CN113520331A - Insomnia treatment and diagnosis method and device based on big data - Google Patents
Insomnia treatment and diagnosis method and device based on big data Download PDFInfo
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Abstract
The invention provides a method and a device for insomnia treatment and diagnosis based on big data, which comprises the following steps: entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information; selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information; the treatment data after the implementation of the low-frequency electrotherapy scheme is recorded, the treatment data comprises sleep quality evaluation information and active scoring and commenting information of the insomnia patients, reference data can be provided for the insomnia patients through the treatment data, so that the patients without experience and medical general knowledge can not blindly adopt an electrotherapy mode to avoid causing adverse reaction to bodies, and each time of low-frequency electrotherapy of the insomnia patients is equivalent to providing one insomnia treatment experiment for big data.
Description
Technical Field
The invention relates to the field of insomnia therapeutic instruments, in particular to an insomnia treatment and diagnosis method and device based on big data.
Background
The insomnia therapeutic apparatus is one of physical therapy methods commonly used in clinic, particularly a low-frequency electro-therapeutic apparatus, has a good insomnia treatment effect, has different insomnia treatment effects aiming at different individual conditions and different disease conditions, but is only an auxiliary treatment method, and can be used for performing targeted comprehensive treatment on insomnia patients, such as psychology dispersion, drug therapy, syndrome differentiation treatment of traditional Chinese medicine, acupuncture and moxibustion, massage and other comprehensive measures, so that the good effect can be obtained;
however, the detection function of the insomnia treatment apparatus has no practical value, and the main reason is that sleep monitoring is not linked with safety monitoring, sleep improvement and even sleep development to form a closed loop, that is, sleep monitoring currently stays for monitoring, and the value of monitoring data is not or rarely exerted.
SUMMARY OF THE PATENT FOR INVENTION
Aiming at the defects in the prior art, the invention provides a method and a device for insomnia treatment and diagnosis based on big data, so as to improve the utilization value of sleep monitoring.
According to a first aspect of the embodiments of the present disclosure, a preferred embodiment of the present invention provides a diagnosis and treatment method for insomnia based on big data, which includes:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud for storage;
and statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data.
In one embodiment, entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate degree information, and comprises the following steps:
entering the classification interface of the insomnia classification information, and selecting the insomnia classification information according with the symptoms of the insomnia patient;
entering a low-frequency electrotherapy scheme classification interface of the insomnia condition classification information, and displaying the treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
and selecting any low-frequency electrotherapy scheme according to the medical treatment data after the low-frequency electrotherapy scheme is implemented.
In one embodiment, the recording of treatment data after the implementation of the low-frequency electrotherapy protocol includes sleep quality assessment information, active scoring and comment information of insomnia patients, and includes:
dynamically monitoring the sleep state of the insomnia patient, wherein the dynamic monitoring mode comprises one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
determining sleep quality assessment information of the insomnia patient;
and inputting the active scoring and comment information of the insomnia patient after the low-frequency electrotherapy scheme is implemented.
In one embodiment, the statistically processing the associated insomnia condition classification information, low frequency electrotherapy protocol and treatment data includes:
generating various types of low-frequency electrotherapy protocols for different classification information of the insomnia symptoms;
counting the implementation times of different low-frequency electrotherapy schemes, and sequencing the low-frequency electrotherapy schemes according to the size sequence;
wherein said counting the number of times different said low frequency electrotherapy protocols are implemented and ordering said low frequency electrotherapy protocols according to a size order comprises:
and sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
According to a second aspect of the disclosed embodiments, the present invention provides a diagnosis and treatment apparatus for insomnia based on big data, comprising:
the display module is used for entering a terminal setting interface and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
the selection module is used for selectively implementing a low-frequency electrotherapy scheme according to the insomnia classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
the monitoring module is used for recording treatment data after the low-frequency electrotherapy scheme is implemented, and the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
the transmission module is used for integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud end for storage;
and the processing module is used for carrying out statistical processing on the associated insomnia condition classification information, the low-frequency electrotherapy scheme and the treatment data.
In one embodiment, the display module includes:
the classification module is used for entering the insomnia classification information classification interface and selecting the insomnia classification information according with the symptoms of the insomnia patient;
the reference module is used for entering a low-frequency electrotherapy scheme classification interface related to the insomnia condition classification information and displaying the treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
and the selection module is used for selecting any low-frequency electrotherapy scheme according to the treatment data after the low-frequency electrotherapy scheme is implemented.
In one embodiment, the monitoring module includes:
the acquisition module is used for dynamically monitoring the sleep state of the insomnia patient, and the dynamic monitoring mode comprises any one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
the determining module is used for determining sleep quality evaluation information of the insomnia patient;
and the cache module is used for inputting the active scoring and comment information of the insomnia patient after the implementation of the low-frequency electrotherapy scheme.
In one embodiment, the processing module includes:
the generation module is used for generating various low-frequency electrotherapy schemes related to different insomnia classification information;
the classification module is used for counting the implementation times of different low-frequency electrotherapy schemes and sequencing the low-frequency electrotherapy schemes according to the size sequence;
wherein, categorizing the module, include:
and the classification submodule is used for sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
According to a third aspect of the disclosed embodiments, the present invention provides a diagnosis and treatment apparatus for insomnia based on big data, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud for storage;
and statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data.
According to the technical scheme, the insomnia treatment and diagnosis method and device based on big data provided by the invention have the following beneficial effects: the treatment data can provide reference data for insomnia patients, so that the patients without experience and medical general knowledge can not blindly adopt an electrotherapy mode to avoid adverse reaction to the body, low-frequency electrotherapy of the insomnia patients every time is equivalent to providing an insomnia treatment experiment for big data, and the treatment data can be processed to select an electrotherapy scheme optimal to different insomnia symptoms to form a virtuous cycle process of analysis and treatment.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the patentable embodiments of the invention, reference will now be made to the appended drawings, which are briefly described as embodiments or as required in the prior art description. In all the drawings, the elements or parts are not necessarily drawn to actual scale.
FIG. 1 is a flow chart of a diagnosis and treatment method for insomnia based on big data according to the present invention;
FIG. 2 is a flowchart of step S101 of a diagnosis and treatment method for insomnia based on big data according to the present invention;
FIG. 3 is a flowchart of step S103 of a diagnosis and treatment method for insomnia based on big data according to the present invention;
FIG. 4 is a flowchart of step S105 of a diagnosis and treatment method for insomnia based on big data according to the present invention;
FIG. 5 is a block diagram of an insomnia treatment and diagnosis device based on big data according to the present invention;
FIG. 6 is a block diagram of another diagnosis and treatment apparatus for insomnia based on big data according to the present invention.
Detailed Description
Embodiments of the patented technology of the present invention will be described in detail below with reference to the drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only used as examples, and the protection scope of the present invention is not limited thereby.
Fig. 1 is a flowchart of an insomnia treatment diagnosis method based on big data according to the present invention, which is applied to an insomnia treatment terminal capable of displaying information such as pictures, videos, short messages, and WeChat. The terminal may be equipped with any terminal device having a display screen, such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and the like. The insomnia treatment and diagnosis method based on big data provided by the embodiment, as shown in fig. 1 and fig. 2, includes the following steps S101-S105:
in step S101, entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
specifically, the treatment data can provide reference data for the insomnia patients, so that the patients without experience and medical general knowledge can not blindly adopt an electrotherapy mode to avoid causing adverse reactions to the body;
in step S102, selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
particularly, when the low-frequency electro-therapeutic apparatus is also provided with auxiliary therapeutic equipment such as a magnetic therapy device, a music playing device and the like, the magnetic therapy or the music therapy can be added into the low-frequency electro-therapeutic scheme for implementation;
in step S103, recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
in step S104, integrating and uploading the treatment data after the implementation of the low-frequency electrotherapy scheme matched with the insomnia condition classification information to a cloud for storage;
in step S105, statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data;
specifically, each time of low-frequency electrotherapy of an insomnia patient is equivalent to providing an insomnia treatment experiment for big data, and an electrotherapy scheme optimal for different insomnia symptoms can be screened out through processing treatment data to form a virtuous circle process of analysis and treatment, so that the sleep health of the whole population can be promoted naturally.
As shown in fig. 2, in step S101, entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data suitable for symptoms of an insomnia patient, where the insomnia classification information includes type information and mild-moderate information, and includes the following steps S201 to S203:
in step S201, entering the classification interface of insomnia classification information, and selecting insomnia classification information that meets the symptoms of the insomnia patient;
in step S202, entering a low-frequency electrotherapy scheme classification interface of the insomnia condition classification information, and displaying treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
in step S203, any low-frequency electrotherapy scheme is selected according to the implemented treatment data of the low-frequency electrotherapy scheme;
specifically, the insomnia classification information comprises one or more combined symptoms in the conditions of difficulty in falling asleep, easy awakening, early awakening, poor sleep quality and the like, a user selects corresponding insomnia classification information according to the type information and the mild-moderate degree information of the user, then a low-frequency electrotherapy scheme list for treating the insomnia classification information is displayed, and a corresponding low-frequency electrotherapy scheme is selected from the low-frequency electrotherapy scheme list, so that the low-frequency electrotherapy scheme can be started for treatment.
In one embodiment, as shown in fig. 3, in step S103, the recording of treatment data after the implementation of the low-frequency electrotherapy protocol, the treatment data including sleep quality assessment information and active scoring and commenting information of insomnia patients, includes the following steps S301 to S303:
in step S301, the sleep state of the insomnia patient is dynamically monitored, wherein the dynamic monitoring mode comprises any one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
in step S302, determining sleep quality assessment information of the insomnia patient;
specifically, the Sleep quality assessment information can visually display the Sleep state after the implementation of the low-frequency electrotherapy scheme, software such as Sleep Cycle can be used for monitoring the turnover times through a gyroscope, and the Sleep time can be added to give the Sleep quality assessment information, or other modes can be adopted;
in step S303, the active scoring and comment information of the insomnia patient after the implementation of the low-frequency electrotherapy scheme is recorded.
Specifically, other patients can directly feel the use condition, discomfort symptom and the like of the patient using the low-frequency electrotherapy method by reading the comment information, the scoring information can directly reflect the low-frequency electrotherapy experience of the patient using the low-frequency electrotherapy method, the total scoring number can be set to 5 stars, and the higher the star rating is, the higher the effectiveness of the use of the low-frequency electrotherapy method is.
In one embodiment, as shown in FIG. 4, in step S104, the statistical processing of the associated insomnia condition classification information, low-frequency electrotherapy protocol and treatment data includes the following steps S401-S402:
in step S401, various types of low-frequency electrotherapy protocols are generated for different types of insomnia condition classification information;
specifically, the low-frequency electrotherapy scheme is established at the cloud end by a professional physician aiming at different insomnia classification information, and different low-frequency electrotherapy schemes comprise the difference between the low-frequency electrotherapy intensity, the low-frequency electrotherapy frequency, the low-frequency electrotherapy duration and the low-frequency electrotherapy mode;
in step S402, the number of times of implementation of different low-frequency electrotherapy protocols is counted, and the low-frequency electrotherapy protocols are sorted according to the order of magnitude;
specifically, the method can visually display the used times of the low-frequency electrotherapy scheme in an arrangement mode in which the total number of the implemented times is in sequence, generally speaking, the more the low-frequency electrotherapy scheme is used, the stronger the applicability is, the higher the cure rate is, and particularly, the low-frequency electrotherapy scheme also provides the ratio between the active scoring number of the insomnia patient and the total scoring number, namely, the good scoring rate, so that the patient can conveniently compare the low-frequency electrotherapy scheme used at high frequency;
wherein, in step S402, counting the number of times of implementing different low-frequency electrotherapy protocols and sorting the low-frequency electrotherapy protocols according to the order of magnitude includes step S403:
in step S403, the treatment data after the implementation of the low-frequency electrotherapy protocol is sorted in chronological order.
Specifically, after a reference suggestion about a certain low-frequency electrotherapy scheme is implemented, information is actively scored and commented on an insomnia patient who uses the low-frequency electrotherapy scheme for the last time on a page, information is actively scored and commented on an insomnia patient who uses the low-frequency electrotherapy scheme for the second time recently, and so on, and the comment information is clicked, so that the insomnia patient can directly enter a reply box of the comment information, and communication among the insomnia patients can be facilitated.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.
FIG. 5 is a block diagram of a big data-based diagnosis and treatment apparatus for insomnia according to the present invention, which can be implemented as part of or all of an electronic device by software, hardware or a combination of both. As shown in fig. 5, the apparatus includes:
the display module 1 is used for entering a terminal setting interface and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
the selection module 2 is used for selectively implementing a low-frequency electrotherapy scheme according to the insomnia classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
the monitoring module 3 is used for recording treatment data after the implementation of the low-frequency electrotherapy scheme, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
the transmission module 4 is used for integrating and uploading the treatment data after the implementation of the low-frequency electrotherapy scheme matched with the insomnia classification information to a cloud for storage;
and the processing module 5 is used for carrying out statistical processing on the associated insomnia condition classification information, the low-frequency electrotherapy scheme and the treatment data.
The device of the embodiment of the disclosure can provide reference data for the insomnia patients through the treatment data, so that the patients without experience and medical general knowledge can not blindly adopt an electrotherapy mode to avoid adverse reaction to the body, and the low-frequency electrotherapy of the insomnia patients every time is equivalent to providing one insomnia treatment experiment for big data.
In one embodiment, as shown in fig. 5, the display module 1 includes:
the classification module 11 is used for entering the insomnia classification information classification interface and selecting the insomnia classification information according with the symptoms of the insomnia patient;
a reference module 12, configured to enter a low-frequency electrotherapy scheme classification interface related to the insomnia condition classification information, and display medical data after implementation of a low-frequency electrotherapy scheme according with the insomnia patient symptom;
and the selection module 13 is used for selecting any low-frequency electrotherapy scheme according to the treatment data after the low-frequency electrotherapy scheme is implemented.
In one embodiment, as shown in fig. 5, the monitoring module 3 includes:
the acquisition module 31 is used for dynamically monitoring the sleep state of the insomnia patient, and the dynamic monitoring mode comprises any one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
a determination module 32 for determining sleep quality assessment information of the insomnia patient;
and the cache module 33 is used for inputting the active scoring and comment information of the insomnia patients after the implementation of the low-frequency electrotherapy scheme.
In an embodiment, as shown in fig. 5, the processing module 5 includes:
a generating module 51, for generating various low-frequency electrotherapy schemes related to different insomnia classification information;
a sorting module 52, configured to count the number of times of implementing different low-frequency electrotherapy schemes, and sort the low-frequency electrotherapy schemes according to a size order;
wherein, the classifying module 52 includes:
and the classification submodule 53 is used for sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The embodiment of the present disclosure further provides an insomnia treatment and diagnosis device based on big data, as shown in fig. 6, including:
a processor 101;
a memory 102 for storing instructions executable by the processor 101;
wherein the processor 101 is configured to:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud for storage;
and statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data.
The processor 101 may be further configured to:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information, and comprises the following steps:
entering the classification interface of the insomnia classification information, and selecting the insomnia classification information according with the symptoms of the insomnia patient;
entering a low-frequency electrotherapy scheme classification interface of the insomnia condition classification information, and displaying the treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
and selecting any low-frequency electrotherapy scheme according to the medical treatment data after the low-frequency electrotherapy scheme is implemented.
The processor 101 may be further configured to:
the recording of the treatment data after the implementation of the low-frequency electrotherapy scheme, wherein the treatment data comprise sleep quality assessment information and active scoring and comment information of the insomnia patients, and the recording comprises the following steps:
dynamically monitoring the sleep state of the insomnia patient, wherein the dynamic monitoring mode comprises one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
determining sleep quality assessment information of the insomnia patient;
and inputting the active scoring and comment information of the insomnia patient after the low-frequency electrotherapy scheme is implemented.
The processor 101 may be further configured to:
the statistical processing of the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data comprises the following steps:
generating various types of low-frequency electrotherapy protocols for different classification information of the insomnia symptoms;
counting the implementation times of different low-frequency electrotherapy schemes, and sequencing the low-frequency electrotherapy schemes according to the size sequence;
wherein said counting the number of times different said low frequency electrotherapy protocols are implemented and ordering said low frequency electrotherapy protocols according to a size order comprises:
and sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
FIG. 6 is a block diagram of another insomnia treatment and diagnosis device based on big data according to the present invention, which is suitable for an insomnia treatment terminal. For example, the terminal device may be equipped with a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and the like.
The terminal device may include one or more of the following components: processing component 100, memory 102, communication component 110, input/output interface 120, power component 130, multimedia component 140, sensor component 150, and audio component 160. The processing component 100 generally controls overall operations of the terminal device, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing assembly 100 may include one or more processors 101 to execute instructions to perform all or part of the steps of the method described above. Further, the processing component 100 can include one or more modules that facilitate interaction between the processing component 100 and other components. For example, the processing component 100 may include a multimedia module to facilitate interaction between the multimedia component 140 and the processing component 100.
The memory 102 is configured to store various types of data to support operations at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, contact data, phonebook data, messages, pictures, videos, etc. The memory 102 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. The power supply component 130 provides power to the various components of the terminal device. The power components 130 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the terminal device.
The multimedia component 140 comprises a screen providing an output interface between the terminal device and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 140 includes a front facing camera and/or a rear facing camera. When the terminal device is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability. The audio component 160 is configured to output and/or input audio signals. For example, the audio component 160 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 102 or transmitted via the communication component 110. In some embodiments, the audio assembly 160 further includes a speaker for outputting audio signals. The input/output interface 120 provides an interface between the processing component 100 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button. The sensor assembly 150 includes one or more sensors for providing various aspects of status assessment for the terminal device. For example, the sensor assembly 150 may detect the open/closed status of the terminal device, the relative positioning of the components, such as the display and keypad of the terminal device, the sensor assembly 150 may also detect a change in the position of the terminal device or a component of the terminal device, the presence or absence of user contact with the terminal device, orientation or acceleration/deceleration of the terminal device, and a change in the temperature of the terminal device. The sensor assembly 150 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 150 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 150 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 110 is configured to facilitate wired or wireless communication between the terminal device and other devices. The terminal device may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication part 110 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 110 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the terminal device may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as the memory 102, comprising instructions executable by the processor 101 of the terminal device to perform the above-described method. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
A non-transitory computer readable storage medium having instructions therein which, when executed by a processor 101 of a terminal device, enable the terminal device to improve the value of sleep monitoring, the method comprising:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud for storage;
and statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data.
In one embodiment, entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate degree information, and comprises the following steps:
entering the classification interface of the insomnia classification information, and selecting the insomnia classification information according with the symptoms of the insomnia patient;
entering a low-frequency electrotherapy scheme classification interface of the insomnia condition classification information, and displaying the treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
and selecting any low-frequency electrotherapy scheme according to the medical treatment data after the low-frequency electrotherapy scheme is implemented.
In one embodiment, the recording of treatment data after the implementation of the low-frequency electrotherapy protocol includes sleep quality assessment information, active scoring and comment information of insomnia patients, and includes:
dynamically monitoring the sleep state of the insomnia patient, wherein the dynamic monitoring mode comprises one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
determining sleep quality assessment information of the insomnia patient;
and inputting the active scoring and comment information of the insomnia patient after the low-frequency electrotherapy scheme is implemented.
In one embodiment, the statistically processing the associated insomnia condition classification information, low frequency electrotherapy protocol and treatment data includes:
generating various types of low-frequency electrotherapy protocols for different classification information of the insomnia symptoms;
counting the implementation times of different low-frequency electrotherapy schemes, and sequencing the low-frequency electrotherapy schemes according to the size sequence;
wherein said counting the number of times different said low frequency electrotherapy protocols are implemented and ordering said low frequency electrotherapy protocols according to a size order comprises:
and sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
In the description of the present patent application, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present patent. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above examples are only used to illustrate the technical solutions of the present invention, but not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; the modifications or substitutions do not make the essence of the corresponding technical solution depart from the scope of the technical solutions of the embodiments of the patent of the present invention, and the technical solutions are all covered in the claims and the specification of the patent of the present invention.
Claims (9)
1. A insomnia treatment and diagnosis method based on big data is characterized by comprising the following steps:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud for storage;
and statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data.
2. The method of claim 1, wherein entering a terminal setting interface, the insomnia condition classification information suitable for symptoms of an insomnia patient, the low frequency electrotherapy scheme and the treatment data are loaded, the insomnia condition classification information includes type information, mild-moderate degree information, and comprises:
entering the classification interface of the insomnia classification information, and selecting the insomnia classification information according with the symptoms of the insomnia patient;
entering a low-frequency electrotherapy scheme classification interface of the insomnia condition classification information, and displaying the treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
and selecting any low-frequency electrotherapy scheme according to the medical treatment data after the low-frequency electrotherapy scheme is implemented.
3. The method of claim 1, wherein said recording of treatment data after implementation of said low frequency electrotherapy protocol, said treatment data including sleep quality assessment information, active scoring, review information for insomnia patients, comprises:
dynamically monitoring the sleep state of the insomnia patient, wherein the dynamic monitoring mode comprises one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
determining sleep quality assessment information of the insomnia patient;
and inputting the active scoring and comment information of the insomnia patient after the low-frequency electrotherapy scheme is implemented.
4. The method of claim 1, wherein said statistically processing said associated wakefulness condition classification information, low frequency electrotherapy protocol and treatment data comprises:
generating various types of low-frequency electrotherapy protocols for different classification information of the insomnia symptoms;
counting the implementation times of different low-frequency electrotherapy schemes, and sequencing the low-frequency electrotherapy schemes according to the size sequence;
wherein said counting the number of times different said low frequency electrotherapy protocols are implemented and ordering said low frequency electrotherapy protocols according to a size order comprises:
and sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
5. An insomnia treatment and diagnosis device based on big data is characterized by comprising:
the display module is used for entering a terminal setting interface and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
the selection module is used for selectively implementing a low-frequency electrotherapy scheme according to the insomnia classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
the monitoring module is used for recording treatment data after the low-frequency electrotherapy scheme is implemented, and the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
the transmission module is used for integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud end for storage;
and the processing module is used for carrying out statistical processing on the associated insomnia condition classification information, the low-frequency electrotherapy scheme and the treatment data.
6. The apparatus of claim 5, wherein the display module comprises:
the classification module is used for entering the insomnia classification information classification interface and selecting the insomnia classification information according with the symptoms of the insomnia patient;
the reference module is used for entering a low-frequency electrotherapy scheme classification interface related to the insomnia condition classification information and displaying the treatment data after the low-frequency electrotherapy scheme which accords with the symptoms of the insomnia patient is implemented;
and the selection module is used for selecting any low-frequency electrotherapy scheme according to the treatment data after the low-frequency electrotherapy scheme is implemented.
7. The apparatus of claim 5, wherein the monitoring module comprises:
the acquisition module is used for dynamically monitoring the sleep state of the insomnia patient, and the dynamic monitoring mode comprises any one or more of electroencephalogram, electrocardio, myoelectricity, skin electricity, blood pressure, body temperature and respiration detection;
the determining module is used for determining sleep quality evaluation information of the insomnia patient;
and the cache module is used for inputting the active scoring and comment information of the insomnia patient after the implementation of the low-frequency electrotherapy scheme.
8. The apparatus of claim 5, wherein the processing module comprises:
the generation module is used for generating various low-frequency electrotherapy schemes related to different insomnia classification information;
the classification module is used for counting the implementation times of different low-frequency electrotherapy schemes and sequencing the low-frequency electrotherapy schemes according to the size sequence;
wherein, categorizing the module, include:
and the classification submodule is used for sequencing the treatment data after the low-frequency electrotherapy scheme is implemented according to the time sequence.
9. An insomnia treatment and diagnosis device based on big data is characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
entering a terminal setting interface, and loading insomnia classification information, a low-frequency electrotherapy scheme and treatment data which are suitable for symptoms of an insomnia patient, wherein the insomnia classification information comprises type information and mild-moderate information;
selectively implementing a low-frequency electrotherapy scheme according to the insomnia condition classification information and the treatment data, wherein the low-frequency electrotherapy scheme comprises intensity, frequency, duration and mode information;
recording treatment data after the low-frequency electrotherapy scheme is implemented, wherein the treatment data comprises sleep quality assessment information and active scoring and comment information of an insomnia patient;
integrating and uploading the treatment data after the low-frequency electrotherapy scheme matched with the insomnia classification information is implemented to a cloud for storage;
and statistically processing the associated insomnia condition classification information, low-frequency electrotherapy scheme and treatment data.
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Application publication date: 20211022 |