ECG Signal Correction Method, Device, Computer Equipment and Storage Medium

Abstract

The present invention discloses a method, device, computer device and storage medium for electrocardiogram signal correction, belonging to the technical field of electrocardiogram detection. The method includes: performing QRS wave positioning on the preprocessed electrocardiogram signal, obtaining the mean electrical axis vector through vector synthesis, and calculating the mean electrical axis angle; using the trained random forest, decision tree and XGBoost models to discriminate the electrode offset situation, and determining the electrode offset label through an integrated voting method; according to the electrode offset label, calling the corresponding trained correction model to correct the electrocardiogram signal and generating the corrected electrocardiogram waveform. This method realizes intelligent discrimination and adaptive correction of electrode offset by constructing vector features reflecting the electrical axis direction, without the need to readjust the electrode position and re-collect data, significantly improving the efficiency and practicality of electrocardiogram signal acquisition and processing.

Description

Technical Field

[0001] The present invention relates to the technical field of electrocardiogram detection, and particularly to a method, device, computer device and storage medium for correcting electrocardiogram signals. Background Art

[0002] A palm electrocardiogram acquisition device is a small and portable electrocardiogram monitoring tool suitable for non-medical professionals. Compared with traditional electrocardiogram acquisition devices that require large equipment and professional operations, the palm electrocardiogram acquisition device has the advantages of small size, easy to carry, and strong real-time performance. It can collect and analyze electrocardiograms at any time in various scenarios such as at home, in the office, and outdoors, and is widely used in fields such as daily health monitoring, telemedicine, and prevention of cardiovascular diseases.

[0003] However, the convenience of the palm electrocardiogram acquisition device also brings the problem of the accuracy of electrode placement. During the electrocardiogram (ECG) signal acquisition process, the accuracy of electrode placement has an important impact on the quality of electrocardiogram signals and the diagnostic results. Especially in portable electrocardiogram acquisition devices, due to the small size of the device and the fact that the users are non-professional medical staff, it is easy to cause the deviation of the placement position. A small deviation in the electrode position often leads to the distortion of the collected electrocardiogram signals, thereby affecting the quality of electrocardiogram signals and further affecting the accuracy of clinical diagnosis. By calculating the deviation between lead signals, the deviation of the electrode position can be judged, but it is still necessary to manually judge the deviation situation according to the deviation calculation result and correct the electrode position, and re-collect the electrocardiogram signal with the correct electrode position. It is impossible to directly correct the electrocardiogram waveform according to the deviation situation, resulting in low acquisition efficiency. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, computer device and storage medium for correcting electrocardiogram signals to solve the technical problem that the corrected waveform cannot be directly output from electrocardiogram signals.

[0005] In a first aspect, an embodiment of the present invention provides a method for correcting electrocardiogram signals, including:

[0006] Preprocessing the acquired electrocardiogram signal to obtain a preprocessed electrocardiogram signal;

[0007] Performing QRS wave positioning on the preprocessed electrocardiogram signal to obtain the R peak amplitude of lead I, the R peak amplitude of lead II, and the R peak amplitude of lead III, and calculating the ratio of the R peak amplitude of lead I to the R peak amplitude of lead III;

[0008] According to the R peak amplitude of lead I, the R peak amplitude of lead II, and the R peak amplitude of lead III, obtaining an average electrical axis vector by vector synthesis and calculating the average electrical axis angle;

[0009] Using the trained random forest model, decision tree model, and XGBoost model, based on the mean electrical axis angle, R peak amplitude in lead I, R peak amplitude in lead II, R peak amplitude in lead III, and the ratio of R peak amplitudes in lead I to lead III, respectively determine the electrode offset conditions of the preprocessed electrocardiogram (ECG) signals, obtain the first electrode offset type, the second electrode offset type, and the third electrode offset type, and perform voting method ensemble learning based on all electrode offset types to obtain the electrode offset label;

[0010] According to the electrode offset label, use the trained correction model based on multi-head attention and gated recurrent unit to correct the preprocessed ECG signal. The correction model first performs weight allocation on the ECG signal in matrix form using the multi-head attention mechanism, and then uses the gated recurrent unit for temporal modeling to generate the corrected ECG signal.

[0011] Further, the step of using the trained correction model based on multi-head attention and gated recurrent unit to correct the preprocessed ECG signal according to the electrode offset label includes:

[0012] According to the electrode offset label, call the electrode offset correction parameters corresponding to the electrode offset label and load them into the correction model;

[0013] Input the preprocessed ECG signal into the correction model, and the correction model outputs the corrected ECG signal.

[0014] Further, the step of inputting the preprocessed ECG signal into the correction model and the correction model outputting the corrected ECG signal includes:

[0015] Convert the preprocessed ECG signal into matrix form to generate an ECG waveform matrix;

[0016] Use the multi-head attention mechanism to perform weight allocation on the ECG waveform matrix to generate an attention ECG waveform matrix;

[0017] Use the gated recurrent unit to perform temporal modeling based on the attention ECG waveform matrix to generate a corrected ECG waveform matrix;

[0018] Convert the corrected ECG waveform matrix into an ECG waveform to generate the corrected ECG signal.

[0019] Further, the step of obtaining the mean electrical axis vector by vector synthesis according to the R peak amplitude in lead I, the R peak amplitude in lead II, and the R peak amplitude in lead III, and calculating the mean electrical axis angle includes:

[0020] Using Einthoven's triangle law and the Bailey hexaxial system, determine the lead I vector, the lead II vector, and the lead III vector according to the R peak amplitude in lead I, the R peak amplitude in lead II, and the R peak amplitude in lead III;

[0021] Use the vector of lead I, the vector of lead II, and the vector of lead III for vector synthesis to generate the mean electrical axis vector;

[0022] Calculate the mean electrical axis angle according to the coordinates of the mean electrical axis vector.

[0023] Further, the method for determining the vector of lead I, the vector of lead II, and the vector of lead III according to Einthoven's triangle law and Bailey's hexaxial system includes:

[0024] Determine the angle of lead I, the angle of lead II, and the angle of lead III by using Einthoven's triangle law and Bailey's hexaxial system;

[0025] Determine the vector of lead I according to the angle of lead I and the R peak amplitude of lead I, determine the vector of lead II according to the angle of lead II and the R peak amplitude of lead II, and determine the vector of lead III according to the angle of lead III and the R peak amplitude of lead III.

[0026] Further, the preprocessing of the acquired electrocardiogram signal to obtain the preprocessed electrocardiogram signal includes:

[0027] Perform denoising processing on the acquired electrocardiogram signal by using a band-pass filter and wavelet transform;

[0028] Perform baseline drift elimination on the acquired electrocardiogram signal by using a high-pass filter.

[0029] Further, the wavelet transform uses Daubechies4 as the wavelet basis function and performs four-layer wavelet decomposition.

[0030] In a second aspect, an embodiment of the present invention provides an electrocardiogram signal correction device, including:

[0031] An electrocardiogram signal processing module, configured to preprocess the acquired electrocardiogram signal;

[0032] A QRS wave positioning module, configured to perform QRS wave positioning on the preprocessed electrocardiogram signal;

[0033] A vector synthesis module, configured to perform vector synthesis and calculate the mean electrical axis angle;

[0034] An electrode offset classification module, configured to discriminate the type of electrode offset and generate an electrode offset label;

[0035] An electrocardiogram signal correction module, configured to correct the electrocardiogram signal by using the trained correction model according to the electrode offset label.

[0036] In a third aspect, an embodiment of the present invention provides a computer device, including:

[0037] One or more processors;

[0038] A storage device for storing one or more programs,

[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned electrocardiogram signal correction method.

[0040] In a fourth aspect, an embodiment of the present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the above-mentioned electrocardiogram signal correction method when executed by a computer processor.

[0041] An electrocardiogram signal correction method, device, computer device and storage medium provided by an embodiment of the present invention. The method extracts the R-peak amplitudes of each lead of the electrocardiogram signal to generate vectors representing each lead, uses vector synthesis to obtain the mean electrical axis vector and mean electrical axis angle indicating the intensity and directionality of cardiac electrical activity, uses the mean electrical axis angle, the R-peak amplitude of each lead, and the amplitude ratio of lead I to lead III, and performs integrated voting using a decision tree model, a random forest model, and an XGBoost model to classify the electrode offset situation and output an electrode offset label. According to the electrode offset label, the corresponding correction parameters are called to load the trained correction model to correct the electrocardiogram signal and obtain the corrected electrocardiogram waveform. It is possible to directly identify the electrode offset situation based on the collected electrocardiogram waveform, then correct the electrocardiogram waveform according to the offset situation, and directly output the corrected electrocardiogram signal. There is no need to use additional equipment to analyze the electrode offset situation, nor to manually correct the electrode placement position and re-collect it twice. The corrected electrocardiogram signal can be directly generated, greatly improving the acquisition efficiency of the electrocardiogram signal and meeting the usage requirements of non-medical professionals. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0043] Figure 1 It is a flowchart of an electrocardiogram signal correction method according to Embodiment 1 of the present invention;

[0044] Figure 2 It is a flowchart of an electrocardiogram signal correction method according to Embodiment 2 of the present invention;

[0045] Figure 3 It is a flowchart of an electrocardiogram signal correction method according to Embodiment 3 of the present invention;

[0046] Figure 4 Schematic diagram of the average electrical axis angle and each type of electrode offset according to Embodiment 3 of the present invention;

[0047] Figure 5 Schematic diagram of the synthesis of the average electrical axis vector according to Einthoven's triangle rule and Bailey's six-axis system in Embodiment 3 of the present invention;

[0048] Figure 6 Schematic structural diagram of an electrocardiogram signal correction device according to Embodiment 4 of the present invention;

[0049] Figure 7 Structural diagram of the computer device according to Embodiment 5 of the present invention. Detailed implementation manners

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that, for the sake of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all the structures.

[0051] When collecting electrocardiogram signals using a portable electrocardiogram acquisition device, for example, using a palm electrocardiogram monitor to detect electrocardiograms, electrodes need to be attached to the corresponding positions of the human body, including the left upper limb, right lower limb, left lower limb and other parts. Due to the portability of the palm electrocardiogram device, many household users, chronic patients, etc., due to their own needs, often choose to attach electrodes by themselves to collect electrocardiogram signals for detecting their own electrocardiogram conditions. However, such operations by non-medical professionals often result in the electrodes not being placed in the correct positions, resulting in deviations in the collected electrocardiogram signals and affecting the judgment of electrocardiogram results. And correcting electrocardiogram signals often requires professional personnel, professional equipment, etc. By judging the offset of the electrodes and correcting the placement positions of the electrodes to re-collect correct electrocardiogram signals, not only is the collection efficiency low, but the means are also relatively complex, and it is often impossible to complete the correction of electrocardiogram signals under the operating conditions of non-medical professionals such as household users and chronic patients for daily health monitoring.

[0052] Embodiment 1

[0053] Figure 1 Flowchart of an electrocardiogram signal correction method according to Embodiment 1 of the present invention. In this embodiment, the electrocardiogram waveform is corrected by judging the offset of the electrodes and calling the corresponding calculation model, and specifically includes the following steps:

[0054] S101, preprocess the acquired electrocardiogram signal to obtain a preprocessed electrocardiogram signal.

[0055] During the electrocardiogram (ECG) acquisition process, there are often interferences such as noise. It is necessary to preprocess the acquired ECG signals by means of denoising, baseline drift elimination, etc., to reduce the irrelevant and interfering content in the ECG signals and eliminate the deviation effects caused by breathing or poor electrode contact. In this embodiment, taking a palm-type ECG acquisition device using a twelve-lead acquisition method as an example, the ECG signals of Lead I, Lead II, and Lead III are collected respectively for subsequent processing.

[0056] S102, perform QRS complex localization on the preprocessed ECG signals to obtain the R peak amplitudes of Lead I, Lead II, and Lead III, and calculate the R peak amplitude ratio between Lead I and Lead III.

[0057] Through QRS complex localization, the R peak amplitude of each lead is determined respectively. The R peak amplitude is an important feature reflecting the intensity of cardiac electrical activity and can be used to evaluate the electrophysiological state of the heart and the accuracy of electrode positions. The R wave and QRS complex of the ECG signal can be detected by using the method of alternate difference and double thresholds to determine the R peak amplitude of each lead. Exemplarily, Findpeaks is used for R peak localization. By searching 10 points forward and backward, the localization position of the R peak is corrected. According to the R peak position, the lowest point is searched forward to determine the Q wave position, and the lowest point is searched backward to determine the S wave position. The R peak amplitude ratio between Lead I and Lead III is calculated by comparing the R peak amplitudes of Lead I and Lead III. Through the R peak amplitude ratio of the two leads, the directional distribution of cardiac electrical activity and electrode positions is evaluated.

[0058] S103, according to the R peak amplitudes of Lead I, Lead II, and Lead III, obtain the mean electrical axis vector by vector synthesis and calculate the mean electrical axis angle.

[0059] Since each lead can reflect the cardiac electrical activity of the human body in a certain direction, the R peak amplitudes of the three leads can be used to generate vectors corresponding to the lead directions respectively, and the vectors of the three leads are superposed and synthesized. Finally, a mean electrical axis vector that can indicate the directionality of the cardiac electrical activity of the human body under the current acquisition situation and is used to analyze the offset of the electrode position is determined. Exemplarily, the Einthoven triangle rule and the Bailey hexaxial system can be used to determine the directions of the three leads respectively, and then the R peak amplitudes of each lead are used to determine the vectors representing the three leads respectively, and vector synthesis is performed to generate a mean electrical axis vector. According to the coordinates of the mean electrical axis vector, the mean electrical axis angle is calculated, which can reflect the overall electrical activity direction of the heart and can be used to evaluate whether the electrode has shifted.

[0060] S104. Using the trained random forest model, decision tree model, and XGBoost model, based on the mean electrical axis angle, R peak amplitude in lead I, R peak amplitude in lead II, R peak amplitude in lead III, and the ratio of R peak amplitudes in lead I to lead III, respectively discriminate the electrode offset situation of the preprocessed electrocardiogram signal, obtain the first electrode offset type, the second electrode offset type, and the third electrode offset type, and perform voting method ensemble learning based on all electrode offset types to obtain the electrode offset label.

[0061] Select the mean electrical axis angle, R peak amplitude in lead I, R peak amplitude in lead II, R peak amplitude in lead III, and the ratio of R peak amplitudes in lead I to lead III as 5 input features, and use the classification model to classify the offset situation of the electrode. The mean electrical axis angle can reflect the overall electrical activity direction of the heart. The R peak amplitudes in lead I, lead II, and lead III can respectively reflect the electrical activity intensity of the heart in the corresponding directions of each lead. The ratio of R peak amplitudes in lead I to lead III can reflect the directional distribution of the heart's electrical activity and electrode position. Using the trained random forest model, decision tree model, and XGBoost model, input the above 5 features into each model respectively, and use the model to classify the offset situation of the electrode. The classification results include 7 electrode position situations, including normal, upward deviation, downward deviation, left deviation, right deviation, counterclockwise rotation, and clockwise rotation. Among them, the random forest model outputs the first electrode offset type, the decision tree model outputs the second electrode offset type, and the XGBoost model outputs the third electrode offset type. Then, use the first electrode offset type, the second electrode offset type, and the third electrode offset type to perform voting method ensemble learning. According to the classification results of the three models, finally obtain the offset situation of the electrode and form a label, that is, the electrode offset label. Exemplarily, the corresponding relationship between the electrode offset type and the label is: normal - 0, upward deviation - 1, downward deviation - 2, left deviation - 3, right deviation - 4, counterclockwise rotation - 5, clockwise rotation - 6, where the label 0 indicates that the electrode has no offset, and the labels 1 - 6 respectively represent different offset types. Respectively utilize the characteristics of the decision tree suitable for classification discrimination tasks, the characteristics of the random forest model that integrates multiple decision trees to handle complex data, and the characteristics of the XGBoost model that has the advantages of the boosting tree model. Integrate and vote on the classification results of the three models. Introduce randomness and diversity through the random forest model and the XGBoost model to make up for the defect that the decision tree model is easily affected by data changes, improve the classification accuracy, robustness, and generalization ability of the model, reduce the risk of overfitting, and reduce the instability that may be brought by a single model.

[0062] S105. According to the electrode offset label, use the trained correction model based on multi-head attention and gated recurrent unit to correct the preprocessed electrocardiogram (ECG) signal. The correction model first assigns weights to the ECG signal in matrix form using the multi-head attention mechanism, and then uses the gated recurrent unit for temporal modeling to generate the corrected ECG signal.

[0063] The electrode offset label can indicate the type of electrode offset of the current preprocessed ECG signal. The corresponding correction parameters can be called according to the electrode offset label and loaded into the trained correction model to form a correction model corresponding to the electrode offset type. The correction model first converts the ECG waveform into matrix form, assigns weights using the multi-head attention mechanism, identifies important partial waveforms and weights them, and then uses the gated recurrent unit to perform modeling according to the time sequence to generate the corrected matrix. Converting the corrected matrix into an ECG waveform can obtain the corrected ECG signal. It should be noted that during the training process of the correction model based on multi-head attention and gated recurrent unit, the correction model is trained using ECG waveforms with different deviation types and normal non-deviated ECG waveforms. The model parameters for correcting different deviation types are obtained through training respectively. Then, during correction, according to the electrode offset label, the model parameters corresponding to the electrode offset type can be selected to correct the ECG waveform, rather than using the same model parameters to correct all offset type ECG waveforms. Exemplarily, the trained model parameters mainly include the weight matrix of the multi-head attention mechanism and the weight matrix of the gated recurrent unit. Through different weight parameters, the ECG signal can be corrected for different electrode offset types.

[0064] In this embodiment, the R peak amplitudes of each lead of the ECG signal are extracted to generate vectors representing each lead, and the mean electrical axis vector and mean electrical axis angle indicating the intensity and directionality of cardiac electrical activity are obtained by vector synthesis. Using the mean electrical axis angle, the R peak amplitude of each lead, and the amplitude ratio of lead I to lead III, integrated voting is performed using the decision tree model, random forest model, and XGBoost model to classify the electrode offset situation and output the electrode offset label. According to the electrode offset label, the corresponding correction parameters are called to load the trained correction model to correct the ECG signal and obtain the corrected ECG waveform. The electrode offset situation of the ECG can be directly identified based on the collected ECG waveform, and then the ECG waveform can be corrected according to the offset situation to directly output the corrected ECG signal without using additional equipment to analyze the electrode offset situation, nor manually correcting the electrode placement position and re-collecting it twice. The corrected ECG signal can be directly generated, greatly improving the acquisition efficiency of the ECG signal and meeting the usage requirements of non-medical professionals.

[0065] Embodiment 2

[0066] Figure 2The flowchart of an electrocardiogram (ECG) signal correction method according to the second embodiment of the present invention. This embodiment is optimized based on the above embodiment. In this embodiment, according to the electrode offset label, the preprocessed ECG signal will be corrected by using the trained correction model based on multi-head attention and gated recurrent unit. The specific optimization is as follows:

[0067] According to the electrode offset label, call the electrode offset correction parameters corresponding to the electrode offset label and load them into the correction model;

[0068] Input the preprocessed ECG signal into the correction model, and the correction model outputs the corrected ECG signal.

[0069] Correspondingly, the ECG signal correction method provided in this embodiment specifically includes:

[0070] S201, preprocess the acquired ECG signal to obtain the preprocessed ECG signal.

[0071] S202, perform QRS wave localization on the preprocessed ECG signal to obtain the R peak amplitude of lead I, the R peak amplitude of lead II, and the R peak amplitude of lead III, and calculate the ratio of the R peak amplitude of lead I to that of lead III.

[0072] S203, according to the R peak amplitude of lead I, the R peak amplitude of lead II, and the R peak amplitude of lead III, obtain the mean electrical axis vector by vector synthesis and calculate the mean electrical axis angle.

[0073] S204, use the trained random forest model, decision tree model, and XGBoost model to respectively discriminate the electrode offset situation of the preprocessed ECG signal according to the mean electrical axis angle, the R peak amplitude of lead I, the R peak amplitude of lead II, the R peak amplitude of lead III, and the ratio of the R peak amplitude of lead I to that of lead III, to obtain the first electrode offset type, the second electrode offset type, and the third electrode offset type, and perform voting method ensemble learning according to all electrode offset types to obtain the electrode offset label.

[0074] S205, according to the electrode offset label, call the electrode offset correction parameters corresponding to the electrode offset label and load them into the correction model.

[0075] During the training phase of the correction model, electrocardiogram (ECG) signals with different types of electrode offsets and normal electrode position ECG signals are used to train the correction model respectively. Corresponding correction parameters can be obtained according to different electrode offset types. According to the electrode offset type, the corresponding correction parameters can be loaded into the correction model to perform corresponding corrections on the ECG signals. Exemplarily, the correspondence between electrode offset types and labels is as follows: normal - 0, upward - 1, downward - 2, left - 3, right - 4, counterclockwise rotation - 5, clockwise rotation - 6. Among them, label 0 indicates no electrode offset, and labels 1 - 6 represent different offset types respectively. When the electrode offset label input to the correction model is 0, the original ECG signal is directly output after skipping the correction model. When the electrode offset label input to the model is 1, the correction parameters corresponding to the upward electrode offset type are loaded to form a correction model for upward electrode offset, and so on.

[0076] S206, input the preprocessed ECG signal into the correction model, and the correction model outputs the corrected ECG signal.

[0077] Based on the loaded correction parameters, the correction model first performs weight allocation on the ECG signal converted into matrix form based on the multi-head attention mechanism, and then uses the gated recurrent unit to perform fitting based on time series to correct the ECG waveform with electrode offset and generate the corrected ECG signal. The correction parameters mainly include the weight matrix of the multi-head attention and the weight matrix of the gated recurrent unit, which can be obtained through the training process.

[0078] Specifically, inputting the preprocessed ECG signal into the correction model and the correction model outputting the corrected ECG signal includes:

[0079] Convert the preprocessed ECG signal into matrix form to generate an ECG waveform matrix;

[0080] Adopt a fixed-length segmentation method based on R-peak positioning: with each R-peak as the center, intercept 150 sampling points forward and 150 sampling points backward to form a one-dimensional heartbeat segment with a length of 300. Each sample is a one-dimensional vector of 1×300. In this way, complete heartbeat samples with clear structure and stable rhythm can be extracted. Finally, all samples are arranged in chronological order to form an ECG waveform matrix, which is used as the model input for the electrode offset correction task. By converting the preprocessed ECG signal into matrix form to generate an ECG waveform matrix, it is convenient for subsequent processing by the correction model. Exemplarily, the ECG waveform matrix can be expressed as:

[0081]

[0082] Among them, represents the ECG waveform matrix, represents the electrode offset label, N represents the number of samples, Indicates the N th offset type is the electrocardiogram signal vector.

[0083] Using the multi-head attention mechanism, weight distribution is performed on the electrocardiogram waveform matrix to generate an attention electrocardiogram waveform matrix.

[0084] The core of the multi-head attention mechanism is to map the input signal into queries (Q, Query), keys (K, Key), and values (V, Value). Taking the electrode offset type being upward as an example, the electrode offset label is For the input data First, perform a linear transformation to obtain their Q, K, and V values respectively:

[0085]

[0086] Among them, , , are the weight matrices obtained through training.

[0087] Calculate the dot product of the query Q and the key K to obtain the attention score and perform scaling:

[0088] Among them, is the dimension of the key vector.

[0089] Perform softmax normalization on the score matrix to obtain the attention weights:

[0090]

[0091] Use the attention weights and the value matrix V to calculate the output:

[0092]

[0093] For multi-head attention, concatenate the outputs of multiple attention heads and perform a linear transformation to obtain the final multi-head attention output:

[0094]

[0095] Among them, is the output of each attention head, is the weight matrix.

[0096] Use the gated recurrent unit to perform temporal modeling based on the attention electrocardiogram waveform matrix to generate a corrected electrocardiogram waveform matrix.

[0097] The reset gate and update gate of the gated recurrent unit are expressed as:

[0098]

[0099] Candidate hidden state Expressed as:

[0100]

[0101] Final hidden state Expressed as:

[0102]

[0103] Wherein, Represents the output of the reset gate, Represents the output of the update gate, And Are the weight matrices of the reset gate and the update gate respectively, And Is the bias term, Is Sigmoid Activation function, Is the candidate hidden state at the current moment, Is the weight matrix of the candidate hidden state, Is the bias term of the candidate hidden state, Represents the hyperbolic tangent activation function, Is the hidden state at the final moment. The hidden state at the final moment, as the output of the gated recurrent unit, is obtained by the weighted average of the hidden state at the previous moment and the current candidate hidden state, and the corrected electrocardiogram waveform matrix is obtained.

[0104] Convert the corrected electrocardiogram waveform matrix into an electrocardiogram waveform to generate a corrected electrocardiogram signal.

[0105] The corrected electrocardiogram waveform matrix needs to be converted into a continuous electrocardiogram waveform to generate the final corrected electrocardiogram signal. The output of the gated recurrent unit is in the form of a matrix representing the corrected electrocardiogram waveform, where each row corresponds to a corrected cardiac beat segment. By extracting the 1×300 vector of each row, the corresponding single cardiac beat signal can be restored, and the correction and reconstruction of the overall electrocardiogram waveform are completed.

[0106] In this embodiment, according to the electrode offset tags classified by the electrode offset situation, the corresponding correction parameters are called by the tags to load the correction model, and the correction model corresponding to the electrode offset situation is adopted to specifically correct the electrocardiogram signals for each electrode offset situation. The multi-head attention mechanism is used for weight distribution to increase the weight ratio of the electrocardiogram waveform features with strong representativeness and reduce the influence of irrelevant factors. Then, the gated recurrent unit is used for temporal modeling. The gated recurrent unit can well capture the long dependencies in the time series and better fit the electrocardiogram waveform. Finally, the corrected electrocardiogram signal is obtained without adjusting the electrode position to re-collect the electrocardiogram signal, improving the acquisition efficiency of the electrocardiogram signal.

[0107] Embodiment III

[0108] Figure 3 The figure is a flowchart of an electrocardiogram signal correction method according to Embodiment III of the present invention. This embodiment is optimized based on the above embodiments. In this embodiment, the mean electrical axis vector is obtained by vector synthesis according to the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III, and the mean electrical axis angle is calculated. The specific optimization is as follows:

[0109] Using Einthoven's triangle rule and Bailey's hexaxial system, determine the Lead I vector, the Lead II vector, and the Lead III vector according to the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III;

[0110] Perform vector synthesis using the Lead I vector, the Lead II vector, and the Lead III vector to generate the mean electrical axis vector;

[0111] Calculate the mean electrical axis angle according to the coordinates of the mean electrical axis vector.

[0112] Correspondingly, the electrocardiogram signal correction method provided in this embodiment specifically includes:

[0113] S301, preprocess the acquired electrocardiogram signal to obtain the preprocessed electrocardiogram signal.

[0114] S302, perform QRS wave localization on the preprocessed electrocardiogram signal to obtain the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III, and calculate the R peak amplitude ratio of Lead I to Lead III.

[0115] S303, using Einthoven's triangle rule and Bailey's hexaxial system, determine the Lead I vector, the Lead II vector, and the Lead III vector according to the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III.

[0116] Einthoven's triangle law is an approximately equilateral triangle formed by three standard limb leads (Lead I, Lead II, and Lead III). The electrodes of these three leads are placed on the right arm, left arm, and left leg respectively, forming a closed triangle, which is mainly used to describe the spatial direction distribution of cardiac electrical activities and can be used to help analyze the direction and deviation of the electrical axis. Bailey's hexaxial system is to keep the axis leads of the six limb leads in their original directions and move them parallelly so that they intersect at a central point, forming a radial geometric figure, which can be used to evaluate the deviation and direction of the electrical axis. By using Einthoven's triangle law and Bailey's hexaxial system to determine the direction of each lead, and then combining the R peak amplitude of each lead as the length of the vector, the Lead I vector, Lead II vector, and Lead III vector that can represent the cardiac electrical activities on each lead can be formed.

[0117] S304. Use the Lead I vector, Lead II vector, and Lead III vector for vector synthesis to generate the mean electrical axis vector.

[0118] Based on the plane coordinate established by Einthoven's triangle law and Bailey's hexaxial system, by superimposing the vectors of the three leads, the mean electrical axis vector is obtained :

[0119] = +

[0120] S305. Calculate the mean electrical axis angle according to the coordinates of the mean electrical axis vector.

[0121] The mean electrical axis vector can indicate the cardiac electrical activities. The abscissa and ordinate of the mean electrical axis vector can reflect the intensity of cardiac electrical activities in different directions to a certain extent. Calculate the mean electrical axis angle 𝜃 using the coordinates of the mean electrical axis vector. The mean electrical axis angle 𝜃 reflects the overall cardiac electrical activity direction. The displacement of the electrode can be judged through the deviation trend of the electrical axis angle:

[0122]

[0123] Among them, and respectively represent the abscissa and ordinate of the mean electrical axis vector. is the inverse trigonometric function of the four quadrants, which is used to calculate the angle (usually in radians) from the positive direction of the x axis to the point . For example, Figure 5As shown, translate V1, V2, and V3 in the figure to obtain V1', V2', and V3' respectively. Superimpose and synthesize V1', V2', and V3' to obtain the mean electrical axis vector, and then calculate the mean electrical axis angle using the coordinates of the mean electrical axis vector. As Figure 4 shown Figure 4 (a) shows the results of the normal electrode placement position; compared with the normal position, Figure 4 in (b), the amplitude of the vector in lead I decreases, the mean electrical axis angle becomes larger, and the electrode placement position is biased upward; Figure 4 in (c), the amplitude of the vector in lead I becomes larger, the amplitude of the vector in lead III becomes smaller, the mean electrical axis angle becomes smaller, and the electrode placement position is biased downward; Figure 4 in (d), the amplitude of the vector in lead III decreases, the mean electrical axis angle becomes smaller, and the electrode placement position is biased to the left; Figure 4 in (e), the amplitude of the vector in lead III becomes larger, the mean electrical axis angle becomes abnormally larger, and the electrode placement position is biased to the right; Figure 4 in (f), the amplitude of the vector in lead I decreases, the amplitude of the vector in lead III becomes larger, the mean electrical axis angle becomes larger, and the electrode placement position rotates clockwise; Figure 4 in (g), the amplitude of the vector in lead I becomes larger, the amplitude of the vector in lead III decreases, the mean electrical axis angle becomes smaller, and the electrode placement position rotates counterclockwise.

[0124] S306. Using the trained random forest model, decision tree model, and XGBoost model, respectively discriminate the electrode offset situation of the preprocessed electrocardiogram signal according to the mean electrical axis angle, the R peak amplitude of lead I, the R peak amplitude of lead II, the R peak amplitude of lead III, and the ratio of the R peak amplitudes of lead I and lead III to obtain the first electrode offset type, the second electrode offset type, and the third electrode offset type. Perform voting method ensemble learning based on all electrode offset types to obtain the electrode offset label.

[0125] S307. According to the electrode offset label, use the trained correction model based on multi-head attention and gated recurrent units to correct the preprocessed electrocardiogram signal. The correction model first performs weight allocation on the electrocardiogram signal in matrix form using the multi-head attention mechanism, and then uses the gated recurrent unit for temporal modeling to generate the corrected electrocardiogram signal.

[0126] In this embodiment, the Einthoven triangle rule and the Baylor six-axis system are used to determine the vectors of each lead by combining the R-peak amplitudes of each lead, and then the average electrical axis vector is obtained through vector synthesis. The average electrical axis angle is calculated using the average electrical axis vector. The average electrical axis angle can reflect the overall electrical activity direction of the heart. The average electrical axis vector and the average electrical axis angle can reflect the intensity of the heart's electrical activity in different directions to a certain extent, and can be used to analyze the electrode position offset. Together with the R-peak amplitude of each lead, they form the input features of a classification model for classifying electrode offsets. The classification model can classify the types of electrode offsets based on the electrical activity intensity of the heart on each lead and the directional correlation between each lead reflected by the input features.

[0127] Specifically, using the Einthoven triangle rule and the Baylor six-axis system, the vectors of Lead I, Lead II, and Lead III are determined according to the R-peak amplitude of Lead I, the R-peak amplitude of Lead II, and the R-peak amplitude of Lead III, including:

[0128] The angles of Lead I, Lead II, and Lead III are determined using the Einthoven triangle rule and the Baylor six-axis system.

[0129] According to the Einthoven triangle rule and the Baylor six-axis system, as Figure 5 shown, the angle of Lead I can be determined to be 0°, the R-peak amplitude is , the angle of Lead II is 60°, the R-peak amplitude is , and the angle of Lead III is 120°, the R-peak amplitude is .

[0130] The vector of Lead I is determined according to the angle of Lead I and the R-peak amplitude of Lead I, the vector of Lead II is determined according to the angle of Lead II and the R-peak amplitude of Lead II, and the vector of Lead III is determined according to the angle of Lead III and the R-peak amplitude of Lead III.

[0131] Taking the R-peak amplitude of Lead I as the vector length and the angle of Lead I as the vector direction, the vector of Lead I can be formed; similarly, taking the R-peak amplitude of Lead II as the vector length and the angle of Lead II as the vector direction, the vector of Lead II can be formed; the vector of Lead III can also be obtained in the same way.

[0132] The vector of Lead I can be expressed as:

[0133]

[0134] The vector of Lead II can be expressed as:

[0135]

[0136] The vector of lead Ⅲ can be expressed as:

[0137]

[0138] An optional implementation manner of this embodiment is to preprocess the acquired electrocardiogram (ECG) signal to obtain a preprocessed ECG signal, including:

[0139] Denoise the acquired ECG signal by using a band-pass filter and wavelet transform.

[0140] During the process of collecting the ECG signal, noise will be generated due to power frequency interference, equipment circuits, etc. A band-pass filter is used for filtering at a specific frequency, and then wavelet transform is used to perform scaling and translation operations to decompose the signal into wavelet basis functions at different scales and positions, obtaining the denoised ECG signal. The decomposition process of wavelet transform can be expressed as:

[0141]

[0142] Wherein, is the original signal, is the wavelet basis function, is the wavelet coefficient, and are the scale and translation parameters. During the signal processing, first perform wavelet decomposition on the original ECG signal , that is, project it onto the basis functions at different scales to calculate the corresponding wavelet coefficients . The calculation of wavelet coefficients is achieved by performing scale transformation (decomposing into different frequency components) and time translation transformation (locating features at different time points) on the signal.

[0143] Eliminate baseline drift of the acquired ECG signal by using a high-pass filter.

[0144] During the process of collecting the ECG signal, low-frequency baseline drift will be caused due to reasons such as breathing and poor electrode contact. A high-pass filter is used to filter the low frequency and perform baseline drift elimination processing on the ECG signal to reduce the influence of irrelevant factors.

[0145] Optionally, Daubechies4 is used as the wavelet basis function for wavelet transform, and four-layer wavelet decomposition is performed.

[0146] Use Daubechies4 (wavelet db4) as the wavelet basis function and perform four-layer wavelet decomposition. Exemplarily, first use the scale factor to control the decomposition level of wavelet transform to analyze the signal at different resolutions; then, calculate the translation parameter Wavelet coefficients under the action The different frequency components of the electrocardiogram signal are extracted separately. To optimize the noise reduction effect, a smoothing coefficient of 0.8 is used for signal smoothing to reduce the influence of random noise. At the same time, a nonlinear correction factor of 0.5 is used to perform threshold processing on the wavelet coefficients to further suppress high-frequency noise and power frequency interference.

[0147] Embodiment 4

[0148] Figure 6 FIG. is a schematic structural diagram of an electrocardiogram signal correction device according to Embodiment 4 of the present invention. In this embodiment, the electrocardiogram signal correction device includes:

[0149] An electrocardiogram signal processing module 810 for preprocessing the acquired electrocardiogram signal;

[0150] A QRS wave positioning module 820 for positioning the QRS wave of the preprocessed electrocardiogram signal;

[0151] A vector synthesis module 830 for performing vector synthesis and calculating the average electrical axis angle;

[0152] An electrode offset classification module 840 for discriminating the type of electrode offset and generating an electrode offset label;

[0153] An electrocardiogram signal correction module 850 for correcting the electrocardiogram signal according to the electrode offset label by using the trained correction model.

[0154] In this embodiment, the electrocardiogram signal is preprocessed by the electrocardiogram signal processing module, the QRS wave of the electrocardiogram signal is positioned by the QRS wave positioning module, vector synthesis is performed by the vector synthesis module, the electrode offset situation is classified by the electrode offset classification module, and the electrocardiogram signal is corrected by the electrocardiogram signal correction module according to the electrode offset type. Vectors of each lead are generated based on the R peak amplitude of each lead of the electrocardiogram signal, then vector synthesis is performed to obtain the average electrical axis vector and calculate the average electrical axis angle. After that, the electrode offset situation is classified, and the correction model corrects the electrocardiogram signal by loading the trained correction model according to the electrode offset type to obtain the corrected electrocardiogram waveform. The electrode offset situation of the collected electrocardiogram waveform can be analyzed, the electrocardiogram waveform can be corrected, and the corrected electrocardiogram signal can be directly output without manually correcting the placement position of the electrode and then re-collecting it twice, improving the collection efficiency of the electrocardiogram signal and meeting the usage requirements of non-medical professionals.

[0155] The electrocardiogram signal correction device provided by the embodiment of the present invention can execute the electrocardiogram signal correction method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0156] Embodiment 5

[0157] Figure 7 This is a structural diagram of a computer device according to the fifth embodiment of the present invention. Figure 7 It shows a block diagram of an exemplary computer device 12 suitable for implementing the embodiments of the present invention. Figure 7 The shown computer device 12 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0158] As Figure 7 shown, the computer device 12 is presented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0159] The bus 18 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0160] The computer device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0161] The system memory 28 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 34 may be used for reading and writing non-removable, non-volatile magnetic media ( Figure 7 not shown, typically referred to as a "hard disk drive"). Although Figure 7 not shown in the figure, a disk drive for reading and writing removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing removable non-volatile optical disks (such as CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 through one or more data media interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to execute the functions of the embodiments of the present invention.

[0162] A program / utilities 40 having a set (at least one) of program modules 42 can be stored, for example, in a memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules 42 generally execute the functions and / or methods in the embodiments described in the present invention.

[0163] The computer device 12 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the device / server / computer device 12, and / or communicate with any device that enables the computer device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 22. Moreover, the computer device 12 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As shown in the figure, the network adapter 20 communicates with other modules of the computer device 12 through a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0164] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the electrocardiogram signal correction method provided in the embodiments of the present invention.

[0165] Embodiment Six

[0166] Embodiment Six of the present invention also provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the electrocardiogram signal correction method provided in the above embodiments when executed by a computer processor.

[0167] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, apparatus, or device.

[0168] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0169] The program codes contained on the computer-readable media may be transmitted with any appropriate media, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0170] The computer program codes for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Python, Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0171] Note that the above is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An electrocardiogram signal correction method, characterized in that Including: Preprocess the acquired electrocardiogram (ECG) signal to obtain a preprocessed ECG signal; Locate the QRS complex in the preprocessed ECG signal to obtain the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III, and calculate the ratio of the R peak amplitude of Lead I to that of Lead III; Based on the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III, obtain the mean electrical axis vector through vector synthesis and calculate the mean electrical axis angle; Using the trained random forest model, decision tree model, and XGBoost model, respectively discriminate the electrode offset situation of the preprocessed ECG signal according to the mean electrical axis angle, the R peak amplitude of Lead I, the R peak amplitude of Lead II, the R peak amplitude of Lead III, and the ratio of the R peak amplitude of Lead I to that of Lead III, to obtain the first electrode offset type, the second electrode offset type, and the third electrode offset type, and perform voting method ensemble learning based on all electrode offset types to obtain the electrode offset label; According to the electrode offset label, use the trained correction model based on multi-head attention and gated recurrent unit to correct the preprocessed ECG signal. The correction model first performs weight allocation on the ECG signal in matrix form using the multi-head attention mechanism, and then uses the gated recurrent unit for temporal modeling to generate the corrected ECG signal.

2. The method according to claim 1, characterized in that, The step of correcting the preprocessed ECG signal using the trained correction model based on multi-head attention and gated recurrent unit according to the electrode offset label includes: According to the electrode offset label, call the electrode offset correction parameters corresponding to the electrode offset label and load them into the correction model; Input the preprocessed ECG signal into the correction model, and the correction model outputs the corrected ECG signal.

3. The method according to claim 2, wherein The step of inputting the preprocessed ECG signal into the correction model and the correction model outputting the corrected ECG signal includes: Convert the preprocessed ECG signal into matrix form to generate an ECG waveform matrix; Use the multi-head attention mechanism to perform weight allocation on the ECG waveform matrix to generate an attention ECG waveform matrix; Use the gated recurrent unit to perform temporal modeling according to the attention ECG waveform matrix to generate a corrected ECG waveform matrix; Convert the corrected ECG waveform matrix into an ECG waveform to generate the corrected ECG signal.

4. The method according to claim 1, wherein The step of obtaining the mean electrical axis vector through vector synthesis based on the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III and calculating the mean electrical axis angle includes: Using Einthoven's triangle law and Bailey's hexaxial system, determine the Lead I vector, the Lead II vector, and the Lead III vector according to the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III; Perform vector synthesis using the Lead I vector, the Lead II vector, and the Lead III vector to generate the mean electrical axis vector; Calculate the mean electrical axis angle according to the coordinates of the mean electrical axis vector.

5. The method according to claim 4, characterized in that The step of using Einthoven's triangle law and Bailey's hexaxial system to determine the Lead I vector, the Lead II vector, and the Lead III vector according to the R peak amplitude of Lead I, the R peak amplitude of Lead II, and the R peak amplitude of Lead III includes: Determine the angles of lead I, lead II, and lead III using Einthoven's triangle rule and Bailey's hexaxial system; Determine the lead I vector according to the angle of lead I and the R peak amplitude of lead I, determine the lead II vector according to the angle of lead II and the R peak amplitude of lead II, and determine the lead III vector according to the angle of lead III and the R peak amplitude of lead III.

6. The method according to claim 1, characterized in that, The preprocessing of the acquired electrocardiogram (ECG) signal to obtain a preprocessed ECG signal includes: Perform denoising processing on the acquired ECG signal using a band-pass filter and wavelet transform; Use a high-pass filter to eliminate baseline drift of the acquired ECG signal.

7. The method according to claim 6, wherein: The wavelet transform uses Daubechies4 as the wavelet basis function and performs four-layer wavelet decomposition.

8. An electrocardiogram signal correction device for an electrocardiogram signal correction method according to any one of claims 1-7, characterized in that, It includes: An ECG signal processing module for preprocessing the acquired ECG signal; A QRS complex localization module for localizing the QRS complex in the preprocessed ECG signal; A vector synthesis module for performing vector synthesis and calculating the mean electrical axis angle; An electrode offset classification module for discriminating the type of electrode offset and generating an electrode offset label; An ECG signal correction module for correcting the ECG signal using a trained correction model according to the electrode offset label.

9. A computer device, characterized in that, The computer device includes: One or more processors; A storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the ECG signal correction method according to any one of claims 1-7.

10. A storage medium containing computer-executable instructions, the computer-executable instructions being used to execute the ECG signal correction method according to any one of claims 1-7 when executed by a computer processor.

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