CN119049686B - Obstructive sleep breathing pathological information processing method, apparatus, equipment and medium - Google Patents
Obstructive sleep breathing pathological information processing method, apparatus, equipment and mediumInfo
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- CN119049686B CN119049686B CN202411007002.3A CN202411007002A CN119049686B CN 119049686 B CN119049686 B CN 119049686B CN 202411007002 A CN202411007002 A CN 202411007002A CN 119049686 B CN119049686 B CN 119049686B
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Abstract
本公开的实施例公开了阻塞性睡眠呼吸病理信息处理方法、装置、设备和介质。该方法的一具体实施方式包括：响应于确定执行资源分配信息表征通过第一资源分配信息执行资源调整，确定第一资源分配信息对应的第一资源调整时间段；响应于当前时间处于第一资源调整时间段内，根据第一资源分配信息，对睡眠检测仪组执行资源调整，以及控制睡眠检测仪组执行阻塞性睡眠呼吸检测任务；响应于检测到阻塞性睡眠呼吸检测任务执行完成，读取睡眠呼吸检测数据组；对睡眠呼吸检测数据组中的每个睡眠呼吸检测数据进行诊断检测，以生成睡眠呼吸诊断检测结果；将睡眠呼吸诊断检测结果组发送至医生端。该实施方式提升了对于阻塞性睡眠呼吸暂停的诊断检测效率。
This disclosure provides embodiments of a method, apparatus, device, and medium for processing pathological information on obstructive sleep apnea. One specific implementation of the method includes: in response to determining that resource allocation information represents the execution of resource adjustments through first resource allocation information, determining a first resource adjustment time period corresponding to the first resource allocation information; in response to the current time being within the first resource adjustment time period, performing resource adjustments on a sleep monitoring device group according to the first resource allocation information, and controlling the sleep monitoring device group to perform an obstructive sleep apnea detection task; in response to detecting the completion of the obstructive sleep apnea detection task, reading a sleep apnea detection data group; performing diagnostic tests on each sleep apnea detection data in the sleep apnea detection data group to generate a sleep apnea diagnostic test result; and sending the sleep apnea diagnostic test result group to a doctor's end. This implementation improves the efficiency of diagnosing obstructive sleep apnea.
Description
Technical Field
Embodiments of the present disclosure relate to the field of obstructive sleep respiratory pathology information processing, and in particular, to obstructive sleep respiratory pathology information processing methods, apparatuses, devices, and media.
Background
Obstructive sleep apnea (obstructive SLEEP APNEA, OSA) is a common disease and has potential health risks. The main clinical manifestation of the disease is snoring during sleep, and is accompanied by apnea and hypopnea, and hypoxia, hypercarbonemia and sleep structural disturbance repeatedly occur at night, and after the disease is developed to a certain degree, the disease often causes multiple organ damages such as daytime sleepiness, cardiovascular and cerebrovascular complications and the like, and seriously affects the life quality and life span of patients. Currently, for the diagnosis of obstructive sleep apnea, a method is generally adopted in which a patient regularly goes to a hospital and a doctor regularly performs detection and diagnosis. However, the above-described diagnostic method for obstructive sleep apnea generally has technical problems of periodic diagnosis, low efficiency, long time for diagnosis and detection, and easy waste of medical resources of hospitals.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose obstructive sleep respiratory pathology information processing methods, apparatus, electronic devices, and computer readable media to address one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an obstructive sleep respiratory pathology information processing method applied to an obstructive sleep respiratory diagnosis all-in-one machine, where the obstructive sleep respiratory diagnosis all-in-one machine includes a sleep detector cluster, an obstructive sleep respiratory diagnosis system, and a doctor, and the method includes acquiring execution resource allocation information corresponding to an obstructive sleep respiratory detection task in response to receiving the obstructive sleep respiratory detection task, determining a sleep detector group corresponding to the obstructive sleep respiratory detection task, wherein the sleep detector group is each sleep detector of the sleep detector cluster indicated by the obstructive sleep respiratory detection task, performing resource allocation by the execution resource allocation information in response to determining that the execution resource allocation information characterizes a first resource allocation period corresponding to the first resource allocation information, performing resource allocation by the sleep detector group in response to a current time within the first resource allocation period, and controlling the sleep detector group to execute the obstructive sleep respiratory detection task, wherein each sleep detector group is detected by the sleep detector group in response to the sleep detector group, reading data corresponding to the sleep detector group in response to the sleep detector group, obtaining a sleep respiration diagnosis detection result set, and sending the sleep respiration diagnosis detection result set to the doctor end.
In a second aspect, some embodiments of the present disclosure provide an apparatus for processing obstructive sleep respiratory pathology information, which is applied to an apparatus for sleep respiratory diagnosis, the apparatus including a sleep detector cluster, an obstructive sleep respiratory diagnosis system, and a doctor side, the apparatus including an acquisition unit configured to acquire execution resource allocation information corresponding to an obstructive sleep respiratory detection task in response to receiving the obstructive sleep respiratory detection task, a first determination unit configured to determine a sleep detector group corresponding to the obstructive sleep respiratory detection task, wherein the sleep detector group is each sleep detector of the sleep detector cluster indicated by the obstructive sleep respiratory detection task, a second determination unit configured to perform resource adjustment by the first resource allocation information in response to determining that the execution resource allocation information characterizes the first resource adjustment period, an adjustment unit configured to acquire the execution resource allocation information corresponding to the obstructive sleep respiratory detection task in response to a current time within the first resource adjustment period, the sleep detector group being configured to read out of the sleep detector group in response to the sleep detector group, and the sleep detector group being configured to perform sleep detector data in response to the sleep detector group, the sleep detector group being configured to read out of the sleep detector group, the sleep respiratory detection data set comprises sleep respiratory detection data, a sending unit and a medical terminal, wherein the sleep respiratory detection data set comprises sleep respiratory detection data, a sleep respiratory detection result set and a sending unit, the sleep respiratory detection data set comprises sleep respiratory detection data, the sending unit is configured to send the sleep respiratory detection result set to the medical terminal, and the sleep respiratory detection data set comprises sleep respiratory detection data.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising one or more processors, and storage means having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following beneficial effects that by the obstructive sleep breathing pathological information processing method of some embodiments of the present disclosure, the diagnosis detection efficiency for obstructive sleep apnea is improved, so that a patient can diagnose obstructive sleep apnea at home, so as to avoid excessive occupation of medical resources of a hospital. Specifically, medical resources of hospitals are easily wasted because of the fact that the regular diagnosis is low in efficiency, the diagnosis and detection time is long, and the medical resources of hospitals are easily wasted. Based on this, the obstructive sleep breathing pathology information processing method of some embodiments of the present disclosure first acquires execution resource allocation information corresponding to an obstructive sleep breathing detection task in response to receiving the obstructive sleep breathing detection task. Thus, communication resources can be allocated to each sleep detector that needs to perform tasks. And then, determining a sleep detector group corresponding to the obstructive sleep breath detection task, wherein the sleep detector group is each sleep detector to be executed in the sleep detector cluster indicated by the obstructive sleep breath detection task. The method comprises the steps of determining the first resource adjustment time period corresponding to first resource allocation information, determining the first resource adjustment time period corresponding to the first resource allocation information in response to determining that the execution resource allocation information characterizes the execution of resource adjustment through the first resource allocation information, executing resource adjustment on the sleep detection instrument set according to the first resource allocation information in response to the current time being in the first resource adjustment time period, and controlling the sleep detection instrument set to execute the obstructive sleep respiration detection task. Thus, the sleep detector can be controlled to collect sleep respiration detection data according to the execution time period described in the task. And then, in response to detecting that the execution of the obstructive sleep breath detection task is completed, reading a sleep breath detection data set from the sleep detection instrument set, wherein the sleep breath detection data in the sleep breath detection data set corresponds to the sleep detector in the sleep detection instrument set. And carrying out diagnosis detection on each sleep breath detection data in the sleep breath detection data set through the obstructive sleep breath diagnosis system so as to generate a sleep breath diagnosis detection result and obtain a sleep breath diagnosis detection result set. Thus, the sleep respiration detection data can be preliminarily subjected to diagnostic analysis to assist the doctor in diagnosis. And finally, sending the sleep respiration diagnosis detection result group to the doctor end. Therefore, the diagnosis and detection efficiency of the obstructive sleep apnea is improved, so that the patient can diagnose the obstructive sleep apnea at home, and medical resources of the hospital are prevented from being excessively occupied.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of an obstructive sleep respiratory pathology information processing method according to the present disclosure;
FIG. 2 is a schematic structural view of some embodiments of an obstructive sleep respiratory pathology information processing apparatus according to the present disclosure;
Fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flow chart of some embodiments of an obstructive sleep respiratory pathology information processing method according to the present disclosure. A flow 100 of some embodiments of an obstructive sleep respiratory pathology information processing method according to the present disclosure is shown. The obstructive sleep breathing pathological information processing method is applied to an obstructive sleep breathing diagnosis all-in-one machine, wherein the obstructive sleep breathing diagnosis all-in-one machine comprises a sleep detector cluster, an obstructive sleep breathing diagnosis system and a doctor end, and comprises the following steps:
step 101, in response to receiving the obstructive sleep breath detection task, acquiring execution resource allocation information corresponding to the obstructive sleep breath detection task.
In some embodiments, an execution body (e.g., a computing device) of the obstructive sleep breathing pathology information processing method may obtain execution resource allocation information corresponding to an obstructive sleep breathing detection task in response to receiving the obstructive sleep breathing detection task. The Obstructive sleep respiratory diagnostic all-in-one machine may be referred to as an OSA (Obstructive SLEEP APNEA) all-in-one machine. Obstructive sleep breath detection tasks may refer to tasks of breath detection for individual patient users to be obstructive sleep breath detected. The sleep detector in the sleep detector cluster may be a detection instrument for detecting a patient user for obstructive sleep breath. One sleep detector corresponds to one patient user. The execution resource allocation information may be information characterizing how the resources of the obstructive sleep breath detection task are configured during execution. For example, the execution resource allocation information may be allocation information characterizing the communication resources. Because the number of sleep detectors involved is large, communication resources need to be reasonably allocated to ensure normal collection of data. The obstructive sleep breath detection task may also include the number of individual sleep detectors that need to perform the task, as well as the period of execution.
Step 102, determining a sleep detector group corresponding to the obstructive sleep respiration detection task.
In some embodiments, the execution subject may determine a sleep detection instrument set corresponding to the obstructive sleep breath detection task. The sleep detector group is each sleep detector to be executed in the sleep detector cluster indicated by the obstructive sleep respiration detection task. That is, the corresponding individual sleep detectors may be determined by the numbers of the individual sleep detectors included in the obstructive sleep breath detection task.
Step 103, in response to determining that the executing resource allocation information characterizes executing resource adjustment through the first resource allocation information, determining a first resource adjustment time period corresponding to the first resource allocation information.
In some embodiments, the execution body may determine a first resource adjustment period corresponding to the first resource allocation information in response to determining that the execution resource allocation information characterizes execution of resource adjustment by the first resource allocation information. The first resource allocation information may be information that characterizes how to perform resource allocation for the obstructive sleep breath detection task according to a preset time period. The first resource adjustment period may be a period in which the obstructive sleep breath detection task performs an augmentation or reduction of the resource. The first resource adjustment period may be a period set in advance in the first resource allocation information. For example, the first resource adjustment period is the a period. And in the time period A, the communication resource corresponding to the obstructive sleep respiration detection task is increased by 1/3.
And 104, responding to the current time in the first resource adjustment time period, executing resource adjustment on the sleep detection instrument set according to the first resource allocation information, and controlling the sleep detection instrument set to execute the obstructive sleep respiration detection task.
In some embodiments, the execution body may perform resource adjustment on the sleep detection apparatus set according to the first resource allocation information and control the sleep detection apparatus set to perform the obstructive sleep breath detection task in response to the current time being within the first resource adjustment period. For example, the communication resources available to the sleep detectors in the sleep detector set may be increased or decreased based on the first resource allocation information. Then, each sleep detector in the sleep detector set can be controlled to acquire sleep breath detection data of the patient user according to a preset execution time period. The sleep breath detection data may include, but is not limited to, a plurality of physiological parameters such as heart rate, respiration, body movement, snoring, and a plurality of sleep parameters such as sleep duration, sleep distribution, sleep efficiency, and dream.
Optionally, in response to determining that the executing resource allocation information characterizes executing resource adjustment through the second resource allocation information, determining resource change information corresponding to the obstructive sleep breath detection task.
In some embodiments, the execution body may determine the resource change information corresponding to the obstructive sleep breath detection task in response to determining that the execution resource allocation information characterizes execution of the resource adjustment by the second resource allocation information. Wherein the second resource allocation information is information for performing resource adjustment based on the resource change information. The resource change information can represent the change condition of the execution resource corresponding to the obstructive sleep respiration detection task. In practice, the resource change information may be a resource change of GPU resources used by the obstructive sleep breath detection task. The second resource allocation information may include a capacity reduction threshold and a capacity expansion threshold. The capacity reduction threshold is less than the capacity expansion threshold. The scaling threshold may be a threshold at which resource scaling is performed. The capacity expansion threshold may be a threshold at which resource expansion is performed. For example, the resource change information corresponding to the obstructive sleep breath detection task may be determined by means of resource statistics.
Optionally, the resource adjustment is performed on the sleep detector set according to the resource change information.
In some embodiments, the execution body may execute resource adjustment on the sleep detection apparatus set according to the resource change information. For example, the communication resources that can be used by each sleep detector in the sleep detector group may be adjusted in equal proportion (change proportion) according to the change proportion indicated by the resource change information.
Optionally, in response to determining that the performing resource allocation information characterizes performing resource adjustment according to the first resource allocation information and the second resource allocation information, determining a second resource adjustment period corresponding to the first resource allocation information.
In some embodiments, the executing entity may determine the second resource adjustment period corresponding to the first resource allocation information in response to determining that the executing resource allocation information characterizes executing resource adjustment according to the first resource allocation information and the second resource allocation information. Wherein the execution resource allocation information characterizes that the first resource allocation information and the second resource allocation information can be executed simultaneously. The second resource adjustment period may refer to the first resource adjustment period or a preset resource adjustment period.
Optionally, the current execution resource allocation information is determined in response to the current time being within the second resource adjustment period.
In some embodiments, the executing entity may determine the current executing resource allocation information in response to the current time being within the second resource adjustment period.
In practice, the execution subject may determine the current execution resource allocation information by:
And a first step of determining a communication resource change value corresponding to the obstructive sleep respiration detection task. For example, first, the execution subject may determine the resource amount of the highest-usage communication resource corresponding to the obstructive sleep breath detection task as the maximum communication resource amount. Then, the resource amount of the least-used communication resource corresponding to the obstructive sleep breath detection task is determined as the lowest communication resource amount. Finally, the ratio of the highest communication resource amount to the lowest communication resource amount is determined as the communication resource variation value.
And a second step of determining the second resource allocation information as current execution resource allocation information in response to determining that the communication resource variation value is greater than a preset value.
And a third step of determining the first resource allocation information as currently executed resource allocation information in response to determining that the communication resource variation value is less than or equal to the preset value.
Optionally, performing resource adjustment on the sleep detection instrument set according to the current execution resource allocation information.
In some embodiments, the execution body may perform resource adjustment on the sleep detection instrument set according to the current execution resource allocation information.
And step 105, in response to detecting that the execution of the obstructive sleep breath detection task is completed, reading a sleep breath detection data set from the sleep detection instrument set.
In some embodiments, the execution body may read the sleep breath detection data set from the sleep detection instrument set in response to detecting that the obstructive sleep breath detection task execution is complete. Wherein the sleep breath detection data in the sleep breath detection data set corresponds to a sleep detector in the sleep detector set. That is, the acquired sleep respiration detection data of the patient user may be read from each of the sleep detectors in the sleep detector group by means of a wired connection or a wireless connection.
And 106, performing diagnosis detection on each sleep breath detection data in the sleep breath detection data set through the obstructive sleep breath diagnosis system to generate a sleep breath diagnosis detection result, thereby obtaining a sleep breath diagnosis detection result set.
In some embodiments, the execution subject may perform diagnostic detection on each sleep breath detection data in the sleep breath detection data set by the obstructive sleep breath diagnostic system to generate a sleep breath diagnostic detection result, resulting in a sleep breath diagnostic detection result set. The obstructive sleep breathing diagnostic system may be a pre-built diagnostic system for intelligent diagnosis of sleep breathing detection data, and may include an on-line expert diagnostic function, a neural network model diagnostic function. The on-line expert diagnosis function may refer to a function of performing diagnosis analysis on the uploaded sleep breathing diagnosis detection result by an on-line doctor. The neural network model diagnosis function may be a function of performing diagnostic analysis on sleep breath detection data through a pre-trained sleep breath detection data pathology recognition model. For example, the pre-trained sleep breath detection data pathology recognition model may be a CNN and LSTM based sleep apnea detection algorithm model, or may be a sleep multi-modal representation learning model based on brain electrical activity, electrocardiography, and respiratory signals.
In practice, the execution subject may perform diagnostic detection on each sleep breath detection data in the sleep breath detection data set by:
And a first step of transmitting the sleep respiration detection data to the obstructive sleep respiration diagnosis system to detect a parameter interval. For example, the obstructive sleep respiratory diagnostic system may determine, after receiving the sleep respiratory detection data, whether each of the parameter data in the sleep respiratory detection data is within a respective corresponding parameter interval.
And secondly, diagnosing and detecting the sleep respiratory detection data through a pre-trained sleep respiratory pathology recognition model so as to generate a sleep respiratory pathology recognition result. The sleep respiratory pathology recognition model comprises a sleep respiratory pathology recognition network group. The sleep respiratory pathology recognition model may be a neural network model pre-trained with sleep respiratory detection data as input and sleep respiratory pathology recognition results as output. For example, the sleep respiratory pathology recognition model may be a sleep apnea detection algorithm model based on CNN and LSTM, and may also be a sleep multi-modal representation learning model based on brain electrical activity, electrocardiogram, and respiratory signals. Wherein one sleep respiratory pathology recognition network corresponds to one sleep respiratory detection type. The type of sleep breath detection may be classified by gender, by age, or by decibel based on the breathing of the person while sleeping. The sleep respiratory pathology recognition network may refer to a sleep respiratory pathology recognition model of a certain sleep respiratory detection type.
Wherein, the second step may comprise the following substeps:
and a first sub-step of generating an identification network output information set corresponding to the sleep respiration detection data by using the sleep respiration pathology identification network group. Wherein each identification network output information comprises a network identification result group and a network prediction confidence group. The network identification result in the network identification result set may be at least one. The network prediction confidence in the network prediction confidence group may be at least one. The network identification result may be a result output by a sleep respiratory pathology identification network. For example, the network identification result may be "sleep breath detection data characterizes user a as having XX disease. One network recognition result set corresponds to one sleep respiratory pathology recognition network. One network identification result corresponds to one network prediction confidence. The network prediction confidence may characterize the accuracy of the network recognition results. The higher the confidence of network prediction, the more accurate the network identification result generated by the corresponding characterization. For example, the sleep respiratory detection data may be directly input into each sleep respiratory pathology recognition network in the sleep respiratory pathology recognition network group, resulting in a recognition network output information set.
And a second sub-step, according to the obtained network prediction confidence coefficient set, carrying out recognition result fusion on each network recognition result in the network recognition result set so as to generate a fusion recognition result set and a corresponding fusion network prediction confidence coefficient set. One fusion recognition result corresponds to one fusion network prediction confidence.
First, in response to determining that there is a resulting relationship between at least two network identification results as an inclusion relationship, at least one target network identification result set is determined from the set of network identification result sets. Wherein, each network identification result in the target network identification result group has a containing relation. The inclusion relation characterizes the range inclusion relation between the corresponding result reflecting ranges of the network identification result. The result embodying range can be the semantic covering range which can be embodied by the identification result. For example, the A network identification result is "sleep breath detection data indicates that the user A has XX disease or YY disease", the B network identification result is "sleep breath detection data indicates that the user A has XX disease", and the A and the B are in inclusion relation.
Next, for each of the at least one target network identification result sets, performing the following processing steps:
And 1, carrying out result fusion on each target network identification result in the target network identification result set to obtain a fusion identification result. Firstly, the executing body can determine the recognition result semantics corresponding to each target network recognition result in the target network recognition result group, and obtain a prediction result semantic group. Then, the predicted result semantic group is input to a text generation model to generate a fusion recognition result. In practice, the text generation model may be a transducer model.
And 2, carrying out confidence fusion on each network prediction confidence in the target network prediction confidence group so as to generate initial fusion network prediction confidence. The target network prediction confidence coefficient set is a network prediction confidence coefficient set corresponding to the target network recognition result set. The individual network prediction confidence levels may be weighted summed to generate an initial fused network prediction confidence level.
And then, generating a fused network prediction confidence coefficient set according to the obtained at least one initial fused network prediction confidence coefficient. At least one initial converged network prediction confidence may be determined as a converged network prediction confidence set.
Then, the obtained at least one fusion recognition result is determined as a fusion recognition result set.
And finally, a third sub-step, namely generating a sleep breathing pathology recognition result according to the fusion network prediction confidence coefficient set. The sleep respiratory pathology recognition result is a fusion recognition result in the fusion recognition result set. And selecting the fusion recognition results with the corresponding fusion network prediction confidence coefficient at the preset number from the fusion recognition result set as the actual prediction result to obtain at least one fusion recognition result. And combining at least one fusion recognition result into a sleep respiratory pathology recognition result.
And thirdly, responding to the received sleep breathing data detection result sent by the obstructive sleep breathing diagnosis system, and combining the sleep breathing data detection result and the sleep breathing pathology recognition result into a sleep breathing diagnosis detection result.
The sleep respiratory pathology recognition model can be obtained through training the following steps:
first, a sleep respiration detection data sample set is acquired. Wherein one sleep breath detection data sample set corresponds to one sleep breath detection type.
And secondly, determining an initial sleep breathing pathology recognition model. The initial sleep respiratory pathology recognition model comprises an initial sleep respiratory pathology recognition network group, and one initial sleep respiratory pathology recognition network corresponds to one sleep respiratory detection type.
Third, for each sleep breath detection data sample set in the set of sleep breath detection data sample sets, performing the training steps of:
1. and determining an initial sleep respiratory pathology recognition network corresponding to the sleep respiratory detection data sample set.
2. Inputting at least one sleep breath detection data sample in the sleep breath detection data sample group into an initial sleep breath pathology recognition network to obtain an initial sleep breath pathology recognition result corresponding to the at least one sleep breath detection data sample.
3. And determining whether the initial sleep respiratory pathology recognition network reaches a preset optimization target according to the initial sleep respiratory pathology recognition result corresponding to the at least one sample and the sample label corresponding to the at least one sleep respiratory detection data sample.
4. And in response to determining that the initial sleep respiratory pathology recognition network reaches the preset optimization target, determining the initial sleep respiratory pathology recognition network as a sleep respiratory pathology recognition network with completed training.
And fourthly, fusing each sleep respiratory pathology recognition network after training into a sleep respiratory pathology recognition model.
Thus, by means of the sleep respiratory pathology recognition network set, sleep respiratory detection data can be analyzed from multiple angles. On the basis, unification of output contents can be realized by realizing fusion of recognition results and fusion of corresponding prediction confidence degrees, so that the recognition results of sleep respiratory pathology can be accurately generated later.
Step 107, the sleep respiration diagnosis detection result set is sent to the doctor end.
In some embodiments, the execution body may send the sleep breath diagnosis detection result set to the doctor's end. For example, the sleep respiratory diagnosis detection result set may be transmitted to a terminal of a doctor for sleep respiratory diagnosis detection by means of a wired connection or a wireless connection.
With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of an obstructive sleep respiratory pathology information processing apparatus, which correspond to those method embodiments shown in fig. 1, which may be particularly applicable in various electronic devices.
As shown in fig. 2, the obstructive sleep respiratory pathology information processing apparatus 200 of some embodiments includes an acquisition unit 201, a first determination unit 202, a second determination unit 203, an adjustment unit 204, a reading unit 205, a detection unit 206, and a transmission unit 207. Wherein the acquisition unit 201 is configured to acquire execution resource allocation information corresponding to an obstructive sleep respiration detection task in response to receiving the obstructive sleep respiration detection task, the first determination unit 202 is configured to determine a sleep detection instrument group corresponding to the obstructive sleep respiration detection task, wherein the sleep detection instrument group is each sleep detection instrument of the sleep detection instrument cluster indicated by the obstructive sleep respiration detection task, the second determination unit 203 is configured to perform resource allocation by the first resource allocation information in response to determining the execution resource allocation information, determine a first resource allocation period corresponding to the first resource allocation information in response to the current time being within the first resource allocation period, perform resource allocation on the sleep detection instrument group in accordance with the first resource allocation information, and control the sleep detection instrument group to perform the obstructive sleep respiration detection task, the reading unit 205 is configured to read data corresponding to the sleep detection instrument group in response to detecting completion of the obstructive sleep respiration detection task, wherein the sleep detection instrument group is configured to obtain a diagnosis result for detecting sleep detection data corresponding to the sleep detection unit 207, the sleep detection unit is configured to diagnose sleep detection unit 207 to detect sleep respiration, is configured to send the sleep breathing diagnostic test result set to the doctor's end.
It will be appreciated that the elements described in the obstructive sleep respiratory pathology information processing apparatus 200 correspond to the individual steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the obstructive sleep respiratory pathology information processing apparatus 200 and the units contained therein, and are not described in detail herein.
Referring now to fig. 3, a schematic diagram of an electronic device 300 (e.g., a computing device) suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, devices may be connected to I/O interface 305 including input devices 306 such as a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc., output devices 307 including a Liquid Crystal Display (LCD), speaker, vibrator, etc., storage devices 308 including, for example, magnetic tape, hard disk, etc., and communication devices 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be included in the electronic device or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs, when the one or more programs are executed by the electronic equipment, the electronic equipment is caused to respond to the receiving of the obstructive sleep respiration detection task, acquire execution resource allocation information corresponding to the obstructive sleep respiration detection task, determine sleep detection instrument groups corresponding to the obstructive sleep respiration detection task, wherein the sleep detection instrument groups are all sleep detection instruments needing to be executed in the sleep detection instrument cluster indicated by the obstructive sleep respiration detection task, respond to the determination of the execution resource allocation information, represent the execution resource allocation information through first resource allocation information, determine a first resource allocation time period corresponding to the first resource allocation information, respond to the current time within the first resource allocation time period, execute resource allocation on the sleep detection instrument groups according to the first resource allocation information, and control the sleep detection instrument groups to execute the obstructive sleep respiration detection task, respond to the detection of the execution of the obstructive sleep respiration detection task, read sleep detection instruments from the sleep detection instrument groups, wherein the sleep detection instrument groups are used for detecting sleep detection by the sleep detection instrument groups, and the sleep detection instrument groups are used for detecting sleep detection by the sleep detection task.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example a processor may be described comprising an acquisition unit, a first determination unit, a second determination unit, an adjustment unit, a reading unit, a detection unit and a transmission unit. The names of these units do not limit the unit itself in some cases, and for example, the transmission unit may also be described as "a unit that transmits the sleep respiration diagnosis detection result set to the doctor side".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic that may be used include Field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-a-chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
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