Electrocardiogram abnormity monitoring method, device and equipment and storage medium

Abstract

The invention relates to an electrocardiogram abnormity monitoring method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring photoelectric volume pulse wave data of a user; inputting the photoelectric volume pulse wave data into a pre-trained electrocardiogram abnormity monitoring model to obtain an output value of the electrocardiogram abnormity monitoring model; wherein the electrocardiogram abnormality monitoring model is obtained by training a training data set comprising photoelectric volume pulse wave data and a data pair of a label for marking whether the electrocardiogram data corresponding to the photoelectric volume pulse wave data is abnormal or not, and is used for monitoring the electrocardiogram abnormality based on the input photoelectric volume pulse wave data. Outputting an output value indicating whether the electrocardiogram data corresponding to the input photoplethysmography data is estimated to be abnormal or not; a user device associated with the user is caused to alert when the output value of the electrocardiogram anomaly monitoring model indicates that the electrocardiogram data estimate of the user has an anomaly. The method can be used for conveniently and continuously monitoring the possible abnormal condition of the electrocardiogram.

Description

[0001] Divisional Application Statement

[0002] This application is a divisional application of the Chinese invention patent application with the application date of July 18, 2023, application number 202310881402.6, and invention name “Electrocardiogram abnormality monitoring method, device, equipment and storage medium”. Technical Field

[0003] The present application relates to the technical field of electrocardiogram monitoring, and in particular to an electrocardiogram abnormality monitoring method, device, equipment and storage medium. Background Art

[0004] Electrocardiogram (ECG) is a widely used medical examination method to monitor / classify cardiac abnormalities (such as myocardial infarction, ventricular hypertrophy, heart failure, etc.).

[0005] The 12-lead ECG is a standard method for obtaining cardiac function information in primary care. It is measured by placing 10 electrodes on the skin surface of the chest and limbs to record the electrical activity of the heart. The ECG waveform consists of a QRS complex representing ventricular depolarization, a P wave representing atrial depolarization, and a T wave representing ventricular repolarization. Such a waveform is an informative and reliable measurement method for reflecting cardiac function, and is therefore widely used in clinical practice for the diagnosis of heart diseases. 12-lead ECG devices can measure ECG data more completely, but they are usually bulky and equipped with electrodes, a central unit, and accessories such as a display and keyboard. It is difficult for ordinary users to conveniently use 12-lead ECG devices to perform daily continuous monitoring of their own ECG.

[0006] In contrast, portable ECG devices such as smart watches and fitness trackers are smaller in size, but can only measure one ECG lead, making the measured ECG data incomplete. In addition, they also require some user-initiated actions, such as holding the probe to close the conductive circuit for measurement, so the user needs to choose the right time to actively initiate the ECG measurement. However, cardiac abnormalities such as arrhythmias are paroxysmal in nature, and choosing the timing of ECG measurement is a challenge even for clinical experts. Therefore, it is difficult to continuously collect and monitor ECG with portable ECG devices.

[0007] Therefore, the above existing ECG measurement methods still have room for improvement. Summary of the invention

[0008] Based on this, it is necessary to provide an electrocardiogram (ECG) abnormality monitoring method, device, equipment and storage medium that can conveniently and continuously monitor possible abnormalities in the electrocardiogram in order to solve the above technical problems.

[0009] In one aspect, a method for monitoring ECG abnormality is provided, the method comprising:

[0010] Obtain the user's photoplethysmography (PPG) data;

[0011] Inputting the PPG data into a pre-trained ECG abnormality monitoring model to obtain an output value of the ECG abnormality monitoring model; wherein the ECG abnormality monitoring model is trained using a training data set including a data pair of PPG data and a label indicating whether ECG data corresponding to the PPG data is abnormal, and the ECG abnormality monitoring model is used to output an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data;

[0012] When the output value of the ECG abnormality monitoring model indicates that the ECG data estimate is abnormal, a user device associated with the user is caused to issue an alarm.

[0013] In another aspect, an ECG abnormality monitoring device is provided, comprising:

[0014] A data acquisition module is used to obtain the user's PPG data;

[0015] A model monitoring module, used for inputting the PPG data into a pre-trained ECG abnormality monitoring model to obtain an output value of the ECG abnormality monitoring model; wherein the ECG abnormality monitoring model is trained using a training data set including a data pair of PPG data and a label indicating whether ECG data corresponding to the PPG data is abnormal, and the ECG abnormality monitoring model is used for outputting an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data;

[0016] The abnormality alarm module is used to cause a user device associated with the user to issue an alarm when the output value of the ECG abnormality monitoring model indicates that the ECG data estimate is abnormal.

[0017] In another aspect, there is provided an ECG abnormality monitoring device, comprising a PPG data detector, an output device, a memory, and a processor;

[0018] The PPG data detector is used to monitor the PPG data of the user and transmit the measured PPG data to the processor;

[0019] The memory stores a computer program; and

[0020] When the processor executes the computer program, it is used to receive the PPG data from the PPG data detector and implement the method as described above, so as to use the output device to cause a user device associated with the user to issue an alarm when the output value of the ECG abnormality monitoring model indicates that there is an abnormality in the estimated ECG data of the user.

[0021] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the method as described above is implemented.

[0022] The above-mentioned ECG abnormality monitoring method, apparatus, device and storage medium, since PPG data can be obtained by measuring the user in a passive manner through a portable wearable device such as a smart watch, can conveniently and continuously monitor the user by acquiring the user's PPG data. By cleverly using a label indicating whether the ECG data corresponding to the PPG data is abnormal to mark the PPG data to form a data pair for training, an ECG abnormality monitoring model can be obtained, which can output an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data. The model can be used to monitor whether the corresponding ECG data is likely to be abnormal based on the measured PPG data, and when an abnormality is detected, the user device associated with the user sends an alarm to suggest the user to actively perform ECG measurement to obtain an accurate result of whether the ECG is abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A diagram showing an application environment of an ECG abnormality monitoring method in an embodiment;

[0024] Figure 2 A schematic diagram of a flow chart of an ECG abnormality monitoring method in one embodiment;

[0025] Figure 3 is an example of a PPG data segment retained by preprocessing in one embodiment;

[0026] Figure 4 is an example of a data segment that is not screened out by preprocessing in one embodiment;

[0027] Figure 5 A schematic diagram of the architecture of an ECG abnormality monitoring model in one embodiment;

[0028] Figure 6 Schematic diagram of the architecture of the residual-double convolution attention block in one embodiment;

[0029] Figure 7 Schematic diagram of the architecture of a dual convolutional attention block in one embodiment;

[0030] Figure 8 Schematic diagram of the architecture of a convolutional attention block in one embodiment;

[0031] Fig. 9 is an example of ECG raw data segments of leads II, V and AVR and corresponding PPG raw data segments in one embodiment;

[0032] Fig.10 is an example of an ECG pre-processed data segment marked as normal and its corresponding PPG pre-processed data segment in one embodiment;

[0033] Fig.11 is an example of an ECG pre-processed data segment marked as abnormal and its corresponding PPG pre-processed data segment in one embodiment;

[0034] Fig.12 A schematic diagram of the structure of an ECG abnormality monitoring device in one embodiment;

[0035] Fig.13 Schematic diagram of the structure of an ECG abnormality monitoring device in one embodiment. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] Due to the close relationship between cardiac function and ECG, and the fact that ECG data contains relatively rich information, especially the ECG waveform structure and the implied time information, 12-lead ECG is the standard method used in clinical practice to monitor abnormal cardiac function. However, existing ECG measurement devices are inconvenient to use. For example, 12-lead ECG devices are bulky and inconvenient for users to monitor daily, and portable ECG devices can usually only measure one ECG lead, and usually require users to "actively" sample ECG through the device, making it difficult to grasp the appropriate measurement time.

[0038] PPG is an optical technology that measures the cardiac cycle by detecting changes in blood volume. PPG can be measured "passively" by, for example, a pulse oximeter, and is often embedded as a feature in wearable devices such as fitness bands and smart watches. The PPG waveform consists of a systolic wave and a diastolic wave, which is simpler and smoother than the ECG waveform. PPG can be conveniently used for long-term continuous monitoring of physiological parameters such as pulse and respiratory rate. However, it is very difficult to classify or monitor cardiac abnormalities based on PPG because it is a peripheral measurement signal that indirectly monitors the operation of the heart, and its relatively smooth morphology reflects less cardiac information compared to ECG. Therefore, existing PPGs are mostly used to measure physiological parameters such as pulse rate, respiratory rate, and blood pressure.

[0039] This application proposes an ECG abnormality monitoring method, which uses the continuously monitored and ubiquitous PPG signal to monitor possible ECG abnormalities, including but not limited to common cardiac function abnormalities such as atrioventricular block, sinus rhythm abnormality, bundle branch block, atrial fibrillation, premature beats, etc., so as to prompt the user to perform an ECG examination to learn about cardiac risks.

[0040] The ECG abnormality monitoring method provided in this application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The terminal 102 can be a terminal device with a function of measuring PPG data. For example, the terminal 102 can be provided with a PPG data detector, such as a pulse oximeter. By way of example, the terminal 102 can be a portable wearable terminal device such as a smart watch, a smart bracelet, a smart headset, etc., which has a built-in PPG data detector. The terminal 102 can also have other forms, such as a medical monitor equipped with a pulse oximeter, or a system consisting of a smart bracelet with a built-in pulse oximeter and a smart phone that wirelessly communicates with the smart bracelet, etc. Those skilled in the art will be able to think of many other variations, which are not limited in this application. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0041] The ECG abnormality monitoring method of the present application may include a training phase and a testing phase. In the training phase, the ECG abnormality monitoring model may be trained using a training data set to obtain a trained ECG abnormality monitoring model, the abnormalities including but not limited to common ECG lead abnormalities such as atrioventricular block, sinus rhythm abnormality, bundle branch block, atrial fibrillation, premature beats, etc. The training process may be executed on the server 104, and after completing the training to obtain the trained ECG abnormality monitoring model, the trained ECG abnormality monitoring model may be deployed from the server 104 to the terminal 102 for use. After completing the training to obtain the trained ECG abnormality monitoring model, in the testing phase, the terminal 102 may monitor the PPG data of the user using its PPG detector, and execute the ECG abnormality monitoring method of the embodiment of the present application, so that when the output value of the ECG abnormality monitoring model indicates that the user's ECG data is estimated to be abnormal, the user device associated with the user issues an alarm, for example, the terminal 102 itself issues an alarm and / or other associated devices outside the terminal 102 issue an alarm. It can be understood that where some or all of the steps of the above-mentioned ECG abnormality monitoring method are performed can be appropriately adjusted between the terminal 102 and the server 104. For example, the trained ECG abnormality monitoring model can also be deployed on the server 104. The terminal 102 sends the collected PPG data to the server 104, and the server 104 uses the model to monitor and sends the monitoring results back to the terminal 102, etc. This application does not limit this.

[0042] Further, the above alarm can be used to indicate that an ECG measurement is further performed on the user. The ECG measurement can be performed by the terminal 102. For example, an ECG measurement device, such as an ECG electrode or an ECG sensor, can be deployed on the terminal 102, so as to perform an ECG measurement on the user using the terminal 102. The ECG measurement can also be measured by any other ECG measurement device outside the terminal 102.

[0043] In one embodiment, Figure 2 As shown, a method for monitoring ECG abnormality is provided. In the testing phase, the method includes the following steps S210-S230:

[0044] S210, obtaining the user's PPG data.

[0045] PPG tracing is a non-invasive detection method that uses photoelectric means to detect changes in blood volume in living tissue. PPG can be detected by a PPG data detector, which is any detector device that can detect the user's PPG in a non-invasive manner, and may include a light source and a photodetector, the light source emits light of a predetermined wavelength to the measured tissue, the photodetector receives light reflected or transmitted by the measured tissue, and obtains a detected PPG signal based on the intensity of the light reflected or transmitted by the measured tissue. The PPG data detector may be, for example, a pulse oximeter.

[0046] In one example, the pulse oximeter may be built into the terminal 102, which may be a smart watch. When the user wears the smart watch, the pulse oximeter located at the bottom of the smart watch dial (close to the user's skin) detects the PPG data on the inside of the user's wrist. In other examples, the terminal 102 may also have other forms, and the test site of the PPG data is not limited to the inside of the user's wrist, but may be other sites such as the inside of the lower limb ankle, the chest, etc. It can be understood that the test site of the test data collected in the test phase is the same as the test site of the training data used in the training phase.

[0047] PPG data is data that characterizes the change in the amplitude of the user's pulse wave over time, and can be represented as one-dimensional data, that is, a PPG one-dimensional data sequence obtained by arranging the sampled values ​​of the pulse wave in the order of sampling time.

[0048] In one embodiment, the acquired PPG data of the user is the PPG data of the user monitored in real time by a portable terminal device.

[0049] In one example, the terminal 102 continuously samples and monitors the test part of the user under test at a predetermined sampling frequency. The terminal 102 intercepts a PPG data segment of a predetermined duration from the PPG signal it continuously monitors as the PPG data to be input into the model. The predetermined sampling frequency and the predetermined duration can be set according to actual needs. For example, the predetermined sampling frequency can be 125 Hz, and the predetermined duration can be 10 s. Then, a PPG data segment of 10 s in length can include approximately 1250 sampling values. Figure 3 The data represented by the dark grey curve in the figure shows an example of a 10 s long PPG raw data segment, wherein the horizontal axis represents time, which is represented by the sampling point sequence number, and the vertical axis represents amplitude. The horizontal and vertical axes in the ECG and PPG data segments shown in the remaining figures of the present application also have the same meaning.

[0050] The collected raw PPG data may have problems such as noise or low quality, which may affect the accuracy of the monitoring results. Therefore, the collected raw data can be preprocessed and then the preprocessed data can be input into the model for monitoring.

[0051] In one embodiment, the above step S210 may include: obtaining the user's PPG raw data, preprocessing the PPG raw data to obtain PPG preprocessed data. Thereby using the PPG preprocessed data as the PPG data to be input into the model. Accordingly, in the subsequent step S220, the PPG preprocessed data may be input into the pre-trained ECG abnormality monitoring model.

[0052] In one embodiment, preprocessing the PPG raw data to obtain PPG preprocessed data may include one or more of the following steps:

[0053] 1) Flatness detection and signal screening:

[0054] In this step, it is detected whether the flatness of the signal reaches a predetermined standard. If so, the signal is retained; otherwise, the signal is discarded.

[0055] For example, for a 10s PPG raw data segment, if there are any 60 consecutive sampling points (about 0.5 seconds) whose sampling values ​​do not exceed the threshold (1e-5), it is regarded as a flat signal and the entire 10s data segment is discarded. Otherwise, the data segment is retained.

[0056] 2) Normalization:

[0057] In this step, the signal is normalized to zero mean and unit variance.

[0058] For example, each sampling value in the data segment retained by the above-mentioned flatness detection and signal screening steps can be normalized to zero mean value and unit variance to obtain a normalized data segment.

[0059] 3) Filtering:

[0060] In this step, the signal is filtered.

[0061] For example, a third-order bandpass Butterworth filter may be used to filter the normalized data segments in the above step to obtain filtered data segments, wherein the low-frequency band cutoff of the filter is 0.5 Hz and the high-frequency band cutoff is 8 Hz.

[0062] 4) Peak detection and signal screening:

[0063] In this step, it is detected whether the number of effective peaks per unit length of the signal reaches a predetermined standard. If so, the signal is retained; otherwise, the signal is discarded.

[0064] For example, the Python toolbox "HeartpPy" can be used to detect the number of valid peaks in the data segment after filtering in the above step. If the number of valid peaks in the 10s data segment is less than 5 (corresponding to less than 30bpm), the data segment is discarded. Otherwise, the data segment is retained.

[0065] 5) Skewness SQI detection and signal screening:

[0066] In this step, it is detected whether the skewness of the signal reaches a predetermined standard. If so, the signal is retained, otherwise, the signal is discarded.

[0067] For example, the skewness is calculated in a sliding window manner, with a window width of 250 sampling values ​​(two seconds) and a step of 125 sampling values ​​(one second). For the 10s data segment retained in the aforementioned peak detection and signal screening steps, the skewness of the sampling values ​​in each of the nine time windows of 0-2s, 1-3s, 2-4s, 3-5s, 4-6s, 5-7s, 6-8s, 7-9s, and 8-10s can be calculated. If most of the nine skewnesses calculated (more than 50%) are negative, the data segment is considered to be a low-quality signal and is discarded. Otherwise, the data segment is retained.

[0068] 6) Replacement of outlier sampling values:

[0069] In this step, outlier sampling values ​​in the signal are detected, and the detected outlier sampling values ​​are replaced with the median of the sampling values ​​to obtain a signal after the outlier sampling values ​​are eliminated.

[0070] For example, a Hampel filter can be applied to detect outliers. In the data segment retained in the aforementioned skewness SQI detection and signal screening steps, the median absolute deviation (MAD) is calculated for every 10 consecutive sample values. The standard deviation (std) of the data segment is then estimated based on the MAD value assuming a normal distribution. If the sample value differs from the median of the data segment by 3std, the sample value is detected as an outlier. The detected outliers are replaced with the median of the data segment. In this way, more outliers at the beginning and end of the data segment can be replaced.

[0071] Step 1) can be considered as a quality screening step. Steps 2) and 3) are cleaning and denoising, and steps 4) to 6) are quality control steps. Figure 3 As shown, Figure 3 FIG. 4 shows a PPG data segment retained by the above preprocessing steps 1) to 6), wherein the dark grey curve represents the original data segment and the light grey curve represents the preprocessed data segment obtained after preprocessing. Figure 4 As shown, Figure 4An example of data segments that are screened out due to failure to pass the different quality control criteria in the above preprocessing steps is shown in FIG. Figure 4 (a) in the figure indicates a data segment that was screened out because the flatness did not meet the standard, (b) indicates a data segment that was screened out because the number of effective peaks did not meet the standard, and (c) indicates a data segment that was screened out because the skewness did not meet the standard.

[0072] In this example, for any original data segment, the preprocessed data segment obtained after the above preprocessing steps can be used for monitoring in the subsequent step input model; if any original data segment is discarded in any of the above steps, the original data segment will no longer be used for monitoring. That is, only the preprocessed data segments that meet the requirements after being selected by the preprocessing steps are used for monitoring, thereby ensuring the accuracy of monitoring. It can be understood that the preprocessing operations performed on the original data in the testing phase can be the same as the preprocessing operations performed on the original data in the training phase to ensure the applicability of the model.

[0073] S220, input the PPG data into a pre-trained ECG abnormality monitoring model to obtain an output value of the ECG abnormality monitoring model.

[0074] Among them, the ECG abnormality monitoring model of the present application is trained using a training data set including a data pair including PPG data and a label indicating whether the ECG data corresponding to the PPG data is abnormal. The architecture and training process of the ECG abnormality monitoring model will be described later.

[0075] The trained ECG anomaly monitoring model can be used to output an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data.

[0076] For example, when the 10s data segment obtained in the above step is input into the ECG abnormality monitoring model, the ECG abnormality monitoring model can output the monitoring category probability As the output value, the probability of the monitoring category By comparing it with the set threshold T, it can be determined whether the corresponding ECG data is estimated to be abnormal. indicates that the ECG data estimate is abnormal, and when indicates that there is no abnormality in the ECG data estimation.

[0077] S230 : When the output value of the ECG abnormality monitoring model indicates that the ECG data is estimated to be abnormal, a user device associated with the user sends out an alarm.

[0078] In one embodiment, the user device associated with the user in S230 may include the above-mentioned terminal 102, such as a portable terminal device worn by the user, to inform the user that he or she has an ECG abnormality risk and should perform ECG measurement in time.

[0079] In another embodiment, the user device associated with the user in S230 may also be a monitoring terminal device of a guardian associated with the user, such as a terminal device used by the user's family member or caregiver, or a terminal device used by the user's family doctor or attending physician, or a terminal device or server of the monitoring agency to which the user belongs, etc., to inform the guardian that the user is at risk of ECG abnormality and that ECG measurement should be performed in a timely manner.

[0080] In one embodiment, causing a user device associated with a user to sound an alarm includes one or more combinations of the following: displaying visual information on a display screen of the user device indicating that an abnormality is estimated in the user's ECG data, causing an indicator light of the user device to light up indicating that an abnormality is estimated in the user's ECG data, causing a vibrator of the user device to vibrate indicating that an abnormality is estimated in the user's ECG data, and causing a sound output device of the user device to sound a sound prompt indicating that an abnormality is estimated in the user's ECG data.

[0081] For example, the content that causes the user device associated with the user to issue an alarm includes, in addition to notifying the monitoring result that the ECG data is estimated to be abnormal, also suggestion information suggesting that the user actively perform ECG measurement.

[0082] Furthermore, in one embodiment, the method may further include: when the output value of the ECG abnormality monitoring model indicates that the ECG data is estimated to be abnormal, making an alarm call to a predetermined alarm receiving device.

[0083] The alarm call may be initiated by an alarm initiating device, which may be any suitable terminal or server. For example, the alarm call may be initiated by a portable terminal device worn by the user. In this case, the portable terminal device needs to be a terminal device with a call-making function, such as a smart watch or a smart bracelet and a mobile phone paired with the smart bracelet; or the alarm call may be initiated by, for example, the server 104 or other servers or terminal devices.

[0084] The alarm call can be automatically initiated by the terminal or server or can be initiated based on the user's instructions. For example, the terminal or server can provide the user with the option of initiating an alarm call via a portable terminal device worn by the user, and then decide whether to make an alarm call to the predetermined alarm receiving device based on the user's selection input for the option. Alternatively, after providing the above options to the user, if the terminal or server does not receive the user's selection input within a predetermined period of time, it may be considered that the user may be in a dangerous condition such as losing consciousness, and the alarm call will be automatically made to the predetermined alarm receiving device.

[0085] The predetermined alarm receiving device for receiving alarm calls can be pre-set as needed. For example, the predetermined alarm receiving device can include the monitoring terminal device of the guardian associated with the user, and / or the alarm terminal of a designated medical institution (for example, a hospital emergency telephone terminal that can be dialed by dialing 112), etc.

[0086] Depending on the pre-settings or the user's selection input, the alarm call can be a real-time call that allows the user to talk to the recipient at the predetermined alarm receiving device. The alarm call can also include pre-set voice broadcast content, for example, it can include voice broadcast content that informs the user that the ECG data is estimated to have abnormal monitoring results. In addition, it can also include personal information such as the user's name, gender, age, residence, and the user's real-time location, so that the alarm call recipient can quickly know the user's current condition and real-time location.

[0087] In one embodiment, the method may further include: when the output value of the ECG abnormality monitoring model indicates that the ECG data is estimated to be abnormal, performing ECG measurement on the user to obtain a measurement result indicating whether the user's ECG is abnormal.

[0088] The ECG measurement may be performed by a terminal 102 provided with an ECG measurement device, or by other ECG measurement equipment different from the terminal 102, such as a 12-lead ECG provided in a medical institution, or a home ECG measurement equipment, etc. For example, after the terminal 102 issues an alarm to remind the user to perform ECG measurement in step S230, the terminal 102 may determine whether to trigger the ECG measurement of the user by the terminal 102 based on the user input. For another example, the terminal 102 may automatically trigger the ECG measurement of the user when the output value indicates that the ECG data is estimated to be abnormal. For another example, after the terminal 102 issues an alarm to remind the user / guardian to perform ECG measurement in step S230, the user / guardian may choose to perform ECG measurement of the user using other ECG measurement equipment other than the terminal 102. For example, the user may go to a medical institution to perform a more professional and comprehensive ECG measurement using a 12-lead ECG, and obtain a measurement result of whether the ECG is abnormal. When the ECG measurement is performed by terminal 102 , terminal 102 may analyze the measured ECG data by itself or via an external device such as a server, and push the measured ECG data and the measurement result of whether the ECG is abnormal to the user through terminal 102 .

[0089] The above-mentioned ECG abnormality monitoring method, since PPG data can be obtained by measuring the user in a passive manner through a portable wearable device such as a smart watch, can conveniently and continuously monitor the user by acquiring the user's PPG data. By cleverly using a label indicating whether the ECG data corresponding to the PPG data is abnormal to mark the PPG data to form a data pair for training, an ECG abnormality monitoring model can be obtained, which can output an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data. The model can be used to monitor whether the corresponding ECG data is likely to be abnormal based on the measured PPG data, and when an abnormality is detected, the user device associated with the user sends an alarm to suggest the user to actively perform ECG measurement to obtain an accurate result of whether the ECG is abnormal.

[0090] The above describes the method steps that the terminal 102 or the server 104 may execute during the testing phase, wherein in step S220 of the testing phase, a pre-trained ECG abnormality monitoring model is used, which requires that in the training phase before the testing phase, the model architecture of the ECG abnormality monitoring model is pre-constructed, and the constructed ECG abnormality monitoring model is trained to optimize and determine the model parameters of the ECG abnormality monitoring model to obtain a trained signal quality assessment model.

[0091] The present application proposes a dual-convolutional attention network (Dual-Convolutional Attention Network, DCA-Net) as the above-mentioned ECG abnormality monitoring model. The dual-convolutional attention network is characterized in that it includes one or more residual-dual convolutional attention (Res-DAC) blocks. Each residual-dual convolutional attention block is formed by adding a dual-convolutional attention (dual-convolutional-attention, DCA) block to the residual block. Among them, the dual convolutional attention block includes a first convolutional attention block for convolving data in the channel dimension and a second convolutional attention block for convolving data in the time dimension.

[0092] In one embodiment, see Figure 5 As shown, the ECG abnormality monitoring model may include at least a plurality of residual-dual convolution attention blocks 1 to N arranged in series.

[0093] In one embodiment, see Figure 5 As shown, the ECG abnormality monitoring model may also include an input convolution layer and a pooling layer located before the multiple residual-double convolution attention blocks and arranged in series with the multiple residual-double convolution attention blocks, and an average pooling layer and a fully connected layer located after the multiple residual-double convolution attention blocks and arranged in series with the multiple residual-double convolution attention blocks.

[0094] Among them, the input convolution layer receives the PPG data input to the ECG abnormality monitoring model, the output of the input convolution layer is fed into the pooling layer, the output of the pooling layer is fed into the first residual-double convolution attention block in multiple residual-double convolution attention blocks, the output of the previous one in multiple residual-double convolution attention blocks is fed into the next one, and the output of the last residual-double convolution attention block in multiple residual-double convolution attention blocks is fed into the average pooling layer, the output of the average pooling layer is fed into the fully connected layer, and the fully connected layer outputs the output value of the ECG abnormality monitoring model.

[0095] The last layer of the fully connected layer may have a single neuron that outputs a 1*1-dimensional output value, i.e., the probability The probability By comparing with the set threshold T, the monitoring result of whether the ECG is estimated to be abnormal can be obtained.

[0096] For example, each convolution layer in the model, such as the input convolution layer, can be a one-dimensional convolution layer to meet the processing requirements of the PPG one-dimensional data sequence.

[0097] Understandably, Figure 5The model architecture in the figure is only an example. Under the basic premise that the model includes one or more residual-dual convolution attention blocks as proposed in this application, technicians in this field can also make many modifications to the architecture of the model. For example, the number and serial / parallel mode of residual-dual convolution attention blocks can be changed, and the number and position of convolutional layers and pooling layers in the model can also be changed.

[0098] In one embodiment, see Figure 6 and Figure 7 As shown, each residual-dual convolution attention block in the multiple residual-dual convolution attention blocks may include a first main path and a first short-circuit branch path. One or more convolution layers and dual convolution attention blocks are arranged in series on the first main path. Among them, the dual convolution attention block includes a first convolution attention block and a second convolution attention block arranged in series or in parallel, the first convolution attention block is used to perform convolution in the channel dimension, and the second convolution attention block is used to perform convolution in the time dimension. The first short-circuit branch path is connected in parallel to one or more convolution layers arranged in series on the first main path and both ends of the dual convolution attention block.

[0099] Among them, the former of one or more convolutional layers and dual convolutional attention blocks and the first short-circuit branch path receive the output of the previous path, the latter of one or more convolutional layers and dual convolutional attention blocks receive the output of the former, and the output of the last of one or more convolutional layers and dual convolutional attention blocks is added to the output matrix of the first short-circuit branch path and fed into the subsequent path.

[0100] In one embodiment, see Figure 6 As shown, the output side of each convolution layer in each residual-dual convolution attention block is also provided with a batch normalization layer and an activation function layer. In each residual-dual convolution attention block, the batch normalization layer and the activation function layer on the output side of each convolution layer except the last convolution layer are arranged before the next convolution layer, and the batch normalization layer on the output side of the last convolution layer is arranged between the convolution layer and the dual convolution attention block, the dual convolution attention block is arranged before the addition operation with the output of the first short-circuit branch path, and the batch normalization layer arranged on the output side of the last convolution layer is arranged after the addition operation with the output of the first short-circuit branch path.

[0101] In a specific example, see Figure 6 As shown, two convolution layers are set in each residual-double convolution attention block, and they are one-dimensional convolution layers. The activation function layer is a ReLU activation function layer. In other examples, the number and dimension of the convolution layers in the residual-double convolution attention block can be changed according to the situation, and the activation function layer can also be other types of activation function layers.

[0102] In one embodiment, see Figure 7 As shown, the first convolutional attention block and the second convolutional attention block are set in series, and the first convolutional attention block is located before the second convolutional attention block. Placing the first convolutional attention block in terms of channel before the second convolutional attention block in terms of time allows the dual convolutional attention blocks to first learn the channel-to-channel interaction and then learn the temporal interaction based on the channel participation output, which performs better than the two in parallel or the first convolutional attention block in terms of channel in the latter order. In other examples, the second convolutional attention block may be located before the first convolutional attention block or the first convolutional attention block and the second convolutional attention block may be set in parallel.

[0103] In one embodiment, see Figure 8 As shown, each of the first convolutional attention block and the second convolutional attention block includes: a second main path and a second short-circuit branch path. The second main path is provided with a maximum pooling layer and an average pooling layer, a convolution layer and an activation function layer, the maximum pooling layer and the average pooling layer are arranged in parallel, and the parallel maximum pooling layer and the average pooling layer are sequentially arranged in series with the convolution layer and the activation function layer. The second short-circuit branch path is connected in parallel to both ends of the maximum pooling layer and the average pooling layer, the convolution layer and the activation function layer arranged on the second main path.

[0104] Among them, the second short-circuit branch path, the maximum pooling layer and the average pooling layer respectively receive the output of the previous path, the output of the maximum pooling layer and the output of the average pooling layer are concatenated and fed into the convolution layer, the output of the convolution layer is fed into the activation function layer, and the output of the activation function layer is multiplied by the output of the second short-circuit branch path and fed into the subsequent path.

[0105] In a specific example, see Figure 8 As shown, the first / second convolutional attention block has a convolutional layer, and it is a one-dimensional convolutional layer. The activation function layer is a Sigmoid activation function layer. In other examples, the number and dimension of the convolutional layers in the first / second convolutional attention block can be changed according to the situation, and the activation function layer can also be other types of activation function layers.

[0106] Among them, the convolutional attention block relies on applying a convolution operation to a certain dimension of the data to learn the interactions between data points along that dimension. Compared with the MLP attention module, convolutional attention is more computationally efficient because no parameters are required in the convolutional layer. Figure 8 As can be seen in the figure, the first convolutional attention block and the second convolutional attention block have similar architectures, but they are applied in different directions. The first convolutional attention block is applied to the convolution channel direction, and the second convolutional attention block is applied to the time domain direction.

[0107] In the above embodiments, the specific configurations of the parameters of each layer / block, such as activation function, kernel size, step size, padding, etc., can be set according to actual needs.

[0108] Specifically, for Figure 7 and Figure 8 The specific example shown is Figure 8 Assume that the convolutional layer in is a one-dimensional convolutional layer with a convolution kernel size of 7. Suppose the input of the first convolutional attention block in the channel (also the input of the DCA module) is Where N is the sample point / batch size, C is the number of convolution channels output from the original residual block, and D is the signal length (remembered as the time dimension). First Convolutional Attention Block The output is:

[0109]

[0110] Where g(X) = [p1(X) || p2(X)] D (2)

[0111] Where p1(X) = max_pool D (X) (3)

[0112] And, p2(X)=mean_pool D (X) (4)In equation (1), represents the Hadamard product (element-wise matrix multiplication); σ(·) represents the Sigmoid activation function, represents a 1D convolutional layer with a kernel size of 7 and convolution in the channel direction. [·||·] in Equation (2) D The subscript D in equations (3) and (4) indicates that the pooling layer is also applied in the time dimension. Output channel-wise attention weights, which are further extended to X of the same shape to scale X. Then the scaled X (denoted as ) is used as input to the temporal attention.

[0113] Similarly, let the output of the second convolutional attention block in time be

[0114]

[0115] in,

[0116] in,

[0117] and,

[0118] Equations (7)-(8) indicate that the maximum pooling and average pooling are first performed on the channel dimension, so and Then, the pooled matrix is ​​combined along the channel dimension, (Equation (6)). The one-dimensional convolutional layer convolves in the time dimension and outputs a single channel, so that Finally, this convolved matrix is ​​activated by the Sigmoid activation function and used to scale the input time domain.

[0119] In a specific example, a DCA-Net model of a specific example can be obtained by replacing all two-dimensional convolutional layers in the known ResNet-34 model with one-dimensional convolutional layers, replacing the last fully connected layer in the ResNet-34 model with a fully connected layer with a 512-dimensional input and a 1-dimensional output, and adding a double convolutional attention block before adding the residual to each residual block of the ResNet-34 model. It can be understood that the DCA-Net model of this specific example will include a serial input convolutional layer, a pooling layer, 16 residual-double convolutional attention blocks, an average pooling layer, and a fully connected layer. The architecture of the DCA-Net model of this specific example can be referred to Figure 5 As shown, where N is N=16, and the architecture of each residual-double convolution attention block can be referred to Figures 6 to 8 As shown. For example, in each residual-double convolutional attention block, the convolution kernel size of the first convolutional attention block and the second convolutional attention block can be 7, the step size is 1, the average pooling layer can be a one-dimensional adaptive average pooling layer (AdaptiveAvgPool1d), and the parameters of other layers and blocks in the model can refer to the parameter settings of the known ResNet-34 model.

[0120] For example, the forward propagation process of the ResNet-34 model of this specific example is explained by taking batch training as an example. When the batch size is 64, a 64x1x1250 one-dimensional PPG data is input to the DCA-Net model of this specific example, after the input convolution layer (with a stride of 2), 64×64×625 data is output, and after the (maximum) pooling layer (with a stride of 2), 64×64×313 data is output. Then, the data is input to the first residual-double convolution attention block, and then passes through the Figure 6After the convolution layer, batch normalization layer, ReLU activation function layer, convolution layer, batch normalization layer shown in, it is input into the double convolution attention layer. The input data first enters the first convolution attention block in the channel. In the first convolution attention block, see Figure 7 , the average pooling layer performs average pooling on the input data in the time dimension and outputs a 64×64×1 matrix. At the same time, the maximum pooling layer performs maximum pooling on the input data in the time dimension and outputs another 64×64×1 matrix. These two matrices are combined along the time dimension (similar to a splicing operation) to obtain a 64×64×2 matrix. The 64×64×2 matrix is ​​convolved along the channel dimension through the convolution layer to learn the 64×64×1 attention weight, and then copied and expanded along the time dimension into a 64×64×313 attention score. This attention score is Hadamard-multiplied with the original input data output through the second short-circuit branch path to output a 64×64×313 matrix. Next, the 64×64×313 matrix output by the first convolutional attention block is input into the second convolutional attention block in terms of time. In the second convolutional attention block, see Figure 7 , the average pooling layer performs average pooling on the 64×64×313 matrix in the channel dimension and outputs a 64×1×313 matrix. At the same time, the maximum pooling layer performs maximum pooling on the input data in the channel dimension and outputs another 64×1×313 matrix. These two matrices are combined to obtain a 64×2×313 matrix. The 64×2×313 matrix is ​​convolved along the time dimension through the convolution layer to obtain a 64×1×313 attention weight, and then replicated and expanded along the channel dimension to become a 64×64×313 attention score. This attention score is Hadamard-multiplied with the original input 64×64×313 matrix output through the second short-circuit branch path, and a 64×64×313 matrix is ​​output. The 64×64×313 matrix is ​​then input into the next residual-double convolutional attention block as the output of the second convolutional attention block. After 16 residual-double convolutional attention blocks, the output data is then passed through the average pooling layer and the fully connected layer to output the output value of the model.

[0121] In the training phase, by training the ECG abnormality monitoring model constructed in any of the above embodiments, a trained ECG abnormality monitoring model can be determined. The ECG abnormality monitoring model with the above structure can be trained using various conventional or novel training methods.

[0122] In order to perform model training, a training data set needs to be constructed. In the present application, the training data set may include multiple sets of data pairs of PPG data and labels indicating whether the corresponding ECG data is abnormal.

[0123] The process of constructing a training dataset can include the following steps:

[0124] 1) Obtain ECG signals of multiple leads and their corresponding PPG signals.

[0125] The correspondence between the ECG signal and the PPG signal means that the ECG signal and the PPG signal are measured simultaneously on the same subject to ensure that the ECG signal and the PPG signal are time-aligned and correlated with each other.

[0126] For example, ECG signals of multiple leads and their corresponding PPG signals can be obtained from the matching subset of the MIMIC-III Waveform Database (WDB). The MIMIC-III WDB is linked to clinical records - all patients in the matching subset are identified and matched to the medical record system. The MIMIC-III WDB includes ECG signals of multiple leads and their paired PPG signals. For each patient, waveform data of different lengths are collected at different time points during their hospitalization. The sampling frequency of these signals is 125Hz. Since different ECG records have different numbers and types of leads, in this example, data of the three most common and representative ECG leads are selected, namely leads II, V and AVR. Therefore, ECG signals using these three leads and corresponding PPG signal records are included in this analysis. However, in other examples, more or fewer ECG leads may be selected, and the type of selected leads may also be changed. In addition, in order to reduce the noise level, only records of signals with a duration exceeding a predetermined duration, such as two minutes, are selected. Signals with a duration of less than two minutes will be screened out.

[0127] 2) Extracting multiple leads' ECG raw data segments and their corresponding PPG raw data segments of a predetermined duration from the acquired multiple leads' ECG signals and their corresponding PPG signals.

[0128] The predetermined duration of the data segment to be intercepted during the training process is the same as the predetermined duration of the data segment intercepted during the test process, for example, 10 seconds. In this way, the second 10-second data segment can be intercepted from each ECG / PPG signal to avoid the instability of the sampling value in the initial period. In this way, the ECG raw data segments of leads II, V and AVR with a duration of 10 seconds and the corresponding PPG raw data segments with a duration of 10 seconds are shown in the following example. Fig. 9 as shown in .

[0129] 3) Preprocessing the intercepted ECG raw data segments of multiple leads of a predetermined time length and their corresponding PPG raw data segments respectively, so as to preprocess and screen out the ECG preprocessed data segments of multiple leads of a predetermined time length that meet the requirements and their corresponding PPG preprocessed data segments.

[0130] For the 10 s PPG raw data segment, the same preprocessing process including steps 1) to 6) as in the test process described above can be performed to screen out the PPG preprocessed data segment that meets the requirements.

[0131] For 10s of ECG raw data segments of leads II, V and AVR, a 5th-order high-pass Butterworth filter of 0.5Hz can be used, followed by power line filtering with a power line frequency of 50Hz to obtain 10s of ECG pre-processed data segments of leads II, V and AVR. Then, a predetermined signal quality assessment method is used to assess the signal quality of each ECG pre-processed data segment. The assessment outputs one of three quality categories - unacceptable, almost unacceptable or excellent. When the ECG pre-processed data segment of a certain lead is assessed as excellent, the ECG pre-processed data segment of the lead is retained, otherwise, when the ECG pre-processed data segment of a certain lead is assessed as unacceptable or almost unacceptable, the ECG pre-processed data segment of the lead is discarded.

[0132] Thus, for any group of 10s ECG raw data segments of multiple leads and their corresponding PPG raw data segments, if any one of the ECG raw data segments of the leads cannot be retained through the above preprocessing process, or the PPG raw data segments cannot be retained through the above preprocessing process, the group of data segments is discarded as a whole. Only when each of the ECG raw data segments of multiple leads in a group and their corresponding PPG raw data segments are retained through the above preprocessing process, can the group of ECG raw data segments of multiple leads and their corresponding PPG raw data segments of the predetermined time length that meet the requirements be obtained.

[0133] 4) for each group of ECG preprocessed data segments of multiple leads of a predetermined time length and their corresponding PPG preprocessed data segments, determine a label for marking whether the ECG preprocessed data segments of the multiple leads are abnormal, and form a data pair with the label and the PPG preprocessed data segment, thereby obtaining a training data set including multiple groups of PPG data and a data pair of labels for marking whether the ECG data corresponding to the PPG data are abnormal.

[0134] Since ECG labels are not given in MIMIC-III WDB, for example, a known ECG classification model with high accuracy, such as AutoNet, can be used to efficiently label whether the ECG pre-processed data segment is abnormal. Fig.10 and Fig.11 As shown, Fig.10 An example of an ECG pre-processed data segment marked as normal and its corresponding PPG pre-processed data segment is shown in FIG. Fig.11 An example of an ECG preprocessing data segment marked as abnormal and its corresponding PPG preprocessing data segment is shown in FIG. In other alternative examples, a professional technician, such as a clinician, may manually mark whether the ECG preprocessing data segment is abnormal to ensure the accuracy of the label. In this way, multiple sets of data pairs such as PPG preprocessing data segments and labels marking whether the corresponding preprocessing data segments ECG are abnormal can be obtained to form the required training data set.

[0135] After the training data set is constructed, the ECG abnormality monitoring model can be trained using the training data set to determine a trained ECG abnormality monitoring model.

[0136] In one embodiment, the training process of the ECG abnormality monitoring model includes:

[0137] Obtain a training data set; the training data set includes multiple sets of PPG data-labeled data pairs indicating whether the corresponding ECG data is abnormal;

[0138] Divide the training data set into training set, validation set and test set;

[0139] The training set is used to train the ECG abnormality monitoring model to obtain the fitted model parameters. The validation set is used to verify the ECG abnormality monitoring model determined by the fitted model parameters and adjust the model hyperparameters. The test set is used to evaluate the performance of the trained ECG abnormality monitoring model determined by the adjusted hyperparameters and the trained model parameters.

[0140] For example, the DCA-Net model of the above-mentioned specific example can be applied to the PPG data segments processed based on MIMIC-III WDB, and the corresponding ECG data segments are marked with a label for binary classification to be trained. The 38,320 sets of PPG data segments-labels of whether the corresponding ECG data segments are abnormal are divided into training sets, validation sets, and test sets, with a ratio of 64%: 16%: 20%. The model parameters are learned using the training set and verified in the validation set to avoid overfitting. The test set is only used to report the results. The model performance was evaluated using specificity, sensitivity / recall, precision, accuracy, AUROC (area under the receiver operating characteristic curve) and AUPRC (area under the precision-recall curve). The model was further randomly repeated 10 times, and the average and standard deviation of the above evaluation indicators were used.

[0141] The DCA-Net model can be implemented using PyTorch version 1.11.0 and CUDA version 11.7. The loss function is binary cross entropy. Further experiments adjust for class imbalance by increasing the loss weight. The model is optimized by the Adam optimizer with default hyperparameter settings and trained using a batch size of 64. A learning rate scheduler in terms of strides is applied to reduce the learning rate by a factor of 0.1 every 10 epochs. Early stopping is also applied - training is terminated if the validation loss does not decrease compared to the current lowest loss for 5 consecutive epochs. The model with the lowest loss is always saved and applied to the test set for evaluation.

[0142] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0143] Performance Testing

[0144] In order to verify the superior performance of the DCA-Net model proposed in this application in terms of architecture, the DCA-Net model of the above specific example of this application and the existing neural network models ResNet-18, ResNet-34, ResNet-50, and ECA-Net are also used to monitor ECG abnormalities based on PPG data for performance testing and comparison. The comparison results are shown in Table 1 below. In Table 1, the numbers shown are the average score percentages of 10 random repetitions, and their standard deviations are shown in brackets. The best average accuracy, AUROC, and AUPRC are highlighted in bold.

[0145] Table 1 Performance comparison results of the DCA-Net model of a specific example of this application and the existing neural network model.

[0146]

[0147] As can be seen from the above table, the DCA-Net proposed in this application has the best average accuracy, AUROC and AUPRC scores. In the one-sided T test, the AUPRC of DCA-Net is significantly better than that of ECA-Net, with p value = 0.01. In addition, due to the smaller standard deviation of all evaluation measures, the performance of DCA-Net is more stable than that of ECA-Net.

[0148] In one embodiment, Fig.12 As shown, an ECG abnormality monitoring device 1200 is provided, comprising:

[0149] The data acquisition module 1210 is used to acquire the PPG data of the user;

[0150] The model monitoring module 1220 is used to input the PPG data into a pre-trained ECG abnormality monitoring model to obtain an output value of the ECG abnormality monitoring model; wherein the ECG abnormality monitoring model is trained using a training data set including a data pair of PPG data and a label indicating whether the ECG data corresponding to the PPG data is abnormal, and the ECG abnormality monitoring model is used to output an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data;

[0151] The abnormality alarm module 1230 is configured to cause a user device associated with a user to issue an alarm when the output value of the ECG abnormality monitoring model indicates that an abnormality exists in the ECG data estimation.

[0152] In one embodiment, the ECG abnormality monitoring device 1200 also includes a model training module 1240, and the model monitoring module 1220 is also used to obtain a trained ECG abnormality monitoring model from the model training module 1240. The model training module 1240 is used to train the ECG abnormality monitoring model to obtain a trained ECG abnormality monitoring model.

[0153] For example, the data acquisition module 1210 , the model monitoring module 1220 , and the abnormal alarm module 1230 may be arranged in the terminal 102 , while the model training module 1300 may be arranged in the server 104 .

[0154] For the specific definition of the ECG abnormality monitoring device 1200, please refer to the definition of the ECG abnormality monitoring method above, which will not be repeated here. Each module in the above-mentioned ECG abnormality monitoring device 1200 can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0155] In one embodiment, Fig.13 As shown, an ECG abnormality monitoring device 1300 is provided, including a PPG data detector 1310, a memory 1320 and a processor 1330;

[0156] The PPG data detector 1310 is used to monitor the user's PPG data and transmit the measured PPG data to the processor; the memory 1320 stores a computer program; and

[0157] When the processor 1330 executes the computer program, it is used to receive PPG data from the PPG data detector 1310 and implement the method of any of the above embodiments.

[0158] In one embodiment, the PPG data detector may be a pulse oximeter.

[0159] In one embodiment, the ECG abnormality monitoring device 1300 further includes an output device 1340, which may include a display screen, an indicator light, a vibrator, and / or a sound output device. The display screen may be a liquid crystal display screen or an electronic ink display screen, etc. The sound output device may be a device with a sound output function such as a speaker or a buzzer. In this way, when the ECG abnormality monitoring device 1300 itself is used to issue an alarm, the alarm may be issued through the output device 1340.

[0160] In one embodiment, the ECG abnormality monitoring device 1300 also has a phone-making function. For example, the ECG abnormality monitoring device 1300 may also include a dialing device with a phone-making function. The dialing device may be implemented, for example, by a smart watch, mobile phone or server associated with the user, so that when an alarm call needs to be made to a predetermined alarm receiving device, the alarm call can be made through the dialing device.

[0161] In one embodiment, the ECG abnormality monitoring device 1300 also has an ECG measurement function. For example, the ECG abnormality monitoring device 1300 may also include an ECG measurement device to further perform ECG measurement on the user using the ECG measurement device when the ECG abnormality is estimated using PPG data.

[0162] It can be understood that the ECG abnormality monitoring device 1300 can be any device that can execute the method of the above-mentioned embodiment of the present application, and it may have various different forms. For example, it may include the aforementioned terminal 102, or it may also include the aforementioned terminal 102 and the server 104.

[0163] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the following steps are implemented:

[0164] Get the user's PPG data;

[0165] Inputting the PPG data into a pre-trained ECG abnormality monitoring model to obtain an output value of the ECG abnormality monitoring model; wherein the ECG abnormality monitoring model is trained using a training data set including a data pair of PPG data and a label indicating whether the ECG data corresponding to the PPG data is abnormal, and the ECG abnormality monitoring model is used to output an output value indicating whether the ECG data corresponding to the input PPG data is estimated to be abnormal based on the input PPG data;

[0166] When the output value of the ECG anomaly monitoring model indicates that an anomaly is estimated in the ECG data, a user device associated with the user is caused to issue an alarm.

[0167] In other embodiments, when the computer program is executed by a processor, the steps of the method of any of the above embodiments are also implemented.

[0168] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0169] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring abnormal electrocardiogram, characterized in that: The method comprises: Obtaining the user's photoplethysmography data; Inputting the photoplethysmographic data into a pre-trained electrocardiogram abnormality monitoring model to obtain an output value of the electrocardiogram abnormality monitoring model; wherein the electrocardiogram abnormality monitoring model is a pre-trained model based on the input photoplethysmographic data, and its output indicates whether the electrocardiogram data corresponding to the input photoplethysmographic data is estimated to have an output value of abnormal heart function; When the output value of the electrocardiogram abnormality monitoring model indicates that the electrocardiogram data estimates that there is abnormal heart function, a user device associated with the user sends out an alarm.

2. The method for monitoring abnormal electrocardiogram according to claim 1, characterized in that: The abnormal electrocardiogram monitoring model is trained using a training data set, wherein the training data set includes photoplethysmographic data and a label indicating whether there is abnormal heart function in electrocardiogram data corresponding to the photoplethysmographic data, and the training data label comes from multiple different patient samples.

3. The method for monitoring abnormal electrocardiogram according to claim 1, characterized in that: The step of obtaining the user's photoplethysmogram data and inputting the photoplethysmogram data into a pre-trained electrocardiogram abnormality monitoring model includes: Obtaining the user's photoplethysmographic raw data; Preprocessing the photoplethysmogram raw data to obtain photoplethysmogram preprocessed data; The photoplethysmography preprocessed data is input into a pre-trained electrocardiogram abnormality monitoring model.

4. The method for monitoring abnormal electrocardiogram according to claim 1, characterized in that: The electrocardiogram abnormality monitoring model includes one of a DCA-Net model, a ResNet-18 model, a ResNet-34 model, a ResNet-50 model and an ECA-Net model.

5. The method for monitoring abnormal electrocardiogram according to claim 1, characterized in that: The electrocardiogram abnormality monitoring model includes one or more residual-double convolution attention blocks, which are formed by adding a double convolution attention block to a residual block, and the double convolution attention block includes a first convolution attention block for convolution in the channel dimension and a second convolution attention block for convolution in the time dimension.

6. The method for monitoring abnormal electrocardiogram according to claim 5, characterized in that: The electrocardiogram abnormality monitoring model includes a plurality of the residual-double convolution attention blocks arranged in series, and each of the plurality of the residual-double convolution attention blocks includes: a first main path, wherein one or more convolutional layers and the dual convolutional attention block are arranged in series on the first main path; wherein the dual convolutional attention block includes the first convolutional attention block and the second convolutional attention block arranged in series or in parallel; and A first short-circuit branch path, wherein the first short-circuit branch path is connected in parallel to two ends of the one or more convolutional layers and the dual convolutional attention block arranged in series on the first main path; Among them, the former of the one or more convolutional layers and the dual convolutional attention blocks and the first short-circuit branch path receive the output of the previous path, the latter of the one or more convolutional layers and the dual convolutional attention blocks receives the output of the former, and the output of the last of the one or more convolutional layers and the dual convolutional attention blocks is added to the output of the first short-circuit branch path and fed into the subsequent path.

7. The method for monitoring abnormal electrocardiogram according to claim 6, characterized in that: The first convolutional attention block and the second convolutional attention block are arranged in series, and the first convolutional attention block is located before the second convolutional attention block.

8. The method for monitoring abnormal electrocardiogram according to claim 6, characterized in that: Each of the first convolutional attention block and the second convolutional attention block comprises: a second main path, wherein a maximum pooling layer and an average pooling layer, a convolution layer and an activation function layer are arranged on the second main path, wherein the maximum pooling layer and the average pooling layer are arranged in parallel, and the maximum pooling layer and the average pooling layer arranged in parallel are arranged in series with the convolution layer and the activation function layer in sequence; and A second short-circuit branch path, wherein the second short-circuit branch path is connected in parallel to both ends of the maximum pooling layer and the average pooling layer, the convolution layer, and the activation function layer provided on the second main path; Among them, the second short-circuit branch path, the maximum pooling layer and the average pooling layer respectively receive the output of the previous path, the output of the maximum pooling layer and the output of the average pooling layer are combined to feed the convolution layer, the output of the convolution layer is fed into the activation function layer, and the output of the activation function layer is multiplied by the output of the second short-circuit branch path and fed into the subsequent path.

9. The method for monitoring abnormal electrocardiogram according to claim 6, characterized in that: The output side of each convolutional layer in each residual-double convolutional attention block is also provided with a batch normalization layer and an activation function layer.

10. The method for monitoring abnormal electrocardiogram according to claim 6, characterized in that: The electrocardiogram abnormality monitoring model also includes: An input convolution layer and a pooling layer are located before the plurality of residual-double convolution attention blocks and are arranged in series with the plurality of residual-double convolution attention blocks, and An average pooling layer and a fully connected layer, located after the plurality of residual-double convolutional attention blocks and arranged in series with the plurality of residual-double convolutional attention blocks; Among them, the input convolution layer receives the photoelectric volume pulse wave data input to the electrocardiogram abnormality monitoring model, the output of the input convolution layer is fed into the pooling layer, the output of the pooling layer is fed into the first residual-double convolution attention block among the multiple residual-double convolution attention blocks, the output of the last residual-double convolution attention block among the multiple residual-double convolution attention blocks is fed into the average pooling layer, the output of the average pooling layer is fed into the fully connected layer, and the fully connected layer outputs the output value of the electrocardiogram abnormality monitoring model.

11. The method for monitoring abnormal electrocardiogram according to any one of claims 1 to 10, characterized in that: The training process of the electrocardiogram abnormality monitoring model includes: Acquire a training data set; the training data set includes a plurality of data pairs of photoplethysmographic data and corresponding electrocardiogram data with labels indicating whether there is abnormal cardiac function; Dividing the training data set into a training set, a validation set and a test set; The training set is used to train the electrocardiogram abnormality monitoring model to obtain fitted model parameters, the validation set is used to validate the electrocardiogram abnormality monitoring model determined by the fitted model parameters and adjust the model hyperparameters, and the test set is used to evaluate the performance of the trained electrocardiogram abnormality monitoring model determined by the adjusted hyperparameters and the trained model parameters.

12. The method for monitoring abnormal electrocardiogram according to any one of claims 1 to 10, characterized in that: The user device associated with the user includes a portable terminal device worn by the user and / or a monitoring terminal device of a guardian associated with the user, and the obtaining of the photoplethysmogram data of the user includes real-time monitoring of the photoplethysmogram data of the user by the portable terminal device; The method of causing the user device associated with the user to issue an alarm includes one or more of the following: displaying visual information on the display screen of the user device indicating that the user's electrocardiogram data is estimated to have abnormal heart function, causing an indicator light of the user device to light up to indicate that the user's electrocardiogram data is estimated to have abnormal heart function, causing a vibrator of the user device to vibrate to indicate that the user's electrocardiogram data is estimated to have abnormal heart function, and causing a sound output device of the user device to issue a sound prompt indicating that the user's electrocardiogram data is estimated to have abnormal heart function.

13. The method for monitoring abnormal electrocardiogram according to any one of claims 1 to 10, characterized in that: The method further comprises: When the output value of the electrocardiogram abnormality monitoring model indicates that the electrocardiogram data estimates that there is abnormal heart function, an alarm call is made to a predetermined alarm receiving device.

14. The method for monitoring abnormal electrocardiogram according to any one of claims 1 to 10, characterized in that: The method further comprises: When the output value of the electrocardiogram abnormality monitoring model indicates that the electrocardiogram data estimates the presence of abnormal heart function, an electrocardiogram measurement is performed on the user to obtain a measurement result indicating whether the electrocardiogram of the user indicates the presence of abnormal heart function.

15. An electrocardiogram abnormality monitoring device, characterized in that: The device comprises: A data acquisition module, used to acquire the user's photoplethysmography data; A model monitoring module, used for inputting the photoplethysmography data into a pre-trained electrocardiogram abnormality monitoring model to obtain an output value of the electrocardiogram abnormality monitoring model; wherein the electrocardiogram abnormality monitoring model is a pre-trained model based on the input photoplethysmography data, and its output indicates whether the electrocardiogram data corresponding to the input photoplethysmography data is estimated to have an output value of abnormal heart function; The abnormality alarm module is used to cause a user device associated with the user to issue an alarm when the output value of the electrocardiogram abnormality monitoring model indicates that the electrocardiogram data is estimated to have abnormal heart function.

16. An electrocardiogram abnormality monitoring device, comprising a photoplethysmographic data detector, a memory and a processor; The photoplethysmography data detector is used to monitor the photoplethysmography data of the user and transmit the monitored photoplethysmography data to the processor; The memory stores a computer program; and When the processor executes the computer program, it is used to receive the photoplethysmography data from the photoplethysmography data detector and implement the electrocardiogram abnormality monitoring method according to any one of claims 1 to 14.

17. The electrocardiogram abnormality monitoring device according to claim 16, characterized in that: It also includes an output device, which includes a display screen, an indicator light, a vibrator and / or a sound output device.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electrocardiogram abnormality monitoring method according to any one of claims 1 to 14 is implemented.

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