Mobile monitoring device, mobile monitoring method, and readable storage medium

Abstract

The application provides a mobile monitoring device, a method and a readable storage medium. In the mobile monitoring method, original electrocardio signals are subjected to noise reduction processing using a motion signal and a skin impedance signal respectively to obtain two channels of noise reduction electrocardio signals, and the signal quality of the two channels of noise reduction electrocardio signals is compared to determine a target noise reduction electrocardio signal therefrom. It can be seen that the motion artifact noise in the target noise reduction electrocardio signal can be eliminated, and the elimination effect is determined by the signal quality of the noise reduction electrocardio signal, and the signal quality of the target noise reduction electrocardio signal can better meet the actual application requirements.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and more specifically, to mobile monitoring devices, mobile monitoring methods, and readable storage media. Background Technology

[0002] Electrocardiogram (ECG) monitoring plays a crucial role in animal health, recording cardiac arrhythmias and assessing chamber dilation. However, ECG monitoring is currently generally only applicable to anesthetized animals, and long-term ECG monitoring in conscious animals remains challenging. The main reason is that movement in conscious animals causes motion interference (also known as noise or motion artifacts) in the ECG waveform. Furthermore, the signal spectrum of motion interference, ranging from 0.05 to 20 Hz, overlaps with the ECG waveform spectrum, making it difficult to eliminate while preserving the integrity of the ECG information.

[0003] Current methods for reducing motion interference typically involve processing the original ECG waveform itself using time averaging, frequency analysis, and wavelet transformation. However, these methods cause significant loss to the original ECG signal and cannot effectively eliminate motion interference in the ECG signal. Summary of the Invention

[0004] In view of this, this application provides a mobile monitoring device, a mobile monitoring method, and a readable storage medium to effectively eliminate motion interference noise in electrocardiogram (ECG) signals and obtain more accurate noise-reduced ECG signals.

[0005] In a first aspect, embodiments of this application provide a mobile monitoring device, which is worn on the monitored object and includes:

[0006] The electrocardiogram (ECG) signal acquisition module is used to acquire the raw ECG signals of the monitored object.

[0007] A motion sensor is used to collect motion signals of the monitored object;

[0008] A skin impedance detection module is used to collect the skin impedance signal of the monitored object;

[0009] The processing module is configured to perform noise reduction processing on the original ECG signal with reference to the motion signal to obtain a noise-reduced ECG signal of the first channel, and to perform noise reduction processing on the original ECG signal with reference to the skin impedance signal to obtain a noise-reduced ECG signal of the second channel; compare the signal quality of the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel, and determine the target noise-reduced ECG signal from the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel according to the comparison result.

[0010] Secondly, embodiments of this application provide a mobile monitoring method, including:

[0011] Collect motion signals of the monitored object and collect skin impedance signals of the monitored object;

[0012] The original ECG signal is denoised by referring to the motion signal to obtain a denoised ECG signal of the first channel, and the original ECG signal is denoised by referring to the skin impedance signal to obtain a denoised ECG signal of the second channel.

[0013] Compare the signal quality of the noise-reduced ECG signal from the first channel and the noise-reduced ECG signal from the second channel;

[0014] Based on the comparison results, the target noise-reduced ECG signal is determined from the noise-reduced ECG signal in the first channel and the noise-reduced ECG signal in the second channel.

[0015] Thirdly, embodiments of this application provide a readable storage medium having a computer program stored thereon, which, when loaded and executed by a processor, implements any of the mobile monitoring methods provided in the following embodiments.

[0016] Based on the above technical solutions, the mobile monitoring device, method, and readable storage medium provided in this application use motion signals and skin impedance signals to denoise the original electrocardiogram (ECG) signals to obtain two channels of denoised ECG signals. The signal quality of the two channels of denoised ECG signals is compared to determine the target denoised ECG signal. It can be seen that motion artifact noise in the target denoised ECG signal can not only be eliminated, but the elimination effect is determined by the signal quality of the denoised ECG signal. The signal quality of the target denoised ECG signal can better meet the needs of practical applications. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A structural block diagram of a mobile monitoring device provided in an embodiment of this application;

[0019] Figure 2 This is a schematic diagram illustrating one way the motion sensor is worn on an animal, as described in this application embodiment.

[0020] Figure 3 A flowchart illustrating the mobile monitoring method provided in this application embodiment;

[0021] Figure 4 This is an example diagram illustrating the processing flow of noise-reduced ECG signals for two channels in an embodiment of this application.

[0022] Figures 5A-5B The following are two flowchart examples illustrating the determination of the target noise-reduced ECG signal in the embodiments of this application;

[0023] Figure 6 A flowchart illustrating the process of determining channel handover hysteresis values ​​in an embodiment of this application;

[0024] Figures 7A-7B Example diagram for determining the feature factors corresponding to two signal features in embodiments of this application;

[0025] Figure 8 This is a flowchart illustrating a process for determining the signal quality of a noise-reduced electrocardiogram (ECG) signal according to an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Mobile monitoring devices, such as wearable electrocardiogram (ECG) recorders, are portable ECG recorders that can be worn on the body of the monitored subject to record the subject's heart activity for extended periods. However, when used on subjects with frequent physical activity, such as animals, the obtained ECG signals are often inaccurate due to motion interference.

[0028] Therefore, external reference signals can be used to eliminate motion interference noise in ECG signals. For example, skin impedance signals or motion signals can be used alone as reference signals for motion interference cancellation. However, the noise reduction effect of a single reference signal may not guarantee the optimal noise reduction effect in real time. Generally, the noise reduction effect of the reference motion signal is better when the monitored subject is in a state of high-amplitude activity, such as running or walking. However, the noise reduction effect of the reference skin impedance signal is better when the monitored subject is in a state of rest or low-amplitude activity, such as sitting, lying down, or walking slowly. Therefore, in order to effectively eliminate motion interference noise in ECG signals, this application provides a mobile monitoring device. This mobile monitoring device can be worn on the monitored subject and can evaluate the noise reduction effect of two reference signals on the monitored subject's ECG signal, and select the noise-reduced signal with better effect as the noise-reduced ECG signal.

[0029] like Figure 1As shown, the mobile monitoring device includes: an electrocardiogram (ECG) signal acquisition module 10, a motion sensor 20, a skin impedance detection module 30, and a processing module 40. It is understood that the mobile monitoring device may also include other physiological parameter acquisition modules, communication modules, storage units, power supplies, and other conventional structures or units or structures expected by those skilled in the art.

[0030] The mobile monitoring device is worn on the monitored object, which can be a living organism such as a human or an animal. The electrocardiogram (ECG) signal acquisition module 10 is used to acquire the ECG signal of the monitored object. To distinguish it from the noise-reduced ECG signal described below, this ECG signal can be referred to as the raw ECG signal. In a specific example, the mobile monitoring device may include a wearable vest. The ECG signal acquisition module 10 may specifically include ECG lead electrodes. The animal carries the mobile monitoring device by wearing the vest, and the ECG lead electrodes are placed at designated locations on the animal's body to acquire the animal's ECG signal in real time. After processing, the ECG signal yields ECG waveform data and heart rate values, among other ECG data.

[0031] Motion sensor 20, which may include one or more, is used to acquire motion signals of the monitored object. In some embodiments, motion sensor 20 may include an accelerometer and a gyroscope to acquire motion signals such as acceleration data and / or angular velocity data of the animal. For example, the motion sensor may be an accelerometer, and the motion signal may be an acceleration signal along three axes, such as the X-axis, Y-axis, and Z-axis. Alternatively, the accelerometer may be integrated onto the ECG lead electrode pads of the ECG signal acquisition module or placed with the electrode pads. Figure 2 For example, the motion sensor 20 can be worn on an animal's body via a vest. For instance, three motion sensors, a, b, and c, can be placed on the dog's neck, back, and abdomen respectively to collect motion signals from different parts of the dog's body. Of course, the placement and number of motion sensors shown in the illustration are merely illustrative examples, and this application does not impose any specific limitations.

[0032] The skin impedance detection module 30 is used to acquire skin impedance signals of the monitored object. Specifically, this module may include electrodes attached to the skin surface of the monitored object, and the skin impedance signal is obtained by detecting the contact impedance between the electrode and the skin. The electrodes of the skin impedance detection module 30 may be shared with the electrodes of the electrocardiogram (ECG) signal acquisition module, or different electrodes may be provided. In the case of shared electrodes, the signals acquired by the same electrode can be processed by different processing circuits to obtain the original ECG signal and the skin impedance signal, respectively.

[0033] It should be noted that methods that rely solely on skin impedance signals to eliminate motion interference are less effective for monitoring highly active subjects such as animals. This is because large movements can lead to significant changes in electrode-skin contact impedance, resulting in inaccurate impedance measurements. However, the embodiments in this application combine motion signals acquired by a reference motion sensor to obtain a noise-reduced ECG signal.

[0034] Processing module 40, which is communicatively connected to electrical signal acquisition module 10, motion sensor 20, and skin impedance detection module 30, is configured to perform actions such as... Figure 3 The mobile monitoring method shown specifically includes steps S301-S303. The processing module 40 can be a unit with data processing capabilities, such as a processor or controller, and can be electrically connected to the electrical signal acquisition module 10, the motion sensor 20, and the skin impedance detection module 30 to achieve data communication between them.

[0035] S301: The original ECG signal is denoised using the reference motion signal to obtain the denoised ECG signal of the first channel.

[0036] The original ECG signal acquired by the ECG signal acquisition module 10 can be sent to the processing module 40, and the motion signal acquired by the motion sensor 20 can also be sent to the processing module 40. The processing module 40 uses the motion signal as a reference signal to perform noise reduction processing on the original ECG signal. The noise-reduced ECG signal can be used as a noise-reduced ECG signal. In order to distinguish it from another noise-reduced ECG signal mentioned below, this noise-reduced ECG signal can be referred to as the noise-reduced ECG signal of the first channel.

[0037] The processing module 40 can use any filtering algorithm to eliminate noise from the original ECG signal by referencing the motion signal.

[0038] S302: The original ECG signal is denoised by referencing the skin impedance signal to obtain the denoised ECG signal of the second channel.

[0039] The original ECG signal acquired by the ECG signal acquisition module 10 can be sent to the processing module 40 (this is the same ECG signal sent to the processing module 40 for noise reduction processing of the motion signal). The skin impedance signal acquired by the skin impedance detection module 30 can also be sent to the processing module 40. The processing module 40 uses the skin impedance signal as a reference signal to perform noise reduction processing on the original ECG signal. The noise-reduced ECG signal can be used as a noise-reduced ECG signal, referred to as the second channel noise-reduced ECG signal. The processing module 40 can use any filtering algorithm to eliminate noise from the original ECG signal using the reference skin impedance signal.

[0040] In one specific implementation, the processing module 40 includes a noise filter, such as an adaptive noise filter, which performs noise reduction processing on the original ECG signal by referring to both the motion signal and the skin impedance signal. The noise filters can use the same filtering algorithm or different filtering algorithms. The noise filters can be the same or different noise filters.

[0041] Taking two noise filters as an example, the motion signal and the original ECG signal can be input into the first noise filter. The first noise filter uses the motion signal as a reference to denoise the original ECG signal, resulting in a denoised ECG signal for the first channel. Similarly, the skin impedance signal and the original ECG signal can be input into the first noise filter. The first noise filter uses the skin impedance signal as a reference to denoise the original ECG signal, resulting in a denoised ECG signal for the second channel. Figure 4 As shown, each of the two noise filters includes an adaptive filter. The original ECG signal is input to the first noise filter as a signal reference value, and the motion signal is input to the adaptive filter of the first noise filter as a reference signal. This is coupled with the parameter factor of the adaptive filter, outputting a filtered reference signal. The filtered reference signal is used to cancel the original ECG signal, outputting a deviation value. This deviation value serves as the adjustment step size for the parameter factor of the adaptive filter, used to adjust the parameter factor. This process is iterated until the output deviation value meets the requirements. Using this satisfactory deviation value, the first channel of the noise-reduced ECG signal can be output. Similarly, the original ECG signal is input to the second noise filter as a signal reference value, and the skin impedance signal is input to the adaptive filter of the second noise filter. After continuous iteration, a satisfactory deviation value is obtained, thus obtaining the second channel of the noise-reduced ECG signal. The adaptive filter can be an FIR (Finite Impulse Response) adaptive filter.

[0042] S303: Compare the signal quality of the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel, and determine the target noise-reduced ECG signal from the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel based on the comparison results.

[0043] Specifically, since different reference signals are used to denoise the same original ECG signal, the signal quality of the denoised ECG signals from the two channels is highly likely to differ. The signal quality of the denoised ECG signals from the two channels is compared, and the target denoised ECG signal is determined based on the comparison result. The comparison result can be a qualitative result reflecting the quality difference (i.e., signal quality high or low), or a quantitative result reflecting the signal quality gap (i.e., signal quality difference). In one specific embodiment, the denoised ECG signal with higher signal quality from the two channels can be directly used as the target denoised ECG signal. In another specific embodiment, the denoised ECG signal with a signal quality difference exceeding a certain threshold can be used as the target denoised ECG signal.

[0044] In this embodiment, the target noise-reduced ECG signal is the original ECG signal after final noise reduction processing. Compared with the original ECG signal, its motion artifact noise is eliminated, and the elimination effect is selected based on the signal quality. The noise reduction effect can better meet the specific noise reduction requirements and has better applicability.

[0045] Targeted noise-reduced ECG signals can be used in other monitoring procedures, such as calculating ECG values; or the waveform of the targeted noise-reduced ECG signal can be directly displayed so that monitoring personnel can visually observe the signal. For example... Figure 1 The mobile monitoring device shown may further include a display module 50. The display module 50 can be connected to the processing module 40 via a signal line, or the display module 50 can be connected to the processing module 40 via wireless communication. The processing module 40 sends the waveform of the target noise-reduced ECG signal to the display module 50, and the display module 50 displays the waveform of the target noise-reduced ECG signal.

[0046] In practical applications, raw ECG signals of the monitored object are typically acquired periodically. In this embodiment, the raw ECG signals acquired in each cycle are processed to obtain two channels of denoised ECG signals, and then the target denoised ECG signal is selected from the two channels. Alternatively, the acquisition cycle of the raw ECG signal (first cycle) and the determination cycle of the target denoised ECG signal (second cycle) are not synchronized. After acquiring the raw ECG signal in the first cycle, two channels of denoised ECG signals can be generated in real time. Then, the signal quality of the two channels of denoised ECG signals can be compared sequentially according to the second cycle, and the target denoised ECG signal for each cycle can be selected based on the comparison results.

[0047] In one specific embodiment, when determining the target denoised ECG signal for the original ECG signal in each cycle, the signal quality of the denoised ECG signals of the two channels in the previous cycle can be referenced; in other words, the signal quality comparison result of the denoised ECG signals of the two channels in the current cycle can be used as the basis for selecting the target denoised ECG signal for the next cycle. Specifically: the signal quality of the denoised ECG signal of the first channel and the denoised ECG signal of the second channel in the current cycle is compared to obtain the comparison result of the current cycle; based on the comparison result of the current cycle, the target denoised ECG signal for the next cycle is selected from the denoised ECG signal of the first channel and the denoised ECG signal of the second channel.

[0048] In specific implementation, when selecting a target denoised ECG signal within any cycle: firstly, the signal quality of the denoised ECG signal from the first channel and the denoised ECG signal from the second channel are compared to obtain the comparison result for the current cycle. Then, based on this comparison result, a target denoised ECG signal is selected for the next cycle. This periodicity means that a target denoised ECG signal needs to be determined once per cycle, and the target denoised ECG signal for each cycle is obtained from the quality comparison result of the two channels' denoised ECG signals in the previous cycle. This embodiment considers the correlation between denoised ECG signals from previous and subsequent cycles, and selects a target denoised ECG signal more suitable for the needs of the current cycle based on this correlation.

[0049] The first requirement focuses on signal quality, so a higher-quality denoised ECG signal from the previous cycle can be selected as the target denoised ECG signal for the next cycle. Specifically, the comparison result is the signal quality ranking; therefore, the higher-quality denoised ECG signal from the first channel and the second channel within the current cycle is determined as the target denoised ECG signal for the next cycle. Since the signal quality of denoised ECG signals is likely to remain stable over a continuous period, this embodiment takes this characteristic into consideration and directly uses the higher-quality denoised ECG signal from the previous cycle as the target denoised ECG signal for the next cycle, thus ensuring that the target denoised ECG signal obtained in each cycle is a higher-quality denoised ECG signal.

[0050] The second requirement focuses on signal stability, avoiding frequent selection of target denoised ECG signals between two different channels. Specifically, the comparison result is the signal quality difference. Therefore, after obtaining the signal quality difference for the current period, it is determined whether the signal quality difference for the current period meets a preset difference condition. If the signal quality difference for the current period meets the preset difference condition, the higher-quality denoised ECG signal from the first and second channels is determined as the target denoised ECG signal for the next period. It should be noted that the preset difference condition is used to evaluate whether the difference between the two signal qualities reaches a certain level. If the preset difference condition is met for the signal quality difference in the current period, it means that one of the denoised ECG signals from the two channels is higher than the other. In this case, the higher-quality denoised ECG signal is taken as the target denoised ECG signal. In this way, when continuously determining the target denoised ECG signal in multiple periods, the signal quality of one denoised ECG signal is significantly higher than that of the other, thus avoiding the discontinuity and instability of the denoised ECG signal caused by frequent channel switching.

[0051] One specific implementation involves determining the channel from which the target denoised ECG signal for the current period originates as the current channel; the current channel is either the first channel or the second channel; comparing the signal quality of the denoised ECG signal from another channel to determine if it is higher than the signal quality of the denoised ECG signal from the current channel, thus obtaining the signal quality difference for the current period; wherein, the other channel is a channel other than the current channel between the first channel and the second channel. Furthermore, it is determined whether the signal quality difference for the current period meets a preset difference condition. If the signal quality difference for the current period meets the preset difference condition, the denoised ECG signal with higher quality from the denoised ECG signal of the first channel and the denoised ECG signal of the second channel is determined as the target denoised ECG signal for the next period.

[0052] It should be noted that the comparison and judgment steps described above can be implemented in the same step. For example, it can be used to determine whether the signal quality difference between the noise-reduced ECG signal from another channel and the signal quality of the noise-reduced ECG signal from the current channel meets a preset difference condition. Furthermore, the preset difference condition can be a signal quality value, and this signal quality value must be positive. In a further embodiment, if the signal quality difference in the current cycle does not meet the preset difference condition, the noise-reduced ECG signal from the current channel is maintained as the target noise-reduced ECG signal for the next cycle.

[0053] The following combination Figure 5A and 5B The two situations described above will be explained separately.

[0054] Figure 5AAssuming the current channel corresponds to the motion signal (channel 1), and the other channel corresponds to the skin impedance signal (channel 2), determine the signal quality Q of the noise-reduced ECG signal in channel 2. 阻抗 Is the signal quality Q greater than that of the noise-reduced ECG signal from the first channel? 加速度 If the sum of the signal quality value ΔQ and the preset signal quality value is greater than the preset difference condition, it means that the signal quality difference of the current cycle meets the preset difference condition, and it can be understood that the signal quality of the noise-reduced ECG signal of the second channel is higher. Therefore, the noise-reduced ECG signal of the second channel is selected as the target noise-reduced ECG signal for the next cycle; otherwise, the noise-reduced ECG signal of the first channel is maintained as the target noise-reduced ECG signal for the next cycle.

[0055] Figure 5B Assuming the current channel corresponds to the skin impedance signal (channel 1), and the other channel corresponds to the motion signal (channel 2), determine the signal quality Q of the noise-reduced ECG signal in channel 2. 加速度 Is the signal quality Q greater than that of the noise-reduced ECG signal from the first channel? 阻抗 If the sum of the signal quality value ΔQ and the preset signal quality value is greater than the preset difference condition, it means that the signal quality difference of the current cycle meets the preset difference condition, and it can be understood that the signal quality of the second channel's denoised ECG signal is higher. Therefore, the second channel's denoised ECG signal is selected as the target denoised ECG signal for the next cycle. Otherwise, the first channel's denoised ECG signal is maintained as the target denoised ECG signal for the next cycle.

[0056] The aforementioned preset signal quality value can be called the channel switching hysteresis value. This value represents the minimum signal quality difference required to switch the channel between the current cycle and the next cycle. The purpose of determining whether the signal quality difference in the current cycle meets the preset difference condition can be achieved by comparing whether the signal quality difference between the two channels in the current cycle is greater than the channel switching hysteresis value. The channel switching hysteresis value set in this embodiment can avoid signal instability and discontinuity caused by frequent switching of the target denoised ECG signal channel when the denoised ECG signal quality of the two channels, although varying slightly, is roughly the same.

[0057] The signal switching hysteresis value can be a preset fixed value or determined based on the actual signal quality of the denoised ECG signal. Specifically: The higher signal quality between the denoised ECG signal of the first channel and the denoised ECG signal of the second channel is determined within the current cycle; if the higher signal quality is greater than a preset signal quality threshold, the channel switching hysteresis value is determined based on the higher signal quality; if the higher signal quality is not greater than the preset signal quality threshold, the channel switching hysteresis value is determined as a preset signal change value. (Reference) Figure 6 One specific implementation shown involves two noise-reduced ECG signals Q. 加速度 and Q 阻抗The larger one is determined from the given information; the larger one is called Q. max Determine Q max If the signal quality threshold Q0 is greater than the preset threshold, then the channel handover hysteresis value ΔQ is set to the coefficient K multiplied by Q. max The coefficient K is a preset value; if it is not greater than this value, the channel handover hysteresis value ΔQ is set to the preset signal change value ΔQ0. It should be noted that the preset signal change value ΔQ0 corresponds to the preset signal quality threshold Q0, meaning that the signal change value ΔQ0 will be set according to the magnitude of the signal quality threshold Q0.

[0058] In this embodiment, a channel switching hysteresis value is set. If the quality of the current channel's denoised ECG signal is high, a larger channel switching hysteresis value is set to avoid frequent switching of the source channel of the target denoised ECG signal. If the quality of the current channel's denoised ECG signal is poor, a smaller channel switching hysteresis value is set to switch the target denoised ECG signal to the denoised ECG signal of another channel to improve the signal quality of the target denoised ECG signal.

[0059] The following details how the signal quality of the two-channel noise-reduced ECG signals is determined.

[0060] Processing module 40 extracts multiple first signal features from the noise-reduced ECG signal of the first channel and multiple second signal features from the noise-reduced ECG signal of the second channel. The first and second signal features are of the same type, representing waveform morphology, specifically those features that significantly alter waveform morphology under interference. For example, each of the first and second signal features includes at least two of the following types: QRS width, RR interval, QRS morphology variability, RT interval, T wave width, and T wave morphology variability. Then, the signal quality of the noise-reduced ECG signal of the first channel is determined using multiple first signal features, and the signal quality of the noise-reduced ECG signal of the second channel is determined using multiple second signal features. Therefore, the noise-reduced ECG signal of the first and second channels each integrates multiple signal features to obtain their respective signal quality, resulting in relatively accurate signal quality.

[0061] Specifically, the characteristic factor and scaling factor corresponding to each first signal feature are determined, as are the characteristic factors and scaling factors corresponding to each second signal feature. The characteristic factor represents the importance of signal features of the same type, while the scaling factor represents the importance of signal features of different types. The scaling factors of all first characteristic signals are summed to 1, and the scaling factors of all second characteristic signals are summed to 1. Larger scaling factors can be set for signal features that are considered more important, and smaller scaling factors can be set for less important ones.

[0062] In one specific implementation, a feature factor is determined for each first signal feature based on its corresponding signal feature interval, and a feature factor is also determined for each second signal feature based on its corresponding signal feature interval. Different scaling coefficients are set for different types of first and second signal features according to their respective types. In other words, when determining the feature factor, multiple signal feature intervals can be set, with different feature factors corresponding to different signal feature intervals, thereby determining the corresponding feature factor based on the signal feature interval to which the signal feature belongs.

[0063] This explanation uses two signal characteristics, QRS width and RR interval, as examples. (Reference) Figure 7A and 7B As shown, the characteristic factor P corresponding to the QRS wave width QRS Four pre-set boundary values, arranged from smallest to largest as T QRS1 T QRS2 T QRS3 T QRS4 This yields five signal feature intervals, each corresponding to one of five feature factors P. QRS1 P QRS2 P QRS3 P QRS4 P QRS5 Among them, the QRS width is less than T. QRS1 The sum is greater than T QRS4 The interval is an unconventional state, therefore characteristic factors P are set respectively. QRS1 and P QRS5 It is a relatively small value; the QRS width is located in the signal characteristic range [T]. QRS2 T QRS3 The area within is the normal state, therefore the characteristic factor P is set. QRS3 It is a relatively large value; the QRS width is located in the signal characteristic range [T]. QRS1 T QRS2 ] and signal characteristic interval [T QRS3 T QRS4 The condition within the range is relatively normal, therefore a characteristic factor P is set. QRS2 and P QRS4 It is a moderate value. The characteristic factor P corresponding to the QRS wave width is determined. QRS At that time, the corresponding characteristic factor P can be determined based on the interval in which the QRS wave width falls. QRS .

[0064] For the characteristic factor P corresponding to the RR interval RR Four pre-set boundary values, arranged from smallest to largest as T RR1 T RR2 TRR3 T RR4 This yields five signal feature intervals, each corresponding to one of five feature factors P. RR1 P RR2 P RR3 P RR4 P RR5 Among them, the RR interval is less than T. RR1 The sum is greater than T RR4 The interval is an unconventional state, therefore characteristic factors P are set respectively. RR1 and P RR5 The value is relatively small; the RR interval is located within the signal characteristic interval [T]. RR2 T RR3 The area within is the normal state, therefore the characteristic factor P is set. RR3 The value is relatively large; the RR interval is located within the signal characteristic interval [T]. RR1 T RR2 ] and signal characteristic interval [T RR3 T RR4 The condition within the range is relatively normal, therefore a characteristic factor P is set. RR2 and P RR4 The value is moderate. The characteristic factor P corresponding to the RR interval is determined. RR At that time, the corresponding characteristic factor P can be determined based on the interval in which the RR interval falls. RR .

[0065] It should be noted that the number of intervals in the two diagrams above is merely an example and can be set to other numbers according to actual needs. Other signal characteristics include the characteristic factor P of the RT interval. RT The characteristic factor P of T-wave width T The characteristic factor P of QRS wave morphological variability can be obtained by referring to the example above. V-QRS The characteristic factor P of T-wave morphological variability V-T It can be set to a preset value.

[0066] Next, the signal quality of the noise-reduced ECG signal of the first channel is determined based on the feature factor and scaling factor of each first signal feature; and the signal quality of the noise-reduced ECG signal of the second channel is determined based on the feature factor and scaling factor of each second signal feature.

[0067] In one specific implementation, the feature factor of each first signal feature is multiplied by a scaling factor to obtain a product corresponding to each first signal feature, and the products corresponding to each first signal feature are summed to obtain the signal quality of the noise-reduced ECG signal of the first channel; similarly, the feature factor of each second signal feature is multiplied by a scaling factor to obtain a product corresponding to each second signal feature, and the products corresponding to each second signal feature are summed to obtain the signal quality of the noise-reduced ECG signal of the second channel.Figure 8 As shown, taking QRS width, RR interval, QRS morphology variability, RT interval, T wave width, and T wave morphology variability as examples, the signal quality calculation method for the noise-reduced ECG signal of any channel is as follows: The characteristic factor P of the QRS width of each channel is... QRS With the proportionality coefficient K QRS Multiply by the characteristic factor P of the RR interval. RR With the proportionality coefficient K RR Multiplication, the characteristic factor P of QRS wave morphological variability V-QRS With the proportionality coefficient K V-QRS Multiplication, characteristic factor P of the RT interval RT With the proportionality coefficient K RT Multiplication, the characteristic factor P of the T-wave width T With the proportionality coefficient K T Multiplication, the characteristic factor P of T-wave morphological variability V-T With the proportionality coefficient K V-T Multiply the results and add them together to obtain the signal quality of the noise-reduced ECG signal for that channel.

[0068] After obtaining the signal quality of the noise-reduced ECG signals from each channel, the noise-reduced ECG signal from one channel can be selected as the target noise-reduced ECG signal by comparing the signal quality and based on the comparison results.

[0069] This application also provides a mobile monitoring method, the method as follows: Figure 3 The method may include: acquiring the motion signal and skin impedance signal of the monitored object; performing noise reduction processing on the original ECG signal with reference to the motion signal to obtain a first-channel noise-reduced ECG signal, and performing noise reduction processing on the original ECG signal with reference to the skin impedance signal to obtain a second-channel noise-reduced ECG signal; comparing the signal quality of the first-channel noise-reduced ECG signal and the second-channel noise-reduced ECG signal; and determining the target noise-reduced ECG signal from the first-channel noise-reduced ECG signal and the second-channel noise-reduced ECG signal based on the comparison result. It should be noted that the description of this mobile monitoring method can be found in the above description of mobile monitoring equipment, and will not be repeated here.

[0070] This invention also provides a computer-readable storage medium (Memory) for storing programs and data. It is understood that the computer-readable storage medium here may include the built-in storage medium in a mobile monitoring device, or it may include an extended storage medium supported by the mobile monitoring device. The computer-readable storage medium provides storage space in which one or more instructions suitable for loading and execution by a processor (such as processing module 40) are stored. These instructions may be one or more computer programs (including program code). It should be noted that the computer storage medium here may be high-speed RAM, non-volatile memory, or at least one computer storage medium located remotely from the aforementioned processor.

[0071] In one embodiment, one or more instructions in the computer storage medium are loaded by a processor to execute at least one mobile monitoring method implemented by the processing module 40 in the mobile monitoring device. Specifically, the processor can load and execute one or more instructions stored in the computer storage medium to implement the aforementioned... Figure 3 , Figure 4 , Figure 5A , Figure 5B , Figure 6 Figure 7 and Figure 8 The corresponding steps of the mobile monitoring method shown.

[0072] The features described above in the disclosed embodiments can be substituted or combined with each other to enable those skilled in the art to implement or use this application. This document has been described with reference to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications can be made to the exemplary embodiments without departing from the scope of this document. For example, various operational steps and components for performing operational steps can be implemented in different ways depending on the specific application or considering any number of cost functions associated with the operation of the system (e.g., one or more steps can be deleted, modified, or combined with other steps).

[0073] The terms "first," "second," etc., used in this specification, claims, and the accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, or apparatuses.

[0074] Furthermore, as those skilled in the art will understand, the principles herein can be reflected in a computer program product on a computer-readable storage medium pre-loaded with computer-readable program code. Any tangible, non-transitory computer-readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, Blu-ray discs, etc.), flash memory, and / or the like. These computer program instructions may be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine, such that instructions, which execute on the computer or other programmable data processing apparatus, can generate means for performing a specified function. These computer program instructions may also be stored in a computer-readable storage medium that can instruct the computer or other programmable data processing apparatus to operate in a particular manner, such that instructions stored in the computer-readable storage medium can form an article of manufacture, including means for implementing the specified function. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to perform a series of operational steps on the computer or other programmable apparatus to produce a computer-implemented process, such that instructions, which execute on the computer or other programmable apparatus, can provide steps for implementing the specified function.

[0075] The foregoing specific descriptions have been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes can be made without departing from the scope of this disclosure. Therefore, considerations for this disclosure are to be illustrative rather than restrictive, and all such modifications are to be included within its scope. Similarly, advantages, other advantages, and solutions to problems with respect to various embodiments have been described above. However, benefits, advantages, solutions to problems, and any elements that produce these, or make them more explicit, should not be construed as critical, essential, or necessary. The term “comprising” and any other variations thereof as used herein are non-exclusive inclusion, meaning that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed or not part of the process, method, system, article, or apparatus. Furthermore, the term “coupled” and any other variations thereof as used herein refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections, and / or any other connections.

[0076] The above embodiments are merely illustrative of several implementation methods, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this invention, and these all fall within the protection scope of this invention. Therefore, the protection scope of this invention should be determined by the appended claims.

Claims

1. A mobile monitoring device, wherein the mobile monitoring device is worn on the monitored object, characterized in that, include: The electrocardiogram (ECG) signal acquisition module is used to acquire the raw ECG signals of the monitored object. A motion sensor is used to collect motion signals of the monitored object; A skin impedance detection module is used to collect the skin impedance signal of the monitored object; The processing module is configured to perform noise reduction processing on the original ECG signal with reference to the motion signal to obtain a noise-reduced ECG signal of the first channel, and to perform noise reduction processing on the original ECG signal with reference to the skin impedance signal to obtain a noise-reduced ECG signal of the second channel; wherein the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel are noise-reduced ECG signals obtained by performing noise reduction processing on the same segment of the original ECG signal; the signal quality of the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel are compared, and a target noise-reduced ECG signal is determined from the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel based on the comparison result.

2. The mobile monitoring device as described in claim 1, characterized in that, When comparing the signal quality of the denoised ECG signal from the first channel and the denoised ECG signal from the second channel, and determining the target denoised ECG signal from the denoised ECG signal from the first channel and the denoised ECG signal from the second channel based on the comparison result, the processing module is specifically used for: The signal quality of the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel are compared in the current period to obtain the comparison result of the current period; Based on the comparison results of the current cycle, the target noise-reduced ECG signal for the next cycle is selected from the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel.

3. The mobile monitoring device as described in claim 2, characterized in that, The comparison result is a result of signal quality; based on the comparison result of the current cycle, when selecting the target denoised ECG signal for the next cycle from the denoised ECG signal of the first channel and the denoised ECG signal of the second channel, the processing module is specifically used for: The higher-quality denoised ECG signal from the first channel and the second channel within the current cycle is selected as the target denoised ECG signal for the next cycle.

4. The mobile monitoring device as described in claim 2, characterized in that, The comparison result represents the signal quality difference; based on the comparison result of the current cycle, when selecting the target denoised ECG signal for the next cycle from the denoised ECG signal of the first channel and the denoised ECG signal of the second channel, the processing module is specifically used for: Determine whether the signal quality difference in the current period meets a preset difference condition; If the signal quality difference in the current cycle meets the preset difference condition, the noise-reduced ECG signal with higher quality between the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel is determined as the target noise-reduced ECG signal for the next cycle.

5. The mobile monitoring device as described in claim 4, characterized in that, When comparing the signal quality of the denoised ECG signal from the first channel and the denoised ECG signal from the second channel within the current period to obtain the comparison result for the current period, the processing module is specifically used for: The channel from which the target noise-reduced ECG signal of the current cycle originates is determined as the current channel; the current channel is either the first channel or the second channel; The signal quality difference of the current cycle is obtained by comparing whether the signal quality of the noise-reduced ECG signal of the other channel is higher than that of the noise-reduced ECG signal of the current channel; wherein, the other channel is a channel other than the current channel among the first channel and the second channel.

6. The mobile monitoring device as described in claim 5, characterized in that, The processing module is also used for: If the signal quality difference in the current cycle does not meet the preset difference condition, the noise-reduced ECG signal of the current channel will continue to be used as the target noise-reduced ECG signal for the next cycle.

7. The mobile monitoring device as described in claim 4, characterized in that, When determining whether the signal quality difference in the current period meets the preset difference condition, the processing module is specifically used for: Determine the channel switching hysteresis value; wherein the channel switching hysteresis value is used to represent the minimum difference in signal quality required to switch the channel between the current period and the next period; Compare whether the signal quality difference in the current period is greater than the channel handover hysteresis value; if the signal quality difference in the current period is greater than the channel handover hysteresis value, it means that the signal quality difference in the current period meets a preset difference condition; if the signal quality difference in the current period is not greater than the channel handover hysteresis value, it means that the signal quality difference in the current period does not meet the preset difference condition.

8. The mobile monitoring device as described in claim 7, characterized in that, When determining the channel handover hysteresis value, the processing module is specifically used for: Determine the higher signal quality between the noise-reduced ECG signal of the first channel and the noise-reduced ECG signal of the second channel within the current cycle; If the signal quality is higher, then the channel switching hysteresis value is determined based on the higher signal quality; If the higher signal quality is not greater than a preset signal quality threshold, then the channel switching hysteresis value is determined as a preset signal change value.

9. The mobile monitoring device as described in claim 1, characterized in that, The processing module is also used for: Multiple first signal features are extracted from the noise-reduced ECG signal of the first channel, and multiple second signal features are extracted from the noise-reduced ECG signal of the second channel; wherein the first signal features and the second signal features are of the same type; The signal quality of the noise-reduced ECG signal of the first channel is determined using the aforementioned multiple first signal features; and the signal quality of the noise-reduced ECG signal of the second channel is determined using the aforementioned multiple second signal features.

10. The mobile monitoring device as described in claim 9, characterized in that, When determining the signal quality of the denoised ECG signal of the first channel using the aforementioned multiple first signal features, and when determining the signal quality of the denoised ECG signal of the second channel using the aforementioned multiple second signal features, the processing module is specifically used for: Determine the feature factor and scaling factor corresponding to each of the first signal features, and determine the feature factor and scaling factor corresponding to each of the second signal features; wherein the feature factor is used to represent the importance between signal features of the same type, and the scaling factor is used to represent the importance between signal features of different types. The signal quality of the noise-reduced ECG signal of the first channel is determined based on the feature factor and scaling factor of each of the first signal features; and the signal quality of the noise-reduced ECG signal of the second channel is determined based on the feature factor and scaling factor of each of the second signal features.

11. The mobile monitoring device as described in claim 10, characterized in that, When determining the feature factor and scaling factor corresponding to each of the first signal features, and when determining the feature factor and scaling factor corresponding to each of the second signal features, the processing module is specifically used for: Based on the signal feature interval corresponding to each of the first signal features, a feature factor is determined for each of the first signal features, and based on the signal feature interval corresponding to each of the second signal features, a feature factor is determined for each of the second signal features; Based on the types of the first signal feature and the second signal feature, different scaling factors are set for different types of the first signal feature and the second signal feature.

12. The mobile monitoring device as described in claim 10, characterized in that, The first signal feature and the second signal feature each include at least two of the following types: QRS width, RR interval, QRS morphology variability, RT interval, T-wave width, and T-wave morphology variability.

13. The mobile monitoring device as described in claim 10, characterized in that, When determining the signal quality of the denoised ECG signal of the first channel based on the feature factor and scaling factor of each of the first signal features; and when determining the signal quality of the denoised ECG signal of the second channel based on the feature factor and scaling factor of each of the second signal features, the processing module is specifically used for: Multiply the feature factor of each first signal feature by the scaling factor to obtain the product corresponding to each first signal feature, and add the products corresponding to each first signal feature to obtain the signal quality of the noise-reduced ECG signal of the first channel. Multiply the feature factor of each second signal feature by the scaling factor to obtain the product corresponding to each second signal feature, and add the products corresponding to each second signal feature to obtain the signal quality of the noise-reduced ECG signal of the second channel.

14. The mobile monitoring device as described in claim 1, characterized in that, When the original ECG signal is denoised using the motion signal to obtain a denoised ECG signal for the first channel, and the original ECG signal is denoised using the skin impedance signal to obtain a denoised ECG signal for the second channel, the processing module is specifically used for: The motion signal and the original electrocardiogram (ECG) signal are input into a first noise filter. The first noise filter performs noise reduction processing on the original ECG signal with reference to the motion signal to obtain a noise-reduced ECG signal for the first channel. The skin impedance signal and the original ECG signal are input into a second noise filter. The second noise filter performs noise reduction processing on the original ECG signal with reference to the skin impedance signal to obtain a noise-reduced ECG signal in the second channel.

15. The mobile monitoring device as described in claim 1, characterized in that, Also includes: Display module; The display module is used to display the waveform of the target noise-reduced electrocardiogram signal.

16. A mobile monitoring method, characterized in that, include: The system acquires raw electrocardiogram (ECG) signals of the monitored subject, as well as motion signals and skin impedance signals of the monitored subject. The original ECG signal is denoised by referring to the motion signal to obtain a denoised ECG signal of the first channel, and the original ECG signal is denoised by referring to the skin impedance signal to obtain a denoised ECG signal of the second channel; wherein the denoised ECG signal of the first channel and the denoised ECG signal of the second channel are denoised ECG signals obtained by denoising the same segment of the original ECG signal. Compare the signal quality of the noise-reduced ECG signal from the first channel and the noise-reduced ECG signal from the second channel; Based on the comparison results, the target noise-reduced ECG signal is determined from the noise-reduced ECG signal in the first channel and the noise-reduced ECG signal in the second channel.

17. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded and executed by the processor, it implements the method as described in claim 16.

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