CN113208609A - Electrocardio information management system - Google Patents
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Abstract
The present disclosure relates to an electrocardiographic information management system, which includes: an electrocardiogram signal acquisition terminal configured to acquire electrocardiogram signals of a patient; a server including a processor and an electrocardiogram storage unit, the processor including at least an electrocardiogram generating unit configured to generate an electrocardiogram image from the electrocardiogram signal, the electrocardiogram storage unit configured to store the electrocardiogram storage image; a diagnostic center configured to generate a diagnostic sequence and push the electrocardiogram images to a physician workstation in the diagnostic sequence. The electrocardiogram management system provided by the invention can be deployed in hospitals, and a unified server is used for transmitting, storing, pre-diagnosing, sequencing and scheduling electrocardiogram images of patients.
Description
Technical Field
The invention relates to a computer vision-based scheduling management system, in particular to a computer vision-based medical image diagnosis scheduling management system. The system can be applied to computer vision-based analysis of diagnostic imaging data for each department of a hospital to enable relative emergencies or to be prioritized.
Background
At present, cardiovascular diseases are recognized as a group of diseases that are considered to be one of the leading causes of death. Cardiovascular disease occurs in the form of Myocardial Infarction (MI). Myocardial infarction, commonly referred to as a heart attack, represents the failure of the heart muscle to contract for a considerable period of time. The risk of death in a person with an ongoing heart attack may be reduced by using appropriate treatment within one hour after the heart attack has started. When a heart disease occurs, the first diagnostic test includes an Electrocardiogram (ECG) and is therefore the primary diagnostic tool for cardiovascular disease (CVD). The electrocardiograph detects the electrical activity of the heart over a test period and then displays it in a graph reflecting periodic electrophysiological events in the myocardium. By careful analysis of the electrocardiogram traces, the physician can diagnose a possible myocardial infarction.
An electrocardiogram information management system (CIS for short) can realize the integrated connection and network access of various electrocardiogram and electrophysiological examination equipment of a hospital. The CIS which is widely used at present supports two network modes of a local area network and a wide area network, provides comprehensive support for information construction and telemedicine of hospitals, and can realize unified storage, transmission, diagnosis, statistics, query and retrieval of patient data information. In a hospital, the CIS and the existing hospital information management system (HIS) of the hospital can be seamlessly fused, and the information management of the whole process of patient appointment registration, electronic number calling, charging, item examination, output report, centralized storage, data sharing and statistical retrieval is realized, so that the problems of low examination efficiency and difficult data query and retrieval in the hospital are solved. However, the CIS system, which is widely used at present, does not perform priority scheduling for electrocardiograms. That is, the current system can only push examination results to the physician workstation of the department according to the examination sequence.
Disclosure of Invention
In view of the above problems of the prior art, the present invention is to provide an electrocardiographic information management system, which can make the electrocardiographic monitoring result of an emergency patient be pushed to a doctor with high priority, so as to be processed more timely.
In order to achieve the above object, an aspect of the present invention provides an electrocardiographic information management system, including:
an electrocardiogram signal acquisition terminal configured to acquire electrocardiogram signals of a patient;
a server including a processor and an electrocardiogram storage unit, the processor including at least an electrocardiogram generating unit configured to generate an electrocardiogram image from the electrocardiogram signal, the electrocardiogram storage unit configured to store the electrocardiogram storage image;
a diagnostic center configured to generate a diagnostic sequence and push the electrocardiogram images to a physician workstation in the diagnostic sequence.
Preferably, the processor generates an electrocardiogram image from the electrocardiogram signal, and includes:
performing noise reduction processing on the electrocardiogram signal;
filtering the electrocardiosignals subjected to noise reduction;
and generating an electrocardiogram image according to the electrocardiosignals subjected to filtering processing.
Preferably, the denoising process is implemented by a wavelet transform algorithm, and the filtering process is a low-pass filter and/or a median filter algorithm.
Preferably, the processor further comprises an electrocardiogram screening unit, after generating the electrocardiogram image, further comprising screening the electrocardiogram image, wherein the screening comprises removing normal electrocardiogram images based on an expert system.
Preferably, the processor further comprises an electrocardiogram analysis unit configured to analyze the screened electrocardiogram images based on a computer vision algorithm model to obtain treatment priority scores; the diagnostic center is further configured to generate the diagnostic sequence according to the treatment priority score.
Preferably, the computer vision algorithm model is a convolutional neural network algorithm model, and the convolutional neural algorithm model includes an input layer, a first convolutional layer, a second convolutional layer, a third convolutional layer, a fourth convolutional layer and an output layer, wherein each convolutional layer of the first convolutional layer, the second convolutional layer, the third convolutional layer and/or the fourth convolutional layer includes at least 80 convolutional cores.
Preferably, the convolutional neural network algorithm model is constructed in the following way:
1) acquiring a plurality of classification categories of electrocardiosignal records by a public API to form a training set, wherein the classification categories at least comprise a normal category, a premature beat category, a myocardial infarction category or an atrial fibrillation category;
2) and constructing a convolutional neural network, and inputting the training set into the convolutional neural network for learning until the sensitivity and the specificity are both greater than 98%.
Preferably, the convolutional neural network algorithm model further comprises processing of iteratively pruning or pruning neurons during construction.
Preferably, the processor further comprises a result summarizing unit configured to modify the treatment priority score according to an expert system analysis result and an analysis result of the convolutional neural network.
In another aspect of the present invention, a method for managing electrocardiographic information is further provided, where the method includes:
acquiring an electrocardiogram signal of a patient through electrocardiogram monitoring equipment;
processing the electrocardiogram signals to generate electrocardiogram images, and analyzing the electrocardiogram images based on a computer vision algorithm model to obtain treatment priority scores;
and generating a diagnosis sequence according to the treatment priority grade, and feeding back the electrocardiogram image to a diagnosis center according to the diagnosis sequence.
Preferably, the method further comprises:
performing noise reduction processing on the electrocardiogram signal; filtering the electrocardiosignals subjected to noise reduction; generating an electrocardiogram image according to the electrocardiosignals subjected to filtering processing;
screening the electrocardiogram images, wherein the screening comprises removing normal electrocardiogram images based on an expert system; analyzing the screened electrocardiogram images based on a computer vision algorithm model to obtain treatment priority scores; the diagnostic center is further configured to generate the diagnostic sequence according to the treatment priority score.
Compared with the prior art, the electrocardiogram management system provided by the invention can be deployed in hospitals, and a unified server is used for transmitting, storing, pre-diagnosing, sequencing and scheduling electrocardiogram images of patients. For cases with urgent disease, a relatively higher priority score will be given so that the physician can understand the patient's condition as early as possible and give timely treatment.
Drawings
Fig. 1 is a topological structure diagram of a general electrocardiographic information management system deployed in a hospital.
Fig. 2 is a block diagram of an electrocardiographic information management system according to an embodiment of the present invention.
Fig. 3 is a flowchart of an electrocardiographic information management system according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a convolutional neural network algorithm model of the electrocardiographic information management system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Various aspects and features of the present invention are described herein with reference to the drawings.
These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It should also be understood that, although the invention has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the invention, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present invention will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the invention in unnecessary or unnecessary detail based on the user's historical actions. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the invention. The terms "electrocardiographic signal" and "electrocardiographic image" as used herein include, but are not limited to, electrocardiographic image data, and may also include other data related thereto, such as time of acquisition, time of upload, source of data, and, for example, the subject's age, height, weight, sex, medical history, etc.
As previously mentioned, conventional hospitals can only prioritize according to the chronological order of existence at present. As shown in fig. 1, the electrocardiographic information management system is connected to a hospital information management system and a diagnosis center, and the electrocardiographic information management system is connected to electrocardiographic rooms, functional departments, cardiology departments, ICU & CCU departments, and intensive care units, and is connected to electrocardiographic signal acquisition devices of the respective departments, such as an electrocardiograph, an electrocardiograph workstation, a dynamic electrocardiograph, a sports flat electrocardiograph, and an ultrasonic electrocardiograph. However, such systems are currently only able to be determined by a clinician based on a patient's characterization. In addition to the possibility of a misjudgment by the doctor, the patient who should be in an emergency (for example, myocardial infarction) may lose the best opportunity for treatment due to a low treatment process. In view of this situation, the electrocardiographic management system provided by the present invention can further process the electrocardiographic signals after acquiring the electrocardiographic signals, so as to generate electrocardiographic images, and the electrocardiographic images can be firstly screened by the expert system, so as to remove obviously normal electrocardiographic images. And then, the abnormal electrocardiogram image is further analyzed based on the computer vision algorithm model to obtain the priority grade of the electrocardiogram image, and the analysis process can be used for carrying out initial judgment based on the computer vision algorithm model to evaluate the illness state of the patient. And the final ranking at the physician workstation is the diagnostic sequence generated according to the treatment priority score. In the invention, the computer vision algorithm model specifically learns the relationship between the input image data and the output image data by using a convolutional neural network algorithm model, wherein the convolutional neural network algorithm model at least comprises an input layer, a first convolutional layer, a second convolutional layer, a third convolutional layer, a fourth convolutional layer and an output layer, and each convolutional layer in the first convolutional layer, the second convolutional layer, the third convolutional layer and/or the fourth convolutional layer at least comprises 80 convolutional cores. Firstly, acquiring a plurality of classification categories of electrocardiosignal records to form a training set through a public API (physical API), wherein the classification categories at least comprise a normal category, a premature beat category, a myocardial infarction category or an atrial fibrillation category; and then constructing a Convolutional Neural Network (CNN), and inputting the training set into the convolutional neural network for learning until the sensitivity and the specificity are both greater than 98%. After each convolution of the four convolution layers, batch normalization is carried out to avoid parameter explosion and 'vanishing gradient' phenomena. Batch normalization allows the deep network to be trained and applied after each convolutional layer and before the execution of the ReLU (rectified linear activation function). The pool level in CNN (before RELU) reduces the problem of network data overfitting, making the input size only half of the actual input. Further, in constructing the convolutional neural network algorithm model, a method of adding data cross validation for obtaining a reliable estimate of the model generalization error is included, and in particular, K-fold cross validation, which involves randomly dividing the training data set into K parts without reintegration, is used in this study: k-1 parts were used for training the model and one part was used for testing. This process was repeated k times to obtain k models and performance estimates. The average performance of the model is then calculated based on the different independent subdivisions to obtain a performance estimate that is less sensitive to the partitioning of the training data. Since k-fold cross-validation is resampling without a re-integration technique, the advantage of this approach is that each sample point will be only part of the training and testing data set, providing a lower variance estimate of template performance. In the present invention, the training data set is divided into ten parts, K10, and in ten iterations nine parts are used for training, one of which is used as the test set for model evaluation. In addition, the estimated performance Ei of each section (e.g., the accuracy of the classification) is then used to calculate an average estimated performance E of the model. In the practical application of some Beijing hospital, the convolutional neural network algorithm model consists of 995 subdivided cases in the "normal" category, 234 subdivided cases in the "ventricular premature beat" category and 93 subdivided cases in the "atrial premature beat" category. 70% of them were used for training and the remaining 30% for testing; the average accuracy of the finally obtained result is 98.33%, the sensitivity is 98.33%, the specificity of 98.35%, the false positive rate is 1.65%, and the false negative rate is 1.66%. Meanwhile, in some embodiments, the convolutional neural network algorithm model is constructed by iterative pruning or pruning of neurons. In iterative pruning, learning the correct connection is an iterative process. Each iteration is a greedy search and finds the best connection from it. In the prune neuron process, however, after pruning connections, those neurons with zero-input or zero-output connections may be safely pruned. A neuron with zero input or zero output connections will not contribute to the final penalty function, resulting in its output or input connections having a gradient of 0.
Specifically, as shown in fig. 2 to 4, one aspect of the present invention provides an electrocardiographic information management system, including:
an electrocardiogram signal acquisition terminal configured to acquire electrocardiogram signals of a patient; a server including a processor and an electrocardiogram storage unit, the processor including at least an electrocardiogram generating unit configured to generate an electrocardiogram image from the electrocardiogram signal, the electrocardiogram storage unit configured to store the electrocardiogram storage image; a diagnostic center configured to generate a diagnostic sequence and push the electrocardiogram images to a physician workstation in the diagnostic sequence.
Preferably, the processor generates an electrocardiogram image from the electrocardiogram signal, and includes:
performing noise reduction processing on the electrocardiogram signal; filtering the electrocardiosignals subjected to noise reduction; and generating an electrocardiogram image according to the electrocardiosignals subjected to filtering processing. The denoising processing is realized by a wavelet transform algorithm, and the filtering processing is a low-pass filter and/or a median filter algorithm.
Further, the processor may further include an electrocardiogram screening unit, after generating the electrocardiogram image, screening the electrocardiogram image, wherein the screening includes removing a normal electrocardiogram image based on an expert system.
In some refinements, the processor further comprises an electrocardiogram analysis unit configured to analyze the filtered electrocardiogram images based on a computer vision algorithm model to obtain a treatment priority score; the diagnostic center is further configured to generate the diagnostic sequence according to the treatment priority score. In other embodiments, the processor further comprises a result summarization unit configured to modify the treatment priority score based on expert system analysis results and analysis results of the convolutional neural network.
In another aspect of the present invention, a method for managing electrocardiographic information is further provided, where the method includes:
acquiring an electrocardiogram signal of a patient through electrocardiogram monitoring equipment;
processing the electrocardiogram signals to generate electrocardiogram images, and analyzing the electrocardiogram images based on a computer vision algorithm model to obtain treatment priority scores;
and generating a diagnosis sequence according to the treatment priority grade, and feeding back the electrocardiogram image to a diagnosis center according to the diagnosis sequence. In some refinements, noise reduction processing is performed on the electrocardiogram signal; filtering the electrocardiosignals subjected to noise reduction; generating an electrocardiogram image according to the electrocardiosignals subjected to filtering processing;
screening the electrocardiogram images, wherein the screening comprises removing normal electrocardiogram images based on an expert system; analyzing the screened electrocardiogram images based on a computer vision algorithm model to obtain treatment priority scores; the diagnostic center is further configured to generate the diagnostic sequence according to the treatment priority score.
Various specific embodiments of the methods described above, including various software modules, may be implemented on the computer-readable storage media.
In the above, various operations or functions are described herein, which may be implemented as or defined as software code or instructions. Such content may be directly executable ("object" or "executable" form) source code or differential code ("delta" or "patch" code). Software implementations of embodiments described herein may be provided via an article of manufacture having code or instructions stored therein or via a method of operating a communication interface to transmit data via the communication interface. A machine or computer-readable storage medium may cause a machine to perform the functions or operations described, and includes any mechanism for storing information in a form accessible by a machine (e.g., a computing device, an electronic system, etc.), such as recordable/non-recordable media (e.g., Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.). A communication interface includes any mechanism that interfaces to any of a hardwired, wireless, optical, etc. medium to communicate with another device, such as a memory bus interface, a processor bus interface, an internet connection, a disk controller, etc. The communication interface may be configured by providing configuration parameters and/or transmitting signals to prepare the communication interface to provide data signals describing the software content. The communication interface may be accessed via one or more commands or signals sent to the communication interface.
The present invention also relates to a system for performing the operations herein. The system may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CDROMs, and magnetic-optical disks, read-only memories (ROMs), Random Access Memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (10)
1. Electrocardio information management system, this system includes:
an electrocardiogram signal acquisition terminal configured to acquire electrocardiogram signals of a patient;
a server including a processor and an electrocardiogram storage unit, the processor including at least an electrocardiogram generating unit configured to generate an electrocardiogram image from the electrocardiogram signal, the electrocardiogram storage unit configured to store the electrocardiogram storage image;
a diagnostic center configured to generate a diagnostic sequence and push the electrocardiogram images to a physician workstation in the diagnostic sequence.
2. The system of claim 1, the processor generating an electrocardiogram image from the electrocardiogram signal, comprising:
performing noise reduction processing on the electrocardiogram signal;
filtering the electrocardiosignals subjected to noise reduction;
and generating an electrocardiogram image according to the electrocardiosignals subjected to filtering processing.
3. The system of claim 2, wherein the denoising process is implemented by a wavelet transform algorithm, and the filtering process is a low pass filter and/or a median filter algorithm.
4. The system of claim 1, the processor further comprising an electrocardiogram filtering unit, after generating the electrocardiogram image, further comprising filtering the electrocardiogram image, the filtering comprising removing normal electrocardiogram images based on an expert system.
5. The system of claim 4, the processor further comprising an electrocardiogram analysis unit configured to analyze the filtered electrocardiogram images based on a computer vision algorithm model to obtain a treatment priority score; the diagnostic center is further configured to generate the diagnostic sequence according to the treatment priority score.
6. The system of claim 5, wherein the computer vision algorithm model is a convolutional neural network algorithm model, and the convolutional neural algorithm model comprises an input layer, a first convolutional layer, a second convolutional layer, a third convolutional layer, a fourth convolutional layer, and an output layer, wherein each of the first convolutional layer, the second convolutional layer, the third convolutional layer, and/or the fourth convolutional layer comprises at least 80 convolutional cores.
7. The system of claim 6, wherein the convolutional neural network algorithm model is constructed by:
1) acquiring a plurality of classification categories of electrocardiosignal records by a public API to form a training set, wherein the classification categories at least comprise a normal category, a premature beat category, a myocardial infarction category or an atrial fibrillation category;
2) and constructing a convolutional neural network, and inputting the training set into the convolutional neural network for learning until the sensitivity and the specificity are both greater than 98%.
8. The system of claim 6, the convolutional neural network algorithm model, when constructed, further comprising iterative pruning or pruning neuron processing.
9. The system of claim 1, the processor further comprising a result summarization unit configured to modify the treatment priority score based on expert system analysis results and analysis results of the convolutional neural network.
10. The electrocardio information management method comprises the following steps:
acquiring an electrocardiogram signal of a patient through electrocardiogram monitoring equipment;
processing the electrocardiogram signals to generate electrocardiogram images, and analyzing the electrocardiogram images based on a computer vision algorithm model to obtain treatment priority scores;
and generating a diagnosis sequence according to the treatment priority grade, and feeding back the electrocardiogram image to a diagnosis center according to the diagnosis sequence.
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| WO2023187987A1 (en) * | 2022-03-29 | 2023-10-05 | 日本電気株式会社 | Electrocardiogram evaluation method |
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