CN116098577A

CN116098577A - Sleep quality assessment method and sleep quality assessment system

Info

Publication number: CN116098577A
Application number: CN202111320532.XA
Authority: CN
Inventors: 肖科; 何先梁; 金星亮; 罗汉源; 孙白雷; 张崇明
Original assignee: Shenzhen Mindray Bio Medical Electronics Co Ltd
Current assignee: Shenzhen Mindray Bio Medical Electronics Co Ltd
Priority date: 2021-11-09
Filing date: 2021-11-09
Publication date: 2023-05-12

Abstract

The invention provides a sleep quality assessment method and a sleep quality assessment system, wherein the sleep quality assessment method comprises the following steps: acquiring a monitored signal of a target object; outputting and displaying at least one item of physiological parameter information of the monitored object based on the monitored signal; extracting a plurality of sleep related parameters of one or more monitored signals; acquiring a sleep parameter evaluation model; inputting a plurality of sleep related parameters into a sleep parameter evaluation model to obtain sleep parameter evaluation data of each sleep related parameter, wherein the sleep parameter evaluation data are used for representing the influence degree of each sleep related parameter on sleep quality; analyzing the sleep parameter evaluation data to obtain a sleep quality evaluation result of the target object; and outputting and displaying the sleep quality assessment result. The sleep quality evaluation method and system can effectively and objectively evaluate the sleep quality of the target object, and provide more accurate sleep quality information for doctors, so that the doctors can be assisted to diagnose the physical condition of the patients.

Description

Sleep quality assessment method and sleep quality assessment system

Technical Field

The present invention relates generally to the technical field of medical devices, and more particularly to a sleep quality assessment method and a sleep quality assessment system.

Background

The sleep quality of a patient is closely related to the physical condition of the patient, and medical staff must pay attention to the sleep condition of the patient every day to assist in diagnosing the physical condition of the patient. When the sleep quality of the patient is better or gradually improved, the rehabilitation of the patient is facilitated, and when the sleep quality of the patient is poorer or the sleep quality of the patient is reduced, the rehabilitation of the patient is not optimistic.

The clinically common sleep quality assessment method mainly comprises the steps that a doctor subjectively inquires the sleep condition of a patient, and then the doctor subjectively assesses the sleep quality of the patient through a sleep quality scale. Although the method can effectively evaluate the sleep quality of the patient, the method for dictating the patient is high in subjectivity and can not accurately describe the sleep information of the patient; second, the patient is unable to describe the occurrence of many sleep events (e.g., apneas, dreams, abnormal wakefulness) that result in incomplete information being provided to the physician. Therefore, the accuracy of the evaluation result of the sleep quality by inquiring the sleep condition of the patient by the doctor is insufficient.

In view of the above problems, the present application proposes a new sleep quality assessment method and a sleep quality assessment system.

Disclosure of Invention

The present invention has been made in order to solve at least one of the above problems.

Specifically, a first aspect of the present invention provides a method for sleep quality assessment, the method comprising:

acquiring a monitored signal of a target object, the monitored signal comprising at least one of: electrocardiographic signals, respiratory signals, plethysmographic waves, blood pressure signals, body temperature signals, electroencephalogram signals, and motion signals;

outputting and displaying at least one item of physiological parameter information of the monitored object based on the monitored signal;

extracting a plurality of sleep related parameters of one or more of the monitored signals;

acquiring a sleep parameter evaluation model;

inputting the sleep related parameters into the sleep parameter evaluation model to obtain sleep parameter evaluation data of each sleep related parameter, wherein the sleep parameter evaluation data are used for representing the influence degree of each sleep related parameter on sleep quality;

analyzing the sleep parameter evaluation data to obtain a sleep quality evaluation result of the target object;

and outputting and displaying the sleep quality assessment result.

A second aspect of the present invention provides a sleep quality assessment system, comprising:

The signal acquisition circuit is used for acquiring a monitored signal of a target object;

a memory for storing executable program instructions;

a processor for executing the program instructions stored in the memory, causing the processor to perform the method of sleep quality assessment described below.

And the display is used for displaying various visual information, wherein the visual information at least comprises the sleep quality assessment result.

A third aspect of the present invention provides a sleep quality assessment system, comprising: a memory for storing executable program instructions;

a processor for executing the program instructions stored in the memory, causing the processor to perform the steps of:

acquiring a plurality of sleep related parameters and a sleep parameter evaluation model of a target object;

inputting a plurality of sleep related parameters into the sleep parameter evaluation model to obtain sleep parameter evaluation data, wherein the sleep parameter evaluation data are used for representing the influence degree of each sleep related parameter on sleep quality;

Acquiring a sleep cycle chart of the target object;

and the display is used for displaying various visual information, wherein the visual information at least comprises the sleep quality assessment result and the sleep cycle chart.

According to the sleep quality assessment method of the first aspect of the invention, based on the sleep time related information, the parameters and the sleep parameter assessment model of the target object, the sleep parameter assessment data can be obtained, and the assessment result of the current sleep quality of the target object is determined based on the sleep parameter assessment data, so that the sleep quality of the target object is effectively and objectively assessed, more accurate sleep quality information is provided for doctors, and the doctors are facilitated to diagnose the physical condition of the patients.

According to the sleep quality assessment system disclosed by the invention, the sleep quality of a target object can be effectively and objectively assessed, more accurate sleep quality information is provided for doctors, so that the doctors can be conveniently assisted to diagnose the physical condition of the patients, the requirement of a medical room of the hospital on the sleep quality assessment of the patients can be met by using the existing monitoring system in the hospital, and compared with professional sleep monitoring equipment, the sleep quality assessment system is lower in cost, simpler to operate and less in time and energy of the doctors.

According to the sleep quality assessment system disclosed by the invention, the sleep quality assessment result and the sleep cycle chart are obtained and displayed in a combined mode, so that the situation that a doctor obtains the sleep quality of a target object more intuitively is provided, and the doctor is facilitated to diagnose the physical condition of a patient.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.

FIG. 1 shows a schematic block diagram of a sleep quality assessment system in one embodiment of the invention;

FIG. 2 shows a schematic diagram of a mobile monitoring system in one embodiment of the invention;

FIG. 3 shows a schematic block diagram of a signal acquisition module of a sleep quality assessment system in one embodiment of the invention;

FIG. 4 illustrates a flow chart of a method of sleep quality assessment in one embodiment of the invention;

FIG. 5 shows a schematic block diagram of signal feature extraction in one embodiment of the invention;

FIG. 6 illustrates a schematic construction of a sleep stage model in one embodiment of the invention;

FIG. 7 shows a schematic diagram of a sleep cycle diagram in one embodiment of the invention;

FIG. 8 shows a flow chart of a method of sleep quality assessment in another embodiment of the invention;

fig. 9 shows a schematic diagram of a relationship between sleep related parameters and sleep quality scores ((a) and a schematic diagram of a sleep parameter scoring model ((b)) in one embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. Based on the embodiments of the invention described in the present application, all other embodiments that a person skilled in the art would have without inventive effort shall fall within the scope of the invention.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced without one or more of these details. In other instances, well-known features have not been described in detail in order to avoid obscuring the invention.

It should be understood that the present invention 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, and will fully convey the scope of the invention to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

In order to thoroughly understand the present invention, a detailed structure of the monitoring system and a sleep quality assessment method will be presented in the following description to illustrate the technical solution proposed by the present invention. Alternative embodiments of the invention are described in detail below, however, the invention may have other implementations in addition to these detailed descriptions.

Specifically, the sleep quality evaluation system and the sleep quality evaluation method of the present application are described in detail below with reference to the accompanying drawings. The features of the examples and embodiments described below may be combined with each other without conflict.

Next, a sleep quality assessment system in an embodiment of the present invention will be described with reference to fig. 1. Sleep quality assessment systems include, but are not limited to, bedside monitoring systems, central stations, PCs that install running monitoring software, mobile monitoring systems, and the like.

As an example, as shown in fig. 1, the sleep quality assessment system according to the embodiment of the present invention detects vital sign parameters of a target object, and the sleep quality assessment system may collect one or more monitored signals of the monitored target object through the signal collection circuit, where the monitored signals include at least one of the following signals: electrocardiographic signals, respiratory signals, plethysmographic waves, blood pressure signals, body temperature signals, electroencephalographic signals, and motion signals. The processor of the sleep quality assessment system processes the signals of the vital sign parameters to obtain the data information of the corresponding vital sign parameters.

The sleep quality assessment system may include a bedside monitoring system or a mobile monitoring system, wherein the sleep quality assessment system may include a plurality of signal acquisition modules for acquiring monitored signals, such as shown in fig. 3, and may include, but is not limited to, an electrocardiograph module for acquiring electrocardiographic signals, a respiratory module for acquiring respiratory signals, an oximetry module for acquiring oximetry signals, a body temperature module for acquiring body temperature signals, a blood pressure module for acquiring blood pressure signals, an electroencephalogram module for acquiring electroencephalogram signals, a motion module for acquiring motion signals, and so forth.

The modules for acquiring the monitored signals can comprise respective signal acquisition circuits, and the signal acquisition circuits can be circuit modules which are built in a host of the monitoring equipment or circuit modules in sensors which are connected with the host of the monitoring equipment through interfaces. When the sleep quality assessment system is a central station or a PC (personal computer) provided with monitoring software, the sleep quality assessment system can receive data information of vital sign parameters of a target object through a communication interface, wherein the communication interface comprises but is not limited to a wired interface, a wireless interface, a USB interface and the like.

As shown in fig. 1, the sleep quality assessment system 100 includes one or more processors 101, one or more signal acquisition circuits 102, a display 103, a memory 104, and a communication interface 105, among other things. These components are interconnected by a bus system and/or other forms of connection mechanisms (not shown). It should be noted that the components and structures of the sleep quality assessment system 100 shown in fig. 1 are exemplary only and not limiting, as the sleep quality assessment system 100 may have other components and structures as desired.

The sleep quality assessment system 100 includes a monitoring system for monitoring vital signs of a patient, such as a bedside monitoring system, having a separate housing with a sensor interface area on a housing panel with a plurality of sensor interfaces integrated therein for connection to external individual physiological parameter sensor accessories (not shown), a display 103, input interface circuitry, alarm circuitry (e.g., LED alarm area), and the like. The processor 101 receives the monitored signal acquired by the signal acquisition circuit 102, and processes the monitored signal to obtain data information of the vital sign parameters of the target object (i.e., the monitored target object), such as data information of various vital sign parameters related to hemodynamics, and data information of other basic parameters (i.e., basic physiological parameters), such as blood oxygen, body temperature, respiration, blood pressure, etc.

The signal acquisition circuit may be selected from an electrocardiograph circuit, a respiratory circuit, a body temperature circuit, an oximetry circuit, a noninvasive blood pressure circuit, an invasive blood pressure circuit, a motion detection circuit, etc., and may be electrically connected to the corresponding sensor interface for electrically connecting to the sensors corresponding to different monitored signals, the output end of the signal acquisition circuit is coupled to a front-end signal processor, the communication port of the front-end signal processor is coupled to the processor 101, and the processor 101 is electrically connected to an external communication and power interface.

The memory 104 is used to store various data and executable programs generated during the relevant sleep monitoring process, such as system programs for storing sleep quality assessment systems, e.g., monitoring systems, various application programs, or algorithms implementing various specific functions. May include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. During the monitoring of sleep by the monitoring system and the monitoring of the target object by the monitoring system, if needed, locally stored data, such as sleep related data, vital sign parameter data, etc., may be stored in the memory.

The processor 101 may be a Central Processing Unit (CPU), an image processing unit (GPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other form of processing unit with data processing capabilities and/or instruction execution capabilities, and may control other components in the monitoring system to perform desired functions. For example, the processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), digital Signal Processors (DSPs), image processing units (GPUs), or combinations thereof.

The processor 101 may be configured to execute program instructions stored in the memory, such that the processor performs the method of sleep quality assessment hereinafter, and in particular the method of sleep quality assessment will be described hereinafter.

In one example, the sleep quality assessment system 100 further includes a communication interface 105 for communication between components of the monitoring system and other devices external to the system (e.g., bedside monitoring system), e.g., the sleep quality assessment system 100 includes a mobile monitoring system and/or bedside monitoring system, which may be communicatively coupled to a monitoring device, e.g., a central station, to output various signals (e.g., near signals) and/or processed parameter information (e.g., physiological parameter information, vital sign parameter information) of the mobile monitoring system bedside monitoring system (or bedside monitoring system) to the central station for the central station to monitor sleep based on the monitored signals, or other monitoring, etc.

The communication interface 105 is an interface that may be any presently known communication protocol, such as a wired interface or a wireless interface, where the communication interface may include one or more serial ports, USB interfaces, ethernet ports, wiFi, wired network, DVI interfaces, device integration interconnect modules, or other suitable various ports, interfaces, or connections. The sleep quality assessment system 100 may also access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, 5G, or combinations thereof. In one exemplary embodiment, the communication interface receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication interface further includes a Near Field Communication (NFC) module to facilitate short range communications. 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 one example, the sleep quality assessment system 100 also includes an input device (not shown), which may be a device used by a user to input instructions, and may include an input device composed of one or more of a keyboard, a trackball, a mouse, a microphone, a touch screen, and the like, or other control buttons. For example, the user may input control instructions for initiating sleep quality assessment, instructions for reviewing sleep monitoring data, instructions for viewing details of sleep events based on sleep session data, instructions for viewing physiological parameter information within various time periods in the sleep session data, etc. through the input device.

The sleep quality assessment system 100 of the embodiment of the present invention further includes an output device that can output various information (e.g., images or sounds) to the outside (e.g., a user), and can include one or more of a display, a speaker, and the like.

In one example, the sleep quality assessment system 100 further comprises one or more displays 103 for displaying at least any visual information, such as sleep staging data below, e.g. sleep cycle charts, sleep events, sleep quality assessment results, various sleep parameters, various preset hotkeys, user setup interfaces, physiological parameter information, kinetic parameter information, etc.

The sleep quality assessment system 100 also includes a user interface through which a user of the monitoring system can control the operation of the monitoring system. The user interface may include a display 103, which may include a touch screen that allows a user to input operational instructions from the display 103 to the monitoring system, and/or include one or more control panels, etc., through which the user may control the operation of the monitoring system.

In one embodiment of the present application, when the monitoring system may comprise a mobile monitoring system, wherein the mobile monitoring system may comprise at least two wearable monitoring devices, each wearable monitoring device comprises a respective one or more signal acquisition circuits for acquiring different monitored signals, e.g. different physiological parameter signals, e.g. blood oxygen signals, electrocardiographic signals, blood pressure signals, etc., or may also be used for acquiring non-physiological parameter signals, e.g. movement signals. Each wearable monitoring device may include a processor, memory, communication interface, etc., wherein the processor, memory, and communication interface are described with reference to the foregoing.

The mobile monitoring system also includes one or more sensors, such as physiological parameter sensors, motion sensors, etc., wherein the physiological parameter sensors include, but are not limited to: an electrocardio sensor, an oximetry sensor, etc., in one example, as shown in fig. 2, the mobile monitoring system includes two wearable monitoring devices, such as a first monitoring device 201 and a second monitoring device 202, where the first monitoring device 201 and the second monitoring device 202 are configured to be wearable on different body parts of the target object, for example, the first monitoring device 201 may be worn on a neck of the target object, a signal acquisition circuit of the first monitoring device 201 is electrically connected to, for example, the electrocardio sensor 203, so as to acquire electrocardio signals of the target object, so as to acquire electrocardio parameter information, such as an electrocardio signal, for example, an electrocardio signal, a heart rate, etc., based on the electrocardio signal, where the electrocardio sensor 203 may include a plurality of electrocardio sensors 203, each of which is used to be attached to a different body part of the target object, and each of the electrocardio sensors 203 may be connected to the first monitoring device 201 through a lead wire, and the second monitoring device 202 may be worn on, for example, a wrist, etc., of the target object, or may also be worn on a lower limb, for example, etc., a signal acquisition circuit of the second monitoring device 202 may be electrically connected to, for example, so as to acquire an oximetry signal, for example, an oximetry sensor 205 may be configured to acquire an oximetry signal, so as to be based on an oxygen signal, for example, an oxygen sensor 205 may be configured to be attached to a finger cuff, or a finger cuff, may be attached to a finger cuff, or a finger cuff, and a finger cuff, may be attached to a finger cuff.

The blood oxygen sensor 205 may include a light emitter, which may include a red light emitter for emitting red light to irradiate a wearing portion of the wearer, such as a finger or a fingertip or a finger belly, for example, red light having a wavelength of about 660nm, and an infrared light emitter for emitting infrared light to irradiate a wearing portion of the wearer, such as a finger, for example, infrared light having a wavelength of about 910nm, and the blood oxygen sensor 205 further includes a receiver, such as a photosensitive component, for acquiring a blood oxygen signal, and the signal acquisition circuit of the second monitoring device 202 acquires the blood oxygen signal acquired by the receiver and outputs the blood oxygen signal to the processor of the second monitoring device 202, and the processor analyzes the blood oxygen signal to acquire a blood oxygen parameter, such as a blood oxygen saturation value.

The first monitoring device 201 may be fixed to the neck of the target object by means of, for example, adhesive, and the housing of the second monitoring device 202 is further provided with a fixing element for sleeving the second monitoring device 202 on the wrist of the target object, wherein the fixing element is arranged on the side opposite to the display of the second monitoring device 202.

In one example, a wire blocking component 206 is further disposed outside the side wall of the housing of the second monitoring device 202, for binding a portion of the lead wire between the blood oxygen sensor and the second monitoring device to the housing of the second monitoring device, so as to avoid the problem of mess caused by too long lead wire, the wire blocking component 206 is perpendicular to a plane where a display on the housing of the second monitoring device 202 is located, a first interface on the housing of the second monitoring device 202 for electrically connecting with the blood oxygen sensor 205 is disposed on a side surface of the housing, for example, when the second monitoring device 202 is worn on an arm of a target object, the side surface faces an upper end of the arm, one end of the lead wire 2041 is connected to the first interface of the second monitoring device 202, and the other end is connected to a sensor interface, for example, of the blood oxygen sensor 205, so that a wired communication connection can be performed between the second monitoring device 202 and the blood oxygen sensor 205, a wire blocking component 206 is disposed on a side facing the housing, and a portion of the lead wire 2042 is disposed in the wire through the wire blocking hole.

In one example, a motion sensor may be worn on an arm or lower limb of a target subject to monitor motion signals. The first monitoring device may include a motion signal acquisition circuit electrically connected to the motion sensor, or the second monitoring device may include a motion signal acquisition circuit electrically connected to the motion sensor, or the mobile monitoring device may further include a third monitoring device including a motion signal acquisition circuit electrically connected to the motion sensor.

For a mobile monitoring system, the following steps of the sleep quality assessment method can be performed based on the first monitoring device and/or the second monitoring device, the first monitoring device and the second monitoring device can perform information interaction through a communication interface, and various visual information can be displayed through the first monitoring device or the second monitoring device with a display, or the remote device such as a bedside monitoring system, a central station or a PC provided with monitoring software can be used as an execution subject to perform the following steps of the sleep detection method, wherein the first monitoring device and the second monitoring device can send the monitoring signals (or information and the like) acquired by the first monitoring device and the second monitoring device to the remote device, or the first monitoring device outputs the monitoring signals acquired by the first monitoring device and the monitoring signals acquired by the first monitoring device to the remote device, and the first monitoring device sends the signals acquired by the second monitoring device and the signals acquired by the second monitoring device to the remote device, optionally the first monitoring device sends the monitoring signals (or the information and the like) to the remote device, or the second monitoring device can send the monitoring signals to a special gateway, the remote device and the remote device can send the monitoring signals to the remote device.

In one example, the first monitoring device transmits its acquired monitored signal to the second monitoring device via a wireless communication module, such as bluetooth, or a wired communication module. In other examples, the first monitoring device further comprises an alarm means for outputting alarm information to trigger the alarm means to alarm when it is identified that the monitored patient is in an abnormal event, such as arrhythmia, based on the monitored signals acquired by the first monitoring device.

In one example, the monitoring system further comprises a remote device and a mobile monitoring system communicatively coupled to the remote device, wherein the mobile monitoring system comprises a signal acquisition circuit and the remote device comprises a processor and a memory.

The second monitoring device may include a display while the first monitoring device may not include a display in view of the comfort of wearing by the target subject, as well as the ease of viewing or manipulation.

It should be noted that, when the sleep quality assessment system includes the mobile monitoring system, the sleep quality assessment system may be used as an execution subject of the method for sleep quality assessment, or may be used as an execution subject of the method for sleep quality assessment, and may be used as an execution subject of the method for sleep quality assessment, or may be used as an execution subject of the method for sleep quality assessment. Alternatively, the sleep quality assessment system of the present application may further include a sleep monitoring device, such as a smart bracelet, a sleep mattress, or the like, to collect the monitored signal related to sleep, and perform the steps of the method for assessing sleep quality of the target object based on the monitored signal, or in other examples, the sleep quality assessment system may be any computer device or the like capable of acquiring the monitored signal of the target object, which may be data of the monitored signal that is locally imported, or may be acquired through a server, or may also be data of the monitored signal that is stored in a mobile storage device, such as a usb disk, a mobile hard disk, or the like, by an acquired user.

Next, a method of sleep quality assessment of the present application will be described with reference to fig. 4 to 9, wherein fig. 4 shows a flowchart of a method of sleep quality assessment in one embodiment of the present invention; FIG. 5 shows a schematic block diagram of signal feature extraction in one embodiment of the invention; FIG. 6 illustrates a schematic construction of a sleep stage model in one embodiment of the invention; FIG. 7 shows a schematic diagram of a sleep cycle diagram in one embodiment of the invention; FIG. 8 shows a flow chart of a method of sleep quality assessment in another embodiment of the invention; fig. 9 shows a schematic diagram of a relationship between sleep related parameters and sleep quality scores ((a) and a schematic diagram of a sleep parameter scoring model ((b)) in one embodiment of the invention.

In order to solve the problem that the sleep quality evaluation result of the subjective query is not accurate and reliable, as shown in fig. 4, the present application proposes a sleep quality evaluation method, which mainly depends on subjective query of a doctor, and includes the following steps S401 to S407: step S401, obtaining a monitored signal of a target object; step S402, outputting and displaying at least one item of physiological parameter information of a monitored object based on the monitored signal; step S403, extracting a plurality of sleep related parameters of one or more monitored signals; step S404, obtaining a sleep parameter evaluation model; step S405, inputting a plurality of sleep related parameters into a sleep parameter evaluation model to obtain sleep parameter evaluation data of each sleep related parameter, wherein the sleep parameter evaluation data is used for representing the influence degree of each sleep related parameter on sleep quality; step S406, analyzing the sleep parameter evaluation data to obtain a sleep quality evaluation result of the target object; step S407, outputting and displaying the sleep quality assessment result. According to the sleep quality assessment method, the sleep parameter assessment data can be acquired based on the sleep related parameters and the sleep parameter assessment model of the target object, and the assessment result of the current sleep quality of the target object is determined based on the sleep parameter assessment data, so that the sleep quality of the target object is effectively and objectively assessed, more accurate sleep quality information is provided for doctors, and the doctors are facilitated to diagnose the physical condition of the patients.

In one example, in step S401, a monitored signal (e.g., one or more monitored signals related to sleep) of the target subject may be acquired based on a monitoring system connected to the target subject; the monitored signal may be a real-time monitored signal, or the monitored signal may be a historical monitored signal, for example, a historical monitored signal in a preset time acquired in the past may be obtained, and the sleep state of the monitored subject is analyzed based on the historical monitored signal in the preset time.

In this embodiment, the monitored signal includes at least one of the following: electrocardiographic signals, respiratory signals, plethysmographic waves, blood pressure signals, body temperature signals, electroencephalographic signals, and motion signals. The electrocardiosignals, respiratory signals, plethysmographic waves, blood pressure signals, body temperature signals and brain electrical signals can be classified as physiological parameter signals, and the motion signals belong to non-physiological signals. The above monitored signals are merely examples, and other signals that can be used for sleep monitoring can be equally applicable to the present application, and it should be noted that the monitored signals related to sleep can be part of the monitored signals or all the monitored signals in all the monitored signals collected by the sleep quality assessment system (such as the monitored system).

In step S402, at least one item of physiological parameter information of the target subject is displayed based on the monitored signal. The monitored signals are processed to obtain physiological parameter information of a target object, such as data information of various vital sign parameters related to hemodynamics, and data information of other basic parameters (i.e. basic physiological parameters), such as blood oxygen, body temperature, respiration, blood pressure and the like.

In the present application, sleep related parameters include at least one of the following information or parameters: the sleep time related information and parameters, sleep structure information, sleep event related parameters, or may also include other sleep related parameters or information. Optionally, the sleep time related information and parameters include at least one of the following parameters: sleep time, total sleep time, light sleep time, deep sleep time, eye rapid movement sleep (rapid eye movement, REM for short) period time, total sleep time duty cycle, light sleep duty cycle, deep sleep duty cycle, REM period duty cycle, number of sleep wakefulness, or other information related to sleep time.

In one example, the sleep structure information includes at least one of the following structures: normal sleep, insomnia, fragmented sleep, REM phase disorders, somnolence, difficulty falling asleep, etc., or other sleep structure.

In one example, the types of sleep events include, but are not limited to: apneas (obstructive, central, mixed), restless legs, dreams, etc. Sleep event related parameters include one or more of the following: an apnea index, restless leg frequency, and a frequency of nocturnal episodes. Wherein the apnea index is, for example, the sleep Apnea Hypopnea Index (AHI), which refers to the number of sleep apneas plus hypopneas per hour. The apnea refers to that the breathing air flow of the mouth and nose stops completely for more than 10 seconds in the sleeping process; hypopnea refers to a 30% or more decrease in respiratory airflow intensity (amplitude) over basal levels during sleep, accompanied by a 3% or more decrease in blood oxygen saturation over basal levels. The restless leg frequency may refer to the number of restless legs occurring during sleep within a preset time, alternatively, the preset time may be one week, two weeks or any other suitable time, and the restless leg frequency refers to the number of restless legs occurring during sleep within one week when the preset time is one week. The frequency of the dream bout refers to the number of dream bouts occurring during the sleeping process within a preset time, alternatively, the preset time may be one week, two weeks or any other suitable time, and the frequency of the dream bout refers to the number of dream bouts occurring during the sleeping process within one week when the preset time is one week.

Sleep time related information and parameters may be obtained based on any suitable method, for example sleep stage data of the target object may be obtained; and determining sleep time related information and parameters and sleep structure information of the target object based on the sleep stage data. Optionally, the sleep stage data may include, but is not limited to, a sleep cycle chart, wherein optionally, the sleep stage data includes a sleep cycle chart, wherein the sleep cycle chart may be a graph, a line graph, or a bar graph, wherein the sleep cycle chart illustrated in fig. 7 is a line graph. Through the sleep cycle chart, a user can intuitively check the sleep condition of a target object, so that a doctor can judge the psychological and physiological states of a patient in an auxiliary way according to the change of the sleep state of the patient.

The sleep stage data may be acquired by any suitable method, for example, the sleep stage data of the target object obtained based on the monitoring of the sleep monitoring system in the past history time may be directly acquired, and in step S403, the method may further include: based on one or more monitored signals collected by the monitoring system, sleep stage data of the target object is obtained, for example, the method comprises the following steps S1 to S4: in step S1, one or more monitored signals (e.g., one or more monitored signals related to sleep) of the target subject are acquired based on a monitoring system connected to the target subject; in step S2, feature extraction is performed on each of the monitored signals (e.g., one or more monitored signals related to sleep) to obtain one or more signal features of each of the monitored signals; in step S3, sleep stage data of the target object is obtained based on one or more of the signal features, and in step S4, sleep time related information and parameters and sleep structure information of the target object are determined based on the sleep stage data.

In one example, when an instruction input by a user to review sleep session data of a preset time period is acquired, one or more monitored signals related to sleep, that is, historical monitored signals, detected in the preset time period are acquired, so that sleep session data of the user in the preset time period is acquired, wherein the preset time period can be reasonably set according to the needs of the user, for example, any time period before the current time, for example, 1 week, 2 weeks, 1 month, etc., and the preset time period is not particularly limited herein. For example, a preset hotkey (for example, a review hotkey) is arranged on a display interface of a display of the sleep quality evaluation system, a user instruction is generated when the operation of the user on the preset hotkey is detected, the review interface is displayed based on the user instruction, input information of a preset time period input by the user based on the review interface is acquired, an instruction for reviewing the sleep state of the preset time period is generated based on the input information, and when the instruction for reviewing the sleep stage data of the preset time period input by the user is received, one or more monitoring signals related to sleep, namely, history monitoring signals, detected in the preset time period are acquired, so that the sleep stage data of the user in the preset time period are acquired.

The sleep quality assessment system is provided with a sleep monitoring key, wherein the sleep monitoring key may be a physical key arranged on a shell of the sleep quality assessment system, or may also be a hot key arranged on a display interface of a display, and before step S403, the method of the present application further includes: based on a user instruction input by a user through the sleep monitoring key, sleep monitoring of the target object is started, and sleep monitoring data such as sleep stage data and the like are stored. By setting the sleep monitoring key, a doctor can conveniently monitor the sleep of the target object when the doctor needs to monitor the sleep of a patient, the system is prevented from still running and calculating the sleep state when the system does not need to monitor the sleep of the patient, the running load of the system is increased, the sleep monitoring data of the target object, such as sleep stage data, are obtained by monitoring the sleep in real time, and the user can call the sleep stage data when the doctor needs to evaluate the sleep quality of the target object.

In one example, in step S1, one or more sleep-related monitored signals of a target subject are acquired, including: based on one or more monitored signals related to sleeping of a target object collected by the monitoring system, at least one signal frame of the one or more monitored signals related to sleeping is obtained, for example, for a real-time monitored signal, the at least one signal frame comprises a current signal frame, and for a historical monitored signal in a preset time period, the at least one signal frame comprises a plurality of continuous signal frames in the preset time period, wherein each signal frame can have the same preset time length, or different preset time lengths, or partially the same partially different preset time lengths, and when the same preset time length is achieved, for example, the preset time length can be any time length of 20 s-60 s, for example, 20s, 30s, 40s, and the like, and particularly the time length can be reasonably set according to actual needs.

In step S2, at least one signal feature (e.g., sleep related signal feature) of the one or more monitored signals is extracted, including: and extracting at least one sleep-related signal characteristic of each monitored signal based on at least one signal frame of each monitored signal, wherein when the continuous signal frames are included, the at least one sleep-related signal characteristic of each monitored signal can be extracted for each signal frame in turn, and further the sleep state of each signal frame is obtained based on the signal characteristics, and when the monitored signal is a real-time monitored signal, after each current signal frame is obtained, the sleep state corresponding to the current signal frame is determined based on the signal characteristics, so that the sleep state of a target object is monitored in real time.

The method for extracting the signal features in the present application may use any suitable method known to those skilled in the art, and is not specifically limited herein.

In the embodiment of the present application, as shown in fig. 5, the signal features include one or more single signal features corresponding to each monitored signal, and/or one or more joint signal features between multiple monitored signals. For example, the single signal characteristics include at least one of the following signal characteristics: time domain features, frequency domain features, non-linear features, trend features, or other suitable signal features may also be included. Optionally, the non-linear features include entropy features, and the like.

In one example, when the monitored signal comprises an electrocardiograph signal, the sleep related signal characteristics of the electrocardiograph signal comprise at least one of the following characteristics: heart rate, heart rate variability time domain features, frequency domain features, wherein heart rate variability time domain features include, but are not limited to, an overall Standard Deviation (SDNN) of all NN intervals, an average Standard Deviation (SDANN), a square root of mean square of differences (Rmssd), an average of standard deviations (sdnnndex), etc., or other suitable time domain features, wherein SDNN is also the standard deviation of all NN intervals in ms, an SDANN frequency domain feature includes, but is not limited to, a total power spectrum (TP), a low frequency band (LF), a high frequency band (HF), an LF/HF ratio, etc., or other suitable frequency domain features.

In one example, when the monitored signal comprises a respiratory signal, the sleep related signal characteristic of the respiratory signal comprises at least one of the following characteristics: respiration rate, variability of respiration rate, or other suitable characteristics, wherein variability of respiration rate includes, but is not limited to, DC, first harmonic peak amplitude (H1) of each spectrum, total power spectrum (TP), low frequency band (LF), high frequency band (HF), LF/HF ratio, etc., and respiratory mechanics characteristics include, but are not limited to, inhalation volume, exhalation volume, etc.

In one example, when the monitored signal comprises a plethysmograph wave, the sleep related signal characteristics of the plethysmograph wave comprise at least one of the following characteristics: blood oxygen saturation, hemodynamic characteristics, heart rate variability related characteristics, or other suitable characteristics, wherein the hemodynamic characteristics include one or more of Heart Rate (HR), pulse rate, central venous pressure (central venous pressure, CVP), cardiac Output (CO) (e.g., continuous cardiac output, conti nuous cardiac output, CCO), etc. parameters.

In one example, when the monitored signal comprises blood pressure, the sleep related signal characteristic of blood pressure comprises at least one of the following characteristics: absolute blood pressure value, trend of blood pressure change, or other sleep related characteristics of blood pressure.

In one example, when the monitored signal comprises a body temperature signal, the sleep related signal characteristic of the body temperature signal comprises at least one of the following characteristics: body temperature value and body temperature change trend; or other sleep related characteristics of the body temperature signal.

In one example, when the monitored signal comprises an electroencephalogram signal, the sleep related signal characteristic of the electroencephalogram signal comprises at least one of: frequency domain features, entropy features, or other suitable features.

In one example, when the monitored signal comprises a motion signal, the sleep related signal characteristic of the motion signal comprises at least one of the following characteristics: time domain signal characteristics, frequency domain characteristics, or other suitable characteristics. Optionally, the time domain signal features include information such as mean SVM, SMA, etc. of the time domain signal features, and optionally, the frequency domain features include, but are not limited to Very Low Frequency (VLF), LF, HF, TP, LF/HF ratio, etc.

The one or more joint signal features (which may also be referred to as multi-signal correlation features) between the plurality of monitored signals include, but are not limited to, respiratory and electrocardiographic based joint features, respiratory and blood oxygen based joint features, respiratory and motion based joint features, electrocardiographic and electroencephalographic based joint features, cardiopulmonary joint features, and the like.

Step S3 may be performed after feature extraction based on one or more of the monitored signals, and sleep stage data of the target object may be obtained based on one or more of the signal features, and in one example, each of the signal features may be further input into a sleep stage model to obtain the sleep stage data. In another example, sleep stage data may be acquired based on a plurality of sleep stage models, including the following steps S31 to S34: in step S31, each signal feature is input to a sleep stage model corresponding to each signal feature, so as to obtain an estimated sleep state corresponding to each signal feature; in step S32, determining a sleep state of the target object based on the estimated sleep states corresponding to the signal features; in step S33, a sleep state of the target object within a predetermined time is acquired; in step S34, the sleep states of the target object within the predetermined time are arranged in time sequence to form sleep stage data of the target object.

In the embodiment of the present application, each signal feature is respectively input into a sleep stage model corresponding to each signal feature, and some details of steps S31 to S34 will be described hereinafter, and sleep states include, but are not limited to, awake, light sleep, deep sleep, rapid eye movement sleep (rapid eye movement, abbreviated as REM), and the like. Optionally, the estimated sleep state is a preliminary estimated sleep state, and the estimated sleep states determined by the signal features may be different, or may be partially the same, or partially different, or the same.

In step S31, as shown in fig. 6, for example, signal features 1 to N are input to sleep stage models 1 to N, respectively, so that each sleep stage model outputs an estimated sleep state. Alternatively, in other examples, a portion of the signal features may be input into the same sleep stage model to obtain the estimated sleep state, and other signal features may be input into one or more sleep stage models to obtain one or more estimated sleep states.

In the present application, a specific sleep stage model may be constructed for each signal feature (for example, a signal feature related to sleep in an electrocardiograph signal) in combination with standard sleep stage information (for example, standard sleep stage information refers to a sleep state marked by a sleep expert).

The sleep stage model may be a corresponding threshold set for each sleep state, and the sleep state may be determined based on the signal features and the result of the threshold, or may be a sleep stage model based on machine learning training without threshold judgment, where the sleep stage model corresponding to each signal feature may be obtained by training with a machine learning method such as a neural network based on past historical monitored signal information of the patient and data such as the sleep state of the patient during the past historical monitored signal as a training set.

For example, the sleep stage model includes a threshold value corresponding to each signal feature for determining each sleep state, where the thresholds corresponding to different sleep states are different, and after comparing each signal feature with the corresponding threshold value, the estimated sleep state is determined, and finally the estimated sleep state is subjected to fusion calculation to determine the final sleep state.

Each estimated sleep state may be characterized by, for example, a numerical value, e.g., different sleep states are characterized by different numerical values.

Since the estimated sleep state determined based on the sleep stage model may be a plurality of different estimated sleep states, step S33 is further performed to determine the sleep state of the target object based on the estimated sleep states corresponding to the signal features.

In one example, determining the sleep state of the target object based on the estimated sleep states corresponding to the respective signal features includes: acquiring the correlation between the signal characteristics of each monitored signal and each sleep state; and determining the sleep state of the target object according to the correlation and the estimated sleep state corresponding to each signal characteristic. By the method, the information of the monitored signals is integrated, so that the sleep state of the target object can be determined more accurately.

The correlation is used to characterize the degree of correlation between each signal feature and the sleep state, and may be obtained by any suitable method known to those skilled in the art, for example, by obtaining the accuracy of the sleep stage model corresponding to each signal feature in the training database, so as to accurately characterize the correlation between each signal feature and each sleep state. For another example, the signal features and the corresponding sleep stage models may be normalized, and then the cross-correlation coefficients, such as pearson cross-correlation coefficients, of the signal features and the sleep stage models may be calculated, where the cross-correlation coefficients characterize the correlation.

After determining the correlation between each signal feature and the estimated sleep state, determining the sleep state of the target object according to the correlation and the estimated sleep state corresponding to each signal feature, including: determining the weight of the estimated sleep state corresponding to each signal characteristic according to the correlation; multiplying each estimated sleep state by the corresponding weight respectively, and adding and summing; and determining the sleep state of the target object according to the summation result. In general, the higher the correlation, the higher the weight can be given. By the method, the sleep state of the target object can be automatically acquired, and the result of the sleep state is more objective and accurate.

After the sleep state is acquired, the method of the present application further comprises the steps of: acquiring a sleep state of a target object within a preset time; the sleep states of the target object in the preset time are arranged according to the time sequence to form sleep stage data of the target object, for example, monitoring signals of a plurality of continuous signal frames are obtained in the preset time in a real-time monitoring process or a reviewing process, each signal frame is analyzed and processed according to the method, so that the sleep state corresponding to each signal frame is obtained, the sleep states corresponding to each signal frame in the preset time are arranged according to the time sequence to form the sleep stage data of the target object in the preset time period. The preset time period can be reasonably set according to actual needs, and can be determined by acquiring input information of the preset time period input by a user before sleep monitoring starts.

After the sleep stage data is acquired, in one example, step S403 further includes: and S4, determining sleep time related information and parameters and sleep structure information of the target object based on the sleep stage data. Optionally, the sleep time related information and parameters include at least one of the following parameters: the sleep time, the total sleep time, the light sleep time, the deep sleep time, the REM period time, the total sleep time duty ratio, the awake duty ratio, the light sleep duty ratio, the deep sleep duty ratio, the REM period duty ratio, the number of sleep awakenings or other sleep-related time parameters, optionally, the sleep time-related parameters can refer to sleep time-related parameters in a preset time period, the preset time period can be one day, half day, or between 8 pm and 7 am every day, and the like, and can be reasonably set according to the needs of users.

Alternatively, the sleep ratio (i.e., the sleep total time ratio) may be obtained by comparing the sleep total time (i.e., the total sleep time) in the monitoring period with the duration of the monitoring period, and the awake ratio may be a ratio of the awake time to the duration of the monitoring period, the deep sleep ratio may be a ratio of the deep sleep time to the sleep total time, the light sleep ratio may be a ratio of the light sleep time to the sleep total time, and the fast-moving eye sleep period ratio may be a ratio of the eyeball fast-moving sleep period time to the sleep total time. The parameters can assist the user in judging the sleep condition of the monitored object, so that the doctor can assist in judging the psychological and physiological states of the patient according to the change of the sleep state of the patient.

In one example, the method of the present application further comprises: based on the sleep stage data, assessing sleep structure information of the target subject, the sleep structure information including at least one of: normal sleep, insomnia, rapid eye movement sleep stage disturbance, fragmented sleep, somnolence syndrome, dreaminess, difficulty in falling asleep, or other sleep structure, the evaluation of which may be based on an evaluation criterion in the industry, for example, may be judged to be insomnia when the time when the target object is in a awake state based on sleep stage data is greater than a threshold time, and may be judged to be rapid eye movement sleep stage disturbance when the REM period of the target object is not sustained based on sleep stage data, may be judged to be somnolence syndrome when the deep sleep time of the target object is greater than a threshold time, and may be judged to be dreaminess when the REM period of the target object is greater than a threshold time based on sleep stage data. The sleeping structure can assist the user to judge the sleeping condition of the target object, so that psychological counseling or physiological treatment can be carried out on the target object when needed.

In one example, extracting a plurality of sleep related parameters of the target subject of one or more of the monitored signals further comprises: obtaining sleep event information of the target object, wherein the sleep event information comprises one or more of the following events: apneas, restless legs, dream, abnormal arousal; based on the sleep event information, the sleep event related parameters are determined.

The sleep event information for the target subject may be obtained based on any suitable method, in one example, the sleep event information for the target subject may be obtained based on a monitoring system coupled to the target subject, and one or more monitored signals for the target subject may be obtained based on the following method; extracting features of each monitored signal to obtain one or more signal features of each monitored signal; one or more of the signal features are input to a sleep event monitoring model to obtain sleep event information for the target subject. Wherein the sleep event monitoring model may be one model or may be multiple models, e.g., different models may be employed for different sleep events. The signal characteristics used may be different for different sleep events, that is, different sleep events may be determined based on different signal characteristics, or when the total signal characteristic data is input into a model, different sleep events may be determined based on different signal characteristics.

Optionally, the sleep event monitoring model may train the model based on machine learning and other methods through the history data of the sleep event to obtain a trained sleep event monitoring model, and input various signal features into the sleep event monitoring model, so as to output sleep event information when a sleep event occurs.

In one example, the method of the present application may further comprise: the sleep events are marked with preset marks on the sleep cycle chart, alternatively different sleep events may be marked with different preset marks, wherein the sleep events are marked, for example, at the corresponding sleep event occurrence moments of the sleep cycle chart.

The preset mark may be any suitable mark, for example the preset mark comprises at least one of the following marks: the thickened line, the line with the differentiated color, the line with the differentiated shape, the symbol mark and the like, wherein the thickened line refers to the thickness of the line of the preset mark is larger than the thickness of the lines at two sides of the line. Alternatively, the line corresponding to the occurrence time of the sleep event in the sleep cycle chart may be set to a differentiated color, or a differentiated shape (for example, a dotted line), or may be a symbol mark, for example, an asterisk or the like. The mark can intuitively remind the user of the occurrence of a sleep event in the sleep cycle chart, so that a doctor can conveniently judge the severity of the illness state of the patient according to the sleep cycle chart and the sleep event, and further determine whether the patient needs to be intervened and treated.

In a specific example, for example, the sleep event is an apnea, the apnea and the sleep cycle chart are displayed in combination, it can be known that the severity of the apnea, the serious apnea may cause a sleep state change, and the sleep quality is reduced, so that a doctor can determine the severity of the patient's condition according to the apnea and sleep stage data, and determine whether to need intervention treatment.

In one example, the method of the embodiments of the present application further includes: when an operation (for example, a click operation) of the user on the preset mark is acquired, detailed information of the sleep event marked by the preset mark is displayed. Optionally, the detailed information of the sleep event includes at least one of the following information: the type of the sleep event, the setting parameters of the sleep event, and the physiological parameter information of the monitored object when the sleep event occurs, such as physiological parameter values, trend graphs, etc., so as to facilitate the user to call the sleep event when needed and assist the doctor to judge the influence degree of the sleep event on the sleep state. Optionally, the physiological parameters include, but are not limited to, basic physiological parameters such as electrocardiographic parameters, blood oxygen parameters, blood pressure parameters, respiratory parameters, electroencephalogram parameters, and the like.

As shown in fig. 8, after various monitored signals are acquired through the signal acquisition module of the monitored system, signal characteristics of each monitored signal are extracted through the signal processing module in the processor, and then the signal characteristics are respectively input into the sleep stage model and the sleep event monitoring model, wherein the signal characteristics input into the sleep stage model and the sleep event monitoring model can be the same characteristics, different characteristics or different partial characteristics.

In the embodiment of the present application, in step S404, a sleep parameter evaluation model may be obtained based on any suitable method, where the obtaining the sleep parameter evaluation model includes: acquiring historical sleep related parameters of a past monitored object stored in a database and a sleep quality evaluation result of the past monitored object; and determining the sleep parameter evaluation model based on the historical sleep related parameters and the sleep quality evaluation result of the past monitoring object. The database may be an existing sleep information database, where one or more sleep quality evaluation results and historical sleep related parameters of the monitored subject are stored in the database.

Optionally, the sleep quality evaluation results include a sleep quality score, and the sleep quality score may be used to characterize a sleep quality, where, for example, the sleep quality evaluation results include a first evaluation result, a second evaluation result, a third evaluation result, and a fourth evaluation result, where the sleep quality score corresponding to the first evaluation result is higher than the sleep quality score corresponding to the second evaluation result, the sleep quality score corresponding to the second evaluation result is higher than the sleep quality score corresponding to the third evaluation result, and the sleep quality score corresponding to the third evaluation result is higher than the sleep quality score corresponding to the fourth evaluation result. The score may be that the higher the score is, the better the sleep quality is, or the lower the score is, the better the sleep quality is, and specifically, the score may be set reasonably according to the actual situation.

The sleep quality score in the database may be an assessment of sleep quality by a sleep expert in combination with clinical manifestations and patient self-describing information such as a classical pittsburgh sleep scale or the like. The case where the sleep quality score is set to a score of 0-3 is shown in table 1, but this is not intended to limit the data of the score of the present application, and other applicable score forms may be applied to the present application.

In one example, the determining the sleep parameter estimation model based on the historical sleep related parameters and the sleep quality estimation result of the past monitored subject includes: determining corresponding relation data of the historical sleep related parameters and the sleep quality evaluation result based on the historical sleep related parameters and the sleep quality evaluation result of the past monitoring object, wherein the corresponding relation can be a relation curve or a function between the sleep related parameters and the sleep quality evaluation result (such as a sleep quality score) and the like; determining the value range of each sleep related parameter corresponding to each sleep quality assessment result based on the corresponding relation data, for example, determining the value range of each sleep related parameter under each sleep quality assessment result based on a relation curve or function between the sleep related parameter and the sleep quality assessment result (such as sleep quality score) and the like; obtaining correlation data of the sleep quality evaluation result and the sleep parameter evaluation data, wherein the correlation data comprises the sleep quality evaluation result and the sleep parameter evaluation data which are in negative correlation or positive correlation, and the correlation data can be reasonably set according to the needs of users and are not particularly limited; and determining the sleep parameter evaluation model based on the correlation data and the value ranges of the sleep related parameters corresponding to the sleep quality evaluation results, wherein the sleep parameter evaluation model is used for determining corresponding sleep parameter evaluation data based on the value ranges of the sleep related parameters, and the sleep parameter evaluation data such as sleep parameter scores are used for reflecting the influence degree of the sleep related parameters on the sleep quality. The sleep parameter evaluation data in the present application may be a sleep parameter score represented by data, which may be reasonably set according to actual needs, for example, it may be a score between 0 and 3, or other scores.

The process of constructing the sleep parameter evaluation model is explained and illustrated below with reference to table 1, wherein table 1 mainly exemplifies the case where the sleep quality score is 0 to 3, and table 1 shows the relationship between the sleep quality score and the sleep parameter score.

Table 1:

in one specific example, sleep quality can be scored from 0 to 3 according to a clinical database, as shown in table 1. Then, a relation curve or function between the sleep-related parameter (p) and the sleep quality score is determined, and the range of values of the parameter p under each sleep quality score is determined based on the relation curve or function, as shown in the graph (a) in fig. 9. Furthermore, a sleep parameter score is determined according to the value range of P, the score is inversely related to the sleep quality score, the influence degree of the sleep parameter on the sleep quality is reflected, and a sleep parameter evaluation model (i.e. a sleep parameter score model) is determined according to the value range of P and the sleep parameter score, for example, the sleep parameter evaluation model is a relation curve or function between the sleep related parameter and the sleep parameter score, as shown in fig. 9 (b), wherein fig. 9 only shows the relation curve between the deep sleep proportion and the sleep quality score, and the relation curve between the deep sleep proportion and the sleep parameter score is the sleep parameter evaluation model.

Further, in step S405, a plurality of sleep related parameters are input to the sleep parameter evaluation model to obtain sleep parameter evaluation data for each of the sleep related parameters, the sleep parameter evaluation data being used to characterize the extent to which the respective sleep related parameters affect the sleep quality, for example, the sleep parameter evaluation data includes a sleep parameter score. For example, the respective sleep related parameters [ p ] of the target object are calculated by the foregoing method ₁ ,p ₂ ,…,p _N ]Based on the sleep parameter evaluation model, determining a sleep quality score S of the influence degree of each sleep parameter on the sleep quality _p1 ,S _p2 ,…,S _pN ]。

Further, in step S406, the sleep parameter evaluation data is analyzed to obtain a sleep quality evaluation result of the target subject.

Since the foregoing-based method may obtain a plurality of sleep parameter evaluation data, such as a sleep parameter score, which may correspond to different sleep quality evaluation results, it is necessary to perform comprehensive analysis on the sleep parameter evaluation data, for example, the analysis on the sleep parameter evaluation data to obtain an evaluation result of the sleep quality of the target subject, including: determining a sleep quality index based on the individual sleep parameter scores; and determining a sleep quality assessment result of the target object based on the sleep quality index. Wherein the sleep quality index may also be referred to herein as a sleep quality score.

In one example, the determining the sleep quality index based on the respective sleep parameter scores includes: acquiring the weight of the sleep parameter score corresponding to each sleep related parameter; multiplying and summing the sleep parameter scores and the corresponding weights to obtain sleep quality indexes. By such a method, a sleep quality index that best reflects the quality of sleep can be obtained.

Alternatively, in the present application, any suitable method may be used to obtain the weight of the sleep parameter score corresponding to each sleep related parameter, for example, since different sleep related parameters have different degrees of influence on the sleep quality, the foregoing database may be, for example, an existing sleep information database, where one or more sleep quality evaluation results of the monitored subject and historical sleep related parameters are stored, and a weight vector (also referred to herein as a weight) is assigned to each sleep parameter score, for example [ W _p1 ,W _p2 ,…,W _pN ]The method for acquiring the weight vector specifically comprises the following steps: acquiring a sleep quality evaluation result of a past monitoring object stored in a database; determining sleep parameter scores corresponding to the sleep related parameters based on the sleep quality evaluation result; calculating a correlation coefficient between each sleep parameter score and a sleep quality score; and taking the absolute value of the correlation coefficient as the weight of the corresponding sleep parameter score. Wherein, the calculation formula of the correlation coefficient is as formula (1):

Wherein X, Y respectively represents sleep parameter score and sleep quality score, and r (X, Y) is a correlation coefficient.

It should be noted that the data of the weights may be based on data between 0 and 1, or may be other values within other suitable ranges, and alternatively, the sum of the weights after addition may be 1 or may be other values.

In one example, a sleep quality index may be obtained based on each sleep parameter score and the respective corresponding weight, for example, each sleep parameter score and the respective corresponding weight are multiplied and summed, and the summed result is subtracted from a preset total score to obtain a sleep quality index, specifically as in the following formula (2).

Wherein, P0 is a preset total score which can be reasonably set according to actual needs, for example, P0 can be 100 minutes,

is a score of the degree of influence of sleep parameters on sleep quality.

In another example, the sleep related parameters (p) and the sleep quality scores (Sp) may be positively correlated, and then the obtained sleep parameter evaluation model may be as shown in fig. 9 (a), that is, the higher the score is, the better the sleep quality is, based on which the sleep quality index may be obtained based on the sleep parameter scores and the weights corresponding to the sleep parameter scores, including, for example: multiplying and summing the sleep parameter scores and the corresponding weights, and subtracting the sum result from a preset total score to obtain a sleep quality index, as shown in a formula (3):

In one example, the determining, based on the sleep quality index, an evaluation result of the sleep quality of the target subject includes: and acquiring a threshold range corresponding to each sleep quality evaluation result, wherein the threshold range can be an evaluation rule of sleep quality scores established according to a clinical database, and determining the evaluation result of the sleep quality corresponding to the target based on the threshold range of the sleep quality index. For example, the sleep quality assessment results include a first assessment result, a second assessment result, a third assessment result, a fourth assessment result, the first assessment result corresponding to a first threshold range, the second assessment result corresponding to a second threshold range, the third assessment result corresponding to a third threshold range, the fourth assessment result corresponding to a fourth threshold range, wherein the first threshold range is greater than the second threshold range, the second threshold range is greater than the third threshold range, the third threshold range is greater than the fourth threshold range, for example, the first threshold range is not less than 90, the second threshold range is less than 90 and not less than 80, the third threshold range is less than 80 and not less than 70, the fourth threshold range is less than 70, or any other suitable threshold range is not specifically defined herein.

Optionally, determining the evaluation result of the sleep quality corresponding to the target based on the threshold range where the sleep quality index is located includes: when the sleep quality index is in the first threshold range, the evaluation result is the first evaluation result; when the sleep quality index is in the second threshold range, the evaluation result is the second evaluation result; when the sleep quality index is in the third threshold range, the evaluation result is the third evaluation result; and when the sleep quality index is in the fourth threshold range, the evaluation result is the fourth evaluation result.

In one example, when the sleep quality index is obtained based on, for example, the formula (2), the sleep quality corresponding to the first evaluation result is better than the sleep quality corresponding to the second evaluation result, the sleep quality corresponding to the second evaluation result is better than the sleep quality corresponding to the third evaluation result, and the sleep quality corresponding to the third evaluation result is better than the sleep quality corresponding to the fourth evaluation result, for example, S < 70 is extremely poor sleep quality, 70< S <80 is poor sleep quality, 80< S <90 is general sleep quality, and 90< S is good sleep quality.

In another example, when the sleep quality index is obtained based on, for example, the calculation of the formula (2), the sleep quality corresponding to the first evaluation result is worse than the sleep quality corresponding to the second evaluation result, the sleep quality corresponding to the second evaluation result is worse than the sleep quality corresponding to the third evaluation result, and the sleep quality corresponding to the third evaluation result is worse than the sleep quality corresponding to the fourth evaluation result, that is, the higher S is, the worse the sleep quality is likely, for example, S < 70 is good sleep quality, 70< S <80 is general sleep quality, 80< S <90 is poor sleep quality, and 90< S is extremely poor sleep quality. It should be noted that these threshold ranges of data are merely examples and are not limiting of the present application.

Because the sleep quality index is further determined by integrating the sleep time related information and the parameters, the sleep structure information and the sleep event related parameters and based on the scores of the parameters in the scheme, compared with the evaluation result of the sleep quality by inquiring the sleep condition of the patient by a doctor, the evaluation result of the sleep quality evaluation method is more objective and accurate, and more accurate sleep quality information can be provided for the doctor, so that the doctor is convenient to be assisted to diagnose the physical condition of the patient.

Further, in step S407, the above-mentioned sleep quality evaluation result may be output, for example, the sleep quality evaluation result may be output to an output device such as a printer, so that the sleep quality evaluation result may be printed out in the form of a report, or the sleep quality evaluation result may be displayed on a display, so that the doctor can intuitively obtain the sleep quality evaluation result, or optionally, at least one parameter of sleep related parameters may be output and displayed, or sleep stage data, for example, a sleep cycle chart may be output and displayed.

In one example, the method of the present application further comprises: displaying the sleep quality assessment result on a display based on a user instruction input by a user; or displaying the sleep quality assessment result on a display in real time. When the sleep quality of the monitored object is monitored and evaluated in real time based on the sleep quality evaluation system, the sleep quality evaluation result can be displayed in a non-real time mode in the sleep monitoring and evaluation process, the evaluation result is generated after monitoring is completed, the evaluation result is stored in the memory, and when the sleep quality evaluation result is required, the sleep quality evaluation result is displayed on the display based on a user instruction input by a user, for example, a hot key is arranged on a display interface of the display, and the sleep quality evaluation result can be displayed on the display based on the user instruction input by the hot key.

In one example, a sleep quality index, which may be obtained after sleep session data and sleep event information is acquired, is displayed on a display interface of a display of a monitoring system.

In one example, the method of the present application further comprises: the sleep cycle chart and the sleep quality assessment result are displayed on a display interface of a display of the monitoring system, for example, after the sleep quality assessment is completed, the sleep quality assessment result is displayed on the outer side of the sleep cycle chart, and optionally, the sleep quality index may be displayed while the sleep quality assessment result is displayed.

Optionally, the method of the present application further comprises: and marking sleep event information on the sleep cycle chart with a preset mark. Wherein, for example, a preset mark is marked at the occurrence time of the sleep event corresponding to the sleep cycle chart, wherein the preset mark comprises at least one of the following marks: the thickened line, a line with a distinctive color, a line with a distinctive shape, a symbol mark, or any other suitable mark, wherein the thickened line refers to a line of a preset mark having a thickness greater than the thickness of the lines on both sides thereof. Alternatively, the line corresponding to the occurrence time of the sleep event in the sleep cycle chart may be set to a differentiated color, or a differentiated shape (for example, a dotted line), or may be a symbol mark, for example, an asterisk or the like. The mark can intuitively remind the user of the occurrence of a sleep event in the sleep cycle chart, so that a doctor can conveniently judge the severity of the illness state of the patient according to the sleep cycle chart and the sleep event, and further determine whether the patient needs to be intervened and treated.

In a specific example, for example, the sleep event is an apnea, and the apnea and sleep cycle chart are displayed in combination, so that it can be known that the severity of the apnea, and the serious apnea may cause a sleep state change, and the sleep quality is reduced, so that a doctor can determine the severity of the patient's condition according to the apnea and the sleep state, and determine whether to perform intervention treatment.

In one example, the method of the embodiments of the present application further includes: when an operation (for example, a click operation) of the user on the preset mark is acquired, detailed information of the sleep event marked by the preset mark is displayed. Optionally, the detailed information of the sleep event includes at least one of the following information: the type of the sleep event, the setting parameters for triggering the sleep event, and the physiological parameter information of the monitored object when the sleep event occurs, such as physiological parameter values, trend graphs and the like, so as to facilitate the user to call the sleep event when needed and assist the doctor to judge the influence degree of the sleep event on the sleep state. Optionally, the physiological parameters include, but are not limited to, basic physiological parameters such as electrocardiographic parameters, blood oxygen parameters, blood pressure parameters, respiratory parameters, electroencephalogram parameters, and the like.

It is worth mentioning that different preset marks can be used for different sleep events, so that a doctor can know that several types of sleep events occur in the time range of the sleep cycle chart of the patient when seeing the preset marks.

In summary, by the sleep quality evaluation method according to the embodiment of the present application, the sleep quality of the target object can be effectively and objectively evaluated, and more accurate sleep quality information is provided for the doctor, so that the doctor is facilitated to be assisted in diagnosing the physical condition of the patient.

In addition, the monitored signals can be acquired based on the current monitoring system in the hospital, compared with the existing polysomnography and the like, the system is simpler, the restriction on the patient in the monitoring process is lower, the cost is cheaper, compared with the consumption-level sleep stage technology, the acquired monitored signals are more, the accuracy is higher, therefore, the accuracy of the evaluation result based on the monitoring system output is higher, the direct acquisition of the evaluation result by the hospital doctor is more convenient, and the time and energy of the doctor are saved.

Further, the present application also provides a sleep quality assessment system that may be used to perform the relevant steps of the methods of sleep quality assessment herein. The foregoing description about the sleep quality assessment system is equally applicable to the sleep quality assessment system in this embodiment.

As further shown in fig. 1, the sleep quality assessment system of the present application includes a memory 104 for storing executable program instructions; a processor 101 for executing the program instructions stored in the memory 101, causing the processor 101 to perform the steps of: acquiring a plurality of sleep related parameters and a sleep parameter evaluation model of a target object; inputting a plurality of sleep related parameters into the sleep parameter evaluation model to obtain sleep parameter evaluation data, wherein the sleep parameter evaluation data are used for representing the influence degree of each sleep related parameter on sleep quality; analyzing the sleep parameter evaluation data to obtain a sleep quality evaluation result of the target object; and acquiring a sleep cycle chart of the target object. Details of the steps performed by the processor 101 may be referred to in the foregoing description of the method for sleep quality assessment, and will not be repeated here.

Further, the sleep quality assessment system also comprises a display for displaying various visual information, wherein the visual information at least comprises the sleep quality assessment result and the sleep cycle chart. The sleep quality evaluation result may be an evaluation result that the sleep quality is good, general, poor, very bad, and the like, which is determined based on the sleep quality index, and in one example, the visual information may further include the sleep quality index, and the display is further configured to display the sleep quality index, so that the doctor obtains the sleep quality of the patient through the sleep quality index. The sleep quality assessment results may be displayed outside the sleep cycle chart, or the sleep cycle chart and the sleep quality assessment results may also be displayed in different areas on the display interface, or the different areas may be two different areas located in the same display window, or may also be two areas located in different display windows.

In one example, the processor 101 is further configured to: obtaining sleep event information of the target object, wherein the sleep event information comprises one or more of the following events: apneas, restless legs, dreams, abnormal wakefulness, or any other sleep event. Details of the processor 101 acquiring sleep event information of the target subject may be referred to the description of the method of sleep quality assessment previously, and will not be repeated here.

In one example, the display is further configured to display the sleep event information, e.g., the processor is further configured to mark the sleep event information on the sleep cycle map with a preset mark, and the display is configured to display the sleep cycle map with the preset mark. Wherein, for example, a preset mark is marked at the occurrence time of the sleep event corresponding to the sleep cycle chart, wherein the preset mark comprises at least one of the following marks: the thickened line, a line with a distinctive color, a line with a distinctive shape, a symbol mark, or any other suitable mark, wherein the thickened line refers to a line of a preset mark having a thickness greater than the thickness of the lines on both sides thereof. Alternatively, the line corresponding to the occurrence time of the sleep event in the sleep cycle chart may be set to a differentiated color, or a differentiated shape (for example, a dotted line), or may be a symbol mark, for example, an asterisk or the like. The mark can intuitively remind the user of the occurrence of a sleep event in the sleep cycle chart, so that a doctor can conveniently judge the severity of the illness state of the patient according to the sleep cycle chart and the sleep event, and further determine whether the patient needs to be intervened and treated.

In one example, the processor is further configured to control the display to display detailed information of the sleep event marked by the preset mark when an operation (e.g., a click operation) of the user with respect to the preset mark is acquired. Optionally, the detailed information of the sleep event includes at least one of the following information: the type of the sleep event, the occurrence time of the sleep event, the setting parameters for triggering the sleep event, the related parameters of the sleep event and the physiological parameter information of the monitored object when the sleep event occurs, such as physiological parameter values, trend graphs and the like, so that a user can conveniently call the sleep event when needed, and a doctor can be assisted in judging the influence degree of the sleep event on the sleep state. Optionally, the physiological parameters include, but are not limited to, basic physiological parameters such as electrocardiographic parameters, blood oxygen parameters, blood pressure parameters, respiratory parameters, electroencephalogram parameters, and the like.

According to the sleep quality assessment system, the sleep quality of a patient can be obtained according to the sleep quality index, and the reason for reducing the sleep quality can be primarily and rapidly assessed according to the combined display of the sleep cycle chart and the sleep abnormal event.

According to the sleep quality assessment system, the sleep quality of a target object can be effectively and objectively assessed, more accurate sleep quality information is provided for doctors, so that the doctors can be assisted to diagnose the physical condition of the patients conveniently, and the requirements of medical rooms in the hospitals on the sleep quality assessment of the patients can be met by utilizing the existing monitoring system in the hospitals.

In addition, the sleep quality assessment system disclosed by the invention can be used for displaying the sleep quality assessment result and the sleep cycle chart in a combined mode by acquiring the sleep quality assessment result and the sleep cycle chart, so that a doctor can more intuitively acquire the sleep quality condition of a target object, and the doctor can be conveniently assisted in diagnosing the physical condition of a patient.

In addition, the embodiment of the invention also provides a computer storage medium, on which the computer program is stored. One or more computer program instructions may be stored on a computer readable storage medium, in which a processor may execute the program instructions stored by the storage device to perform the functions of (by) the processor and/or other desired functions of the embodiments of the present invention herein, for example, to perform the corresponding steps of the sleep quality assessment method according to the embodiments of the present invention, various applications and various data, such as various data used and/or generated by the applications, etc., may also be stored.

For example, the computer storage medium may include, for example, a memory card of a smart phone, a memory component of a tablet computer, a hard disk of a personal computer, read-only memory (ROM), erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, or any combination of the foregoing storage media.

Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above illustrative embodiments are merely illustrative and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another device, or some features may be omitted or not performed.

In the description provided herein, 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.

Similarly, it should be appreciated that in order to streamline the invention and aid in understanding one or more of the various inventive aspects, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, the method of the present invention should not be construed as reflecting the following intent: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be combined in any combination, except combinations where the features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some of the modules according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.

Claims

1. A method of sleep quality assessment, the method comprising:

acquiring a sleep parameter evaluation model;

and outputting and displaying the sleep quality assessment result.

2. The method of claim 1, wherein the sleep related parameter comprises at least one of the following information or parameters: sleep time related information and parameters, sleep structure information, sleep event related parameters, wherein,

the sleep time related information and parameters include at least one of the following parameters: sleep time, total sleep time, light sleep time, deep sleep time, REM period time, total sleep time duty cycle, awake duty cycle, light sleep duty cycle, deep sleep duty cycle, REM period duty cycle, number of sleep wakefulness;

the sleep structure information includes at least one of the following structures: normal sleep, insomnia, fragmented sleep, REM phase disorders, somnolence or difficulty falling asleep;

The sleep event related parameters include one or more of the following: an apnea index, restless leg frequency, and a frequency of nocturnal episodes.

3. The method of claim 1 or 2, wherein the extracting a plurality of sleep related parameters of one or more of the monitored signals comprises:

extracting features of each monitored signal to obtain one or more signal features of each monitored signal;

acquiring sleep stage data of the target object based on one or more of the signal features;

and determining sleep time related information and parameters and sleep structure information of the target object based on the sleep stage data.

4. The method of claim 3, wherein the obtaining sleep stage data for the target subject based on one or more of the signal characteristics comprises:

inputting the signal features into the sleep stage models respectively so as to obtain estimated sleep states corresponding to the signal features;

determining the sleep state of the target object based on the estimated sleep state corresponding to each signal characteristic;

acquiring the sleep state of the monitored object within a preset time;

Arranging the sleep states of the target object within the preset time according to the time sequence to form sleep stage data of the target object; or alternatively, the process may be performed,

each of the signal features is input into a sleep stage model to obtain the sleep stage data.

5. The method of any of claims 1 to 4, wherein said extracting a plurality of sleep related parameters of one or more of said monitored signals comprises:

inputting one or more of the signal features to a sleep event monitoring model to obtain sleep event information for the target subject, the sleep event information including one or more of the following events: apneas, restless legs, dream, abnormal arousal;

based on the sleep event information, the sleep event related parameters are determined.

6. The method of any of claims 1 to 5, wherein the obtaining the sleep parameter assessment model comprises:

acquiring historical sleep related parameters of a past monitored object stored in a database and a sleep quality evaluation result of the past monitored object;

And determining the sleep parameter evaluation model based on the historical sleep related parameters and the sleep quality evaluation result of the past monitoring object.

7. The method of claim 6, wherein the determining the sleep parameter assessment model based on the historical sleep related parameters and the past monitored subject's sleep quality assessment results comprises:

determining corresponding relation data of the historical sleep related parameters and the sleep quality evaluation result based on the historical sleep related parameters and the sleep quality evaluation result of the past monitoring object;

determining the value range of each sleep related parameter corresponding to each sleep quality evaluation result based on the corresponding relation data;

acquiring correlation data of a sleep quality evaluation result and the sleep parameter evaluation data, wherein the correlation data comprises that the sleep quality evaluation result and the sleep parameter evaluation data are in negative correlation or positive correlation;

and determining the sleep parameter evaluation model based on the correlation data and the value ranges of the sleep related parameters corresponding to the sleep quality evaluation results, wherein the sleep parameter evaluation model is used for determining corresponding sleep parameter evaluation data based on the value ranges of the sleep related parameters.

8. The method of any of claims 1 to 7, wherein the sleep quality assessment results comprise a sleep quality score, the sleep quality assessment results comprise a first assessment result, a second assessment result, a third assessment result, and a fourth assessment result, wherein the sleep quality score corresponding to the first assessment result is higher than the sleep quality score corresponding to the second assessment result, the sleep quality score corresponding to the second assessment result is higher than the sleep quality score corresponding to the third assessment result, and the sleep quality score corresponding to the third assessment result is higher than the sleep quality score corresponding to the fourth assessment result.

9. The method according to any one of claims 1 to 8, wherein the sleep parameter evaluation data includes a sleep parameter score corresponding to each of the sleep related parameters, and the analyzing the sleep parameter evaluation data to obtain an evaluation result of sleep quality of the target subject includes:

determining a sleep quality index based on the individual sleep parameter scores;

and determining a sleep quality assessment result of the target object based on the sleep quality index.

10. The method of claim 9, wherein determining a sleep quality index based on the respective sleep parameter scores comprises:

Acquiring the weight of the sleep parameter score corresponding to each sleep related parameter;

multiplying and summing the sleep parameter scores and the corresponding weights to obtain sleep quality indexes; or alternatively

multiplying and summing the sleep parameter scores and the corresponding weights, and subtracting the sum result from a preset total score to obtain a sleep quality index.

11. The method of claim 10, wherein the obtaining weights of the sleep parameter scores corresponding to the respective sleep related parameters comprises:

acquiring a sleep quality evaluation result of a past monitoring object stored in a database;

determining sleep parameter scores corresponding to the sleep related parameters based on the sleep quality evaluation result;

calculating a correlation coefficient between each sleep parameter score and a sleep quality score;

and taking the absolute value of the correlation coefficient as the weight of the corresponding sleep parameter score.

12. The method of claim 9, wherein the determining an evaluation of the sleep quality of the target subject based on the sleep quality index comprises:

Acquiring threshold ranges corresponding to the sleep quality evaluation results;

and determining an evaluation result of the sleep quality corresponding to the target based on a threshold range of the sleep quality index.

13. The method of claim 12, wherein the sleep quality assessment results include a first assessment result, a second assessment result, a third assessment result, a fourth assessment result, the first assessment result corresponding to a first threshold range, the second assessment result corresponding to a second threshold range, the third assessment result corresponding to a third threshold range, the fourth assessment result corresponding to a fourth threshold range, wherein the first threshold range is greater than the second threshold range, the second threshold range is greater than the third threshold range, the third threshold range is greater than the fourth threshold range, the determining the assessment result of the sleep quality corresponding to the target based on the threshold range in which the sleep quality index is located comprises:

when the sleep quality index is in the first threshold range, the evaluation result is the first evaluation result;

when the sleep quality index is in the second threshold range, the evaluation result is the second evaluation result;

When the sleep quality index is in the third threshold range, the evaluation result is the third evaluation result;

and when the sleep quality index is in the fourth threshold range, the evaluation result is the fourth evaluation result.

14. The method of claim 13, wherein the first evaluation result corresponds to a sleep quality that is better than a sleep quality that corresponds to the second evaluation result, the second evaluation result corresponds to a sleep quality that is better than a sleep quality that corresponds to the third evaluation result, and the third evaluation result corresponds to a sleep quality that is better than a sleep quality that corresponds to the fourth evaluation result; or alternatively

The sleep quality corresponding to the first evaluation result is inferior to the sleep quality corresponding to the second evaluation result, the sleep quality corresponding to the second evaluation result is inferior to the sleep quality corresponding to the third evaluation result, and the sleep quality corresponding to the third evaluation result is inferior to the sleep quality corresponding to the fourth evaluation result.

15. The method of claim 9, wherein the method further comprises:

and displaying the sleep quality index and/or the sleep quality assessment result on a display interface of a display.

16. The method of claim 3, wherein the sleep stage data comprises a sleep cycle map, the method further comprising:

displaying the sleep cycle chart and the sleep quality assessment result on a display interface of a display; and/or

And marking sleep event information on the sleep cycle chart with a preset mark.

17. A sleep quality assessment system, the sleep quality assessment system comprising:

a memory for storing executable program instructions;

a processor for executing the program instructions stored in the memory, causing the processor to perform the method of sleep quality assessment as claimed in any one of claims 1 to 16 below.

18. The sleep quality assessment system according to claim 17, wherein the display is further configured to display at least one of the sleep related parameters.

19. A sleep quality assessment system, the sleep quality assessment system comprising: a memory for storing executable program instructions;

acquiring a sleep cycle chart of the target object;

20. The sleep quality assessment system according to claim 19, wherein the processor is further configured to: obtaining sleep event information of the target object, wherein the sleep event information comprises one or more of the following events: apneas, restless legs, dream, abnormal arousal;

the display is also used for displaying the sleep event information.

21. The sleep quality assessment system according to claim 20, wherein the processor is further configured to mark sleep event information on the sleep cycle map with a preset mark;

the display is also used for displaying the sleep cycle chart and the preset mark.

22. The sleep quality assessment system according to claim 21, wherein the processor is further configured to: when the operation of the user aiming at the preset mark is obtained, the display is controlled to display the detailed information of the sleep event information marked by the preset mark, wherein the detailed information of the sleep event information comprises at least one of the following information: the type of sleep event, the time of occurrence of the sleep event, and the physiological parameter information of the subject when the sleep event occurs.