CN111419214A - Electrocardio abnormality detection method, terminal and server - Google Patents
Electrocardio abnormality detection method, terminal and server Download PDFInfo
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Abstract
The invention discloses an electrocardio abnormality detection method, a terminal and a server, wherein firstly, a terminal (namely an equipment end) is utilized to collect electrocardio data, and the electrocardio data is subjected to one-by-one heartbeat analysis to obtain the electrocardio waveform characteristics; sequentially sending the electrocardio data and the electrocardio waveform characteristics to a server (namely a cloud) so as to indicate the server to perform off-line electrocardio abnormality detection by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result; meanwhile, the terminal carries out real-time electrocardio abnormality detection based on the electrocardio data, and sends a real-time electrocardio abnormality detection result to the server, so that the server further carries out electrocardio abnormality diagnosis according to the real-time electrocardio abnormality detection result of the terminal and an off-line electrocardio abnormality detection result of the server, and an electrocardio abnormality diagnosis result is obtained.
Description
Technical Field
The invention relates to an electrocardio detection technology, in particular to an electrocardio abnormality detection method, a terminal and a server.
Background
At present, in order to analyze electrocardiographic data acquired by wearable dynamic electrocardiograph equipment, more accurate analysis information is provided for doctors, workload is reduced, and related electrocardiograph algorithms are increasingly emphasized. On the equipment end or the mobile handheld equipment, due to the limitation of computing power and resources, although the real-time electrocardio algorithm can be analyzed in time, the detection and identification accuracy of the electrocardio abnormal signal is very low; the off-line electrocardio algorithm after data collection improves the accuracy of electrocardio abnormality detection and identification by means of methods such as machine learning, deep learning and the like, but the calculation time is long, and the real-time response can not meet the requirements of doctors.
Disclosure of Invention
The embodiment of the invention provides an electrocardio abnormality detection method, a terminal and a server creatively, aiming at solving the problems of high calculation power and serious time consumption of electrocardio abnormality detection.
According to a first aspect of the present invention, there is provided a method for detecting an abnormal cardiac electrical condition, applied to a server, the method including: sequentially receiving electrocardio data and performing one-by-one heart beat analysis on the electrocardio data to obtain electrocardio waveform characteristics; performing off-line electrocardio abnormality detection by using the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result; receiving a real-time electrocardio abnormality detection result obtained by carrying out real-time electrocardio abnormality detection based on the electrocardio data; and carrying out electrocardio abnormity diagnosis according to the real-time electrocardio abnormity detection result and the off-line electrocardio abnormity detection result to obtain an electrocardio abnormity diagnosis result.
According to an embodiment of the present invention, before performing offline abnormal electrocardiographic detection using the electrocardiographic data and electrocardiographic waveform characteristics, the method further includes: preprocessing the electrocardio data; and performing signal quality evaluation and sliding window processing on the preprocessed electrocardio data to obtain the electrocardio data subjected to sliding window processing.
According to an embodiment of the present invention, the off-line electrocardiographic abnormality detection using the electrocardiographic data and the electrocardiographic waveform characteristics includes: performing deep learning anomaly detection on the electrocardio data subjected to sliding window processing to obtain a deep learning anomaly detection result; and performing off-line electrocardio abnormality detection on the deep learning abnormality detection result and the electrocardio waveform characteristics.
According to an embodiment of the invention, the method further comprises: and if the abnormal electrocardio diagnosis result represents that the abnormal electrocardio exists, sending an abnormal alarm to a remote monitoring center.
According to a second aspect of the present invention, there is also provided an electrocardiographic abnormality detection method applied to a terminal, the method including: acquiring electrocardiogram data; performing heart beat analysis on the electrocardiogram data one by one to obtain electrocardiogram waveform characteristics; sequentially sending the electrocardio data and the electrocardio waveform characteristics to indicate that offline electrocardio abnormality detection is performed by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an offline electrocardio abnormality detection result; performing real-time electrocardio abnormality detection based on the electrocardio data to obtain a real-time electrocardio abnormality detection result; and sending the real-time electrocardio abnormality detection result to indicate that electrocardio abnormality diagnosis is carried out according to the real-time electrocardio abnormality detection result and the off-line electrocardio abnormality detection result to obtain an electrocardio abnormality diagnosis result.
According to a third aspect of the present invention, there is also provided a server, comprising: the receiving module is used for sequentially receiving the electrocardio data and the electrocardio waveform characteristics obtained by performing one-by-one heartbeat analysis on the electrocardio data; the off-line electrocardio abnormality detection module is used for carrying out off-line electrocardio abnormality detection by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result; the receiving module is also used for receiving a real-time electrocardio abnormity detection result obtained by carrying out real-time electrocardio abnormity detection based on the electrocardio data; and the electrocardio abnormity diagnosis module is used for carrying out electrocardio abnormity diagnosis according to the real-time electrocardio abnormity detection result and the off-line electrocardio abnormity detection result to obtain an electrocardio abnormity diagnosis result.
According to an embodiment of the present invention, the method further includes: the data processing module is used for preprocessing the electrocardio data before offline electrocardio abnormality detection is carried out by utilizing the electrocardio data and the electrocardio waveform characteristics through the offline electrocardio abnormality detection module; and the system is also used for carrying out signal quality evaluation and sliding window processing on the preprocessed electrocardio data to obtain the electrocardio data subjected to sliding window processing.
According to an embodiment of the present invention, the offline abnormal electrocardiographic detection module is specifically configured to perform deep learning abnormal detection on the electrocardiographic data after the sliding window processing, so as to obtain a deep learning abnormal detection result; and performing off-line electrocardio abnormality detection on the deep learning abnormality detection result and the electrocardio waveform characteristics.
According to an embodiment of the present invention, the method further includes: and the alarm module is used for sending an abnormal alarm to the remote monitoring center if the abnormal electrocardio diagnosis result indicates that the abnormal electrocardio exists.
According to a fourth aspect of the present invention, there is further provided a terminal, comprising: the acquisition module is used for acquiring the electrocardio data; the heart beat analysis module is used for carrying out heart beat analysis on the electrocardio data one by one to obtain the electrocardio waveform characteristics; the sending module is used for sequentially sending the electrocardio data and the electrocardio waveform characteristics so as to indicate that the electrocardio data and the electrocardio waveform characteristics are utilized to carry out off-line electrocardio abnormality detection to obtain an off-line electrocardio abnormality detection result; the real-time electrocardio abnormality detection module is used for carrying out real-time electrocardio abnormality detection based on the electrocardio data to obtain a real-time electrocardio abnormality detection result; the sending module is further configured to send the real-time abnormal electrocardiogram detection result to instruct to perform abnormal electrocardiogram diagnosis according to the real-time abnormal electrocardiogram detection result and the offline abnormal electrocardiogram detection result, so as to obtain an abnormal electrocardiogram diagnosis result.
According to the electrocardio abnormality detection method, the terminal and the server, firstly, a terminal (namely an equipment terminal) is used for collecting electrocardio data, and the electrocardio data are subjected to one-by-one heartbeat analysis to obtain the electrocardio waveform characteristics; sequentially sending the electrocardio data and the electrocardio waveform characteristics to a server (namely a cloud) so as to indicate the server to perform off-line electrocardio abnormality detection by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result; meanwhile, the terminal carries out real-time electrocardio abnormality detection based on the electrocardio data, and sends a real-time electrocardio abnormality detection result to the server, so that the server further carries out electrocardio abnormality diagnosis according to the real-time electrocardio abnormality detection result of the terminal and an off-line electrocardio abnormality detection result of the server, and an electrocardio abnormality diagnosis result is obtained. Therefore, the device side uploads the electrocardio data to the cloud in real time, and simultaneously uploads the cloud waveform characteristics and real-time electrocardio abnormal detection results obtained by performing heart beat-to-heart beat calculation analysis one by one, so that more information between heart beats is analyzed when an offline analysis algorithm of a cloud system is called, and the analysis accuracy is improved while the problem of time consumption in calculation is effectively solved.
It is to be understood that the teachings of the present invention need not achieve all of the above-described benefits, but rather that specific embodiments may achieve specific technical results, and that other embodiments of the present invention may achieve benefits not mentioned above.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
FIG. 1 is a diagram illustrating a network architecture for implementing abnormal cardiac electrical activity detection according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a flow interaction for detecting an abnormal electrocardiogram according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing a first implementation flow of the method for detecting an electrocardiographic abnormality according to the embodiment of the present invention;
FIG. 4 is a flow chart illustrating a specific implementation of a new anomaly detection implemented by an application example of the present invention;
FIG. 5 shows a schematic flow chart of implementing the method for detecting an electrocardiographic abnormality according to the embodiment of the present invention;
FIG. 6 is a schematic diagram showing a third implementation flow of the electrocardiographic abnormality detection method according to the embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a component structure of a server according to an embodiment of the present invention;
fig. 8 is a schematic diagram illustrating a configuration of a terminal according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given only to enable those skilled in the art to better understand and to implement the present invention, and do not limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The technical solution of the present invention is further elaborated below with reference to the drawings and the specific embodiments.
FIG. 1 is a diagram illustrating a network architecture for implementing abnormal cardiac electrical activity detection according to an embodiment of the present invention; fig. 2 shows a schematic flow interaction diagram for implementing electrocardiographic abnormality detection according to the embodiment of the present invention.
Referring to fig. 1, the implementation of the application example of the present invention mainly includes data interaction between a terminal (i.e., a device side), a server (i.e., a cloud side), and a remote monitoring center. Specifically, the equipment end sequentially sends electrocardiogram data, electrocardiogram waveform characteristics (such as QRS heart beat characteristics) and real-time electrocardiogram abnormity detection results (namely algorithm analysis results) to the remote electrocardiogram monitoring cloud; further, the remote electrocardiographic monitoring cloud carries out operation processes of data management and task distribution, offline electrocardiographic analysis, monitoring alarm implementation and the like, so that when the electrocardiographic abnormality diagnosis result represents that electrocardiographic abnormality exists, an abnormality alarm is sent to the remote monitoring center for timely processing by a doctor.
Based on the network architecture for implementing abnormal cardiac electrical detection shown in fig. 1, the flow for implementing abnormal cardiac electrical detection according to the embodiment of the present invention is shown in fig. 2, and specifically includes: acquiring electrocardio data by a terminal (namely an equipment end), and carrying out heart beat analysis on the electrocardio data one by one to obtain the electrocardio waveform characteristics; sequentially sending the electrocardio data and the electrocardio waveform characteristics to a server (namely a cloud) so as to indicate the server to perform off-line electrocardio abnormality detection by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result; meanwhile, the terminal carries out real-time electrocardio abnormality detection based on the electrocardio data, and sends a real-time electrocardio abnormality detection result to the server, so that the server further carries out electrocardio abnormality diagnosis according to the real-time electrocardio abnormality detection result of the terminal and an off-line electrocardio abnormality detection result of the server, and an electrocardio abnormality diagnosis result is obtained.
Therefore, the device side uploads the electrocardio data to the cloud in real time, and simultaneously uploads the cloud waveform characteristics and real-time electrocardio abnormal detection results obtained by performing heart beat-to-heart beat calculation analysis one by one, so that more information between heart beats is analyzed when an offline analysis algorithm of a cloud system is called, and the analysis accuracy is improved while the problem of time consumption in calculation is effectively solved.
FIG. 3 is a schematic diagram showing a first implementation flow of the method for detecting an electrocardiographic abnormality according to the embodiment of the present invention; fig. 4 is a schematic diagram illustrating a specific implementation flow of implementing a new anomaly detection by an application example of the present invention.
Referring to fig. 3, the method for detecting an electrocardiographic abnormality at a server side according to the embodiment of the present invention includes: operation 301, sequentially receiving electrocardiographic data and performing one-by-one heartbeat analysis on the electrocardiographic data to obtain electrocardiographic waveform characteristics; operation 302, performing offline electrocardiographic abnormality detection by using the electrocardiographic data and the electrocardiographic waveform characteristics to obtain an offline electrocardiographic abnormality detection result; operation 303, receiving a real-time abnormal electrocardiographic detection result obtained by performing real-time abnormal electrocardiographic detection based on the electrocardiographic data; and operation 304, performing electrocardiogram abnormity diagnosis according to the real-time electrocardiogram abnormity detection result and the off-line electrocardiogram abnormity detection result to obtain an electrocardiogram abnormity diagnosis result.
In operation 301, referring to fig. 4, the server receives raw electrocardiographic data collected by the terminal and electrocardiographic waveform characteristics analyzed on a beat-by-beat basis. The electrocardiographic waveform features at least include P wave, QRS wave, S wave, T wave and the like.
Before operation 302, referring to fig. 4, the server needs to perform preprocessing, such as denoising, on the electrocardiographic data; further, signal quality evaluation and window sliding processing are carried out on the preprocessed electrocardio data, so that the electrocardio data with excellent signal quality is obtained for subsequent off-line electrocardio abnormality detection.
Correspondingly, in operation 302, the server first performs deep learning anomaly detection on the electrocardiographic data after the sliding window processing to obtain a deep learning anomaly detection result; and then, performing off-line electrocardio abnormality detection on the deep learning abnormality detection result and the electrocardio waveform characteristics.
In operation 303-304, after the terminal performs heart beat-by-heart beat calculation analysis through a real-time electrocardio analysis algorithm to obtain a real-time electrocardio abnormality detection result, reporting the real-time electrocardio abnormality detection result to a server; further, on the basis that the terminal completes calculation and analysis of heart beats one by one, the server carries out heart anomaly diagnosis according to the real-time heart anomaly detection result and the off-line heart anomaly detection result so as to analyze more information among heart beats, thereby effectively solving the problem of time consumption of calculation and better improving the analysis accuracy.
Fig. 5 shows a schematic flow chart of implementing the electrocardiographic abnormality detection method according to the embodiment of the present invention.
Referring to fig. 5, the method for detecting an electrocardiographic abnormality at a server side according to the embodiment of the present invention includes: operation 501, sequentially receiving electrocardiographic data and performing one-by-one heartbeat analysis on the electrocardiographic data to obtain electrocardiographic waveform characteristics; operation 502, performing offline electrocardiographic abnormality detection by using the electrocardiographic data and electrocardiographic waveform characteristics to obtain an offline electrocardiographic abnormality detection result; operation 503, receiving a real-time abnormal cardiac electrical detection result obtained by performing real-time abnormal cardiac electrical detection based on the cardiac electrical data; operation 504, performing an abnormal cardiac electrical diagnosis according to the real-time abnormal cardiac electrical detection result and the offline abnormal cardiac electrical detection result to obtain an abnormal cardiac electrical diagnosis result; and operation 505, if the abnormal electrocardio diagnosis result indicates that the abnormal electrocardio exists, sending an abnormal alarm to a remote monitoring center.
The specific implementation processes of operations 501 to 504 are similar to the specific implementation processes of operations 301 to 304 in the embodiment shown in fig. 3, and are not described here again.
In operation 505, referring to fig. 4, the server determines whether the abnormal cardiac electrical diagnosis result represents abnormal cardiac electrical, and if so, sends an abnormal alarm to the remote monitoring center. Therefore, the embodiment of the invention effectively improves the analysis accuracy and simultaneously sends the abnormal alarm information to the electrocardiogram monitoring center in time for doctors to process in time.
Fig. 6 shows a third implementation flow diagram of the electrocardiographic abnormality detection method according to the embodiment of the present invention.
Referring to fig. 6, the method for detecting a side electrocardiogram abnormality of a terminal according to the embodiment of the present invention includes: operation 601, collecting electrocardiogram data; operation 602, performing beat-to-beat analysis on the electrocardiographic data one by one to obtain electrocardiographic waveform characteristics; operation 603, sequentially sending the electrocardiographic data and the electrocardiographic waveform characteristics to indicate that offline electrocardiographic abnormality detection is performed by using the electrocardiographic data and the electrocardiographic waveform characteristics, so as to obtain an offline electrocardiographic abnormality detection result; operation 604, performing real-time abnormal cardiac electrical detection based on the cardiac electrical data to obtain a real-time abnormal cardiac electrical detection result; and operation 605, sending the real-time abnormal electrocardiogram detection result to instruct to perform abnormal electrocardiogram diagnosis according to the real-time abnormal electrocardiogram detection result and the off-line abnormal electrocardiogram detection result to obtain an abnormal electrocardiogram diagnosis result.
In operations 601-603, referring to fig. 4, the terminal performs electrocardiographic data acquisition according to a specific time interval, wherein the selection of the specific time interval is related to the accuracy requirement of electrocardiographic abnormality detection, and the value is usually 30s, that is, the terminal acquires electrocardiographic data for 30s in real time; and then reporting the real-time acquired electrocardiogram data to a server.
After acquiring the electrocardiogram data in real time, the terminal firstly carries out preprocessing, such as denoising, on the electrocardiogram data; and then, carrying out effective signal detection on the preprocessed electrocardio data, namely filtering to obtain the electrocardio data with signal quality meeting a quality threshold. Correspondingly, the terminal performs beat-to-beat analysis on the electrocardiographic data meeting the quality threshold, such as QRS detection, to obtain electrocardiographic waveform characteristics; and then reporting the electrocardiographic waveform characteristics to a server. The electrocardiographic waveform features at least include P wave, QRS wave, S wave, T wave and the like.
In operation 604-605, after the terminal performs beat-to-beat calculation analysis by the real-time electrocardiogram analysis algorithm to obtain a real-time electrocardiogram abnormality detection result, the real-time electrocardiogram abnormality detection result is reported to the server, so that the server performs electrocardiogram abnormality diagnosis according to the real-time electrocardiogram abnormality detection result and the offline electrocardiogram abnormality detection result on the basis that the terminal completes beat-to-beat calculation analysis by the real-time electrocardiogram analysis algorithm, so as to analyze more information among beats, thereby effectively solving the calculation time consumption problem and better improving the analysis accuracy.
Further, based on the above-mentioned electrocardiographic abnormality detection method, an embodiment of the present invention further provides a server, and referring to fig. 7, the server 70 includes: the receiving module 701 is configured to sequentially receive electrocardiographic data and electrocardiographic waveform characteristics obtained by performing one-by-one heartbeat analysis on the electrocardiographic data; an offline electrocardiogram anomaly detection module 702, configured to perform offline electrocardiogram anomaly detection by using the electrocardiogram data and electrocardiogram waveform characteristics to obtain an offline electrocardiogram anomaly detection result; the receiving module 701 is further configured to receive a real-time abnormal cardiac electrical detection result obtained by performing real-time abnormal cardiac electrical detection on the basis of the cardiac electrical data; the abnormal electrocardiogram diagnosis module 703 is configured to perform abnormal electrocardiogram diagnosis according to the real-time abnormal electrocardiogram detection result and the offline abnormal electrocardiogram detection result to obtain an abnormal electrocardiogram diagnosis result.
According to an embodiment of the present invention, the server 70 further includes: the data processing module is used for preprocessing the electrocardio data before offline electrocardio abnormality detection is carried out by utilizing the electrocardio data and the electrocardio waveform characteristics through the offline electrocardio abnormality detection module; and the system is also used for carrying out signal quality evaluation and sliding window processing on the preprocessed electrocardio data to obtain the electrocardio data subjected to sliding window processing.
According to an embodiment of the present invention, the offline abnormal electrocardiographic detection module 702 is specifically configured to perform deep learning abnormal detection on the electrocardiographic data after the sliding window processing, so as to obtain a deep learning abnormal detection result; and performing off-line electrocardio abnormality detection on the deep learning abnormality detection result and the electrocardio waveform characteristics.
According to an embodiment of the present invention, the server 70 further includes: and the alarm module is used for sending an abnormal alarm to the remote monitoring center if the abnormal electrocardio diagnosis result indicates that the abnormal electrocardio exists.
Similarly, based on the above-mentioned electrocardiographic abnormality detection method, an embodiment of the present invention further provides a terminal, and referring to fig. 8, the terminal 80 includes: the acquisition module 801 is used for acquiring electrocardiogram data; a heartbeat analysis module 802, configured to perform heartbeat analysis on the electrocardiographic data one by one to obtain electrocardiographic waveform characteristics; a sending module 803, configured to send the electrocardiographic data and the electrocardiographic waveform feature in sequence, so as to instruct offline electrocardiographic abnormality detection to be performed by using the electrocardiographic data and the electrocardiographic waveform feature, so as to obtain an offline electrocardiographic abnormality detection result; a real-time abnormal electrocardiogram detection module 804, configured to perform real-time abnormal electrocardiogram detection based on the electrocardiogram data to obtain a real-time abnormal electrocardiogram detection result; the sending module 803 is further configured to send the real-time abnormal cardiac electrical signal detection result to instruct to perform abnormal cardiac electrical signal diagnosis according to the real-time abnormal cardiac electrical signal detection result and the offline abnormal cardiac electrical signal detection result, so as to obtain an abnormal cardiac electrical signal diagnosis result.
Here, it should be noted that: the above description of the server or the terminal embodiment is similar to the description of the method embodiment shown in fig. 1 to 6, and has similar beneficial effects to the method embodiment shown in fig. 1 to 6, and therefore, the description is omitted. For technical details that are not disclosed in the server or terminal embodiment of the present invention, please refer to the description of the method embodiments shown in fig. 1 to 6 of the present invention for understanding, and therefore, for brevity, will not be described again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An electrocardio abnormality detection method is applied to a server, and the method comprises the following steps:
sequentially receiving electrocardio data and performing one-by-one heart beat analysis on the electrocardio data to obtain electrocardio waveform characteristics;
performing off-line electrocardio abnormality detection by using the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result;
receiving a real-time electrocardio abnormality detection result obtained by carrying out real-time electrocardio abnormality detection based on the electrocardio data;
and carrying out electrocardio abnormity diagnosis according to the real-time electrocardio abnormity detection result and the off-line electrocardio abnormity detection result to obtain an electrocardio abnormity diagnosis result.
2. The method of claim 1, wherein before using the electrocardiographic data and electrocardiographic waveform characteristics for offline electrocardiographic abnormality detection, the method further comprises:
preprocessing the electrocardio data;
and performing signal quality evaluation and sliding window processing on the preprocessed electrocardio data to obtain the electrocardio data subjected to sliding window processing.
3. The method according to claim 2, wherein the off-line electrocardiographic abnormality detection using the electrocardiographic data and electrocardiographic waveform characteristics comprises:
performing deep learning anomaly detection on the electrocardio data subjected to sliding window processing to obtain a deep learning anomaly detection result;
and performing off-line electrocardio abnormality detection on the deep learning abnormality detection result and the electrocardio waveform characteristics.
4. The method according to any one of claims 1 to 3, further comprising:
and if the abnormal electrocardio diagnosis result represents that the abnormal electrocardio exists, sending an abnormal alarm to a remote monitoring center.
5. An electrocardio abnormality detection method is applied to a terminal, and the method comprises the following steps:
acquiring electrocardiogram data;
performing heart beat analysis on the electrocardiogram data one by one to obtain electrocardiogram waveform characteristics;
sequentially sending the electrocardio data and the electrocardio waveform characteristics to indicate that offline electrocardio abnormality detection is performed by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an offline electrocardio abnormality detection result;
performing real-time electrocardio abnormality detection based on the electrocardio data to obtain a real-time electrocardio abnormality detection result;
and sending the real-time electrocardio abnormality detection result to indicate that electrocardio abnormality diagnosis is carried out according to the real-time electrocardio abnormality detection result and the off-line electrocardio abnormality detection result to obtain an electrocardio abnormality diagnosis result.
6. A server, characterized in that the server comprises:
the receiving module is used for sequentially receiving the electrocardio data and the electrocardio waveform characteristics obtained by performing one-by-one heartbeat analysis on the electrocardio data;
the off-line electrocardio abnormality detection module is used for carrying out off-line electrocardio abnormality detection by utilizing the electrocardio data and the electrocardio waveform characteristics to obtain an off-line electrocardio abnormality detection result;
the receiving module is also used for receiving a real-time electrocardio abnormity detection result obtained by carrying out real-time electrocardio abnormity detection based on the electrocardio data;
and the electrocardio abnormity diagnosis module is used for carrying out electrocardio abnormity diagnosis according to the real-time electrocardio abnormity detection result and the off-line electrocardio abnormity detection result to obtain an electrocardio abnormity diagnosis result.
7. The server of claim 6, further comprising:
the data processing module is used for preprocessing the electrocardio data before offline electrocardio abnormality detection is carried out by utilizing the electrocardio data and the electrocardio waveform characteristics through the offline electrocardio abnormality detection module; and the system is also used for carrying out signal quality evaluation and sliding window processing on the preprocessed electrocardio data to obtain the electrocardio data subjected to sliding window processing.
8. The server according to claim 7,
the off-line electrocardio abnormality detection module is specifically used for performing deep learning abnormality detection on the electrocardio data processed by the sliding window to obtain a deep learning abnormality detection result; and performing off-line electrocardio abnormality detection on the deep learning abnormality detection result and the electrocardio waveform characteristics.
9. The server according to any one of claims 6 to 8, further comprising:
and the alarm module is used for sending an abnormal alarm to the remote monitoring center if the abnormal electrocardio diagnosis result indicates that the abnormal electrocardio exists.
10. A terminal, characterized in that the terminal comprises:
the acquisition module is used for acquiring the electrocardio data;
the heart beat analysis module is used for carrying out heart beat analysis on the electrocardio data one by one to obtain the electrocardio waveform characteristics;
the sending module is used for sequentially sending the electrocardio data and the electrocardio waveform characteristics so as to indicate that the electrocardio data and the electrocardio waveform characteristics are utilized to carry out off-line electrocardio abnormality detection to obtain an off-line electrocardio abnormality detection result;
the real-time electrocardio abnormality detection module is used for carrying out real-time electrocardio abnormality detection based on the electrocardio data to obtain a real-time electrocardio abnormality detection result;
the sending module is further configured to send the real-time abnormal electrocardiogram detection result to instruct to perform abnormal electrocardiogram diagnosis according to the real-time abnormal electrocardiogram detection result and the offline abnormal electrocardiogram detection result, so as to obtain an abnormal electrocardiogram diagnosis result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112932500A (en) * | 2021-01-29 | 2021-06-11 | 联想(北京)有限公司 | Electrocardio abnormity processing method, device and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107669262A (en) * | 2017-10-31 | 2018-02-09 | 王量弘 | Multi-lead Telediagnosis of Electrocardiogram Signals and monitor system and method based on SVM and WLT |
| CN109157202A (en) * | 2018-09-18 | 2019-01-08 | 东北大学 | A kind of cardiovascular disease early warning system based on more physiological signal depth integrations |
| CN109431490A (en) * | 2018-09-28 | 2019-03-08 | 上海优加利健康管理有限公司 | Artificial intelligence real-time analysis method and system for cardioelectric monitor |
| CN110353656A (en) * | 2019-07-12 | 2019-10-22 | 东南大学 | A kind of wearable ECG monitor system and its monitoring method based on cloud framework |
| CN110507315A (en) * | 2019-09-26 | 2019-11-29 | 杭州电子科技大学 | A kind of efficient electrocardiographic diagnosis system |
| WO2020013004A1 (en) * | 2018-07-13 | 2020-01-16 | オムロンヘルスケア株式会社 | Biological data provision device, biological data provision method, and program for biological data provision |
-
2020
- 2020-03-31 CN CN202010241845.5A patent/CN111419214A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107669262A (en) * | 2017-10-31 | 2018-02-09 | 王量弘 | Multi-lead Telediagnosis of Electrocardiogram Signals and monitor system and method based on SVM and WLT |
| WO2020013004A1 (en) * | 2018-07-13 | 2020-01-16 | オムロンヘルスケア株式会社 | Biological data provision device, biological data provision method, and program for biological data provision |
| CN109157202A (en) * | 2018-09-18 | 2019-01-08 | 东北大学 | A kind of cardiovascular disease early warning system based on more physiological signal depth integrations |
| CN109431490A (en) * | 2018-09-28 | 2019-03-08 | 上海优加利健康管理有限公司 | Artificial intelligence real-time analysis method and system for cardioelectric monitor |
| CN110353656A (en) * | 2019-07-12 | 2019-10-22 | 东南大学 | A kind of wearable ECG monitor system and its monitoring method based on cloud framework |
| CN110507315A (en) * | 2019-09-26 | 2019-11-29 | 杭州电子科技大学 | A kind of efficient electrocardiographic diagnosis system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112932500A (en) * | 2021-01-29 | 2021-06-11 | 联想(北京)有限公司 | Electrocardio abnormity processing method, device and system |
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