A physiological parameter optimization method and monitoring device based on motion sensor

Abstract

A physiological parameter optimization method and monitoring device based on a motion sensor (30), which synchronously acquires a patient's physiological signal and motion signal (S100), determines the signal characteristics of the physiological signal from the physiological signal, and judges whether the physiological signal is reliable based on the signal characteristics of the physiological signal (S300); if the physiological signal is judged to be unreliable, determines the signal characteristics of the motion signal from the motion signal, and determines the motion state of the monitored object during each heartbeat based on the signal characteristics of the motion signal (S500); determines the validity of the physiological signal during the heartbeat based on the motion state during the heartbeat, or corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameter, or directly corrects the physiological parameter (S700).

Description

Technical Field

[0001] The present invention relates to the field of medical equipment, and in particular to a physiological parameter optimization method and a monitoring device based on a motion sensor. Background Art

[0002] With the development of medical technology and the improvement of people's understanding of medicine, the importance and attention of rapid recovery after surgery have been greatly enhanced and improved. During the postoperative recovery period, medical staff hope that patients can get out of bed and move around more to promote rapid physical recovery. Traditional bedside monitoring devices limit the patient's activity space, and the long and complicated cables cannot allow patients to move comfortably. The patient's physiological signals can also be monitored when the patient is active, but the patient's activities will interfere with the monitored physiological signals, resulting in the inability to accurately monitor the physiological signals of active patients.

[0003] Therefore, current monitoring devices still have many limitations. SUMMARY OF THE INVENTION

[0005] Technical issues

[0006] In view of the above problems, the present invention mainly provides a physiological parameter optimization method and a monitoring device based on a motion sensor.

[0007] Solution to the problem

[0008] Technical Solutions

[0009] According to the first aspect, an embodiment provides a method for optimizing physiological parameters based on a motion sensor, comprising:

[0010] Acquiring a physiological signal of the monitored object through a physiological sensor attached to the monitored object, and simultaneously acquiring a motion signal of the monitored object through a motion sensor attached to the monitored object;

[0011] Determining a signal feature of the physiological signal from the physiological signal, and judging whether the physiological signal is reliable according to the signal feature of the physiological signal;

[0012] If it is determined that the physiological signal is unreliable, determining a signal feature of the motion signal from the motion signal, and determining a motion state of the monitored subject during each heartbeat according to the signal feature of the motion signal;

[0013] The validity of the physiological signal during the heartbeat is determined according to the motion state during the heartbeat, or the signal characteristics of the physiological signal during the heartbeat are corrected when calculating the physiological parameters, or the physiological parameters are directly corrected.

[0014] In one embodiment, the physiological parameter optimization method provides a signal feature extraction unit for extracting and outputting the signal features of the input signal. When the input is a physiological signal, the unit is used to extract and output the signal features of the physiological signal. When the input is a motion signal, the unit is used to extract and output the signal features of the motion signal.

[0015] In one embodiment, before determining the signal feature of the physiological signal from the physiological signal and determining the signal feature of the motion signal from the motion signal, the physiological signal and the motion signal are pre-processed in parallel.

[0016] In one embodiment, the preprocessing includes: performing at least one of filtering, amplifying, and analog-to-digital conversion on the signal.

[0017] In one embodiment, judging whether the physiological signal is reliable according to the signal characteristics of the physiological signal includes: assigning a weight to each signal characteristic, obtaining a reliability score by voting and scoring, and judging whether the physiological signal is reliable based on the reliability score.

[0018] In one embodiment, determining the motion state of the monitored object during each heartbeat based on the signal characteristics of the motion signal includes: grading the motion state of the monitored object during each heartbeat based on the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat.

[0019] In one embodiment, grading the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat includes: calculating the mean and / or variance of the signal characteristics of the motion signal, grading the motion state of the monitored object during each heartbeat by threshold segmentation, and obtaining the motion level of the monitored object during each heartbeat.

[0020] In one embodiment, the motion level includes: a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the low motion level and the high motion level.

[0021] In one embodiment, determining the validity of a physiological signal during a heartbeat based on the motion state during the heartbeat or correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters or directly correcting the physiological parameters includes: determining the validity of a physiological signal based on the motion level during the heartbeat or correcting the signal characteristics of the physiological signal during the heartbeat based on the motion level during the heartbeat when calculating the physiological parameters or directly correcting the physiological parameters based on the motion level during the heartbeat.

[0022] In one embodiment, the signal characteristics include a signal quality SQI; the method of correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion level during the heartbeat includes: correcting the signal quality SQI of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion level during the heartbeat.

[0023] In one embodiment, determining the validity of the physiological signal according to the motion level during the heartbeat includes: when the motion level during the heartbeat is a low motion level, determining the physiological signal during the heartbeat is valid; when the motion level during the heartbeat is a medium motion level, reconfirming the validity of the physiological signal during the heartbeat; when the motion level during the heartbeat is a high motion level, determining the physiological signal during the heartbeat is invalid.

[0024] In one embodiment, the method of correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters based on the motion level during the heartbeat includes: when the motion level during the heartbeat is a low motion level, not modifying the signal characteristics of the physiological signal; when the motion level during the heartbeat is a medium motion level or a high motion level, correcting the signal characteristics of the physiological signal.

[0025] In one embodiment, the signal characteristics include time domain characteristics and / or frequency domain characteristics of the signal; the time domain characteristics of the signal include: the position of the peak, the amplitude of the peak, the slope of the peak, the width of the peak, the validity of the peak, the type of peak, the peak interval value, the validity of the peak interval, and at least one of the signal quality SQI.

[0026] In one embodiment, the modifying the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameter includes: modifying the weight of the time domain characteristics and / or frequency domain characteristics of the physiological signal during the heartbeat when calculating the physiological parameter.

[0027] In one embodiment, if the physiological signal is judged to be unreliable, the physiological parameter optimization method further includes: directly calculating the physiological parameters based on the signal characteristics of the physiological signal determined from the physiological signal; and determining whether the calculated physiological parameters during the heartbeat are valid based on the validity of the physiological signal during the heartbeat.

[0028] In one embodiment, the physiological parameter optimization method further includes: if it is determined that the physiological signal is reliable, directly calculating the physiological parameter based on the signal characteristics of the physiological signal.

[0029] According to the second aspect, an embodiment provides a monitoring device, comprising:

[0030] A physiological sensor, used to be attached to a monitored object to obtain a physiological signal of the monitored object;

[0031] A motion sensor, used for acquiring a motion signal of the monitored object by relying on a motion sensor attached to the monitored object;

[0032] A processor determines the signal characteristics of the physiological signal from the physiological signal, and judges whether the physiological signal is reliable according to the signal characteristics of the physiological signal; if the physiological signal is judged to be unreliable, determines the signal characteristics of the motion signal from the motion signal, and determines the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal; determines the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat, or corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters, or directly corrects the physiological parameters.

[0033] In one embodiment, the processor includes a signal feature extraction unit, which is used to extract and output the signal features of the input signal. When the input is a physiological signal, it is used to extract and output the signal features of the physiological signal. When the input is a motion signal, it is used to extract and output the signal features of the motion signal.

[0034] In one embodiment, the monitoring device also includes a preprocessing circuit, which is used to preprocess the physiological signal and the motion signal in parallel before the processor determines the signal characteristics of the physiological signal from the physiological signal and determines the signal characteristics of the motion signal from the motion signal.

[0035] In one embodiment, the preprocessing circuit includes at least one of the following:

[0036] A filter circuit, used for filtering the input signal;

[0037] An amplifier circuit, used for amplifying an input signal;

[0038] The analog-to-digital conversion circuit is used to perform analog-to-digital conversion on the input signal.

[0039] In one embodiment, the processor determines whether the physiological signal is reliable based on the signal characteristics of the physiological signal, including: assigning a weight to each signal characteristic, obtaining a reliability score through voting and scoring, and determining whether the physiological signal is reliable based on the reliability score.

[0040] In one embodiment, the processor determines the motion state of the monitored object during each heartbeat based on the signal characteristics of the motion signal, including: grading the motion state of the monitored object during each heartbeat based on the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat.

[0041] In one embodiment, the processor classes the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat, including: calculating the mean and / or variance of the signal characteristics of the motion signal, and classifying the motion state of the monitored object during each heartbeat by threshold segmentation to obtain the motion level of the monitored object during each heartbeat.

[0042] In one embodiment, the motion level includes: a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the low motion level and the high motion level.

[0043] In one embodiment, the processor determines the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat, or corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters, or directly corrects the physiological parameters, including: determining the validity of the physiological signal according to the motion level during the heartbeat, or correcting the signal characteristics of the physiological signal during the heartbeat according to the motion level during the heartbeat when calculating the physiological parameters, or directly correcting the physiological parameters according to the motion level during the heartbeat.

[0044] In one embodiment, the signal characteristics include a signal quality SQI; the processor corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion level during the heartbeat, including: correcting the signal quality SQI of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion level during the heartbeat.

[0045] In one embodiment, the processor determines the validity of the physiological signal according to the motion level during the heartbeat, including: when the motion level during the heartbeat is a low motion level, determining that the physiological signal during the heartbeat is valid; when the motion level during the heartbeat is a medium motion level, reconfirming the validity of the physiological signal during the heartbeat; when the motion level during the heartbeat is a high motion level, determining that the physiological signal during the heartbeat is invalid.

[0046] In one embodiment, the processor corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion level during the heartbeat, including: when the motion level during the heartbeat is a low motion level, the signal characteristics of the physiological signal are not modified; when the motion level during the heartbeat is a medium motion level or a high motion level, the signal characteristics of the physiological signal are corrected.

[0047] In one embodiment, the signal characteristics include time domain characteristics and / or frequency domain characteristics of the signal; the time domain characteristics of the signal include: the position of the peak, the amplitude of the peak, the slope of the peak, the width of the peak, the validity of the peak, the type of peak, the peak interval value, the validity of the peak interval, and at least one of the signal quality SQI.

[0048] In one embodiment, the processor correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameter includes: correcting the weight of the time domain characteristics and / or frequency domain characteristics of the physiological signal during the heartbeat when calculating the physiological parameter.

[0049] In one embodiment, if the processor determines that the physiological signal is unreliable, it also directly calculates the physiological parameters based on the signal characteristics of the physiological signal determined from the physiological signal; and determines whether the calculated physiological parameters during the heartbeat are valid based on the validity of the physiological signal during the heartbeat.

[0050] In one embodiment, if the processor determines that the physiological signal is reliable, the processor directly calculates the physiological parameter based on the signal feature of the physiological signal.

[0051] In one embodiment, the monitoring device further includes a shell, in which the processor is disposed, and the processor is connected to the physiological sensor and the motion sensor via a wired or wireless manner.

[0052] In one embodiment, the motion sensor includes an acceleration sensor, an angular velocity sensor, or a gravity sensor.

[0053] In one embodiment, the monitoring device is a mobile monitoring device.

[0054] According to a third aspect, an embodiment provides a computer-readable storage medium, comprising a program, wherein the program can be executed by a processor to implement any of the methods described above.

[0055] Advantageous Effects of the Invention

[0056] Beneficial Effects

[0057] According to the physiological parameter optimization method based on motion sensor and monitoring device, and computer-readable storage medium of the above-mentioned embodiment, the patient's physiological signals and motion signals are synchronously acquired, the signal characteristics of the physiological signals are determined from the physiological signals, and whether the physiological signals are reliable is judged based on the signal characteristics of the physiological signals; if the physiological signals are judged to be unreliable, the signal characteristics of the motion signals are determined from the motion signals, and the motion state of the monitored object during each heartbeat is determined based on the signal characteristics of the motion signals; the validity of the physiological signals during a heartbeat is determined based on the motion state during the heartbeat, or the signal characteristics of the physiological signals during the heartbeat are corrected when calculating the physiological parameters, thereby improving the accuracy of the physiological signals and physiological parameters.

[0058] Brief description of the accompanying drawings BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a system framework diagram of a parameter processing module in a multi-parameter monitor;

[0060] Figure 2 It is a system framework diagram of a parameter processing module in a single parameter monitor;

[0061] Figure 3 It is a structural diagram of a monitoring device networking system used in a hospital;

[0062] Figure 4 A flowchart of a physiological parameter optimization method based on a motion sensor according to an embodiment;

[0063] Figure 5 A flowchart of a physiological parameter optimization method based on a motion sensor according to another embodiment;

[0064] Figure 6 A schematic diagram of correcting the signal characteristics of the physiological signal during a heartbeat when calculating the physiological parameter according to the motion state during the heartbeat in an embodiment;

[0065] Figure 7 A schematic diagram of the structure of a monitoring device according to an embodiment;

[0066] Figure 8 A schematic diagram of the structure of a monitoring device according to another embodiment;

[0067] Fig. 9 A schematic diagram of the structure of a monitoring device according to another embodiment;

[0068] Fig.10 The figure is a schematic diagram of the structure of a monitoring device according to another embodiment.

[0069] Embodiments of the invention

[0070] Embodiments of the present invention DETAILED DESCRIPTION

[0071] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0072] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0073] The serial numbers of the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0074] like Figure 1 As shown, a system framework diagram of a parameter processing module in a multi-parameter monitor is provided. The multi-parameter monitor has an independent housing, and a sensor interface area is provided on the housing panel, in which a plurality of sensor interfaces are integrated for connecting with external physiological parameter sensor accessories 111. The housing panel also includes a small LCD display area, a display 119, an input interface circuit 122, and an alarm circuit 120 (such as an LED alarm area). The parameter processing module is an external communication and power supply interface for communicating with the host and taking power from the host. The parameter processing module also supports an external parameter module, and a plug-in monitor host can be formed by inserting the parameter module. As a part of the monitor, it can also be connected to the host through a cable. The external parameter module is an external accessory of the monitor.

[0075] The internal circuit of the parameter processing module is placed in the housing, such as Figure 1As shown, it includes a signal acquisition circuit 112 corresponding to at least two physiological parameters, a front-end signal processing circuit 113 and a main processor 115. The signal acquisition circuit 112 can be selected from an electrocardiogram circuit, a respiratory circuit, a body temperature circuit, a blood oxygen circuit, a non-invasive blood pressure circuit, an invasive blood pressure circuit, etc. These signal acquisition circuits 112 are electrically connected to corresponding sensor interfaces respectively, and are used to be electrically connected to sensor accessories 111 corresponding to different physiological parameters. Its output end is coupled to the front-end signal processor, and the communication port of the front-end signal processor is coupled to the main processor, and the main processor is electrically connected to the external communication and power supply interface. Various physiological parameter measurement circuits can adopt the general circuits in the prior art. The front-end signal processing circuit completes the sampling and analog-to-digital conversion of the output signal of the signal acquisition circuit, and outputs a control signal to control the measurement process of the physiological signal. These parameters include but are not limited to: electrocardiogram, respiratory, body temperature, blood oxygen, non-invasive blood pressure and invasive blood pressure parameters. The front-end signal processing circuit can be implemented by a single-chip microcomputer or other semiconductor devices, for example, a single-chip microcomputer can be selected, and it can also be implemented by ASIC or FPGA. The front-end signal processing circuit can also be powered by an isolated power supply. The sampled data is simply processed and packaged, and then sent to the main processor through the isolated communication interface. For example, the front-end signal processing circuit 113 can be coupled to the main processor 115 through the isolated power supply and the communication interface 114. The reason why the front-end signal processing circuit is powered by an isolated power supply is that the DC / DC power supply isolated by the transformer plays the role of isolating the patient from the power supply equipment. The main purpose is: 1. Isolate the patient, and float the application part through the isolation transformer to make the patient leakage current small enough; 2. Prevent the voltage or energy during the application of defibrillation or electric knife from affecting the boards and devices of the intermediate circuit such as the main control board (guaranteed by creepage distance and electrical clearance). Of course, the front-end signal processor circuit 113 can also be connected to the main processor 115 through the cable 124. The main processor completes the calculation of physiological parameters, and sends the calculation results and waveforms of the parameters to the host (such as a host with a display, a PC, a central station, etc.) through the external communication and power interface 116, wherein the main processor 115 can also be connected to the external communication and power interface 116 through a cable 125, and the external communication and power interface 116 can be one or a combination of LAN interfaces composed of Ethernet, Token Ring, Token Bus, and the fiber optic distributed data interface (FDDI) as the backbone network of these three networks, or it can also be one or a combination of wireless interfaces such as infrared, Bluetooth, wifi, WMTS communication, or it can also be one or a combination of wired data connection interfaces such as RS232 and USB. The external communication and power interface 116 can also be a combination of one or both of a wireless data transmission interface and a wired data transmission interface. The host can be any computer device such as the host of a monitor, an electrocardiograph, an ultrasonic diagnostic instrument, a computer, etc., and a monitoring device can be formed by installing the matching software.The host can also be a communication device, such as a mobile phone. The parameter processing module sends data to a mobile phone that supports Bluetooth communication through a Bluetooth interface to achieve remote transmission of data. After the main processor 115 completes the calculation of the physiological parameters, it can also determine whether the physiological parameters are abnormal. If abnormal, an alarm can be issued through the alarm circuit 120. In addition, the power supply and battery management circuit 117 in the figure is used to manage and process the power supply of the monitor, and the memory 118 can store the intermediate and final data of the monitor, as well as store program instructions or codes for execution by the main processor 115, etc. If the monitor has the function of measuring blood pressure, it can also include a pump valve drive circuit 121, which is used to perform inflation or deflation operations under the control of the main processor 115. If the monitor does not have the function of measuring blood pressure, the pump valve drive circuit 121 can be removed.

[0076] like Figure 2 As shown, the processing system architecture of a monitor for a single physiological parameter is provided. The same content can be found in the above content.

[0077] like Figure 3 As shown, a monitoring device networking system for in-hospital use is provided. The system can save the data of the monitoring device as a whole, centrally manage patient information and nursing information, and store the two in association, which is convenient for saving historical data and associating alarms. Figure 3In the system shown, a bedside monitor 212 can be provided for each bed, and the bedside monitor 212 can be the aforementioned multi-parameter monitor or plug-in monitor. In addition, each bedside monitor 212 can also be paired with a portable monitoring device 213 for transmission. The portable monitoring device 213 provides a simple and portable parameter processing module. The simple and portable parameter processing module can be worn on the patient's body to perform mobile monitoring on the patient. After the portable monitoring device 213 and the bedside monitor 212 perform wired or wireless communication, the physiological data generated by the mobile monitoring can be transmitted to the bedside monitor 212 for display, or transmitted to the central station 211 through the bedside monitor 212 for viewing by doctors or nurses, or transmitted to the data server 215 through the bedside monitor 212 for storage. In addition, the portable monitoring device 213 can also directly transmit the physiological data generated by the mobile monitoring to the central station 211 for storage and display through the wireless network node 214 set up in the hospital, or transmit the physiological data generated by the mobile monitoring to the data server 215 for storage through the wireless network node 214 set up in the hospital. It can be seen that the data corresponding to the physiological parameters displayed on the bedside monitor 212 may be derived from a sensor accessory directly connected to the monitor, or from a portable monitoring device 213, or from a data server. The portable monitoring device 213 may be connected to the sensor accessory 111 by wire and / or wirelessly, and include part or all of the circuits of the aforementioned parameter processing module. For example, isolation measures for isolated patients may not be set on the portable monitoring device 213, but may be set outside the portable monitoring device 213, such as on the sensor accessory 111. The portable monitoring device 213 may have its own display screen for displaying parameter calculation results and / or prompting alarm information. For example, the portable monitoring device 213 may be a wireless sensor patch attached to the body, or a transport monitor, or a telemetry device.

[0078] Please refer to Figure 4 In one embodiment, a physiological parameter optimization method based on a motion sensor (hereinafter referred to as the physiological parameter optimization method) is provided, which can be applied to a monitor such as the one mentioned above, and includes steps S100 to S700, which are described in detail below.

[0079] Step S100: Acquire a physiological signal of the monitored object through a physiological sensor attached to the monitored object, and simultaneously acquire a motion signal of the monitored object through a motion sensor attached to the monitored object.

[0080] In one embodiment, the motion sensor is a sensor for sensing the motion of the detected object. For example, the motion sensor may include at least one of a velocity sensor, an acceleration sensor, an angular velocity sensor (such as a gyroscope) or a gravity sensor, and the corresponding motion signals are a velocity signal (which may be a velocity vector with a direction or a velocity without a direction), an acceleration signal, an angular velocity signal and a gravity acceleration signal; of course, the motion signal may also be the duration of the motion. The motion sensor is attached to the detected object, and may be directly attached to the body or clothing of the detected object. For example, the motion sensor may be mounted on an electrode lead wire, or on a device adjacent to the electrode / lead wire, or integrated with the electrode. When the monitor is a mobile monitor, the motion sensor may be disposed on a circuit board in the monitor.

[0081] Step S300: determining a signal feature of the physiological signal from the physiological signal, and judging whether the physiological signal is reliable according to the signal feature of the physiological signal.

[0082] In one embodiment, judging whether the physiological signal is reliable according to the signal characteristics of the physiological signal may include: assigning a weight to each signal characteristic, obtaining a reliability score by voting, and judging whether the physiological signal is reliable based on the reliability score. For example, a weight is assigned to each signal characteristic of the physiological signal through a machine learning algorithm, and the reliability score of the physiological signal is obtained by voting and scoring these signal characteristics of the physiological signal, and then judging whether the physiological signal is reliable based on the reliability score. For example, a reliability threshold is set. If the reliability score is greater than the reliability threshold, the physiological signal is judged to be reliable. Otherwise, the physiological signal is judged to be unreliable. For example, it is assumed that the physiological signal has three signal characteristics, namely, signal quality SQI, QRS wave matching, and QRS wave validity. Weights can be set for them respectively, such as the weight of signal quality SQI is 50%, the weight of QRS wave matching is 30%, and the weight of QRS wave validity is 20%; then these three signal characteristics are voted and scored, for example, the larger the signal quality SQI, the higher its score. The higher the score, for example, the full score is 10 points, the larger the signal quality SQI, the closer the score is to 10 points; similarly, the better the QRS wave matching, the higher the score, and the better the QRS wave validity, the higher the score. Of course, the QRS wave matching and QRS wave validity can also be simply divided into two points, one score for a good QRS wave matching and another score for a bad QRS wave matching, or one score for a valid QRS wave and another score for an invalid QRS wave; then the scores of these three signal features are multiplied by their weights and added together to obtain the reliability score of the physiological signal, and finally the reliability of the physiological signal is judged based on the reliability score; of course, multiple thresholds for reliability can also be set, and then the reliability level of the physiological signal is classified according to the reliability score of the physiological signal, and finally normalized as reliable or unreliable.

[0083] Please refer to Figure 5 In one embodiment, the physiological parameter optimization method of the present invention may further include step S400: if it is determined that the physiological signal is reliable, the physiological parameter is directly calculated based on the signal characteristics of the physiological signal.

[0084] Step S500: If the physiological signal is determined to be unreliable, the signal characteristics of the motion signal are determined from the motion signal, and the motion state of each heartbeat period of the monitored object is determined according to the signal characteristics of the motion signal. The heartbeat period refers to the duration of a heartbeat, which can be understood as the QRS wave interval, for example.

[0085] In one embodiment, determining the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal may include: grading the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat. For example, the motion state of the monitored object during each heartbeat may be graded by calculating the mean and / or variance of the signal characteristics of the motion signal and by threshold segmentation to obtain the motion level of the monitored object during each heartbeat, for example, by setting different segmentation thresholds to grade the motion signal.

[0086] In one embodiment, the motion level may include three levels, such as a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the two. In other embodiments, the motion level may also be divided into other numbers of levels, such as two levels (such as only retaining the low motion level and the high motion level), or four levels.

[0087] Step S300 involves signal characteristics of physiological signals, and step S500 involves signal characteristics of motion signals. In one embodiment, the signal characteristics may include time domain characteristics and / or frequency domain characteristics of the signal; the time domain characteristics of the signal may include: at least one of the position of the peak, the amplitude of the peak, the slope of the peak, the width of the peak, the validity of the peak, the type of the peak, the peak-to-peak interval value, the validity of the peak-to-peak interval, and the signal quality SQI. There are many specific implementation methods. For example, taking the time domain signal as an example, the signal can be searched for peaks, the peak amplitude, peak slope, and peak width can be calculated, and the signal quality SQI and other time domain features, such as peak effectiveness, peak type, peak-to-peak interval value, and interval effectiveness, can be further calculated. For example, taking the frequency domain feature as an example, the signal can be Fourier transformed to obtain the frequency characteristics of the signal, and then the high-frequency signal and the low-frequency signal can be processed to obtain the required spectrum characteristics. In some embodiments, in order to better extract the signal characteristics of the signal, the signal can also be filtered and denoised first, such as filtering out the signal's power frequency interference, base drift, and high-frequency noise interference.

[0088] It can be seen that the method described in the present invention introduces the signal characteristics of physiological signals and the signal characteristics of motion signals in the process of physiological parameter optimization. Through the signal characteristics of these two types of signals, the purpose of physiological parameter optimization is finally achieved. In the specific implementation process, a signal feature extraction unit can be provided to extract and output the signal characteristics of the input signal. When the input is a physiological signal, it is used to extract and output the signal characteristics of the physiological signal. When the input is a motion signal, it is used to extract and output the signal characteristics of the motion signal. By using the same hardware structure to extract the signal characteristics of physiological signals and the signal characteristics of motion signals, hardware unlocking can be effectively reduced and costs can be saved.

[0089] In one embodiment, in order to better obtain the signal characteristics of the signal, before determining the signal characteristics of the physiological signal from the physiological signal and determining the signal characteristics of the motion signal from the motion signal, the physiological signal and the motion signal are preprocessed in parallel, for example, the signal can be filtered, amplified, and analog-to-digital converted. For example, the physiological sensor and the motion sensor obtain the physiological signal and the motion signal respectively, and these two types of signals are first filtered to remove noise, then amplified to increase the signal amplitude, and finally converted from analog signals to digital signals through analog-to-digital conversion.

[0090] Step S700: determining the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat or correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters or directly correcting the physiological parameters.

[0091] In one embodiment, the determination of the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat may include: determining the validity of the physiological signal according to the motion level during the heartbeat. For example, when the motion level during the heartbeat is a low motion level, the physiological signal during the heartbeat is determined to be valid; when the motion level during the heartbeat is a medium motion level, the validity of the physiological signal during the heartbeat is reconfirmed; when the motion level during the heartbeat is a high motion level, the physiological signal during the heartbeat is determined to be invalid. The reconfirmation of the validity of the physiological signal during the heartbeat may be to further confirm the validity of these physiological signals in combination with the motion characteristics of the motion signals, or to reconfirm the validity of these physiological signals after modifying them.

[0092] Please refer to Figure 6In one embodiment, correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion state during the heartbeat may include: correcting the signal characteristics of the physiological signal during the heartbeat according to the motion level during the heartbeat when calculating the physiological parameters. For example, when the motion level during the heartbeat is a low motion level, the signal characteristics of the physiological signal may not be modified; when the motion level during the heartbeat is a medium motion level or a high motion level, the signal characteristics of the physiological signal may be corrected; for example, when the motion level during the heartbeat is a medium motion level, the validity of the time domain characteristics and / or frequency domain characteristics of the physiological signal is determined, and the level / threshold of the SQI may also be corrected; when the motion level during the heartbeat is a high motion level, the weight of the time domain characteristics and / or frequency domain characteristics of the physiological signal may be corrected (may be set to 0), and the level / threshold of the SQI may also be corrected. Alternatively, for example, the signal quality SQI of the physiological signal during the heartbeat can be corrected when calculating the physiological parameters according to the level of motion during the heartbeat, wherein the signal feature can include the signal quality SQI, such as the level or threshold of the signal quality SQI can be modified, for example, when the signal feature of the physiological signal is determined from the physiological signal, the signal quality SQI is obtained, and then the value of the signal quality SQI can be modified according to the level of motion during the heartbeat. For another example, a corresponding SQI value can be pre-stored for each motion level, and when the signal feature of the physiological signal is determined from the physiological signal, the signal quality SQI is obtained, and the obtained signal quality SQI can be directly replaced according to the SQI value corresponding to the level of motion during the heartbeat. Alternatively, for example, the weights of the time domain features and / or frequency domain features of the physiological signal during the heartbeat can be corrected when calculating the physiological parameters. The signal features can include the time domain features and / or frequency domain features of the signal.

[0093] In another embodiment, when the level of motion during a heartbeat is a low level of motion, the physiological signal during the heartbeat is determined to be valid (so that the physiological parameters generated according to the physiological signal are also valid); when the level of motion during a heartbeat is a medium level of motion, the physiological signal (such as QRS wave interval, SQI value, QRS wave type) is processed, such as correction processing; when the level of motion during a heartbeat is a high level of motion, the physiological parameters generated according to the physiological signal (heart rate value, arrhythmia, blood oxygen value, blood oxygen saturation) are directly processed, such as correction processing. In this embodiment, it is possible to directly determine that the physiological signal is valid and thus the physiological parameter is also valid according to the level of motion, it is also possible to correct the physiological signal, and it is also possible to directly correct the physiological parameter.

[0094] In one embodiment, the physiological parameter optimization method of the present invention may further include a step: if it is determined that the physiological signal is unreliable, it also includes: directly calculating the physiological parameter based on the signal feature of the physiological signal determined from the physiological signal; determining whether the calculated physiological parameter during the heartbeat is valid according to the validity of the physiological signal during the heartbeat. For example, after directly calculating the physiological parameter based on the physiological signal feature determined from the physiological signal, if the current level of motion is low, the directly calculated physiological parameter is determined to be valid; if the current level of motion is medium, the directly calculated physiological parameter is determined to be questionable; if the current level of motion is high, the directly calculated physiological parameter is determined to be invalid.

[0095] The above are some contents of the physiological parameter optimization method of the present invention, which obtains and determines the signal characteristics of the physiological signals through the physiological signals of the monitored object, and then judges the reliability of the physiological signals based on its signal characteristics; and obtains and determines the signal characteristics of the motion signals through the motion signals of the monitored object, so as to further determine the motion state of the monitored object during each heartbeat; when the physiological signal is judged to be unreliable, it is optimized according to the motion state during the heartbeat, thereby improving the accuracy of the physiological parameters, reducing false alarms, etc.

[0096] Please refer to Figure 7 In one embodiment of the present invention, a monitoring device or monitor is also disclosed, which may include a physiological sensor 10, a motion sensor 30 and a processor 50. In one embodiment, the monitoring device further includes a housing, in which the processor 50 is disposed, and the processor 50 is connected to the physiological sensor 10 and the motion sensor 30 in a wired or wireless manner. The following is a detailed description.

[0097] The physiological sensor 10 is used to be attached to a monitored object to obtain a physiological signal of the monitored object.

[0098] The motion sensor 30 is used to obtain the motion signal of the monitored object by relying on the motion sensor attached to the monitored object. In one embodiment, the motion sensor is a sensor for sensing the motion of the detected object. For example, the motion sensor may include at least one of a velocity sensor, an acceleration sensor, an angular velocity sensor (such as a gyroscope) or a gravity sensor, and the corresponding motion signals are a velocity signal (which may be a velocity vector with a direction or a velocity without a direction), an acceleration signal, an angular velocity signal and a gravity acceleration signal; of course, the motion signal may also be the duration of the motion. The motion sensor is attached to the detected object, and may be directly attached to the body or clothing of the detected object, or may be attached to the housing of the monitoring device. For example, the motion sensor may be mounted on the electrode lead wire, or on a device adjacent to the electrode / lead wire, or integrated with the electrode. When the monitor is a mobile monitor, the motion sensor may be arranged on a circuit board in the monitor.

[0099] The processor 50 determines the signal characteristics of the physiological signal from the physiological signal, and judges whether the physiological signal is reliable according to the signal characteristics of the physiological signal; if the physiological signal is judged to be unreliable, the processor 50 determines the signal characteristics of the motion signal from the motion signal, and determines the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal; determines the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat, or corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters, or directly corrects the physiological parameters. In one embodiment, the processor 50 can obtain the physiological signal of the monitored object through a physiological sensor attached to the monitored object, and simultaneously obtain the motion signal of the monitored object through a motion sensor attached to the monitored object.

[0100] It can be seen from the above that the processor 50 first determines the reliability of the physiological signal; when the physiological signal is determined to be unreliable, it is optimized through the motion signal, which is explained below one by one.

[0101] When judging the reliability of a physiological signal, the processor 50 may first determine the signal features of the physiological signal from the physiological signal, and judge whether the physiological signal is reliable based on the signal features of the physiological signal. For example, the processor 50 may assign a weight to each signal feature of the physiological signal, obtain a reliability score by voting, and judge whether the physiological signal is reliable based on the reliability score. Specifically, for example, a weight is assigned to each signal feature of the physiological signal through a machine learning algorithm, and the reliability score of the physiological signal is obtained by voting and scoring these signal features of the physiological signal, and then the reliability of the physiological signal is judged based on the reliability score. For example, a reliability threshold is set. If the reliability score is greater than the reliability threshold, the physiological signal is judged to be reliable. Otherwise, the physiological signal is judged to be unreliable. For example, it is assumed that the physiological signal has three signal features, namely, signal quality SQI, QRS wave matching, and QRS wave validity. Weights can be set for them respectively, such as the weight of signal quality SQI is 50%, the weight of QRS wave matching is 30%, and the weight of QRS wave validity is 20%; then these three signal features are voted and scored, for example, the larger the signal quality SQI, the higher its The higher the score, for example, the full score is 10 points, the larger the signal quality SQI, the closer the score is to 10 points; similarly, the better the QRS wave matching, the higher the score, and the better the QRS wave validity, the higher the score. Of course, the QRS wave matching and QRS wave validity can also be simply divided into two points, one score for a good QRS wave matching and another score for a bad QRS wave matching, or one score for a valid QRS wave and another score for an invalid QRS wave; then the scores of these three signal features are multiplied by their weights and added together to obtain the reliability score of the physiological signal, and finally the reliability of the physiological signal is judged based on the reliability score; of course, multiple reliability thresholds can also be set, and then the reliability level of the physiological signal is classified according to the reliability score of the physiological signal, and finally normalized to reliable or unreliable.

[0102] If the processor 50 determines that the physiological signal is reliable, the physiological parameter is directly calculated based on the signal characteristics of the physiological signal; if the processor 50 determines that the physiological signal is unreliable, subsequent optimization is performed.

[0103] If the processor 50 determines that the physiological signal is unreliable, the signal feature of the motion signal is determined from the motion signal, and the motion state of the monitored object during each heartbeat is determined according to the signal feature of the motion signal. For example, in one embodiment, the processor 50 can classify the motion state of the monitored object during each heartbeat according to the signal feature of the motion signal, and obtain the motion level of the monitored object during each heartbeat. For example, the processor 50 can classify the motion state of the monitored object during each heartbeat by calculating the mean and / or variance of the signal feature of the motion signal, and obtain the motion level of the monitored object during each heartbeat by threshold segmentation, such as the processor 50 classifies the motion signal by setting different segmentation thresholds. In one embodiment, the motion level can include three levels, such as a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the two. In other embodiments, the motion level can also be divided into other numbers of levels, such as two levels (such as only retaining the low motion level and the high motion level) and four levels.

[0104] The processor 50 then determines the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat, or modifies the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters, or directly modifies the physiological parameters.

[0105] In one embodiment, the processor 50 determines the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat, which may include: determining the validity of the physiological signal according to the motion level during the heartbeat. For example, the processor 50 may determine that the physiological signal during the heartbeat is valid when the motion level during the heartbeat is a low motion level; reconfirm the validity of the physiological signal during the heartbeat when the motion level during the heartbeat is a medium motion level; and determine that the physiological signal during the heartbeat is invalid when the motion level during the heartbeat is a high motion level. The reconfirmation of the validity of the physiological signal during the heartbeat may be to further confirm the validity of these physiological signals in combination with the motion characteristics of the motion signals, or to reconfirm the validity of these physiological signals after modifying them.

[0106] In one embodiment, the processor 50 corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion state during the heartbeat, which may include: correcting the signal characteristics of the physiological signal during the heartbeat according to the motion level during the heartbeat when calculating the physiological parameters. For example, the processor 50 may not modify the signal characteristics of the physiological signal when the motion level during the heartbeat is a low motion level; correct the signal characteristics of the physiological signal when the motion level during the heartbeat is a medium motion level or a high motion level; for example, when the motion level during the heartbeat is a medium motion level, determine the validity of the time domain characteristics and / or frequency domain characteristics of the physiological signal, and may also correct the level / threshold of the SQI; when the motion level during the heartbeat is a high motion level, correct the weight of the time domain characteristics and / or frequency domain characteristics of the physiological signal (which may be set to 0), and may also correct the level / threshold of the SQI. Alternatively, for example, the processor 50 may correct the signal quality SQI of the physiological signal during the heartbeat when calculating the physiological parameters according to the level of motion during the heartbeat, wherein the signal feature may include the signal quality SQI, such as the level or threshold of the signal quality SQI may be modified, for example, when the signal feature of the physiological signal is determined from the physiological signal, the signal quality SQI is obtained, and then the value of the signal quality SQI may be modified according to the level of motion during the heartbeat. For another example, a corresponding SQI value may be pre-stored for each level of motion, and when the signal feature of the physiological signal is determined from the physiological signal, the signal quality SQI is obtained, and the obtained signal quality SQI may be directly replaced according to the SQI value corresponding to the level of motion during the heartbeat. Alternatively, for example, the processor 50 may correct the weight of the time domain feature and / or frequency domain feature of the physiological signal during the heartbeat when calculating the physiological parameters. The signal feature may include the time domain feature and / or frequency domain feature of the signal.

[0107] In another embodiment, the processor 50 can determine that the physiological signal during the heartbeat is valid (so that the physiological parameters generated according to the physiological signal are also valid) when the exercise level during the heartbeat is a low exercise level; when the exercise level during the heartbeat is a medium exercise level, the physiological signal (such as QRS wave interval, SQI value, QRS wave type) is processed, such as correction processing; when the exercise level during the heartbeat is a high exercise level, the physiological parameters generated according to the physiological signal (heart rate value, arrhythmia, blood oxygen value, blood oxygen saturation) are directly processed, such as correction processing. In this embodiment, it is possible to directly determine that the physiological signal is valid and thus the physiological parameter is also valid according to the exercise level, it is also possible to correct the physiological signal, or it is also possible to directly correct the physiological parameter.

[0108] In one embodiment, if the processor 50 determines that the physiological signal is unreliable, the processor 50 may also directly calculate the physiological parameter based on the signal feature of the physiological signal determined from the physiological signal; and determine whether the calculated physiological parameter during the heartbeat is valid according to the validity of the physiological signal during the heartbeat. For example, after the processor 50 directly calculates the physiological parameter based on the physiological signal feature determined from the physiological signal, if the current exercise level is low, the directly calculated physiological parameter is determined to be valid; if the current exercise level is medium, the directly calculated physiological parameter is determined to be questionable; and if the current exercise level is high, the directly calculated physiological parameter is determined to be invalid.

[0109] From the above description of the processor 50, it can be seen that the processor 50 is involved in determining the signal characteristics of physiological signals and motion signals. In one embodiment, the signal characteristics may include the time domain characteristics and / or frequency domain characteristics of the signal; the time domain characteristics of the signal may include: the position of the peak, the amplitude of the peak, the slope of the peak, the width of the peak, the validity of the peak, the type of the peak, the peak-to-peak interval value, the validity of the peak-to-peak interval, and at least one of the signal quality SQI. There are many specific ways for the processor 50 to determine the signal characteristics of the signal. For example, taking the time domain signal as an example, the processor 50 can search for peaks on the signal, calculate the amplitude of the peak, the slope of the peak, the width of the peak, and further calculate the signal quality SQI and other time domain characteristics - such as the validity of the peak, the type of peak, the peak-to-peak interval value and the validity of the interval, etc. For example, taking the frequency domain characteristics as an example, the processor 50 can perform Fourier transform on the signal to obtain the frequency characteristics of the signal, and then obtain the required spectrum characteristics by processing the high-frequency signal and the low-frequency signal. In order to reduce hardware unlocking and save costs, in one embodiment, please refer to Figure 8 The processor 50 may include a signal feature extraction unit 51, which is used to extract and output the signal features of the input signal. When the input is a physiological signal, it is used to extract and output the signal features of the physiological signal. When the input is a motion signal, it is used to extract and output the signal features of the motion signal. Please refer to Fig. 9 In one embodiment, in order to better obtain the signal characteristics of the signal, the monitoring device of the present invention may further include a preprocessing circuit 40, which is used to preprocess the physiological signal and the motion signal in parallel before the processor determines the signal characteristics of the physiological signal from the physiological signal and determines the signal characteristics of the motion signal from the motion signal. For example, please refer to Fig.10The preprocessing circuit 40 may include at least one of a filter circuit 41, an amplifier circuit 42 and an analog-to-digital conversion circuit 43, wherein the filter circuit 41 is used to filter the input signal, the amplifier circuit 42 is used to amplify the input signal, and the analog-to-digital conversion circuit 43 is used to perform analog-to-digital conversion on the input signal. The filter circuit 41 can be used as the front stage of the preprocessing circuit 40, the amplifier circuit 42 can be used as the middle stage of the preprocessing circuit 40, and the analog-to-digital conversion circuit 43 can be used as the back stage of the preprocessing circuit. For example, the preprocessing circuit 40 first filters the signal through the filter circuit 41 to remove noise, and then amplifies the filtered signal through the amplifier circuit 42 to increase the signal amplitude, and finally performs analog-to-digital conversion on the amplified signal through the analog-to-digital conversion circuit 43 to convert the signal from an analog signal to a digital signal.

[0110] It should be noted that Figures 7 to 10 The disclosed monitoring device, some of its structures and components can also refer to Figure 1 and Figure 2 Public monitors, e.g. Figures 7 to 10 The processor 50 in the monitoring device may be composed of Figure 1 and Figure 2 The physiological sensor 10 may be a sensor accessory 111, and Figures 7 to 10 The disclosed monitoring device may also include Figure 1 and Figure 2 Other components disclosed in the invention, such as the isolation power supply and communication interface 114, the external communication and power supply interface 116, etc., are not described in detail here. Figures 7 to 10 The disclosed monitoring device may also be Figure 3 All or part of the portable monitoring device 213 shown, for example, Figures 4 to 6 Part of the steps may be performed in the portable monitoring device 213, and part may be performed in the bedside monitor 212; or, Figures 4 to 6 All of the steps are executed in the portable monitoring device 213, and the processed data is displayed, output, and stored through the central station 211 or the bedside monitor 212.

[0111] The monitoring device disclosed in the present invention can be a mobile monitoring device, which can meet the needs of mobile monitoring and will not limit the patient's activity space. In addition, various interferences caused by pulling the electrodes due to activities such as walking, getting in and out of bed, and friction of clothes in traditional monitoring devices will occur, which may seriously interfere with the waveform signal and may also affect the accuracy of physiological parameter measurement, further affecting the doctor's judgment of the patient's condition, thereby affecting the patient's recovery; in the present invention, the patient's physiological signal and motion signal are obtained synchronously, the signal characteristics of the physiological signal are determined from the physiological signal, and whether the physiological signal is reliable is determined according to the signal characteristics of the physiological signal; if the physiological signal is determined to be unreliable, the signal characteristics of the motion signal are determined from the motion signal, and the motion state of the monitored object during each heartbeat is determined according to the signal characteristics of the motion signal; the validity of the physiological signal during the heartbeat is determined according to the motion state during the heartbeat, or the signal characteristics of the physiological signal during the heartbeat are corrected when calculating the physiological parameters, thereby meeting the measurement of mobile monitoring and ensuring the performance of the measurement.

[0112] The above is the physiological parameter optimization method and monitoring device based on motion sensor disclosed in the present invention. A practical example is given below to illustrate.

[0113] For example, the physiological sensor 10 is an ECG sensor, and the physiological signal obtained by the physiological sensor 10 when attached to the monitored object is an ECG signal. Therefore, the ECG signal of the monitored object is obtained by the ECG sensor attached to the monitored object, and the motion signal of the monitored object is obtained by the motion sensor attached to the monitored object at the same time.

[0114] The signal characteristics of the ECG signal are determined from the ECG signal, and the signal characteristics of the ECG signal may include: amplitude, slope, width, frequency, etc. of the QRS wave, validity, type, peak-to-peak interval value and interval value validity of the QRS wave, signal quality SQI, etc.

[0115] Determine whether the ECG signal is reliable based on its signal features, for example, collect statistics on the signal features of the ECG signal, vote and score these signal features with different weights to obtain a reliability score of the ECG signal, and determine whether the ECG signal is reliable based on the obtained reliability score.

[0116] When it is determined that the ECG signal is reliable, ECG parameters, such as heart rate, etc., may be directly calculated based on the signal characteristics of the ECG signal.

[0117] When it is determined that the ECG signal is unreliable, optimization may be performed based on the motion signal.

[0118] Specifically, the signal characteristics of the motion signal are determined from the motion signal, and the motion state of the monitored object during each heartbeat is determined according to the signal characteristics of the motion signal; for example, the motion state of the monitored object during each heartbeat is graded according to the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat. For example, the motion level may include three levels: a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the two.

[0119] Then, the validity of the ECG signal during the heartbeat is determined according to the motion state during the heartbeat, or the signal characteristics of the ECG signal during the heartbeat are corrected when calculating the ECG parameters, and each heartbeat corresponds to a QRS waveform. For example, at a low motion level, the ECG signal is not subjected to additional processing, and the signal characteristics of the ECG signal can be directly output; at a medium motion level, the current QRS wave interval of the ECG signal can be corrected to invalid, the signal quality SQI of the ECG signal can be corrected to 3, and the current QRS wave type of the ECG signal can be re-determined; at a high motion level, the QRS wave interval of the ECG signal can be directly corrected to invalid, the signal quality SQI of the ECG signal can be corrected to 4, and the QRS wave type of the electrical signal can be corrected to normal, so as to reduce ventricular arrhythmia alarms.

[0120] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0121] The above specific examples are used to illustrate the present invention, which are only used to help understand the present invention and are not used to limit the present invention. For those skilled in the art, according to the concept of the present invention, the above specific implementation methods can be changed.

Claims

1. A physiological parameter optimization method based on motion sensor, characterized in that: include: Acquiring a physiological signal of the monitored object through a physiological sensor attached to the monitored object, and simultaneously acquiring a motion signal of the monitored object through a motion sensor attached to the monitored object; Determining a signal feature of the physiological signal from the physiological signal, and judging whether the physiological signal is reliable according to the signal feature of the physiological signal; If it is determined that the physiological signal is reliable, directly calculating the physiological parameter based on the signal characteristics of the physiological signal; If it is determined that the physiological signal is unreliable, the signal characteristics of the motion signal are determined from the motion signal, and the motion state of the monitored object during each heartbeat is determined according to the signal characteristics of the motion signal; wherein, the determination of the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal comprises: grading the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion grade of the monitored object during each heartbeat; Determine the validity of the physiological signal during the heartbeat according to the movement state during the heartbeat; wherein, determining the validity of the physiological signal during the heartbeat according to the movement state during the heartbeat includes: determining the physiological signal during the heartbeat as valid according to the movement level, or determining the physiological signal during the heartbeat as invalid according to the movement level, or re-determining the validity of the physiological signal during the heartbeat according to the movement level.

2. The physiological parameter optimization method according to claim 1, characterized in that: A signal feature extraction unit is provided to extract and output the signal features of the input signal. When the input is a physiological signal, it is used to extract and output the signal features of the physiological signal; when the input is a motion signal, it is used to extract and output the signal features of the motion signal.

3. The physiological parameter optimization method according to claim 1, characterized in that: Before determining the signal feature of the physiological signal from the physiological signal and determining the signal feature of the motion signal from the motion signal, the method further includes: preprocessing the physiological signal and the motion signal in parallel.

4. The physiological parameter optimization method according to claim 3, characterized in that: The preprocessing includes: performing at least one of filtering, amplifying, and analog-to-digital conversion on the signal.

5. The physiological parameter optimization method according to claim 1, characterized in that: The determining whether the physiological signal is reliable according to the signal characteristics of the physiological signal includes: assigning a weight to each signal characteristic, obtaining a reliability score by voting and scoring, and determining whether the physiological signal is reliable based on the reliability score.

6. The physiological parameter optimization method according to claim 1, characterized in that: The method of grading the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat includes: calculating the mean and / or variance of the signal characteristics of the motion signal, grading the motion state of the monitored object during each heartbeat by threshold segmentation, and obtaining the motion level of the monitored object during each heartbeat.

7. The physiological parameter optimization method according to claim 1, characterized in that: The motion levels include: a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the low motion level and the high motion level.

8. The physiological parameter optimization method according to claim 7, characterized in that: The determining of the validity of the physiological signal according to the motion level during the heartbeat includes: when the motion level during the heartbeat is a low motion level, determining the physiological signal during the heartbeat to be valid; when the motion level during the heartbeat is a medium motion level, reconfirming the validity of the physiological signal during the heartbeat; when the motion level during the heartbeat is a high motion level, determining the physiological signal during the heartbeat to be invalid.

9. The physiological parameter optimization method according to claim 1 or 2, characterized in that: The signal characteristics include time domain characteristics and / or frequency domain characteristics of the signal; The time domain characteristics of the signal include: at least one of the position of the peak, the amplitude of the peak, the slope of the peak, the width of the peak, the validity of the peak, the type of the peak, the peak-to-peak interval value, the validity of the peak-to-peak interval, and the signal quality SQI.

10. The physiological parameter optimization method according to claim 1, characterized in that: If it is determined that the physiological signal is unreliable, it also includes: directly calculating physiological parameters based on the signal characteristics of the physiological signal determined from the physiological signal; and determining whether the calculated physiological parameters during the heartbeat are valid according to the validity of the physiological signal during the heartbeat.

11. A monitoring device, characterized in that: include: A physiological sensor, used to be attached to a monitored object to obtain a physiological signal of the monitored object; A motion sensor, used for acquiring a motion signal of the monitored object by relying on a motion sensor attached to the monitored object; A processor determines the signal characteristics of the physiological signal from the physiological signal, and judges whether the physiological signal is reliable according to the signal characteristics of the physiological signal; if the physiological signal is judged to be reliable, directly calculates the physiological parameters based on the signal characteristics of the physiological signal; if the physiological signal is judged to be unreliable, determines the signal characteristics of the motion signal from the motion signal, and determines the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal, wherein the determination of the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal includes: grading the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion grade of the monitored object during each heartbeat; determining the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat or correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters; wherein determining the validity of the physiological signal during the heartbeat according to the motion state during the heartbeat includes: Determine the physiological signal during the heartbeat as valid according to the movement level, or determine the physiological signal during the heartbeat as invalid according to the movement level, or redetermine the validity of the physiological signal during the heartbeat according to the movement level; wherein, correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the movement state during the heartbeat includes: not modifying the signal characteristics of the physiological signal according to the movement level, or correcting the signal characteristics of the physiological signal according to the movement level.

12. The monitoring device according to claim 11, characterized in that: The processor includes a signal feature extraction unit, which is used to extract and output the signal feature of the input signal. When the input is a physiological signal, it is used to extract and output the signal features of the physiological signal; when the input is a motion signal, it is used to extract and output the signal features of the motion signal.

13. The monitoring device according to claim 11, characterized in that: It also includes a preprocessing circuit, which is used to preprocess the physiological signal and the motion signal in parallel before the processor determines the signal characteristics of the physiological signal from the physiological signal and determines the signal characteristics of the motion signal from the motion signal.

14. The monitoring device according to claim 13, characterized in that: The preprocessing circuit comprises at least one of the following: A filter circuit, used for filtering the input signal; An amplifier circuit, used for amplifying an input signal; The analog-to-digital conversion circuit is used to perform analog-to-digital conversion on the input signal.

15. The monitoring device according to claim 11, characterized in that: The processor determines whether the physiological signal is reliable according to the signal characteristics of the physiological signal, including: assigning a weight to each signal characteristic, obtaining a reliability score by voting and scoring, and determining whether the physiological signal is reliable based on the reliability score.

16. The monitoring device according to claim 11, characterized in that: The processor classes the motion state of the monitored object during each heartbeat according to the signal characteristics of the motion signal to obtain the motion level of the monitored object during each heartbeat, including: calculating the mean and / or variance of the signal characteristics of the motion signal, and classifying the motion state of the monitored object during each heartbeat by threshold segmentation to obtain the motion level of the monitored object during each heartbeat.

17. The monitoring device according to claim 11, characterized in that: The motion levels include: a low motion level for indicating that the monitored object has no motion or slight motion, a high motion level for indicating that the monitored object has intense motion, and a medium motion level between the low motion level and the high motion level.

18. The monitoring device according to claim 11, characterized in that: The signal characteristics include signal quality SQI; The processor corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the movement level during the heartbeat, including: correcting the signal quality SQI of the physiological signal during the heartbeat when calculating the physiological parameters according to the movement level during the heartbeat.

19. The monitoring device according to claim 11, characterized in that: The processor determines the validity of the physiological signal according to the motion level during the heartbeat, including: when the motion level during the heartbeat is a low motion level, determining that the physiological signal during the heartbeat is valid; when the motion level during the heartbeat is a medium motion level, reconfirming the validity of the physiological signal during the heartbeat; when the motion level during the heartbeat is a high motion level, determining that the physiological signal during the heartbeat is invalid.

20. The monitoring device according to claim 11, characterized in that: The processor corrects the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameters according to the motion level during the heartbeat, including: when the motion level during the heartbeat is a low motion level, the signal characteristics of the physiological signal are not modified; when the motion level during the heartbeat is a medium motion level or a high motion level, the signal characteristics of the physiological signal are corrected.

21. The monitoring device according to claim 11 or 12, characterized in that: The signal characteristics include time domain characteristics and / or frequency domain characteristics of the signal; The time domain characteristics of the signal include: at least one of the position of the peak, the amplitude of the peak, the slope of the peak, the width of the peak, the validity of the peak, the type of the peak, the peak-to-peak interval value, the validity of the peak-to-peak interval, and the signal quality SQI.

22. The monitoring device according to claim 21, characterized in that: The processor correcting the signal characteristics of the physiological signal during the heartbeat when calculating the physiological parameter includes: correcting the weight of the time domain characteristics and / or frequency domain characteristics of the physiological signal during the heartbeat when calculating the physiological parameter.

23. The monitoring device according to claim 11, characterized in that: If the processor determines that the physiological signal is unreliable, it directly calculates the physiological parameters based on the signal characteristics of the physiological signal determined from the physiological signal; and determines whether the calculated physiological parameters during the heartbeat are valid based on the validity of the physiological signal during the heartbeat.

24. The monitoring device according to claim 11, characterized in that: It also includes a shell, in which the processor is arranged, and the processor is connected to the physiological sensor and the motion sensor in a wired or wireless manner.

25. The monitoring device according to claim 11, characterized in that: The motion sensor includes an acceleration sensor, an angular velocity sensor or a gravity sensor.

26. The monitoring device according to claim 11, characterized in that: The monitoring device is a mobile monitoring device.

27. A computer-readable storage medium, characterized in that: The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 10.

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