。
疼痛评分可以用数字表示,例如60、80或者90,数值越高,疼痛评分越高;也可以用字母表示,例如A、B、C或者D,字母排序越靠前,疼痛评分越高。
本申请对预设疼痛评分阈值不作限定,预设疼痛评分阈值例如是70、80或者85。
在一些可能的方式中,所述程控设备被进一步采用如下方式获取所述患者的疼痛评分:
将所述第一生物电信号输入疼痛预测模型,得到所述患者的疼痛评分;
其中,所述疼痛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,每个所述第一训练数据包括一个样本对象的生物电信号以及所述样本对象的疼痛评分的标注数据;
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对象的生物电信号输入预设的第一深度学习模型,得到所述样本对象的疼痛评分的预测数据;
基于所述样本对象的疼痛评分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疼痛预测模型;如果否,则利用下一个所述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,由此训练得到的疼痛预测模型,可以基于第一生物电信号获取患者的疼痛评分,且计算结果准确性高、可靠性高。
本申请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。
本申请对疼痛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。
在一些可能的方式中,所述生物电采集设备是植入于所述患者体内的刺激器,所述方法还可以包括:
在利用所述生物电采集设备完成正式采集以后,基于所述第二生物电信号,获取所述患者对应的刺激参数配置信息,以使所述生物电采集设备对所述患者施加对应的电刺激能量。
由此,生物电采集设备可以兼具提供电刺激和感测生物电信号的功能,由于第二生物电信号对应的采集时间比较长,对患者的病情分析非常有帮助,可以根据第二生物电信号,获取刺激参数配置信息,利用生物电采集设备为患者提供精准的电刺激。
本申请对刺激参数配置信息不作限定,刺激参数配置信息例如是:频率130Hz、脉宽60μs和幅值2-3V。
本申请实施例还提供了一种程控设备,其具体方式与上述方法的实施例中记载的实施方式、所达到的技术效果一致,部分内容不再赘述。
所述程控设备被配置成:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
在一些可能的方式中,所述预设条件包括以下至少一种:
所述第一生物电信号的有效性评分不小于预设有效性评分阈值;
所述第一生物电信号没有发生异常。
在一些可能的方式中,所述程控设备被配置成采用如下方式获取所述第一生
物电信号的有效性评分:
对所述第一生物电信号进行采样,得到多个采样点;
针对每个采样点,逐个计算所述采样点的幅值与预设幅值的比对值,将比对值不小于预设比对阈值的采样点作为有效采样点;
基于所述有效采样点的数量,确定所述第一生物电信号的有效性评分。
在一些可能的方式中,所述程控设备被配置成采用如下方式检测所述第一生物电信号是否发生异常:
将所述第一生物电信号输入异常检测模型,以输出所述第一生物电信号对应的预测相似度;其中,所述异常检测模型用于将所述第一生物电信号分别与异常数据库中的每个异常生物电信号进行比对,得到所述第一生物电信号与每个异常生物电信号的相似度,将所述第一生物电信号对应的多个相似度中最高的相似度作为所述第一生物电信号对应的预测相似度;
当所述预测相似度不小于预设相似度阈值时,确定所述第一生物电信号发生异常;
当所述预测相似度小于预设相似度阈值时,确定所述第一生物电信号没有发生异常。
在一些可能的方式中,所述预设条件包括:所述第一生物电信号的有效性评分不小于预设有效性评分阈值;
当所述第一生物电信号不满足所述预设条件时,所述程控设备还被配置成:
基于所述第一生物电信号,获取所述生物电采集设备对应的采集参数配置信息,控制所述生物电采集设备再次进入所述预采集模式。
在一些可能的方式中,所述第一生物电信号包括脑电信号,在控制所述生物电采集设备进入所述预采集模式之后,所述程控设备还被配置成:
基于所述第一生物电信号,获取所述患者的疼痛评分;
当所述疼痛评分不小于预设疼痛评分阈值时,停止预采集。
在一些可能的方式中,所述程控设备被进一步采用如下方式获取所述患者的疼痛评分:
将所述第一生物电信号输入疼痛预测模型,得到所述患者的疼痛评分;
其中,所述疼痛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,每个所述第一训练数据包括一个样本对象的生物电信号以及所述样本对象的疼痛评分的标注数据;
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对象的生物电信号输入预设的第一深度学习模型,得到所述样本对象的疼痛评分的预测数据;
基于所述样本对象的疼痛评分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疼痛预测模型;如果否,则利用下一个所述第一训练数据继续训练所述第一深度学习模型。
在一些可能的方式中,所述生物电采集设备是植入于所述患者体内的刺激器,所述程控设备还被配置成:
在利用所述生物电采集设备完成正式采集以后,基于所述第二生物电信号,获取所述患者对应的刺激参数配置信息,以使所述生物电采集设备对所述患者施加对应的电刺激能量。
参见图5,图5是本申请实施例提供的一种程控***300的结构框图。
本申请实施例还提供了一种程控***300,其具体方式与上述方法的实施例中记载的实施方式、所达到的技术效果一致,部分内容不再赘述。
所述程控***300包括程控设备100和生物电采集设备200;
所述程控设备100与所述生物电采集设备200可通信地连接,所述程控设备100被配置成:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备200进入预采集模式,以采集所述患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备200进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
本申请实施例还提供了一种电子设备,所述电子设备包括存储器和处理器,所述存储器存储有计算机程序,所述处理器执行所述计算机程序时实现如下步骤:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
本申请实施例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如下步骤:
接收用户的预采集操作;
响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;
当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;
当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;
响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
需要说明的是,本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外,术语“包括”和“具有”以及他们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、***、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
当一个部件被称作“在”另一部件“上”或“之上”、“连接到”或“结合到”另一部件时,该部件可以直接在所述另一部件上、直接连接到或直接结合到所述另一部件,或者可以存在中间部件。然而,当部件被称作“直接在”另一部件“上”、“直接连接到”或“直接结合到”另一部件时,不存在中间部件。为此,术语“连接”可以指物理连接、电气连接等,并且具有或不具有中间部件。
Claims (11)
- 一种程控设备,所述程控设备被配置成:接收用户的预采集操作;响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
- 根据权利要求1所述的程控设备,其中,所述预设条件包括以下至少一种:所述第一生物电信号的有效性评分不小于预设有效性评分阈值;所述第一生物电信号没有发生异常。
- 根据权利要求2所述的程控设备,其中,所述程控设备被配置成采用如下方式获取所述第一生物电信号的有效性评分:对所述第一生物电信号进行采样,得到多个采样点;针对每个采样点,逐个计算所述采样点的幅值与预设幅值的比对值,将比对值不小于预设比对阈值的采样点作为有效采样点;基于所述有效采样点的数量,确定所述第一生物电信号的有效性评分。
- 根据权利要求2所述的程控设备,其中,所述程控设备被配置成采用如下方式检测所述第一生物电信号是否发生异常:将所述第一生物电信号输入异常检测模型,以输出所述第一生物电信号对应的预测相似度;其中,所述异常检测模型用于将所述第一生物电信号分别与异常数据库中的每个异常生物电信号进行比对,得到所述第一生物电信号与每个异常生物电信号的相似度,将所述第一生物电信号对应的多个相似度中最高的相似度 作为所述第一生物电信号对应的预测相似度;当所述预测相似度不小于预设相似度阈值时,确定所述第一生物电信号发生异常;当所述预测相似度小于预设相似度阈值时,确定所述第一生物电信号没有发生异常。
- 根据权利要求1所述的程控设备,其中,所述预设条件包括:所述第一生物电信号的有效性评分不小于预设有效性评分阈值;当所述第一生物电信号不满足所述预设条件时,所述程控设备还被配置成:基于所述第一生物电信号,获取所述生物电采集设备对应的采集参数配置信息,控制所述生物电采集设备再次进入所述预采集模式。
- 根据权利要求1所述的程控设备,其中,所述第一生物电信号包括脑电信号,在控制所述生物电采集设备进入所述预采集模式之后,所述程控设备还被配置成:基于所述第一生物电信号,获取所述患者的疼痛评分;当所述疼痛评分不小于预设疼痛评分阈值时,停止预采集。
- 根据权利要求6所述的程控设备,其中,所述程控设备被进一步采用如下方式获取所述患者的疼痛评分:将所述第一生物电信号输入疼痛预测模型,得到所述患者的疼痛评分;其中,所述疼痛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,每个所述第一训练数据包括一个样本对象的生物电信号以及所述样本对象的疼痛评分的标注数据;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对象的生物电信号输入预设的第一深度学习模型,得到所述样本对象的疼痛评分的预测数据;基于所述样本对象的疼痛评分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 习模型作为所述疼痛预测模型;如果否,则利用下一个所述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所述的程控设备,其中,所述生物电采集设备是植入于所述患者体内的刺激器,所述程控设备还被配置成:在利用所述生物电采集设备完成正式采集以后,基于所述第二生物电信号,获取所述患者对应的刺激参数配置信息,以使所述生物电采集设备对所述患者施加对应的电刺激能量。
- 一种程控***,所述程控***包括程控设备和生物电采集设备;所述程控设备与所述生物电采集设备可通信地连接,所述程控设备被配置成:接收用户的预采集操作;响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
- 一种电子设备,所述电子设备包括存储器和处理器,所述存储器存储有计算机程序,所述处理器执行所述计算机程序时实现如下步骤:接收用户的预采集操作;响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正 式采集模式的采集时长。
- 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如下步骤:接收用户的预采集操作;响应于所述预采集操作,控制生物电采集设备进入预采集模式,以采集患者的第一生物电信号;当所述第一生物电信号不满足预设条件时,生成第一提示信息并发送至用户设备,和/或,停止预采集;当所述第一生物电信号满足所述预设条件时,接收所述用户的采集确认操作;响应于所述采集确认操作,控制所述生物电采集设备进入正式采集模式,以采集所述患者的第二生物电信号,其中,所述预采集模式的采集时长小于所述正式采集模式的采集时长。
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