Electrocardio lead mode detection method and system and storage medium

Abstract

The invention relates to an electrocardio lead mode detection method and system and a storage medium, and the method comprises the steps: carrying out the electrocardio feature extraction of current electrocardio data, and obtaining a target signal feature; the current electrocardio data is used for indicating the original electrocardio data meeting the full connection state within the current preset time; based on the target signal features and the corresponding original electrocardio data, conducting lead channel analysis on the current electrocardio data to obtain a to-be-output lead mode; and when the target signal feature and the to-be-output lead mode meet a preset output condition, outputting the first lead mode. According to the method and the device, the target signal characteristics in the current electrocardiogram data can be extracted, the lead channel analysis is performed according to the target signal characteristics, and the output of the lead mode is limited, so that the problem that the lead mode cannot be automatically detected is solved.

Description

Technical Field

[0001] The present application relates to the technical field of electrocardiogram information processing, and in particular to an electrocardiogram lead pattern detection method, system and storage medium. Background Art

[0002] ECG signals are the response of the heart's electrical activity over a period of time. They are collected through skin electrodes, which are then connected to ECG lead wires to form different leads, and the ECG signals are transmitted to the ECG measurement circuit to obtain an ECG. Skin electrodes are generally placed on various parts of the body, such as the limbs and chest. Commonly used ones include single lead, 3 leads, 12 leads, 18 leads, or 24 leads.

[0003] In different application scenarios, when users need to switch to different lead modes for ECG signal collection, the lead mode needs to be identified accordingly. Currently, after replacing the electrode assembly, some lead modes are determined based on user preset information and the lead detachment status. This requires users to set it in advance, resulting in the problem of being unable to automatically detect the lead mode.

[0004] With respect to the problem that the lead mode cannot be automatically detected in the related technologies, no effective solution has been proposed yet. Summary of the invention

[0005] In this embodiment, an electrocardiogram lead mode detection method, system and storage medium are provided to solve the problem that the lead mode cannot be automatically detected in the related art.

[0006] In a first aspect, a method for detecting an electrocardiogram lead pattern is provided in this embodiment, comprising:

[0007] Extracting ECG features from the current ECG data to obtain target signal features; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time;

[0008] Based on the target signal characteristics and the corresponding original ECG data, performing lead channel analysis on the current ECG data to obtain a lead mode to be output;

[0009] When the target signal characteristics and the lead pattern to be output meet a preset output condition, a first lead pattern is output.

[0010] In some embodiments, the step of performing lead channel analysis on the current ECG data based on the target signal characteristics and the corresponding original ECG data to obtain a lead mode to be output includes:

[0011] Determine a number of signal channels corresponding to the original electrocardiogram data;

[0012] Based on the target signal features and the corresponding original ECG data, the similarity between each two signal channels is analyzed to obtain a single channel similarity result;

[0013] For any signal channel among the plurality of signal channels: based on the single-channel similarity result, determining the difference of the signal channel relative to all other signal channels, and obtaining a difference score corresponding to the signal channel;

[0014] Based on the difference score corresponding to each of the signal channels, the lead mode to be output corresponding to the current electrocardiogram data is determined.

[0015] In some embodiments, determining the lead mode to be output corresponding to the current ECG data based on the difference score corresponding to each of the signal channels includes:

[0016] Based on the difference score corresponding to each of the signal channels, determining a total difference score corresponding to all signal channels;

[0017] The total difference score is judged based on a preset threshold to determine the lead mode to be output corresponding to the current electrocardiogram data.

[0018] In some embodiments, when the target signal characteristics and the lead pattern to be output meet a preset output condition, outputting the first lead pattern comprises:

[0019] Based on the preset time period where the current preset time is located, a corresponding output strategy is adopted; wherein,

[0020] Within a first preset time period, if the target signal characteristic satisfies a first preset output condition, and the lead pattern to be output satisfies a second preset output condition, then based on the lead pattern to be output, the first lead pattern is output; or,

[0021] Within a second preset time period, if the lead pattern to be output meets the second preset output condition, the first lead pattern is output based on the lead pattern to be output.

[0022] In some embodiments, the method further comprises:

[0023] When the target signal feature and the lead pattern to be output do not meet the preset output condition, if the current preset time meets the third preset time period, the lead pattern of the historical ECG data is weighted and a second lead pattern is output.

[0024] In some embodiments, the step of extracting ECG features from the current ECG data to obtain target signal features includes:

[0025] Preprocessing the current ECG data to obtain first quasi-ECG data and second quasi-ECG data;

[0026] Extracting features from the first quasi-ECG data to generate first target signal features;

[0027] Extracting features from the second quasi-ECG data based on historical target signal features to generate second target signal features;

[0028] Based on the first target signal feature and the second target signal feature, performing quality detection on the current ECG data;

[0029] The first target signal feature and the second target signal feature corresponding to the current ECG data whose quality detection result meets the preset conditions are determined as the target signal features.

[0030] In some embodiments, the preprocessing of the current ECG signal data to obtain the first quasi-ECG data and the second quasi-ECG data includes:

[0031] Performing pacing processing on the current electrocardiogram signal data to generate pacing processing data;

[0032] Performing baseline shift processing on the pacing processed data to obtain first quasi-ECG data and second pre-processed ECG data;

[0033] The second preprocessed ECG data is subjected to noise reduction processing and data amplification processing in sequence to obtain second quasi-ECG data.

[0034] In some embodiments, the method further comprises:

[0035] Collecting the original ECG data of each signal channel within the current preset time;

[0036] Determining the lead-off state of the signal channel according to the original ECG data;

[0037] Based on the lead-off state of the signal channel, the original ECG data in a fully connected state is determined.

[0038] In a second aspect, in this embodiment, an electrocardiogram lead pattern detection system is provided, comprising: a feature extraction module, a lead detection module, and a lead output module;

[0039] The feature extraction module is used to extract ECG features from the current ECG data to obtain target signal features; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time;

[0040] The lead detection module is used to perform lead channel analysis on the current ECG data based on the target signal characteristics and the corresponding original ECG data to obtain a lead mode to be output;

[0041] The lead output module is used to output a first lead pattern when the target signal characteristics and the lead pattern to be output meet a preset output condition.

[0042] In a third aspect, a storage medium is provided in this embodiment, on which a computer program is stored, and when the program is executed by a processor, the electrocardiogram lead pattern detection method described in the first aspect is implemented.

[0043] Compared with the related art, the ECG lead mode detection method, system and storage medium provided in this embodiment obtain target signal features by extracting ECG features from current ECG data; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time; based on the target signal features and the corresponding original ECG data, the current ECG data is subjected to lead channel analysis to obtain the lead mode to be output; when the target signal features and the lead mode to be output meet the preset output conditions, the first lead mode is output. This embodiment can extract the target signal features in the current ECG data, perform lead channel analysis according to the target signal features, and limit the output of the lead mode, thereby solving the problem of being unable to automatically detect the lead mode.

[0044] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0046] Figure 1 is a hardware structure block diagram of a terminal of an electrocardiogram lead pattern detection method in an embodiment;

[0047] Figure 2 is a flow chart of an electrocardiogram lead pattern detection method in an embodiment;

[0048] Figure 3 is a schematic diagram of a signal channel in different lead modes in an embodiment;

[0049] Figure 4 is a schematic diagram of an ECG detection scenario in which an ECG lead pattern detection method is applied in an embodiment;

[0050] Figure 5 is a schematic diagram of electrocardiogram feature extraction in an embodiment;

[0051] Figure 6 is a schematic diagram of different time periods during lead mode detection in one embodiment;

[0052] Figure 7 is a flow chart of an electrocardiogram lead pattern detection method in another embodiment;

[0053] Figure 8 The structure block diagram of an electrocardiogram lead pattern detection system in an embodiment.

[0054] In the figure: 102, processor; 104, memory; 106, transmission device; 108, input and output device; 10, feature extraction module; 20, lead detection module; 30, lead output module. DETAILED DESCRIPTION

[0055] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0056] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the", "these" and the like in this application do not represent quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. Usually, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0057] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, when running on a terminal, the terminal can be a personal computer, a laptop, a smart phone, a tablet computer and a portable wearable device. Figure 1 FIG. 1 is a hardware structure diagram of a terminal of the ECG lead pattern detection method of this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data, wherein processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0058] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the ECG lead pattern detection method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0059] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by the communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0060] ECG signals are the response of the heart's electrical activity over a period of time. They are collected through skin electrodes, which are then connected to ECG lead wires to form different leads, and the ECG signals are transmitted to the ECG measurement circuit to obtain an ECG. Skin electrodes are generally placed on various parts of the body, such as the limbs and chest. Commonly used ones include single lead, 3 leads, 12 leads, 18 leads, or 24 leads.

[0061] In different application scenarios, when users need to switch to different lead modes for ECG signal collection, the lead mode needs to be identified accordingly. Currently, after replacing the electrode assembly, some lead modes are determined based on user preset information and the lead detachment status. This requires users to set it in advance, resulting in the problem of being unable to automatically detect the lead mode.

[0062] In this embodiment, a method for detecting an electrocardiogram lead pattern is provided. Figure 2 FIG. 1 is a flow chart of the ECG lead mode detection method in this embodiment. Figure 2 As shown, the method comprises the following steps:

[0063] Step S210, extracting ECG features from the current ECG data to obtain target signal features; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time.

[0064] In the lead mode detection process, the ECG data in the detection process is divided into several preset time periods, and the current ECG data is obtained within the current preset time period.

[0065] Specifically, dynamic original ECG data can be acquired in real time through ECG acquisition devices such as wearable ECG devices and integrated monitoring devices; or, local ECG data can be acquired as offline original ECG data. Accordingly, the embodiments of the present application can be applied to ECG lead mode detection in various application scenarios, such as switching and detection of ECG lead modes based on wearable ECG devices, switching and detection of lead systems of integrated monitoring devices, and ECG lead mode detection for local ECG data.

[0066] For the acquired original ECG data, after the lead-off state is determined, the original ECG data that meets the full connection state is used as the current ECG data. The full connection state means that all signal channels in the original ECG data are in a connection state.

[0067] Preprocess the current ECG data to obtain the first quasi-ECG data and the second quasi-ECG data; perform feature extraction on the first quasi-ECG data and the second quasi-ECG data respectively to generate the first target signal feature and the second target signal feature. After completing the feature extraction of the current ECG data, detect the quality of the current ECG data. If the quality of the current ECG data does not meet the preset conditions, the ECG data needs to be acquired again until the ECG data meets the preset conditions, and the first target signal feature and the second target signal feature corresponding to the ECG data whose quality detection results meet the preset conditions are determined as the target signal features, and further perform lead mode detection.

[0068] Step S220, based on the target signal characteristics and the corresponding original ECG data, the current ECG data is subjected to lead channel analysis to obtain a lead pattern to be output.

[0069] Among them, since the connection method of the electrode assembly is different in different lead modes, the collected ECG data is also different in the performance of each lead signal channel. Figure 3 is a schematic diagram of the signal channel in different lead modes in this embodiment. Figure 3 There are 8 signal channels in lead mode A, lead mode B and lead mode C. Figure 1 The signal channel collected by the Patch (Patch-type dynamic ECG) is shown in lead mode A; Figure 3 The signal channels collected by the 12-lead Holter monitor (12-lead dynamic electrocardiogram) are shown in lead mode B; 8 of the signal channels collected by the 12-lead Holter monitor (12-lead dynamic electrocardiogram) are shown in lead mode C.

[0070] When the quality detection result of the current ECG data meets the preset conditions in the above steps, the lead mode analysis is performed on several signal channels corresponding to the original ECG data based on the target signal characteristics and the corresponding original ECG data.

[0071] Specifically, the signal channels may be compared and analyzed according to specific parameters of the signal channels (such as peaks, troughs, and RT waves) to obtain the lead mode to be output.

[0072] In addition, from the perspective of the similarity between signal channels, a one-to-one analysis can be performed on the signal channels to obtain a single-channel similarity result; then, based on the single-channel similarity result, a one-to-many analysis can be performed on the signal channels from the perspective of the difference between any signal channel and all other signal channels, and the channel analysis results of all signal channels can be combined to output the lead mode to be output corresponding to the current ECG data.

[0073] Step S230: when the target signal characteristics and the lead pattern to be output meet the preset output conditions, output the first lead pattern.

[0074] The lead pattern to be outputted obtained as above is used as a preliminary detection result, and whether to output the lead pattern is further determined according to the target signal characteristics and the lead pattern to be outputted, and output restrictions are imposed on the lead pattern to be outputted.

[0075] Specifically, for different time periods during the lead mode detection process, for example, the first preset time period when the ECG data is unstable in the early detection period, the second preset time period in the middle detection period, and the third preset time period in the late detection period, corresponding output strategies are respectively adopted, and the lead mode to be output is used as the first lead mode, or the lead mode to be output is processed and then output, and the host computer system is provided for data viewing and subsequent data analysis. The host computer system can be the terminal in the above embodiment, including but not limited to a personal computer, a laptop computer, a smart phone, and a tablet computer.

[0076] Figure 4 is a schematic diagram of an ECG detection scenario in which the ECG lead mode detection method in this embodiment is applied. Figure 4 As shown, the ECG detection scenario includes a signal source, a lower computer system, and a host computer system. The signal source is the human ECG signal; the lower computer system is an ECG acquisition device used to obtain ECG data from the human ECG signal. It can be a wearable ECG device and an integrated monitoring device, etc., and can support lead systems such as Holter12 leads, Holter3 leads, Patch1 leads, and Patch3 leads; the host computer system covers functions such as lead mode detection, data viewing, and data analysis. Functional carriers include but are not limited to Android systems, IOS systems, Windows systems, and Linux systems. In this scenario, through the lead mode detection method, the host computer system can automatically identify different lead systems and perform ECG data viewing and data analysis.

[0077] The above steps extract the target signal features in the current ECG data, perform lead channel analysis based on the target signal features, and limit the output of the lead mode. Compared with the existing technology, the user does not need to configure the lead mode in advance. When the lead mode is switched, the lead channel in the fully connected state can be analyzed according to the ECG data, and the lead mode can be automatically detected and identified, which reduces the difficulty of operation. At the same time, it can be applied to ECG lead mode detection in various application scenarios.

[0078] With the advancement of dynamic ECG technology, wearable ECG devices are gradually developing towards small size and easy to wear. Currently, there are many types of wearable ECG products. The data acquisition box of the product corresponds to the electrode assembly one by one. Users often need to choose multiple types of products to use according to their needs, which greatly increases the difficulty of user operation. In addition, the one-to-one structure also increases the design cost of wearable ECG products.

[0079] In view of the application scenarios of the above-mentioned wearable ECG device, a wearable ECG device is provided in this embodiment, including: an electrode assembly and a data acquisition box; the electrode assembly and the data acquisition box are detachably connected through a unified interface.

[0080] The electrode assembly includes but is not limited to the electrode sheet and lead wire set corresponding to the lead system such as Holter 12 lead, Holter 3 lead, Patch 1 lead, Patch 3 lead, etc. The data acquisition box is a hardware and software system for collecting human ECG signals. The data acquisition box and the electrode assembly are in a one-to-many form, that is, a unified interface is used, and one data acquisition box can be connected to multiple electrode assemblies.

[0081] By adopting the wearable ECG device in this embodiment, on the one hand, there is no need to perform additional lead mode interface design on the data acquisition box and the electrode assembly. Different types of electrode assemblies can be designed as a unified interface, which improves the adaptability of each component, reduces the design cost of each component, and broadens the use scenarios of the product, thereby improving the applicability and application value of the product; on the other hand, it can automatically and quickly identify the lead mode when using the wearable ECG device, greatly improving the user experience.

[0082] In one embodiment, the above current ECG data can be obtained by the following steps:

[0083] Collect the original ECG data of each signal channel within the current preset time; determine the lead-off status of the signal channel according to the original ECG data; and determine the original ECG data in the full-connection state based on the lead-off status of the signal channel.

[0084] The dynamic original ECG data is collected by an ECG collection device, or local ECG data is obtained as offline original ECG data. The ECG collection device includes but is not limited to wearable ECG devices and integrated monitoring devices.

[0085] The fully connected state means that all signal channels in the original ECG data are in a connected state, and the original ECG data of each signal channel within the current preset time is collected, wherein the current preset time can be determined by the sampling rate of the ECG data. For the acquired original ECG data, after the lead-off state is judged, the original ECG data that satisfies the fully connected state is used as the current ECG data. Since the signal channel in the lead-off state lacks ECG characteristics, the lead-off state can be judged by the lower computer such as the acquisition device according to the flag bit. For example, when acquiring the original ECG data, the acquisition device adds a signal channel for feedback of the lead-off state, and the lead-off state of the current ECG data can be directly obtained in the upper computer system.

[0086] If the lead is off, an alarm will be output, and the user will be prompted to adjust and re-collect the ECG data until the electrode assembly is in normal contact with the human skin, and the original ECG data in the fully connected state is obtained.

[0087] In this embodiment, the lead-off state of the signal channel is judged according to the original ECG data; and the original ECG data in the fully connected state is determined, so as to obtain the current ECG data within the current preset time, which can provide the current ECG data in the fully connected state for the subsequent lead channel analysis, and can also standardize the lead mode detection scene, and reduce the probability of lead mode judgment errors due to improper operation or electrode material problems. Compared with the method of using the lead-off state to judge the lead mode in the related art, the judgment of the lead-off state is separated from the lead mode judgment logic, so that the detection result is not affected by the user's wearing method, and the consistency of the detection result is better.

[0088] In one embodiment, Figure 5 Schematic diagram of ECG feature extraction in this embodiment. Figure 5 As shown in the figure, the upstream input of ECG feature extraction is the original ECG data collected by the lower computer system. After the lead-off state is judged, the current ECG data in the fully connected state is obtained; the output information of ECG feature extraction is the target signal feature, and the downstream output is the lead mode detection. In ECG feature extraction, the quality of ECG data is also judged. If the quality meets the preset conditions, the target signal feature is output and transmitted to the downstream for lead mode detection. If the quality does not meet the preset conditions, the lower computer system is waited for to transmit new ECG data.

[0089] In this embodiment, the specific steps of extracting ECG features and obtaining target signal features are as follows:

[0090] Step S211, preprocessing the current ECG data to obtain first quasi-ECG data and second quasi-ECG data.

[0091] When the current ECG data in the fully connected state is obtained and the length of the current ECG data meets the requirement, the current ECG data can be preprocessed and ECG features can be extracted. Specifically, the requirement for data length is the requirement for communication with the lower computer.

[0092] In one embodiment, a multi-feature fusion method is designed to pre-process the ECG data, which can maximize the extraction of effective information in the ECG data. Figure 5 As shown, the current ECG signal data is paced to generate paced processing data; the paced processing data is baseline offset to obtain first quasi-ECG data and second pre-processed ECG data; the second pre-processed ECG data is noise-reduction processed and data amplification processed in turn to obtain second quasi-ECG data.

[0093] Among them, pacing processing includes pacing pin detection and filling operations to generate pacing processing data. The pacing processing avoids the influence of high pulses on the current ECG data. The pacing processing data is then subjected to baseline offset processing. The baseline offset processing is used to correct the ups and downs of the ECG data signal caused by chest breathing movement and slight movement of the electrode, thereby ensuring the consistency of data in each signal channel and obtaining the first quasi-ECG data and the second preprocessed ECG data.

[0094] Furthermore, the second pre-processed ECG data is subjected to noise reduction and data amplification to obtain the second quasi-ECG data. The noise reduction is used to identify and process various types of noise in the ECG data; the data amplification is used to amplify the data by multiple times when the first segment of data is input. Since only a segment of ECG data within the current preset time is insufficient for feature extraction, some historical databases are required for matching. The database is pre-filled by amplification, which can meet the data length required for subsequent feature extraction of the second quasi-ECG data, thereby enabling rapid feature extraction. The ECG data in the database is updated each time feature extraction is performed.

[0095] Step S212: extract features from the first quasi-ECG data to generate first target signal features.

[0096] Among them, Figure 5 As shown, a time domain signal processing method is used to obtain data features such as mean and amplitude for the first quasi-ECG data to generate a first target signal feature.

[0097] Step S213: extracting features from the second quasi-ECG data based on the historical target signal features to generate second target signal features.

[0098] Among them, Figure 5 As shown, the peak detection method is used for the second quasi-ECG data to obtain the P wave (waveform generated when the electrical activity is transmitted to the atrium), QRS wave (the process of ventricular depolarization), T wave (reflecting the potential and time changes in the late ventricular repolarization process), RR interval (the time from the start of atrial excitation to the start of ventricular excitation), QT (from the starting point of the QRS wave to the end point of the T wave, reflecting the start of ventricular depolarization to the end of repolarization), ST (the period from the end of the QRS to the starting point of the T wave) and other characteristic information in the current ECG data as the second target signal feature. Since only a period of ECG data within the current preset time is insufficient for feature extraction, it is necessary to refer to the historical target signal features in the historical database to extract features of the current ECG data.

[0099] Step S214: Perform quality detection on the current ECG data based on the first target signal feature and the second target signal feature.

[0100] Among them, Figure 5As shown, based on the first target signal feature and the second target signal feature, the quality of each signal channel of the current ECG data is detected, and finally the quality of the current ECG data is determined according to the quality of each signal channel.

[0101] The following is a method for expressing the quality test results of the current ECG data:

[0102] Q(A)=f Q (Feature1,Feature2,…,FeatureM);

[0103] Q(All)=f All (Q(A),Q(B),…,Q(N));

[0104] Among them, f Q Operation indicating the quality of the signal channel; f All Indicates the operation of the quality of the current ECG data; Feature indicates the first target signal feature and the second target signal feature, and there are M of them in total; Q(A), Q(B), …, Q(N) respectively indicate the quality of signal channels A, B, … in the current ECG data, and there are N signal channels in total; Q(All) indicates the final quality of the entire current ECG data.

[0105] When detecting the quality of each signal channel of the current ECG data based on the first target signal feature and the second target signal feature, the first target signal feature and the second target signal feature can be specifically compared with the corresponding threshold value to determine whether the quality of the signal channel meets the requirements, and finally the number of signal channels that meet the requirements is obtained. Among them, the comparison of parameters such as QRS wave, RR interval, heart rate, amplitude and signal-to-noise ratio can be specifically selected. Further determine whether the number of signal channels that meet the requirements in the current ECG data meets the quality requirements. If so, it is considered that the Q(All) value is 1; otherwise, the Q(All) value is 0.

[0106] For example, the number of features M is set to 2, Feature1 represents the number of R peaks in the current ECG data, and Feature2 represents the RR interval. If the number of R peaks is greater than the corresponding threshold, and the difference between each RR interval and the average RR interval is less than the corresponding threshold, it is considered that Q(A) is equal to 1 and the quality of signal channel A meets the requirements, otherwise it is considered that Q(A) is equal to 0 and the quality of signal channel A does not meet the requirements.

[0107] Step S215 , determining the first target signal feature and the second target signal feature corresponding to the current ECG data whose quality detection result meets the preset conditions as the target signal features.

[0108] Among them, through the above-mentioned quality detection, the first target signal feature and the second target signal feature corresponding to the current ECG data whose quality detection results meet the preset conditions are determined as the target signal features. The preset conditions are specifically whether the number of signal channels that meet the requirements in the current ECG data meets the quality requirements. Usually, in order to obtain a better lead mode detection result, it is required that all signal channels in the current ECG data meet the requirements.

[0109] The output target signal characteristics are transmitted to the downstream for lead mode detection; if the quality does not meet the preset conditions, the lower computer system is waited for to transmit new ECG data.

[0110] In this embodiment, after preprocessing the current ECG data, feature extraction is performed to generate the first target signal feature and the second target signal feature; and after the feature extraction of the ECG signal is completed, the quality of the current ECG data is judged. If the quality of the current ECG data does not meet the preset conditions, the ECG data needs to be acquired again until the ECG data meets the preset conditions, and the first target signal feature and the second target signal feature corresponding to the ECG data whose quality detection results meet the preset conditions are determined as the target signal features, and further lead mode detection is performed, which can effectively reduce data fluctuations caused by movements or other interferences and improve the accuracy of detection.

[0111] In one embodiment, in the above step S220, based on the target signal characteristics and the corresponding original ECG data, the lead channel analysis is performed on the current ECG data to obtain the lead mode to be output, which can be achieved by the following steps:

[0112] Step S221, determining a number of signal channels corresponding to the original ECG data.

[0113] In which, since the connection method of the electrode assembly is different in different lead modes, the performance of the collected ECG data on each lead signal channel is also different. According to the performance of each signal channel in the original ECG data, several signal channels corresponding to the original ECG data are determined.

[0114] Step S222: Based on the target signal characteristics and the corresponding original ECG data, the similarity between every two signal channels is analyzed to obtain a single channel similarity result.

[0115] Among them, from the perspective of the similarity between signal channels, the signal channels are analyzed one-to-one to obtain the single channel similarity results. Specifically, based on the data of the original ECG data in each signal channel and the feature information of the target signal feature in each signal channel, the similarity between each two signal channels is analyzed. For example, for signal channels A, B, C, and D, the similarity analysis between each two signal channels refers to analyzing the similarity between signal channels AB, AC, and AD respectively.

[0116] In practical applications, some target signal features can be selected to perform similarity analysis between every two signal channels, such as R peak, RR interval, heart rate, and signal amplitude.

[0117] For example, the following is a single channel similarity result eChan(A) between two signal channels A and B: B ) is calculated as follows:

[0118] eChan(A B )=E S (ichan(A),ichan(B),ibeat(A),iarrRpeak(A));

[0119] Where ichan(A) and ichan(B) represent the data of the original ECG data in signal channels A and B respectively; ibeat(A) represents the number of heartbeats in signal channel A; iarrRpeak(A) represents the R peak position in signal channel A; E S Represents the operation of the similarity between signal channels A and B. It should be noted that ibeat(A) and iarrRpeak(A) in the above calculation method can also be replaced by other target signal features.

[0120] The following continues to give the above E S A specific calculation method:

[0121] When ibeat(A)>0, that is, a heartbeat is detected in signal channel A:

[0122]

[0123] Among them, N represents the site range before and after the selected R peak, and M The operation represents the average difference of the original ECG data within the current preset time; [-1] represents the horizontal coordinate of the R peak position. When the heartbeat is detected, since the ECG data has a large difference near the R peak, by taking the average value in this range, when the average difference is greater than the threshold, the single channel similarity result that signal channel A is not similar to signal channel B can be obtained.

[0124] When ibeat(A)=0, that is, no heartbeat is detected in signal channel A:

[0125]

[0126]

[0127]

[0128] Where n represents the horizontal coordinate of the original ECG data, i.e., the sampling point, and f A (ichan(A)(n)) represents the Sigmoid mapping of the ECG signal data of signal channel A, f B (ichan(B)(n)) represents the Sigmoid mapping of the ECG signal data of signal channel B; m represents the amount of sliding, that is, the deviation of the movement of the sampling point; Represents the cross-correlation coefficient after mapping. Among them, the Sigmoid function has the characteristics of smoothness and monotony and boundedness. Performing Sigmoid mapping on the ECG signal can suppress impulse noise and retain the original information of the signal to be detected. Then, the correlation function is calculated for the mapped signal, and the similarity information of the ECG data signal can be obtained from the peak information of the correlation function.

[0129] Set the range of m to [-d, d], the resolution to 1, and calculate the R corresponding to each value of m within the set range. AB (m) and calculate R AB (m) is the maximum value in the array. If there is an obvious maximum value, then m = D. That is, when m = D, R AB (m) has a maximum value, indicating that ichan(A) and ichan(B) have strong similarity at this time, that is, it is considered that signal channel A is similar to signal channel B. If there is no obvious maximum value, it means that ichan(A) and ichan(B) do not have strong similarity, and the single channel similarity result is obtained that signal channel A is not similar to signal channel B.

[0130] Step S223, for any signal channel among the plurality of signal channels: based on the single-channel similarity result, determine the difference of the signal channel relative to all other signal channels, and obtain a difference score corresponding to the signal channel.

[0131] Among them, based on the single-channel similarity results, a one-to-many analysis is performed on the signal channels from the perspective of the difference between any signal channel and all other signal channels. For example, for signal channels A, B, C, and D, performing a difference analysis on any signal channel among several signal channels means obtaining the difference scores of signal channels A, B, C, and D relative to all other channels respectively. Specifically, different analysis weights are applied to each of the above single-channel similarity results to obtain the total difference score of the current ECG data.

[0132] Exemplarily, a method for expressing the difference score EChan(A) of signal channel A relative to all other signal channels is given below:

[0133] EChan(A)=E N (eChan(A B ), eChan(AC ),…,eChan(A N ), iarrWeight(A));

[0134] Among them, iarrWeight(A) represents the analysis weight between signal channel A and other channels B, C, ...; EChan(A) represents the difference score of signal channel A relative to all channels; E N Represents the operation of difference score. The analysis weight can be determined by the logic of different ECG detection scenarios and the channel correlation known by prior knowledge.

[0135] The following gives the E values ​​of eight signal channels (I, II, V1 to V6) selected from the 12 leads. N A specific calculation method:

[0136]

[0137] Among them, iarrWeight(I II ) represents the analysis weight between channel I and channel II; the analysis weight between channel I and channels II, V1 to V6 is [1,0,0,0,0,0,0], and S is the total score E of channel I relative to all channels. N , when the score is less than the threshold, it can be considered that there is a difference between the I channel and other related channels.

[0138] Step S224, based on the difference score corresponding to each signal channel, determine the lead mode to be output corresponding to the current ECG data.

[0139] Among them, based on the difference score corresponding to each signal channel, the total difference score corresponding to all signal channels is determined; based on the preset threshold, the total difference score is judged to determine the lead mode to be output corresponding to the current ECG data.

[0140] Specifically, the difference scores corresponding to each signal channel are counted to determine the total difference scores corresponding to all signal channels. The total difference scores are judged based on a preset threshold, and the lead mode to be output is finally determined. A method for representing the lead mode to be output LeadMode is given below:

[0141] LeadMode=f(EChan(A), EChan(B), EChan(C),…, EChan(N));

[0142] Wherein, f represents the operation of determining the total difference score according to the difference score corresponding to each signal channel.

[0143] In practical applications, multiple lead mode categories can be detected based on all signal channels in the fully connected state.

[0144] Furthermore, in order to avoid complex or redundant judgment logic design, the judgment logic of the lead mode to be output is determined according to the type of lead mode to be detected. For example, when the lead mode detection category includes Holter 12 leads and Patch 3 leads, the following is a lead mode judgment logic:

[0145]

[0146] Among them, EChan(v1)~EChan(v6) represent the difference scores of V1~V6 channels obtained in the above steps. The difference scores are counted and the total difference score is output. If it is greater than the threshold, it is considered that there are differences in the signal channels in this range, and the lead mode to be output is determined to be Holter12 lead. If it is less than the threshold, it is considered that there are no differences in the signal channels in this range, and the lead mode to be output is determined to be Patch3 lead.

[0147] In this example, the basic system adopted is the conventional 12-lead system, including 12 lead channels, namely I, II, III, avL, avF, avR, V1, V2, V3, V4, V5, and V6. Leads I, II, and III are standard bipolar limb leads. Lead I is the potential difference between the left and right hands, lead II is the potential difference between the left leg and the right hand, and lead III is the potential difference between the left leg and the left hand. Lead III can be converted from leads I and II. Leads avL, avF, and avR are pressurized unipolar limb leads, which are used to directly record the voltage at the lead site. Leads V1, V2, V3, V4, V5, and V6 are chest leads, which are used to record the actual potential of various parts of the heart under the electrodes. Limb leads observe changes in the ECG from the front (up, down, left, and right), and chest leads observe changes in the ECG from a cross section (assuming that the heart is cut into a cross section at a certain horizontal plane). Based on the 12-lead channel, the signals collected by different electrode assemblies and lead modes perform differently on the 12-lead channel. To simplify the judgment logic, this example uses eight channels, I, II, V1, V2, V3, V4, V5, and V6, to perform lead mode analysis and judgment.

[0148] Similarly, if category judgment is performed on Patch 1 and Patch 3, only the difference scores of channels I and II need to be statistically analyzed.

[0149] In this embodiment, a one-to-one analysis is performed on the signal channels from the perspective of the similarity between the signal channels to obtain the single-channel similarity result; then, based on the single-channel similarity result, a one-to-many analysis is performed on the signal channels from the perspective of the difference between any signal channel and all other signal channels, and the channel analysis results of all signal channels are combined to output the lead mode to be output corresponding to the current ECG data. At the same time, different judgment logics are adopted in different application scenarios, which can simplify the algorithm steps, improve the algorithm efficiency, and enable accurate lead mode detection and judgment.

[0150] The lead mode to be outputted obtained above is used as a preliminary detection result, and whether to output the lead mode is further determined according to the target signal characteristics and the lead mode to be outputted, and output restrictions are imposed on the lead mode to be outputted. In one embodiment, in the above step S230, when the target signal characteristics and the lead mode to be outputted meet the preset output conditions, the first lead mode is outputted, which can be achieved by adopting a corresponding output strategy based on the preset time period of the current preset time, wherein:

[0151] In a first preset time period, if the target signal characteristic satisfies a first preset output condition, and the lead pattern to be output satisfies a second preset output condition, a first lead pattern is output based on the lead pattern to be output.

[0152] Alternatively, within the second preset time period, if the lead pattern to be output meets the second preset output condition, the first lead pattern is output based on the lead pattern to be output.

[0153] In addition, when the target signal characteristics and the lead pattern to be output do not meet the preset output conditions, if the current preset time meets the third preset time period, the lead pattern of the historical ECG data is weighted and the second lead pattern is output.

[0154] in, Figure 6 is a schematic diagram of different time periods during the lead mode detection process in this embodiment, such as Figure 6 As shown, for different time periods in the lead mode detection process, corresponding output strategies are adopted respectively, and the lead mode (first lead mode or second lead mode) is output according to the lead mode to be output. For example, the first preset time period when the ECG data is unstable in the early stage of detection, the second preset time period in the middle stage of detection, and the third preset time period in the late stage of detection.

[0155] In the first preset time period, it is determined whether the target signal feature meets the first preset output condition, and whether the lead mode to be output meets the second preset output condition. According to the comparison result of the target signal feature and the corresponding threshold, it is determined whether the target signal feature meets the first preset output condition. For example, if the number of R peak detections in the current ECG data is less than 1, or the detected R peak amplitude is less than the threshold, it is determined that the target signal feature does not meet the first preset output condition.

[0156] Based on the consistency principle, it is judged whether the lead pattern to be output meets the second preset output condition. Specifically, after obtaining the lead pattern to be output, the results of the lead pattern to be output of multiple segments of ECG data can be continuously analyzed. If the lead pattern to be output is consistent within the specified time range, the second preset output condition is met; otherwise, the second preset output condition is not met.

[0157] If the target signal characteristic satisfies the first preset output condition, and the lead pattern to be output meets the second preset output condition, the lead pattern to be output is output as the first lead pattern; otherwise, no result is output.

[0158] Since the instability of ECG data may cause some interference during the first preset time period starting from the early stage of detection, the first preset output condition and the second preset output condition can reduce the interference effect and improve the accuracy of lead mode detection.

[0159] In the second preset time period, the lead mode to be output is judged according to the second preset output condition, and if the second preset output condition is met, the lead mode to be output is output as the first lead mode; otherwise, no result is output. This can avoid the influence of excessive signal fluctuation on lead mode detection.

[0160] Within the third preset time period, the second lead mode is output based on the timeliness principle. Specifically, if a valid lead mode cannot be output within the specified time limit, then after a certain time range (for example, after the first preset time period and the second preset time period), weights are set for the historical ECG data based on the proximity principle, and weighted processing is performed to output the second lead mode.

[0161] The following lead mode detection categories include Holter 12 leads and Patch 3 leads as examples to provide a calculation method for the second lead mode:

[0162] Assume that after the set time T, the output lead mode needs to be Holter 12 lead or Patch 3 lead. First, obtain the N lead modes to be output before the time point T, which include the default corresponding value -1, the Holter 12 lead corresponding value 12, and the Patch 3 lead corresponding value 3; then NSet weights. Based on the proximity principle, the closer the result is to the time point T, the greater the corresponding weight. L1 is the farthest in time, and the weight is set as W1 = 1 + 1 / N. L N is the closest in time, and the weight is set as W N = 1 + N / N; Next, calculate the scores of the Holter 12-lead and Patch 3-lead based on the N lead patterns to be output and their corresponding weights. The score of the Holter 12-lead is where a to g represent the result subscripts corresponding to the Holter 12-lead result. The score of the Patch 3-lead is where h to n represent the result subscripts corresponding to the Patch 3-lead result. If Q 12 ≥ Q3, then the output result is the Holter 12-lead, and a prompt signal is output indicating that the lead pattern needs to be confirmed, but it does not affect the subsequent operations of the host computer system; if Q 12 < Q3, then the output result is the Patch 3-lead, and a prompt signal is output indicating that the lead pattern needs to be confirmed, and similarly, it does not affect the subsequent operations of the host computer system.

[0163] The above-mentioned output of the second lead pattern when exceeding the appropriate detection time period based on the timeliness principle can avoid an infinite logical loop, ensure the output of the result within a certain time, and output a prompt signal, which can improve the stability of lead pattern detection.

[0164] Figure 7 is the flowchart of the electrocardiogram lead pattern detection method in this embodiment. As Figure 7 shown, the method includes the following steps:

[0165] Step S710: Collect the original electrocardiogram data of each signal channel within the current preset time, and judge the lead detachment status of the signal channel to determine the original electrocardiogram data in the fully connected state.

[0166] Step S720: Preprocess the current electrocardiogram data to obtain the first quasi-electrocardiogram data and the second quasi-electrocardiogram data; extract features from the first quasi-electrocardiogram data to generate the first target signal features; based on the historical target signal features, extract features from the second quasi-electrocardiogram data to generate the second target signal features.

[0167] Step S730: Determine the first target signal features and the second target signal features corresponding to the current electrocardiogram data whose quality detection results meet the preset conditions as the target signal features.

[0168] Step S740: Determine the several signal channels corresponding to the original electrocardiogram data; analyze the similarity between every two signal channels based on the target signal features and the corresponding original electrocardiogram data to obtain the single-channel similarity result.

[0169] Step S750, for any signal channel among the plurality of signal channels: based on the single-channel similarity result, determine the difference of the signal channel relative to all other signal channels, and obtain a difference score corresponding to the signal channel.

[0170] Step S760: Determine the lead mode to be output corresponding to the current ECG data based on the difference score corresponding to each signal channel.

[0171] Step S770: Based on the preset time period in which the current preset time is located, a corresponding output strategy is adopted to output a lead mode.

[0172] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0173] Based on the same inventive concept, the embodiment of the present application also provides an ECG lead pattern detection system for implementing the ECG lead pattern detection method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more ECG lead pattern detection system embodiments provided below can refer to the limitations of the ECG lead pattern detection method above, and will not be repeated here.

[0174] Figure 8 is a structural block diagram of the ECG lead mode detection system of this embodiment, such as Figure 8 As shown, the system includes: a feature extraction module 10, a lead detection module 20 and a lead output module 30.

[0175] The feature extraction module 10 is used to extract ECG features from the current ECG data to obtain target signal features; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time.

[0176] The lead detection module 20 is used to perform lead channel analysis on the current ECG data based on the target signal characteristics and the corresponding original ECG data to obtain the lead mode to be output.

[0177] The lead output module 30 is used to output the first lead pattern when the target signal characteristics and the lead pattern to be output meet the preset output conditions.

[0178] It should be noted that the above modules can be either functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor of the computer device; or the above modules can be located in different processors of the computer device in any combination.

[0179] Through the system provided in this embodiment, the target signal features in the current ECG data are extracted, the lead channel analysis is performed according to the target signal features, and the output of the lead mode is restricted. Compared with the existing technology, the user does not need to configure the lead mode in advance. When the lead mode is switched, the lead channel in the fully connected state can be analyzed according to the ECG data, and the lead mode can be automatically detected and identified, which reduces the difficulty of operation. At the same time, it can be applied to ECG lead mode detection in various application scenarios.

[0180] In this embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0181] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0182] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0183] In addition, in combination with the ECG lead pattern detection method provided in the above embodiments, a storage medium may be provided in this embodiment to implement the method. The storage medium stores a computer program, and when the computer program is executed by a processor, any ECG lead pattern detection method in the above embodiments is implemented.

[0184] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0185] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0186] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

[0187] The term "embodiment" in this application refers to a specific feature, structure or characteristic described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is clearly or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0188] 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 patent protection. 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 scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the attached claims.

Claims

1. A method for detecting an electrocardiogram lead pattern, characterized in that: include: Extracting ECG features from the current ECG data to obtain target signal features; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time; Based on the target signal characteristics and the corresponding original ECG data, performing lead channel analysis on the current ECG data to obtain a lead mode to be output; When the target signal characteristics and the lead pattern to be output meet a preset output condition, a first lead pattern is output.

2. The method according to claim 1, characterized in that The method of performing lead channel analysis on the current ECG data based on the target signal characteristics and the corresponding original ECG data to obtain a lead mode to be output includes: Determine a number of signal channels corresponding to the original electrocardiogram data; Based on the target signal features and the corresponding original ECG data, the similarity between each two signal channels is analyzed to obtain a single channel similarity result; For any signal channel among the plurality of signal channels: based on the single-channel similarity result, determining the difference of the signal channel relative to all other signal channels, and obtaining a difference score corresponding to the signal channel; Based on the difference score corresponding to each of the signal channels, the lead mode to be output corresponding to the current electrocardiogram data is determined.

3. The method according to claim 2, characterized in that The step of determining the lead mode to be output corresponding to the current ECG data based on the difference score corresponding to each of the signal channels comprises: Based on the difference score corresponding to each of the signal channels, determining a total difference score corresponding to all signal channels; The total difference score is judged based on a preset threshold to determine the lead mode to be output corresponding to the current electrocardiogram data.

4. The method according to claim 1, characterized in that: The method of outputting a first lead mode when the target signal characteristic and the lead mode to be output meet a preset output condition comprises: Based on the preset time period where the current preset time is located, a corresponding output strategy is adopted; wherein, Within a first preset time period, if the target signal characteristic satisfies a first preset output condition, and the lead pattern to be output satisfies a second preset output condition, then based on the lead pattern to be output, the first lead pattern is output; or, Within a second preset time period, if the lead pattern to be output meets the second preset output condition, the first lead pattern is output based on the lead pattern to be output.

5. The method according to claim 1, characterized in that Also includes: When the target signal feature and the lead pattern to be output do not meet the preset output condition, if the current preset time meets the third preset time period, the lead pattern of the historical ECG data is weighted and a second lead pattern is output.

6. The method according to claim 1, characterized in that The method extracts ECG features from the current ECG data to obtain target signal features; includes: Preprocessing the current ECG data to obtain first quasi-ECG data and second quasi-ECG data; Extracting features from the first quasi-ECG data to generate first target signal features; Extracting features from the second quasi-ECG data based on historical target signal features to generate second target signal features; Based on the first target signal feature and the second target signal feature, performing quality detection on the current ECG data; The first target signal feature and the second target signal feature corresponding to the current ECG data whose quality detection result meets the preset conditions are determined as the target signal features.

7. The method according to claim 6, characterized in that The preprocessing of the current ECG signal data to obtain first quasi-ECG data and second quasi-ECG data comprises: Performing pacing processing on the current electrocardiogram signal data to generate pacing processing data; Performing baseline shift processing on the pacing processed data to obtain first quasi-ECG data and second pre-processed ECG data; The second preprocessed ECG data is subjected to noise reduction processing and data amplification processing in sequence to obtain second quasi-ECG data.

8. The method according to claim 1, characterized in that Also includes: Collecting the original ECG data of each signal channel within the current preset time; Determining the lead-off state of the signal channel according to the original ECG data; Based on the lead-off state of the signal channel, the original ECG data in a fully connected state is determined.

9. An electrocardiogram lead pattern detection system, characterized in that: include: Feature extraction module, lead detection module and lead output module; The feature extraction module is used to extract ECG features from the current ECG data to obtain target signal features; the current ECG data is used to indicate the original ECG data that meets the full connection state within the current preset time; The lead detection module is used to perform lead channel analysis on the current ECG data based on the target signal characteristics and the corresponding original ECG data to obtain a lead mode to be output; The lead output module is used to output a first lead pattern when the target signal characteristics and the lead pattern to be output meet a preset output condition.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electrocardiogram lead pattern detection method according to any one of claims 1 to 8 are implemented.

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