WO2019161607A1 - 心电信息动态监护方法和动态监护系统 - Google Patents

心电信息动态监护方法和动态监护系统 Download PDF

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WO2019161607A1
WO2019161607A1 PCT/CN2018/083461 CN2018083461W WO2019161607A1 WO 2019161607 A1 WO2019161607 A1 WO 2019161607A1 CN 2018083461 W CN2018083461 W CN 2018083461W WO 2019161607 A1 WO2019161607 A1 WO 2019161607A1
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data
information
heartbeat
electrocardiogram
ecg
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PCT/CN2018/083461
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English (en)
French (fr)
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曹君
刘畅
周位位
辛洪波
姜艳
李喆
田亮
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乐普(北京)医疗器械股份有限公司
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Priority to US16/766,516 priority Critical patent/US11517212B2/en
Priority to EP18907233.3A priority patent/EP3698707B1/en
Publication of WO2019161607A1 publication Critical patent/WO2019161607A1/zh

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0245Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A61B5/02455Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals provided with high/low alarm devices
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • AHUMAN NECESSITIES
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    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/0002Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006ECG or EEG signals
    • AHUMAN NECESSITIES
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    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/30Input circuits therefor
    • A61B5/307Input circuits therefor specially adapted for particular uses
    • A61B5/308Input circuits therefor specially adapted for particular uses for electrocardiography [ECG]
    • AHUMAN NECESSITIES
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    • A61B5/346Analysis of electrocardiograms
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    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346Analysis of electrocardiograms
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    • A61B5/366Detecting abnormal QRS complex, e.g. widening
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    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
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    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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    • A61B2576/023Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the heart
    • GPHYSICS
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    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention relates to the field of data processing technologies, and in particular, to a dynamic monitoring method for an electrocardiogram information and a dynamic monitoring system.
  • ECG monitoring is a commonly used clinical monitoring method.
  • the ECG signal it monitors is the weak current reflected by the electrical activity of cardiomyocytes on the body surface, recorded by the body surface electrode and the amplified recording system.
  • ECG monitoring For non-bedside ECG monitoring, compared with the conditions of bedside ECG monitoring, it is often subject to various signal interferences. Other non-cardiogenic electrical signals are also recorded during ECG recording. For example, EMG signal interference caused by skeletal muscle activity. These signals can result in an output of incorrect heartbeat signal detection results.
  • the ECG signal is a manifestation of the process of myocardial electrical activity, which can reflect a large amount of information about the state of the heart.
  • the ECG signal will change accordingly.
  • the accuracy of the automatic analysis is far from enough, so that the reference of the output ECG test report is not significant, and it still depends on the doctor's subjective judgment to form the ECG test. report.
  • the object of the present invention is to provide a dynamic monitoring method and a dynamic monitoring system for electrocardiogram information, realizing real-time data interaction through wired or wireless communication technology, and performing complete and rapid automatic analysis on electrocardiogram data, timely detecting abnormalities and generating alarm information, and simultaneously Support users to actively report alarms when they are consciously abnormal. Data recording and storage for the occurrence of abnormal alarms, so that the cause of the abnormality can be quickly analyzed and traceable.
  • a first aspect of the embodiments of the present invention provides a method for dynamically monitoring an electrocardiogram information, including:
  • the dynamic monitoring device receives the user input or the monitoring reference data sent by the server; the monitoring reference data includes the measured object information and the electrocardiographic abnormal event information;
  • the dynamic monitoring device performs monitoring data collection on the measured object to obtain electrocardiogram data of the measured object
  • the dynamic monitoring device performs wave group feature recognition on the electrocardiogram data, obtains a characteristic signal of the electrocardiogram data, performs heartbeat classification on the electrocardiogram data according to the characteristic signal, and obtains a heart beat classification according to the basic rule reference data of the electrocardiogram Information and generating ECG event data;
  • the ECG event data includes device ID information of the dynamic monitoring device;
  • the dynamic monitoring device determines corresponding ECG event information according to the ECG event data, and determines whether the ECG event information is the ECG abnormal event information; when the ECG abnormal event information is, the alarm information is output.
  • the method further includes:
  • the dynamic monitoring device receives an active alarm command input by the measured object
  • the active alarm event record information and the active alarm event record data are sent to the server.
  • the alarm information includes the ECG abnormal event information, the alarm time information, and the device ID information of the dynamic monitoring device, and the method further includes:
  • the dynamic monitoring device stores the electrocardiogram data, the electrocardiogram event data, the alarm information, and one or more abnormal event record data generated by the plurality of electrocardiogram data within a preset period of time corresponding to the alarm time information corresponding time Sent to the server.
  • the method further comprises:
  • the dynamic monitoring device receives and outputs alarm feedback information corresponding to the ECG event data and/or alarm information sent by the server.
  • performing the wave group feature recognition on the electrocardiogram data, obtaining a characteristic signal of the electrocardiogram data, performing heart beat classification on the electrocardiogram data according to the feature signal, and obtaining a heart beat classification according to the basic rule reference data of the electrocardiogram Information and generate ECG event data specifically include:
  • each of the heartbeat data corresponding to one heart cycle, including a corresponding P wave, QRS Wave group, T wave amplitude and start and end time data;
  • the heartbeat analysis data of the specific heartbeat in the primary classification information result is input to the ST segment and the T wave change model for identification, and the ST segment and T wave evaluation information is determined;
  • the detailed feature information includes amplitude, direction, Form and start and end time data
  • the heartbeat classification information is analyzed and matched to generate the ECG event data.
  • the dynamic monitoring method for electrocardiogram information provided by the first embodiment of the present invention performs a complete and rapid automatic analysis of the electrocardiogram data through the dynamic monitoring device, detects an abnormality in time and generates an alarm message, and simultaneously supports the user to actively report the alarm when the conscious abnormality occurs. Data recording and storage for the occurrence of abnormal alarms, so that the cause of the abnormality can be quickly analyzed and traceable.
  • a second aspect of the embodiments of the present invention provides another method for dynamically monitoring ECG information, including:
  • the dynamic monitoring device performs physical condition monitoring data collection on the measured object, obtains electrocardiogram data of the measured object, and acquires information of the measured object, and sends the ECG data and the measured object information to the server;
  • the ECG data has a time attribute Device ID information for information and dynamic monitoring devices;
  • the server performs wave group feature recognition on the electrocardiogram data, obtains a characteristic signal of the electrocardiogram data, performs heartbeat classification on the electrocardiogram data according to the characteristic signal, and obtains heartbeat classification information according to the basic rule reference data of the electrocardiogram, And generating ECG event data;
  • the ECG event data includes device ID information of the dynamic monitoring device;
  • the server determines monitoring reference data according to the measured object information;
  • the monitoring reference data includes ECG abnormal event information corresponding to the measured object information;
  • the server Determining, by the server, the corresponding ECG event information according to the ECG event data, and determining whether the ECG event information is an ECG abnormal event information; and generating an alarm information when the ECG abnormal event information is generated;
  • the alarm information includes Describe the ECG abnormal event information, the alarm time information, and the device ID information of the dynamic monitoring device;
  • the server sends the alarm information to the dynamic monitoring device according to the device ID information of the dynamic monitoring device, so that the dynamic monitoring device generates a corresponding alarm output signal according to the alarm information.
  • the method further includes:
  • the dynamic monitoring device receives an active alarm command input by the measured object
  • the active alarm event record information and the active alarm event record data are sent to the server.
  • the method further includes:
  • the server acquires a plurality of electrocardiogram data in a preset period of time corresponding to the corresponding time of the electrocardiogram data according to the time attribute information, and generates abnormal event record data;
  • the server generates association information of the abnormal event record data and the alarm information.
  • performing the wave group feature recognition on the electrocardiogram data, obtaining a characteristic signal of the electrocardiogram data, performing heart beat classification on the electrocardiogram data according to the feature signal, and obtaining a heart beat classification according to the basic rule reference data of the electrocardiogram Information and generate ECG event data specifically include:
  • each of the heartbeat data corresponding to one heart cycle, including a corresponding P wave, QRS Wave group, T wave amplitude and start and end time data;
  • the heartbeat analysis data of the specific heartbeat in the primary classification information result is input to the ST segment and the T wave change model for identification, and the ST segment and T wave evaluation information is determined;
  • the detailed feature information includes amplitude, direction, Form and start and end time data
  • the heartbeat classification information is analyzed and matched to generate the ECG event data.
  • the electrocardiogram information dynamic monitoring method collects the electrocardiogram data through the dynamic monitoring device, and the uploading server performs complete and rapid automatic analysis, detects the abnormality in time, generates the alarm information and sends the alarm information to the dynamic monitoring device, and simultaneously performs dynamic monitoring.
  • the device supports users to actively report alarms when they are consciously abnormal.
  • the server records and stores data for the case of abnormal alarms so that the cause of the anomaly can be quickly analyzed and traceable.
  • a third aspect of the embodiments of the present invention provides a dynamic monitoring system, which includes one or more dynamic monitoring devices and servers according to the above first aspect;
  • the dynamic monitoring device includes a memory and a processor; the memory is for storing a program, and the processor is configured to perform the method of the first aspect and the implementations of the first aspect.
  • a fourth aspect of the embodiments of the present invention provides a computer program product comprising instructions for causing a computer to execute the methods of the first aspect and the implementations of the first aspect when the computer program product is run on a computer.
  • a fifth aspect of the embodiments of the present invention provides a computer readable storage medium.
  • the computer readable storage medium stores a computer program.
  • the computer program is executed by the processor, the first aspect and the method in each implementation manner of the first aspect are implemented. .
  • a sixth aspect of the embodiments of the present invention provides a dynamic monitoring system, which includes the server and one or more dynamic monitoring devices described in the second aspect above;
  • the server includes a memory and a processor; the memory is for storing a program, and the processor is configured to perform the methods of the second aspect and the implementations of the first aspect.
  • a seventh aspect of the embodiments of the present invention provides a computer program product comprising instructions for causing a computer to perform the methods of the second aspect and the implementations of the second aspect when the computer program product is run on a computer.
  • An eighth aspect of the embodiments of the present invention provides a computer readable storage medium.
  • the computer readable storage medium stores a computer program.
  • the computer program is executed by the processor, the method in the implementation manners of the second aspect and the second aspect is implemented. .
  • FIG. 1 is a flowchart of a method for dynamically monitoring ECG information according to an embodiment of the present invention
  • FIG. 2 is a flowchart of a method for processing electrocardiogram data according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a two-classification model for interference recognition according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a heart beat classification model according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a ST segment and T wave change model according to an embodiment of the present invention.
  • FIG. 6 is a flowchart of another method for dynamically monitoring ECG information according to an embodiment of the present invention.
  • FIG. 7 is a flowchart of a method for a user to actively trigger event recording according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a dynamic monitoring system according to an embodiment of the present invention.
  • FIG. 9 is a flowchart of still another method for dynamically monitoring ECG information according to an embodiment of the present invention.
  • FIG. 10 is a schematic structural diagram of another dynamic monitoring system according to an embodiment of the present invention.
  • Holter ECG is a method for continuously recording and compiling ECG changes in the active and quiet state of the human heart for a long time. It has become one of the important diagnostic methods for non-invasive examination in the clinical cardiovascular field. Compared with ordinary electrocardiogram, dynamic electrocardiogram can continuously record up to 100,000 ECG signals within 24 hours, which can improve non-persistent arrhythmia, especially transient arrhythmia and transient myocardial ischemia. The rate of detection of seizures. More than 90% of heart disease outbreaks occur outside the medical institution, so it is necessary to record and monitor the heart condition in daily life for people with a history of heart disease.
  • the present invention proposes a dynamic monitoring method for ECG information, which can be applied to a dynamic monitoring system consisting of a wearable dynamic monitoring device and a server, realizing real-time data interaction through wired or wireless communication technology, through electrocardiogram
  • the data is completely and automatically analyzed automatically, and the abnormality is detected in time to generate alarm information, and the user is allowed to actively report the alarm when the user is consciously abnormal.
  • the method can be mainly executed in the dynamic monitoring device or executed in the server, and the dynamic monitoring device mainly performs the interaction and output of the collection and alarm information of the electrocardiogram data.
  • the two different cases will be described below in two embodiments.
  • the ECG information dynamic monitoring method of the present invention will be described in detail in conjunction with the flowchart of the electrocardiographic information dynamic monitoring method shown in FIG.
  • the central electrical information dynamic monitoring method is mainly implemented in the dynamic monitoring device.
  • the dynamic monitoring method for electrocardiogram information of the present invention mainly includes the following steps:
  • Step 110 The dynamic monitoring device receives the user input or the monitoring reference data sent by the server.
  • the dynamic monitoring device may specifically be a single-lead or multi-lead wearable ECG monitor, and each dynamic monitoring device has a unique device ID.
  • the corresponding monitoring reference data can be configured in the dynamic monitoring device according to the situation of the user.
  • the monitoring reference data can be understood as the reference data or information for indicating whether the monitored user's ECG signal is normal or not, and the setting of the monitoring reference data may be different for different users, and the specific The manner in which the input is configured on the monitoring device is obtained by the server being configured according to the user information and delivered to the dynamic monitoring device.
  • the monitoring reference data may include the measured object information and the set ECG abnormal event information.
  • the ECG abnormal event information includes information about various electrocardiographic abnormal events that need to generate an abnormality of the electrocardiogram, and when the dynamic monitoring device collects and analyzes the electrocardiogram data, and obtains an electrocardiographic abnormality event indicated by the electrocardiogram data, Whether or not an alarm is generated may be determined by determining whether the cardiac abnormality event is an event specified in the cardiac abnormal event information.
  • Step 120 The dynamic monitoring device performs monitoring data collection on the measured object to obtain electrocardiogram data of the measured object;
  • the dynamic monitoring device collects and records the signals generated by the electrophysiological activity of the cardiac cells in a single-lead or multi-lead manner by means of non-invasive electrocardiogram examination, and obtains electrocardiogram data.
  • the ECG data includes the object ID to be measured, the device ID of the dynamic monitoring device, and the detection time information.
  • Step 130 The dynamic monitoring device performs wave group feature recognition on the electrocardiogram data, obtains characteristic signals of the electrocardiogram data, performs heartbeat classification on the electrocardiogram data according to the characteristic signal, obtains heartbeat classification information according to the basic rule reference data of the electrocardiogram, and generates ECG event data. ;
  • ECG signals are needed. Effective interference identification and elimination can effectively reduce false alarms caused by interference signals.
  • the ECG signal is the embodiment of the myocardial electrical activity process. Therefore, in addition to the heart rate, the ECG signal can also display a large amount of information about the heart state. When there is a problem with the heart state, the ECG signal will change accordingly.
  • the research on the existing ECG signal processing methods in the industry we found that only very limited analysis and alarms were performed on ECG signals. In addition to this, in addition to effective interference identification and elimination of ECG signals to reduce false positives caused by interference signals, we believe that it can be improved from the following points:
  • the classification of heart beats is more detailed, and not only in the three categories of sinus, supraventricular and ventricular, so as to meet the complex and comprehensive analysis requirements of clinical electrocardiographers.
  • the ECG data processing process of the present invention adopts an artificial intelligence self-learning automatic ECG analysis method, which is implemented based on an artificial intelligence convolutional neural network (CNN) model.
  • the CNN model is a supervised learning method in deep learning. It is a multi-layer network (hidden layer) connection structure that simulates a neural network. The input signal passes through each hidden layer in turn, and a series of complicated mathematical processing (Convolution volume) is performed.
  • CNN belongs to the supervised learning method in artificial intelligence.
  • the input signal passes through multiple hidden layer processing to reach the final fully connected layer.
  • the classification result obtained by softmax logistic regression is related to the known classification result (label label).
  • label label There will be an error.
  • a core idea of deep learning is to continually minimize this error through a large number of sample iterations, and then calculate the parameters that connect the hidden layer neurons. This process generally requires constructing a special cost function, using a nonlinearly optimized gradient descent algorithm and a backpropagation algorithm (BP) to quickly and efficiently minimize the entire depth (the number of layers in the hidden layer). ) and breadth (dimension dimension) are all complex parameters in the neural network structure.
  • BP backpropagation algorithm
  • Deep learning inputs the data to be identified into the training model, passes through the first hidden layer, the second hidden layer, the third hidden layer, and finally outputs the recognition result.
  • the wave group feature recognition, the interference recognition, the heart beat classification and the like of the electrocardiogram data are all based on the artificial intelligence self-learning training model to obtain an output result, and the analysis speed is fast and the accuracy is high.
  • the step obtains the characteristic signal of the electrocardiogram data by performing wave group feature recognition on the electrocardiogram data, performs heartbeat classification on the electrocardiogram data according to the characteristic signal, and obtains heartbeat classification information according to the basic rule reference data of the electrocardiogram, according to the heartbeat classification information.
  • Step 131 The data format of the electrocardiogram data is resampled into a preset standard data format, and the first filtering process is performed on the electrocardiogram data of the converted preset standard data format.
  • the format of the ECG data is read and read, and different reading operations are implemented for different devices.
  • the baseline needs to be adjusted and converted into millivolt data according to the gain.
  • the data is converted to a sampling frequency that can be processed by the entire process. Then, high frequency, low frequency noise interference and baseline drift are removed by filtering to improve the accuracy of artificial intelligence analysis.
  • the processed ECG data is saved in a preset standard data format.
  • sampling frequency and transmission data format are solved, and high frequency, low frequency noise interference and baseline drift are removed by digital signal filtering.
  • Digital signal filtering can use high-pass filter, low-pass filter and median filter to eliminate power frequency interference, myoelectric interference and baseline drift interference, and avoid the impact on subsequent analysis.
  • a low-pass, high-pass Butterworth filter can be used for zero-phase shift filtering to remove baseline drift and high-frequency interference, retaining an effective ECG signal; median filtering can be performed using a preset duration of sliding window The median of the data point voltage magnitude replaces the amplitude of the center sequence of the window. The low frequency baseline drift can be removed.
  • Step 132 performing heartbeat detection processing on the electrocardiogram data after the first filtering process, and identifying a plurality of heartbeat data included in the electrocardiogram data;
  • each heart beat data corresponds to one heart cycle, including corresponding P wave, QRS wave group, T wave amplitude and start and end time data.
  • the heartbeat detection in this step is composed of two processes, one is a signal processing process, and the characteristic frequency band of the QRS complex is extracted from the electrocardiogram data after the first filtering; the second is to determine the QRS complex by setting a reasonable threshold The time of occurrence.
  • a P wave, a QRS complex, a T wave component, and a noise component are generally included.
  • the QRS complex has a frequency range of 5 to 20 Hz, and the QRS complex signal can be proposed by a bandpass filter within this range.
  • the specific detection process is a process based on peak detection. Threshold judgment is performed for each peak sequence in the signal. When the threshold value is exceeded, the QRS group judgment process is entered, and more features are detected, such as RR interval and shape.
  • the amplitude and frequency of the beat signal in the center of the recording process of the ECG information are constantly changing, and in the disease state, this characteristic will be more powerful.
  • the threshold adjustment needs to be dynamically performed according to the change of the data characteristics in the time domain.
  • QRS complex detection mostly adopts the dual amplitude threshold combined with the time threshold.
  • the high threshold has a higher positive rate
  • the lower threshold has a higher sensitivity rate
  • the RR interval exceeds a certain period.
  • the time threshold is detected using a low threshold to reduce missed detection. Since the low threshold is low due to the low threshold, it is susceptible to T wave and myoelectric noise, and it is easy to cause multiple tests. Therefore, high threshold is preferred for detection.
  • Step 133 determining a detection confidence level of each heart beat according to the heart beat data
  • the confidence calculation module can provide an estimate of the confidence of the QRS complex detection based on the amplitude of the QRS complex and the amplitude ratio of the noise signal during the RR interval.
  • Step 134 Perform interference recognition on the heart beat data according to the interference recognition two-class model, obtain whether the heart beat data has interference noise, and a probability value for determining the interference noise;
  • the interference phenomenon is easily affected by various effects during the long-term recording process, the acquired heartbeat data is invalid or inaccurate, and cannot correctly reflect the condition of the subject, and also increases the difficulty and workload of the doctor; and the interference data is also The main factor that leads to the inability of intelligent analysis tools to work effectively. Therefore, it is especially important to minimize external signal interference.
  • This step is based on the end-to-end two-category recognition model with deep learning algorithm as the core. It has the characteristics of high precision and strong generalization performance, which can effectively solve the disturbance problem caused by the main interference sources such as electrode stripping, motion interference and static interference. It overcomes the problem that the traditional algorithm has poor recognition effect due to the diversity and irregularity of the interference data.
  • Step A using interference recognition two-class model for heart rate data for interference recognition
  • Step B identifying a data segment in the heartbeat data that the heartbeat interval is greater than or equal to the preset interval determination threshold
  • Step C performing signal abnormality determination on the data segment whose heartbeat interval is greater than or equal to the preset interval determination threshold, and determining whether it is an abnormal signal;
  • the identification of the abnormal signal mainly includes whether the electrode piece is off, low voltage, and the like.
  • Step D if it is not an abnormal signal, determine a starting data point and a ending data point of the sliding sample in the data segment according to the set time value by a preset time width, and start sliding sampling the data segment from the starting data point, Obtaining a plurality of sampled data segments until the data point is terminated;
  • step E interference identification is performed for each sampled data segment.
  • the above steps A-E will be described with a specific example.
  • the heartbeat data of each lead is cut and sampled by the set first data amount, and then input to the interference recognition two-classification model for classification, and a probability value of the interference recognition result and the corresponding result is obtained; for the heartbeat interval If the heartbeat data is greater than or equal to 2 seconds, first determine whether the signal is overflowing, low voltage, and the electrode is off; if not, according to the first data amount, starting from the left heartbeat, continuing to the right continuously without overlapping the first data amount Sliding sampling for identification.
  • the input may be the first data volume heartbeat data of any lead, and then the interference identification two-class model is used for classification, and the direct output is the interference classification result, and the obtained result is fast, the accuracy is high, the stability is good, and the subsequent Analysis provides more effective and quality data.
  • the interference data is often caused by the external disturbance factor, there are mainly the electrode piece falling off, low voltage, static interference and motion interference.
  • the interference data generated by different disturbance sources is different, and the interference data generated by the same disturbance source is different.
  • the diversity is wide, but it is very different from the normal data, it is also possible to ensure the diversity when collecting the interference training data, and take the sliding sampling of the moving window. It is possible to increase the diversity of the interference data so that the model is more robust to the interference data. Even if the future interference data is different from any previous interference, the similarity with the interference is greater than the normal data. The ability of the model to identify interfering data is enhanced.
  • the interference identification two-class model used in this step can be as shown in Figure 3.
  • the network first uses two layers of convolutional layers.
  • the convolution kernel size is 1x5, and each layer is followed by a maximum pool.
  • the number of convolution kernels starts at 128, and the number of convolution kernels doubles each time the largest pooling layer is passed.
  • the convolutional layer is followed by two fully connected layers and a softmax classifier. Since the classification number of the model is 2, softmax has two output units, which in turn correspond to the corresponding categories, and uses cross entropy as the loss function.
  • the sampling rate is 200 Hz
  • the data length is a segment D[300] of 300 ECG voltage values (millivolts)
  • the input data is: InputData(i,j), where i is the ith Lead, j is the jth segment D of the lead i. All the input data is randomly dispersed to start training, which ensures the convergence of the training process. At the same time, it controls the collection of too many samples from the same patient's ECG data, improving the generalization ability of the model, and the accuracy of the real scene. After the training converges, using 1 million independent test data for testing, the accuracy rate can reach 99.3%. Another specific test data is shown in Table 1 below.
  • Step 135 determining the validity of the heartbeat data according to the detection confidence, and, according to the lead parameter and the heartbeat data determining the valid heartbeat data, combining the result of the interference recognition and the time rule to generate the heartbeat time series data, and Generating heartbeat analysis data based on cardiac time series data;
  • each lead heart rate data can be cut using a preset threshold to generate heartbeat analysis data of each lead required for specific analysis.
  • the heartbeat data merging process performed by the lead heartbeat merging module is as follows: according to the refractory period of the reference data of the basic rule of the electrocardiogram, the time characterization data combination of the different lead heartbeat data is obtained, and the heartbeat data with large deviation is discarded.
  • the above-mentioned time characterization data combination voting generates a merged heart beat position, the merged heart beat position is added to the combined cardiac beat time sequence, and the next set of heart beat data to be processed is moved, and the loop execution is performed until all the heart beat data are merged.
  • the ECG activity refractory period may preferably be between 200 milliseconds and 280 milliseconds.
  • the acquired time characterization data combination of the different lead heartbeat data should satisfy the following condition: the time characterization data of the heartbeat data combination contains at least one time characterization data of the heartbeat data.
  • the number of leads using the detected heartbeat data is determined as a percentage of the effective number of leads; if the time of the heartbeat data is indicative of the position of the corresponding lead is a low voltage
  • the lead, the interfering segment, and the electrode are considered to be inactive leads for this heartbeat data when the electrode is detached.
  • the time average of the heart beat data can be used to obtain the average value of the data.
  • this method sets a refractory period to avoid false merges.
  • a unified heartbeat time series data is output through the merging operation.
  • This step can simultaneously reduce the multi-detection rate and missed detection rate of the heart beat, and effectively improve the sensitivity and positive predictive rate of heart beat detection.
  • Step 136 Perform feature extraction and analysis on the amplitude and time characterization data of the heartbeat analysis data according to the heartbeat classification model, and obtain primary classification information of the heartbeat analysis data;
  • the multi-lead classification method includes two methods: lead voting decision classification method and lead synchronization association classification method.
  • the lead voting decision classification method is based on the heartbeat analysis data of each lead to conduct independent lead classification, and then the result vote is merged to determine the voting result decision method of the classification result; the lead synchronous association classification method uses the heart beat for each lead Analyze data for simultaneous association analysis.
  • the single-lead classification method is to analyze the heartbeat analysis data of the single-lead device, and directly use the corresponding lead model for classification, and there is no voting decision process. The following describes several classification methods described above.
  • Single-lead classification methods include:
  • the single-lead heartbeat data is cut to generate single-lead heartbeat analysis data, and the characteristics of the amplitude and time characterization data of the heartbeat classification model corresponding to the training are input. Extract and analyze to obtain a single-lead classification information.
  • the lead voting decision classification method may specifically include:
  • the first step is to cut the heartbeat data of each lead according to the heartbeat time series data, thereby generating heartbeat analysis data of each lead;
  • the feature extraction and analysis of the amplitude and time characterization data of each lead heartbeat analysis data are performed, and the classification information of each lead is obtained;
  • the classification voting decision calculation is performed according to the classification information of each lead and the reference weight value reference coefficient, and the primary classification information is obtained.
  • the lead weight value reference coefficient is based on the Bayesian statistical analysis of the electrocardiogram data to obtain the voting weight coefficient of each lead for different heart beat classifications.
  • the lead synchronization association classification method may specifically include:
  • each lead heartbeat data is cut to generate heartbeat analysis data of each lead; and then the heartbeat analysis data of each lead is performed according to the trained multi-lead synchronous correlation classification model. Synchronous amplitude and time characterizing the feature extraction and analysis of the data, and obtaining a classification information of the heartbeat analysis data.
  • the synchronous correlation classification method input of the heart beat data is all the lead data of the dynamic electrocardiogram device.
  • the data points of the same position and a certain length on each lead are intercepted and synchronously transmitted to the trained
  • the artificial intelligence deep learning model performs computational analysis, and the output is that each heartbeat position point comprehensively considers all the lead ECG signal characteristics, and the accurate heart beat classification of the heartbeat associated with the heart rhythm characteristics in time.
  • the method fully considers that the different lead data of the electrocardiogram actually measures the information flow transmitted by the cardiac electrical signal in different vector directions of the electrocardiogram, and comprehensively analyzes the multi-dimensional digital features transmitted by the electrocardiogram signal in time and space. Greatly improved the traditional method only relying on a single lead independent analysis, and then the results are summarized into some statistical voting methods and the easy to get the classification error defects, greatly improving the accuracy of heart rate classification.
  • the heartbeat classification model used in this step can be as shown in FIG. 4, and specifically can be an end-to-end multi-label classification model inspired by the model of convolutional neural network AlexNet, VGG16, Inception and the like based on artificial intelligence deep learning.
  • the model's network is a 7-layer convolutional network, with each convolution followed by an activation function.
  • the first layer is a convolutional layer of two different scales, followed by six convolutional layers.
  • the convolution kernels of the seven-layer convolution are 96, 256, 256, 384, 384, 384, 256, respectively. Except for the first-level convolution kernel, which has two scales of 5 and 11, respectively, the other layer has a convolution kernel scale of 5.
  • the third, fifth, sixth and seventh layers of the convolutional layer are followed by the pooling layer. Finally followed by two fully connected layers.
  • the heartbeat classification model in this step we used a training set containing 17 million data samples from 300,000 patients for training. These samples are generated by accurately labeling the data according to the requirements of dynamic electrocardiogram analysis and diagnosis.
  • the annotations are mainly for common arrhythmia, conduction block and ST segment and T wave changes, which can meet the model training of different application scenarios.
  • the marked information is saved in a preset standard data format.
  • a small sliding is performed on the classification with less sample size to amplify the data. Specifically, it is based on each heart beat and according to a certain step. (For example, 10-50 data points) move 2 times, which can increase the data by 2 times, and improve the recognition accuracy of the classified samples with less data. After the actual results are verified, the generalization ability has also been improved.
  • the length of the interception of the training data may be 1 second to 10 seconds.
  • the sampling rate is 200 Hz, with a sampling length of 2.5 s, and the obtained data length is a segment D [500] of 500 ECG voltage values (millivolts)
  • the input data is: InputData (i, j), where i is The i-th lead, j is the j-th segment D of the lead i. All the input data is randomly dispersed to start training, which ensures the convergence of the training process. At the same time, it controls the collection of too many samples from the same patient's ECG data, improving the generalization ability of the model, and the accuracy of the real scene.
  • the segment data D corresponding to all the leads is synchronously input, and the lead data of multiple spatial dimensions (different cardiac axis vectors) of each time position is synchronously learned according to the multi-channel analysis method of image analysis, thereby Get a more accurate classification result than the conventional algorithm.
  • Step 137 inputting, to the ST segment and the T wave change model, the heartbeat analysis data of the specific heartbeat in the primary classification information result, and determining the ST segment and the T wave evaluation information;
  • the ST segment and T wave evaluation information is specifically the lead position information in which the ST segment and the T wave corresponding to the heartbeat analysis data are changed. Because clinical diagnosis requires localization of changes to ST segments and T waves to specific leads.
  • the specific heartbeat data of the primary classification information refers to the heartbeat analysis data including the sinus beat (N) and other heartbeat types that may contain ST changes.
  • the ST segment and T wave change lead positioning module inputs the specific heart beat data of the primary classification information into each lead according to each lead into an artificial intelligence deep learning training model for identifying the ST segment and the T wave change, and performs calculation analysis and output.
  • the results indicate whether the characteristics of the lead segment conform to the conclusions of the ST segment and the T wave change, so that the information of the ST segment and the T wave change occurring in the specific lead, that is, the ST segment and the T wave evaluation information can be determined.
  • the specific method may be: inputting the data of each lead heartbeat analysis of the sinus heartbeat in the primary classification information, inputting the ST segment and the T wave change model, and identifying and analyzing the sinus beat analysis data one by one to determine whether the sinus beat analysis data is There are ST segment and T wave changing characteristics and specific lead position information that occurs, and ST segment and T wave evaluation information is determined.
  • the ST segment and T wave change model used in this step can be as shown in Fig. 5, and can be an end-to-end classification model inspired by models such as artificial neural network deep learning based convolutional neural networks AlexNet and VGG16.
  • the model is a 7-layer network with 7 convolutions, 5 pools, and 2 full connections.
  • the convolution kernel used for convolution is 1x5, and the number of filters for each layer is different.
  • the number of layer 1 convolution filters is 96; the second layer convolution is combined with the third layer convolution, the number of filters is 256; the fourth layer convolution is combined with the fifth layer convolution, and the number of filters is 384; the number of the sixth layer convolution filter is 384; the number of the seventh layer convolution filter is 256; the first, third, fifth, sixth, and seventh layers of the convolution layer are pooled. Then there are two full connections, and finally the results are divided into two categories using the Softmax classifier. In order to increase the nonlinearity of the model and extract the features of higher dimensionality of the data, two convolutional modes are used.
  • the ratio of the training data to the T wave change is about 2:1, which guarantees the good generalization ability of the model in the classification process and does not appear to have a tendency to have more training data.
  • the shape of the heart beat is diverse, the forms of different individuals are not the same. Therefore, in order to better estimate the distribution of each classification, the characteristics can be effectively extracted.
  • the training samples are collected from individuals of different ages, weights, genders, and areas of residence.
  • the ECG data of a single individual in the same time period is often highly similar, in order to avoid over-learning, when acquiring data of a single individual, a small number of samples of different time periods are randomly selected from all the data; The patient's heartbeat morphology has large differences between individuals and high intra-individual similarity. Therefore, when dividing training and test sets, different patients are divided into different data sets to avoid the same individual data appearing in the training set at the same time. With the test set, the resulting model test results are closest to the real application scenario, ensuring the reliability and universality of the model.
  • Step 138 Perform P wave and T wave feature detection on the heartbeat analysis data according to the heartbeat time series data, and determine detailed feature information of the P wave and the T wave in each heart beat;
  • detailed feature information includes amplitude, direction, shape, and start and end time data; in the analysis of heartbeat signals, the features of P wave, T wave, and QRS wave are also important basis for ECG analysis.
  • the P-wave, T-wave and QRS complexes are extracted by calculating the position of the segmentation point in the QRS complex and the position of the P- and T-wave segmentation points. . It can be realized by QRS group cut point detection, single lead PT detection algorithm and multi-lead PT detection algorithm.
  • QRS group segmentation point detection According to the QRS group segment power maximum point and the start and end points provided by the QRS complex detection algorithm, the R point, R' point, S point and S' point of the QRS group in a single lead are searched. When there is multi-lead data, the median of each segmentation point is calculated as the last segmentation point position.
  • P-wave and T-wave detection algorithms are relatively low in amplitude relative to QRS complex, and the signal is gentle, which is easy to be submerged in low-frequency noise, which is a difficult point in detection.
  • QRS complex detection the method uses a low-pass filter to perform third filtering on the signal after eliminating the influence of the QRS complex on the low frequency band, so that the relative amplitude of the PT wave is increased.
  • the T wave is then found between the two QRS complexes by peak detection. Because the T wave is a wave group generated by ventricular repolarization, there is a clear lock-time relationship between the T wave and the QRS complex.
  • the midpoint between each QRS complex and the next QRS complex (such as the range between 400ms and 600ms after the first QRS complex) is used as the T-wave detection.
  • the largest peak is selected as the T wave in this interval.
  • the direction and morphological characteristics of the P wave and the T wave are determined based on the peak and position data of the P wave and the T wave.
  • the cutoff frequency of the low pass filtering is set between 10-30 Hz.
  • Multi-lead P-wave and T-wave detection algorithms In the case of multi-lead, since the generation time of each wave in the heart beat is the same, the spatial distribution is different, and the temporal and spatial distribution of noise is different, the traceability algorithm can be used to perform P, Detection of T waves.
  • the signal is first subjected to QRS complex elimination processing and the signal is third filtered using a low pass filter to remove interference.
  • Each individual component in the original waveform is then calculated by an independent component analysis algorithm. Among the separated independent components, according to the distribution characteristics of the peaks and the position of the QRS complex, the corresponding components are selected as the P-wave and T-wave signals, and the direction and morphological characteristics of the P-wave and the T-wave are determined.
  • Step 139 the heartbeat analysis data is subjected to secondary classification processing according to the basic rule of the electrocardiogram, the detailed feature information of the P wave and the T wave, and the ST segment and the T wave evaluation information under the primary classification information to obtain the heartbeat classification information;
  • the heartbeat classification information is analyzed and matched to generate ECG event data.
  • the basic rule of ECG reference data is generated by following the basic rules describing the electrophysiological activity of cardiomyocytes and the clinical diagnosis of electrocardiogram in authoritative ECG textbooks, such as the minimum time interval between two heart beats, and the minimum of P and R waves.
  • Interval is used to subdivide the classification information after heartbeat classification; mainly based on the inter-heart rate RR interval and the medical saliency of different heartbeat signals on each lead; the heartbeat review module is based on the electrocardiogram
  • the basic rule reference data combined with the classification and recognition of a plurality of continuous heartbeat analysis data, and the detailed feature information of the P wave and the T wave, the ventricular heart beat classification is divided into finer heart beat classification, including: ventricular premature beat (V) , ventricular escape (VE), accelerated ventricular premature beats (VT), subventricular subtypic breakdown into supraventricular premature beats (S), atrial escape (SE), borderline escape (JE ) and atrial accelerated premature beats (AT) and so on.
  • V ventricular premature beat
  • VE ventricular escape
  • VT accelerated ventricular premature beats
  • S supraventricular premature beats
  • SE atrial escape
  • JE borderline escape
  • AT atrial accelerated premature beats
  • the secondary classification process it is also possible to correct the misclassification identification of the reference data that does not conform to the basic rule of the electrocardiogram occurring in one classification.
  • the subdivided heart beats are classified according to the reference data of the basic rules of the electrocardiogram, and the classification and identification which does not conform to the basic rule of the electrocardiogram are found, and the classification is corrected according to the RR interval and the classification marks before and after.
  • a variety of heartbeat classifications can be output, such as: normal sinus beat (N), complete right bundle branch block (N_CRB), complete left bundle branch block (N_CLB), indoor resistance Hysteresis (N_VB), first degree atrioventricular block (N_B1), pre-excitation (N_PS), premature ventricular contraction (V), ventricular escape (VE), accelerated ventricular premature beat (VT), supraventricular premature beats ( S), atrial escape (SE), borderline escape (JE), accelerated atrial premature beats (AT), atrial flutter (AF), artifacts (A) and other classification results.
  • N normal sinus beat
  • N_CRB complete right bundle branch block
  • N_CLB complete left bundle branch block
  • N_VB indoor resistance Hysteresis
  • N_PS first degree atrioventricular block
  • V premature ventricular contraction
  • VE ventricular escape
  • VT accelerated ventricular premature beat
  • S supraventricular premature beats
  • SE atrial escape
  • JE borderline escape
  • AF accelerated
  • the heart rate parameters of the basic calculation include: RR interval, heart rate, QT time, QTc time and other parameters.
  • the pattern matching is performed according to the basic rule of the electrocardiogram, and the following typical ECG events corresponding to the ECG event data can be obtained, including but not limited to:
  • Second degree type II (2:1) atrioventricular block
  • the heartbeat analysis data is generated according to the heartbeat classification information and the electrocardiogram basic rule reference data.
  • the ECG event data includes device ID information of the dynamic monitoring device.
  • Step 140 The dynamic monitoring device determines corresponding ECG event information according to the ECG event data, and determines whether the ECG event information is ECG abnormal event information;
  • the corresponding relationship between the ECG event data and the ECG time information can be obtained through artificial intelligence, and the corresponding ECG event information is obtained correspondingly.
  • the ECG event information corresponding to the ECG event data is the sinus heart. Beat events, ventricular premature beats, etc. Only part of this is an ECG abnormal event that requires an alarm.
  • the above data processing process is real-time, so the dynamic monitoring device will continue to have ECG event information.
  • the output interval of ECG event information can also be reasonably set, which not only reduces the amount of data calculation but also avoids missed detection.
  • step 150 is performed. Otherwise, step 120 is continued to continue monitoring data collection for the measured object. .
  • Step 150 outputting an alarm message
  • the dynamic monitoring device when determining that the ECG event information is the ECG abnormal event information, the dynamic monitoring device generates corresponding alarm information according to the ECG abnormal event information.
  • the alarm information can be output locally in the dynamic monitoring device, and can be simply a predetermined sound alarm, a photoelectric alarm, or a voice alarm, information display alarm, etc. output according to the ECG abnormal time information.
  • the monitored object By generating an alarm, the monitored object is abnormal in its current ECG signal, so that the monitored object can be quickly alerted.
  • the dynamic monitoring device outputs the electrocardiogram data of the electrocardiogram event information, the electrocardiogram event data, the alarm information, and the electrocardiogram data of the alarm information.
  • the abnormal event record data generated by the plurality of electrocardiogram data in the preset time period before and after the data acquisition time (that is, the time corresponding to the alarm time information).
  • the abnormal event record data is stored in the dynamic monitoring device for retrospective use in the analysis of the abnormality of the electrocardiogram.
  • the present invention prompts the user to have an abnormality of the electrocardiogram, and can also report the abnormality of the electrocardiogram through data interaction with the server. Remote monitoring can also be achieved through reporting, so that the guardian who accesses through the server can respond accordingly, such as dispatching medical ambulance personnel or remotely guiding the user for emergency treatment and guiding medical treatment.
  • the ECG information dynamic monitoring method of the present invention can be as shown in FIG. 6, and includes the following steps:
  • Step 610 The dynamic monitoring device receives the user input or the monitoring reference data sent by the server.
  • Step 620 The dynamic monitoring device performs monitoring data collection on the measured object to obtain electrocardiogram data of the measured object.
  • Step 630 The dynamic monitoring device performs wave group feature recognition on the electrocardiogram data, obtains a characteristic signal of the electrocardiogram data, performs heartbeat classification on the electrocardiogram data according to the characteristic signal, obtains heartbeat classification information according to the basic rule reference data of the electrocardiogram, and generates ECG event data. ;
  • Step 640 The dynamic monitoring device determines corresponding ECG event information according to the ECG event data, and determines whether the ECG event information is ECG abnormal event information;
  • step 650 is performed; otherwise, step 620 is continued to continue monitoring data collection for the measured object.
  • Step 650 outputting alarm information
  • Step 660 The dynamic monitoring device sends the electrocardiogram data, the electrocardiogram event data, the alarm information, and one or more of the abnormal event record data generated by the plurality of electrocardiogram data in the preset time period before and after the corresponding time of the alarm time information to the server. .
  • the dynamic monitoring device can connect to the server for data transmission through a wired or wireless network.
  • the data transmission is implemented by using a wireless network when performing real-time data transmission.
  • the wireless network includes wireless but not limited to wireless local area network (WIFI) based on IEEE 802.11b standard, Bluetooth, 3G/4G/5G mobile communication network, and Internet of Things.
  • WIFI wireless local area network
  • the alarm device After the alarm device outputs the alarm information or outputs the alarm information, the ECG data, the ECG event data, the alarm information, and the abnormality generated by the plurality of ECG data in the preset time period according to the alarm time information corresponding time can be generated.
  • One or more of the event log data is sent to the server via the wired or wireless network described above.
  • the server can realize remote monitoring according to the received information, and can further process the above information through the server, for example, to the terminal device of the relevant medical institution, determine the subsequent processing procedure according to the above information, send an ambulance personnel or remotely guide the user to perform emergency Handling, guiding medical treatment, etc.
  • the dynamic monitoring device can be located through the network, and can report the current location information of the monitored object in the sent alarm information, so that the server can obtain the information of the real-time location of the monitored object.
  • Step 670 The dynamic monitoring device receives and outputs alarm feedback information corresponding to the ECG event data and/or the alarm information sent by the server.
  • the dynamic monitoring device may also perform feedback. For example, when the ambulance personnel are sent to the monitored user according to the alarm information, the generated notification information is sent to the dynamic monitoring device. Output, notify the monitored object that the ambulance personnel have been dispatched; or when the emergency response information is generated according to the alarm information, the emergency treatment information is sent to the dynamic monitoring device and output, and the monitored object is instructed to take corresponding actions, such as sitting still in the ground, lying flat Take medication immediately or seek medical attention promptly.
  • Output methods include, but are not limited to, voice playback or display output.
  • the dynamic monitoring device can also support the user to actively trigger event recording.
  • the dynamic monitoring device can input voice, text and the like into the symptom of the user, and the dynamic monitoring device generates the information according to the information input by the user. Active alarm events are logged and sent to the server.
  • the specific implementation process can be as shown in FIG. 7 and includes the following steps:
  • Step 710 The dynamic monitoring device receives an active alarm command input by the measured object.
  • the triggering of the active alarm command may be designed on the dynamic monitoring device by using a hardware button, using a one-button trigger mode, or a function option on the human-computer interaction interface of the dynamic monitoring device.
  • the input of the active alarm command is realized by user operation.
  • Step 720 Receive active alarm information input by the measured object according to the active alarm instruction
  • the dynamic monitoring device when receiving the active alarm instruction, the dynamic monitoring device generates an input device startup instruction, and starts the input device on the dynamic monitoring device, which may be a voice input device, an audio and video input device, a text information input device, or the like. Specifically, it may be a microphone, a camera or a soft keyboard, a touch screen, or the like.
  • the input device After the input device is activated, the user's input is monitored, and the monitored information is recorded as active alarm information.
  • Step 730 Acquire ECG data in a preset time period before and after the current time according to the active alarm instruction
  • the dynamic monitoring device when receiving the active alarm command, the dynamic monitoring device also records the ECG data of the preset time period before and after the active alarm command generation time for analysis, and confirms the reason for the user's conscious discomfort.
  • Step 730 and step 720 can be performed in synchronization.
  • Step 740 Generate active alarm event record information according to the active alarm command and time information of the current time, and generate active alarm event record data according to the active alarm information and the electrocardiogram data within a preset time period before and after the current time;
  • the dynamic monitoring device records the current time, and generates active alarm event record information, which is used to indicate that an event of the user's active alarm is generated.
  • the active alarm event record data is generated according to the active alarm information recorded in the foregoing step 720 and the electrocardiogram data recorded in step 730.
  • the active alarm event record information and the active alarm event record data have an association relationship, and the active alarm event record data can be acquired through the active alarm time record information.
  • step 750 the active alarm event record information and the active alarm event record data are sent to the server.
  • the dynamic monitoring device automatically uploads the active alarm event record information and the active alarm event record data to the server after the recording is completed.
  • the dynamic monitoring device first determines the communication connection state with the server, and if it is in the normal connection state, the data can be directly uploaded, and of course, can be stored locally locally. If the connection is disconnected, the active alarm event record information and the active alarm event record data may be cached in the local memory of the dynamic monitoring device, and a communication connection request with the server is initiated at a preset time interval, when the connection is completed. After that, the active alarm event record information and the active alarm event record data are automatically uploaded.
  • FIG. 8 is a schematic structural diagram of a dynamic monitoring system according to an embodiment of the present invention.
  • the dynamic monitoring system includes one or more dynamic monitoring devices 1 and a server 2.
  • the dynamic monitoring device 1 includes a processor 11 and a memory 12.
  • the memory 12 can be connected to the processor 11 via a bus 13.
  • the memory 12 may be a nonvolatile memory such as a hard disk drive and a flash memory in which a software program and a device driver are stored.
  • the software program can perform various functions of the above method provided by the embodiments of the present invention; the device driver can be a network and an interface driver.
  • the processor 11 is configured to execute a software program, and when the software program is executed, the method provided by the embodiment of the present invention can be implemented.
  • the embodiment of the present invention further provides a computer readable storage medium.
  • the computer readable storage medium stores a computer program, and when the computer program is executed by the processor, the method provided by the embodiment of the present invention can be implemented.
  • Embodiments of the present invention also provide a computer program product comprising instructions.
  • the processor is caused to perform the above method.
  • the electrocardiogram information dynamic monitoring method and the dynamic monitoring system provided by the embodiments of the present invention perform complete and rapid automatic analysis of the electrocardiogram data through the dynamic monitoring device, timely discover abnormalities and generate alarm information, and simultaneously support the user to actively report the alarm when the conscious abnormality occurs. Data recording and storage for the occurrence of abnormal alarms, so that the cause of the abnormality can be quickly analyzed and traceable.
  • the ECG information dynamic monitoring method is mainly implemented in the dynamic monitoring device.
  • the ECG information dynamic monitoring method of the present invention can also be implemented in a server, and the dynamic monitoring device mainly performs ECG data collection and transmission. , the output of the alarm message and the trigger of the active alarm.
  • the central electronic information dynamic monitoring method is mainly implemented in a server.
  • the dynamic monitoring method for electrocardiogram information of the present invention mainly includes the following steps:
  • Step 910 The dynamic monitoring device performs physical condition monitoring data collection on the measured object, obtains electrocardiogram data of the measured object, and acquires information of the measured object, and sends the electrocardiogram data and the measured object information to the server;
  • the ECG data has time attribute information and device ID information of the dynamic monitoring device
  • Step 920 The server performs wave group feature recognition on the electrocardiogram data, obtains a characteristic signal of the electrocardiogram data, performs heartbeat classification on the electrocardiogram data according to the characteristic signal, obtains heartbeat classification information according to the basic rule reference data of the electrocardiogram, and generates ECG event data;
  • the specific execution process of this step is the same as the foregoing step 120, except that the execution subject becomes a server.
  • Step 930 The server determines monitoring reference data according to the measured object information.
  • the server determines the corresponding monitoring reference data according to the information of the measured object sent by the dynamic monitoring device, and may be determined according to the age, gender, medical history, etc. in the information of the measured object, and preferably obtains the historical monitoring according to the information of the measured object.
  • the data combined with historical monitoring data, identifies personalized monitoring baseline data for the subject.
  • the monitoring reference data should at least include the ECG abnormal event information corresponding to the measured object information.
  • Step 940 The server determines corresponding ECG event information according to the ECG event data, and determines whether the ECG event information is ECG abnormal event information;
  • the specific execution process of this step is the same as the above-mentioned step 140, except that the execution subject is different.
  • step 950 is performed, otherwise step 910 is continued.
  • Step 950 generating an alarm message
  • the server determines that the ECG event information is the ECG abnormal event information
  • the server generates corresponding alarm information according to the ECG abnormal event information.
  • the alarm information includes ECG abnormal event information, alarm time information, and device ID information of the dynamic monitoring device.
  • the server collects the ECG data of the ECG event information, the ECG event data, the alarm information, and the data acquisition time of the ECG data generating the alarm information (ie, the time corresponding to the alarm time information)
  • An abnormal event record data generated by a plurality of electrocardiogram data within a preset period of time before and after.
  • the abnormal event record data is stored in the server and can be used retroactively for performing ECG anomaly analysis.
  • step 960 the server sends the alarm information to the dynamic monitoring device according to the device ID information of the dynamic monitoring device, so that the dynamic monitoring device generates a corresponding alarm output signal according to the alarm information.
  • the server determines the dynamic monitoring device according to the device ID, and sends the alarm information to the dynamic monitoring device through a communication connection with the dynamic monitoring device.
  • the dynamic monitoring device generates an alarm output signal based on the received alarm information. Specifically, it may be simply a predetermined sound alarm or a photoelectric alarm, or may be a voice alarm, an information display alarm, or the like output according to the ECG abnormal time information. By generating an alarm, the monitored object has an abnormality in its current heart signal, so that the monitored object can be quickly alerted.
  • the server can perform remote monitoring as described in the above embodiments, so that the guardian who accesses through the server can respond accordingly, such as dispatching medical ambulance personnel or remotely guiding the user for emergency treatment, guiding medical treatment, etc. .
  • the network may perform positioning to determine current location information of the dynamic monitoring device, so that the server can acquire information of the real-time location of the monitored object.
  • the server may also send feedback information to the dynamic monitoring device. For example, when the ambulance personnel are sent to the monitored user according to the alarm information, the generated notification information is sent to the dynamic monitoring device for output, and the monitored object is sent to the ambulance personnel; or When the emergency treatment information is generated according to the alarm information, the emergency treatment information is sent to the dynamic monitoring device and output, and the monitored object is instructed to take corresponding actions, such as sitting still in the ground, lying flat, taking the medicine immediately or seeking medical treatment promptly.
  • Output methods include, but are not limited to, voice playback or display output.
  • the user's active triggering event record is also implemented in this embodiment, and the processing procedure is the same as that of the foregoing embodiment, and details are not described herein again.
  • FIG. 10 is a schematic structural diagram of a dynamic monitoring system according to an embodiment of the present invention.
  • the dynamic monitoring system includes one or more dynamic monitoring devices 2 and a server 1.
  • the server 1 includes a processor 11 and a memory 12.
  • the memory 12 can be connected to the processor 11 via a bus 13.
  • the memory 12 may be a nonvolatile memory such as a hard disk drive and a flash memory in which a software program and a device driver are stored.
  • the software program can perform various functions of the above method provided by the embodiments of the present invention; the device driver can be a network and an interface driver.
  • the processor 11 is configured to execute a software program, and when the software program is executed, the method provided by the embodiment of the present invention can be implemented.
  • the embodiment of the present invention further provides a computer readable storage medium.
  • the computer readable storage medium stores a computer program, and when the computer program is executed by the processor, the method provided by the embodiment of the present invention can be implemented.
  • Embodiments of the present invention also provide a computer program product comprising instructions.
  • the processor is caused to perform the above method.
  • the ECG information dynamic monitoring method and the dynamic monitoring system collect the ECG data through the dynamic monitoring device, upload the server for complete and rapid automatic analysis, timely discover the abnormality and generate the alarm information and send it to the dynamic monitoring device.
  • the dynamic monitoring device supports the user to actively report the alarm when the user is consciously abnormal.
  • the server records and stores data for the case of abnormal alarms so that the cause of the anomaly can be quickly analyzed and traceable.
  • the steps of a method or algorithm described in connection with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both.
  • the software module can be placed in random access memory (RAM), memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or technical field. Any other form of storage medium known.

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Abstract

一种心电信息动态监护方法和动态监护系统,心电信息动态监护方法包括:动态监护设备接收用户输入或者服务器下发的监测基准数据(S110,S610);动态监护设备对被测对象进行监护数据采集,得到被测对象的心电图数据(S120,S620);动态监护设备对心电图数据进行波群特征识别,得到心电图数据的特征信号,根据特征信号对心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据(S130,S630);心电图事件数据包括动态监护设备的设备ID信息;动态监护设备根据心电图事件数据确定对应的心电图事件信息,并确定心电图事件信息是否为心电异常事件信息(S140,S640);当为心电异常事件信息时,输出报警信息(S150,S650)。

Description

心电信息动态监护方法和动态监护系统
本申请要求于2018年2月24日提交中国专利局、申请号为201810157366.8、发明名称为“心电信息动态监护方法和动态监护系统”的中国专利申请的优先权。
技术领域
本发明涉及数据处理技术领域,尤其涉及一种心电信息动态监护方法和动态监护系统。
背景技术
心电监测是一种常用的临床医疗监测手段。其监测的心电信号是心肌细胞的电活动在体表反映出的微弱电流,通过体表电极和放大记录系统记录下来。
对于非床旁的心电监测,相比于床旁心电监测的条件要求更高,往往容易受到各种信号干扰,在心电信号记录过程中同时还会记录到其他非心源性的电信号,比如骨骼肌活动带来的肌电信号干扰等等。这些信号都会导致不正确心搏信号检测结果的输出。
非床旁监测的实时性,也是需要考虑的一个因素。目前最常用的动态心电图检测仪holter,虽然可连续记录24小时心电活动的全过程,包括休息、活动、进餐、工作、学习和睡眠等不同情况下的心电图资料,但其数据分析和处理都是滞后的,都是在被监测者将holter交回医院之后才能得知的,这显然大大的限制了非床旁监测的存在意义和价值,它无法像床旁监测一样实时地反映出被监测者的心电信号变化,并及时做出相应反应,尤其是出现被 监测者发生异常需要用药干预或者抢救等情况。
此外,心电信号是心肌电活动过程的体现,可以体现出大量的心脏状态的信息。在心脏状态出现问题的时候,心电信号会出现相应的改变,目前自动分析的准确率远远不够,致使输出的心电图检测报告的参考意义不大,依然依赖于医生的主观判断来形成心电图检测报告。
如何有效提高心电图的自动分析水平,实现非床旁监测的实时性以使得监测具有更大的意义发挥更大的作用,是本发明所要解决的难题和挑战。
发明内容
本发明的目的是提供一种心电信息动态监护方法和动态监护系统,通过有线或无线通讯技术实现数据实时交互,通过对心电图数据进行完整快速的自动分析,及时发现异常并产生报警信息,同时支持用户在自觉异常时主动上报报警。对于产生异常报警的情况进行数据记录和存储,以便能够迅速分析异常发生的原因,并具有可追溯性。
为实现上述目的,本发明实施例第一方面提供了一种心电信息动态监护方法,包括:
动态监护设备接收用户输入或者服务器下发的监测基准数据;所述监测基准数据包括被测对象信息和心电异常事件信息;
所述动态监护设备对被测对象进行监护数据采集,得到所述被测对象的心电图数据;
所述动态监护设备对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据;所述心电事件数据包括所述动态监护设备的设备ID信息;
所述动态监护设备根据所述心电图事件数据确定对应的心电图事件信息,并确定所述心电图事件信息是否为所述心电异常事件信息;当为所述心 电异常事件信息时,输出报警信息。
优选的,所述方法还包括:
所述动态监护设备接收被测对象输入的主动报警指令;
根据所述主动报警指令接收所述被测对象输入的主动报警信息;
根据所述主动报警指令获取当前时刻前后预设时段内的心电图数据;
根据所述主动报警指令和所述当前时刻的时间信息生成主动报警事件记录信息,并且根据所述主动报警信息和所述当前时刻前后预设时段内的心电图数据生成主动报警事件记录数据;
将所述主动报警事件记录信息和所述主动报警事件记录数据发送给所述服务器。
优选的,所述报警信息包括所述心电异常事件信息、报警时间信息和所述动态监护设备的设备ID信息,所述方法还包括:
所述动态监护设备将所述心电图数据、心电图事件数据、报警信息,以及根据所述报警时间信息对应时间的前后预设时段内的多个心电图数据生成的异常事件记录数据中的一个或多个发送至所述服务器。
进一步优选的,所述方法还包括:
所述动态监护设备接收并输出所述服务器发送的对应所述心电图事件数据和/或报警信息的报警反馈信息。
优选的,所述对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据具体包括:
将所述心电图数据的数据格式经过重采样转换为预设标准数据格式,并对转换后的预设标准数据格式的心电图数据进行第一滤波处理;
对所述第一滤波处理后的心电图数据进行心搏检测处理,识别所述心电图数据包括的多个心搏数据,每个所述心搏数据对应一个心搏周期,包括相应的P波、QRS波群、T波的幅值和起止时间数据;
根据所述心搏数据确定每个心搏的检测置信度;
根据干扰识别二分类模型对所述心搏数据进行干扰识别,得到心搏数据是否存在干扰噪音,以及用于判断干扰噪音的一个概率值;
根据所述检测置信度确定心搏数据的有效性,并且,根据确定有效的心搏数据的导联参数和心搏数据,基于所述干扰识别的结果和时间规则合并生成心搏时间序列数据;根据所述心搏时间序列数据生成心搏分析数据;
根据心搏分类模型对所述心搏分析数据进行幅值和时间表征数据的特征提取和分析,得到所述心搏分析数据的一次分类信息;
对所述一次分类信息结果中的特定心搏的心搏分析数据输入到ST段和T波改变模型进行识别,确定ST段和T波评价信息;
根据所述心搏时间序列数据,对所述心搏分析数据进行P波和T波特征检测,确定每个心搏中P波和T波的详细特征信息,详细特征信息包括幅值、方向、形态和起止时间的数据;
对所述心搏分析数据在所述一次分类信息下根据所述心电图基本规律参考数据、所述P波和T波的详细特征信息以及所述ST段和T波评价信息进行二次分类处理,得到心搏分类信息;
对所述心搏分类信息进行分析匹配,生成所述心电图事件数据。
本发明实施例一提供的心电信息动态监护方法,通过动态监护设备对心电图数据进行完整快速的自动分析,及时发现异常并产生报警信息,同时支持用户在自觉异常时主动上报报警。对于产生异常报警的情况进行数据记录和存储,以便能够迅速分析异常发生的原因,并具有可追溯性。
本发明实施例第二方面提供了另一种心电信息动态监护方法,包括:
动态监护设备对被测对象进行体征监护数据采集,得到所述被测对象的心电图数据并获取被测对象信息,将所述心电图数据和被测对象信息发送给服务器;所述心电图数据具有时间属性信息和动态监护设备的设备ID信息;
所述服务器对所述心电图数据进行波群特征识别,得到所述心电图数据 的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据;所述心电事件数据包括所述动态监护设备的设备ID信息;
所述服务器根据所述被测对象信息确定监测基准数据;所述监测基准数据包括所述被测对象信息对应的心电异常事件信息;
所述服务器根据所述心电图事件数据确定对应的心电图事件信息,并确定所述心电图事件信息是否为心电异常事件信息;当为心电异常事件信息时,生成报警信息;所述报警信息包括所述心电异常事件信息、报警时间信息和所述动态监护设备的设备ID信息;
所述服务器根据所述动态监护设备的设备ID信息将所述报警信息发送至所述动态监护设备,使所述动态监护设备根据所述报警信息产生相应的报警输出信号。
优选的,所述方法还包括:
所述动态监护设备接收被测对象输入的主动报警指令;
根据所述主动报警指令接收所述被测对象输入的主动报警信息;
根据所述主动报警指令获取当前时刻前后预设时段内的心电图数据;
根据所述主动报警指令和所述当前时刻的时间信息生成主动报警事件记录信息,并且根据所述主动报警信息和所述当前时刻前后预设时段内的心电图数据生成主动报警事件记录数据;
将所述主动报警事件记录信息和所述主动报警事件记录数据发送给所述服务器。
优选的,所述方法还包括:
当为心电异常事件信息时,所述服务器根据所述时间属性信息获取所述心电图数据对应时间的前后预设时段内的多个心电图数据,生成异常事件记录数据;
所述服务器生成所述异常事件记录数据与所述报警信息的关联信息。
优选的,所述对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据具体包括:
将所述心电图数据的数据格式经过重采样转换为预设标准数据格式,并对转换后的预设标准数据格式的心电图数据进行第一滤波处理;
对所述第一滤波处理后的心电图数据进行心搏检测处理,识别所述心电图数据包括的多个心搏数据,每个所述心搏数据对应一个心搏周期,包括相应的P波、QRS波群、T波的幅值和起止时间数据;
根据所述心搏数据确定每个心搏的检测置信度;
根据干扰识别二分类模型对所述心搏数据进行干扰识别,得到心搏数据是否存在干扰噪音,以及用于判断干扰噪音的一个概率值;
根据所述检测置信度确定心搏数据的有效性,并且,根据确定有效的心搏数据的导联参数和心搏数据,基于所述干扰识别的结果和时间规则合并生成心搏时间序列数据;根据所述心搏时间序列数据生成心搏分析数据;
根据心搏分类模型对所述心搏分析数据进行幅值和时间表征数据的特征提取和分析,得到所述心搏分析数据的一次分类信息;
对所述一次分类信息结果中的特定心搏的心搏分析数据输入到ST段和T波改变模型进行识别,确定ST段和T波评价信息;
根据所述心搏时间序列数据,对所述心搏分析数据进行P波和T波特征检测,确定每个心搏中P波和T波的详细特征信息,详细特征信息包括幅值、方向、形态和起止时间的数据;
对所述心搏分析数据在所述一次分类信息下根据所述心电图基本规律参考数据、所述P波和T波的详细特征信息以及所述ST段和T波评价信息进行二次分类处理,得到心搏分类信息;
对所述心搏分类信息进行分析匹配,生成所述心电图事件数据。
本发明实施例二提供的心电信息动态监护方法,通过动态监护设备对心 电图数据进行采集,上传服务器进行完整快速的自动分析,及时发现异常并产生报警信息下发给动态监护设备,同时动态监护设备支持用户在自觉异常时主动上报报警。服务器对于产生异常报警的情况进行数据记录和存储,以便能够迅速分析异常发生的原因,并具有可追溯性。
本发明实施例第三方面提供了一种动态监护系统,该系统包括上述第一方面所述的一个或多个动态监护设备和服务器;
所述动态监护设备包括存储器和处理器;存储器用于存储程序,处理器用于执行第一方面及第一方面的各实现方式中的方法。
本发明实施例第四方面提供了一种包含指令的计算机程序产品,当计算机程序产品在计算机上运行时,使得计算机执行第一方面及第一方面的各实现方式中的方法。
本发明实施例第五方面提供了一种计算机可读存储介质,计算机可读存储介质上存储有计算机程序,计算机程序被处理器执行时实现第一方面及第一方面的各实现方式中的方法。
本发明实施例第六方面提供了一种动态监护系统,该系统包括上述第二方面所述的服务器和一个或多个动态监护设备;
所述服务器包括存储器和处理器;存储器用于存储程序,处理器用于执行第二方面及第一方面的各实现方式中的方法。
本发明实施例第七方面提供了一种包含指令的计算机程序产品,当计算机程序产品在计算机上运行时,使得计算机执行第二方面及第二方面的各实现方式中的方法。
本发明实施例第八方面提供了一种计算机可读存储介质,计算机可读存储介质上存储有计算机程序,计算机程序被处理器执行时实现第二方面及第二方面的各实现方式中的方法。
附图说明
图1为本发明实施例提供的一种心电信息动态监护方法的流程图;
图2为本发明实施例提供的心电图数据的处理方法的流程图;
图3为本发明实施例提供的干扰识别二分类模型的示意图;
图4为本发明实施例提供的心搏分类模型的示意图;
图5为本发明实施例提供的ST段和T波改变模型的示意图;
图6为本发明实施例提供的另一种心电信息动态监护方法的流程图;
图7为本发明实施例提供的用户主动触发事件记录的方法流程图;
图8为本发明实施例提供的一种动态监护系统的结构示意图;
图9为本发明实施例提供的又一种心电信息动态监护方法的流程图;
图10为本发明实施例提供的另一种动态监护系统的结构示意图。
具体实施方式
下面通过附图和实施例,对本发明的技术方案做进一步的详细描述。
动态心电图是一种可以长时间连续记录并编集分析人体心脏在活动和安静状态下心电图变化的方法,目前已成为临床心血管领域中非创伤性检查的重要诊断方法之一。与普通心电图相比,动态心电图于24小时内可连续记录多达10万次左右的心电信号,这样可以提高对非持续性心律失常,尤其是对一过性心律失常及短暂的心肌缺血发作的检出率。90%以上的心脏疾病突发都是在医疗机构之外发生的,因此对于有心脏疾病史的人群,记录和监控日常状态下的心脏情况,是非常有必要的。
为此,本发明提出了一种心电信息动态监护方法,可以应用于由可穿戴式的动态监护设备与服务器组成的动态监护系统中,通过有线或无线通讯技术实现数据实时交互,通过对心电图数据进行完整快速的自动分析,及时发现异常并产生报警信息,同时支持用户在自觉异常时主动上报报警。对于产生异常报警的情况进行数据记录和存储,以便能够迅速分析异常发生的原因,并具有可追溯性。
该方法可以主要执行与动态监护设备中,或者执行于服务器中,动态监护设备主要执行心电图数据的采集和报警信息的交互、输出。以下分别以两个实施例,对这两种不同的情况进行说明。
下面首先结合图1所示的心电信息动态监护方法的流程图,对本发明的心电信息动态监护方法进行详述。在本实施例中心电信息动态监护方法主要执行于动态监护设备中。如图1所示,本发明的心电信息动态监护方法主要包括如下步骤:
步骤110,动态监护设备接收用户输入或者服务器下发的监测基准数据;
具体的,动态监护设备具体可以是单导联或多导联的可穿戴式心电监测仪,每台动态监护设备都有唯一的设备ID。当动态监护设备被分派给一个待监测用户使用时,可以根据该用户的情况,在动态监护设备中配置相应的监测基准数据。
监测基准数据可以理解为用以指示所监测到的用户心电信号正常与否的是否需要产生报警的基准数据或信息,对于不同的用户,监测基准数据的设置可以不同,具体的可以通过在动态监护设备上配置输入的方式或者通过服务器根据用户信息进行配置并下发到动态监护设备的方式获得。
在本实施例中,监测基准数据可以包括有被测对象信息和设定好的心电异常事件信息。心电异常事件信息包含有需要产生心电异常报警的各种心电异常事件的信息,在动态监护设备对心电图数据进行采集、分析等一系列处理,得到心电图数据指示的心电异常事件时,可以通过确定该心电异常事件是否是心电异常事件信息中规定的事件而确定是否产生报警。
步骤120,动态监护设备对被测对象进行监护数据采集,得到被测对象的心电图数据;
具体的,动态监护设备通过无创心电图检查的方式对心脏细胞电生理活动产生的信号以单导联或多导联的形式进行采集记录,得到心电图数据。心电图数据中包括被测对象ID、动态监护设备的设备ID和检测时间信息。
步骤130,动态监护设备对心电图数据进行波群特征识别,得到心电图数据的特征信号,根据特征信号对心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据;
具体的,考虑到在心电信号的动态监护记录过程中同时还会记录到其他非心源性的电信号,比如骨骼肌活动带来的肌电信号干扰等等,因此我们认为需要对心电信号进行有效的干扰识别和排除,才能够有效降低因为干扰信号造成的误报。
此外,心电信号是心肌电活动过程的体现,因此心电信号除了可以用来检测心率以外,还可以体现出大量的心脏状态的信息。在心脏状态出现问题的时候,心电信号会出现相应的改变。在对业内现有的心电信号处理方法的研究中我们发现,目前只对心电信号进行了非常有限的分析和报警。对此,除了对心电信号进行有效的干扰识别和排除,以降低因为干扰信号造成的误报之外,我们认为还可以从以下几点进行改进:
第一,在心搏特征提取中需要对P波、T波进行准确识别,可以避免心搏检测的多检和漏检,比如对一些特殊心电图信号,例如心律比较缓慢患者的高大T波,或者T波肥大的信号的多检。
第二,对心搏的分类进行更加细致的划分,而不能仅停留在窦性、室上性和室性这三种分类,从而满足临床心电图医生复杂全面的分析要求。
第三,准确识别房扑房颤和ST-T改变,从而能够有助于提供对ST段和T波改变对心肌缺血分析的帮助。
第四,对心搏和心电事件的准确识别。
在本发明中,我们针对上述几点,通过对心电图数据的分析计算,特别是引入人工智能(AI)技术,对采集的数字信号进行心律失常分析、长间歇停搏,扑动和颤动,传导阻滞,早搏和逸搏,心动过缓,心动过快,ST段改变检测、心电事件的分析与归类,以达到产生准确报警信号的目的,从而有效的进行病人生命体征的监护。
基于上述几点,本发明的心电图数据的处理过程,采用了基于人工智能自学习的心电图自动分析方法,是基于人工智能卷积神经网络(CNN)模型来实现的。CNN模型是深度学习中的监督学习方法,就是一个模拟神经网络的多层次网络(隐藏层hidden layer)连接结构,输入信号依次通过每个隐藏层,在其中进行一系列复杂的数学处理(Convolution卷积、Pooling池化、Regularization正则化、防止过拟合、Dropout暂时丢弃、Activation激活、一般使用Relu激活函数),逐层自动地抽象出待识别物体的一些特征,然后把这些特征作为输入再传递到高一级的隐藏层进行计算,直到最后几层的全连接层(Full Connection)重构整个信号,使用Softmax函数进行逻辑(logistics)回归,达到多目标的分类。
CNN属于人工智能中的监督学习方法,在训练阶段,输入信号经过多个的隐藏层处理到达最后的全连接层,softmax逻辑回归得到的分类结果,与已知的分类结果(label标签)之间会有一个误差,深度学习的一个核心思想就是通过大量的样本迭代来不断地极小化这个误差,从而计算得到连接各隐藏层神经元的参数。这个过程一般需要构造一个特别的损失函数(cost function),利用非线性优化的梯度下降算法和误差反向传播算法(backpropagation algorithm,BP),快速有效地极小化整个深度(隐藏层的层数)和广度(特征的维数)都十分复杂的神经网络结构中所有连接参数。
深度学习把需要识别的数据输入到训练模型,经过第一隐藏层、第二隐藏层、第三隐藏层,最后是输出识别结果。
在本发明中,对心电图数据进行波群特征识别、干扰识别、心搏分类等都是基于人工智能自学习的训练模型来得到输出结果,分析速度快,准确程度高。
具体的,该步骤通过对心电图数据进行波群特征识别,得到心电图数据的特征信号,根据特征信号对心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,根据心搏分类信息处理生成心电图事件数据, 并最终生成报告数据来实现。进一步的,该过程具体可以通过如图2所示的如下步骤来实现。
步骤131,将心电图数据的数据格式经过重采样转换为预设标准数据格式,并对转换后的预设标准数据格式的心电图数据进行第一滤波处理;
具体的,心电图数据的格式适配读取,对不同的设备有不同的读取实现,读取后,需要调整基线、根据增益转换成毫伏数据。经过数据重采样,把数据转换成全流程能够处理的采样频率。然后通过滤波去除高频,低频的噪音干扰和基线漂移,提高人工智能分析准确率。将处理后的心电图数据以预设标准数据格式保存。
通过本步骤解决不同在使用不同导联,采样频率和传输数据格式的差异,以及通过数字信号滤波去除高频,低频的噪音干扰和基线漂移。
数字信号滤波可以分别采用高通滤波器,低通滤波器和中值滤波,把工频干扰、肌电干扰和基线漂移干扰消除,避免对后续分析的影响。
更具体的,可以采用低通、高通巴特沃斯滤波器进行零相移滤波,以去除基线漂移和高频干扰,保留有效的心电信号;中值滤波则可以利用预设时长的滑动窗口内数据点电压幅值的中位数替代窗口中心序列的幅值。可以去除低频的基线漂移。
步骤132,对第一滤波处理后的心电图数据进行心搏检测处理,识别心电图数据包括的多个心搏数据;
具体的,每个心搏数据对应一个心搏周期,包括相应的P波、QRS波群、T波的幅值和起止时间数据。本步骤中的心搏检测由两个过程构成,一是信号处理过程,从所述第一滤波处理后的心电图数据中提取QRS波群的特征频段;二是通过设置合理的阈值确定QRS波群的发生时间。在心电图中,一般会包含P波、QRS波群、T波成分以及噪声成分。一般QRS波群的频率范围在5到20Hz之间,可以通过一个在此范围内的带通滤波器提出QRS波群信号。然而P波、T波的频段以及噪声的频段和QRS波群频段有部分重叠,因此通过信号 处理的方法并不能完全去除非QRS波群的信号。因此需要通过设置合理的阈值来从信号包络中提取QRS波群位置。具体的检测过程是一种基于峰值检测的过程。针对信号中每一个峰值顺序进行阈值判断,超过阈值时进入QRS波群判断流程,进行更多特征的检测,比如RR间期、形态等。
在其心电信息的记录过程中心搏信号的幅度和频率时时刻刻都在变化,并且在疾病状态下,这种特性会表现的更强。在进行阈值设定时,需要根据数据特征在时域的变化情况动态的进行阈值调整。为了提高检测的准确率和阳性率,QRS波群检测大多采用双幅度阈值结合时间阈值的方式进行,高阈值具有更高的阳性率,低阈值具有更高的敏感率,在RR间期超过一定时间阈值,使用低阈值进行检测,减少漏检情况。而低阈值由于阈值较低,容易受到T波、肌电噪声的影响,容易造成多检,因此优先使用高阈值进行检测。
对于不同导联的心搏数据都具有导联参数,用以表征该心搏数据为哪个导联的心搏数据。因此在得到心电图数据的同时也就可以根据其传输来源确定了其导联的信息,将此信息作为心搏数据的导联参数。
步骤133,根据心搏数据确定每个心搏的检测置信度;
具体的,置信度计算模块在心搏检测的过程中,根据QRS波群的幅度以及RR间期内噪声信号的幅度比例可以提供针对QRS波群检测置信度的估计值。
步骤134,根据干扰识别二分类模型对心搏数据进行干扰识别,得到心搏数据是否存在干扰噪音,以及用于判断干扰噪音的一个概率值;
因为在长时间记录过程中易受多种影响出现干扰现象,导致获取的心搏数据无效或不准确,不能正确反映受测者的状况,同时也增加医生诊断难度及工作量;而且干扰数据也是导致智能分析工具无法有效工作的主要因素。因此,将外界信号干扰降到最低显得尤为重要。
本步骤基于以深度学习算法为核心的端到端二分类识别模型,具有精度高,泛化性能强的特点,可有效地解决电极片脱落、运动干扰和静电干扰等主要干扰来源产生的扰动问题,克服了传统算法因干扰数据变化多样无规律 而导致的识别效果差的问题。
具体可以通过如下方法来实现:
步骤A,对心搏数据使用干扰识别二分类模型进行干扰识别;
步骤B,识别心搏数据中,心搏间期大于等于预设间期判定阈值的数据片段;
步骤C,对心搏间期大于等于预设间期判定阈值的数据片段进行信号异常判断,确定是否为异常信号;
其中,异常信号的识别主要包括是否为电极片脱落、低电压等情况。
步骤D,如果不是异常信号,则以预设时间宽度,根据设定时值确定数据片段中滑动取样的起始数据点和终止数据点,并由起始数据点开始对数据片段进行滑动取样,至终止数据点为止,得到多个取样数据段;
步骤E,对每个取样数据段进行干扰识别。
以一个具体的例子对上述步骤A-E进行说明。对每个导联的心搏数据以设定的第一数据量进行切割采样,然后分别输入到干扰识别二分类模型进行分类,获得干扰识别结果和对应结果的一个概率值;对心搏间期大于等于2秒的心搏数据,先判断是否是信号溢出,低电压,电极脱落;如果不是上述情况,就按照第一数据量,从左边心搏开始,向右连续以第一数据量不重叠滑动取样,进行识别。
输入可以是任一导联的第一数据量心搏数据,然后采用干扰识别二分类模型进行分类,直接输出是否为干扰的分类结果,获得结果快,精确度高,稳定性好,可为后续分析提供更有效优质的数据。
因为干扰数据往往是由外界扰动因素的作用而引起的,主要有电极片脱落、低电压、静电干扰和运动干扰等情况,不但不同扰动源产生的干扰数据不同,而且相同扰动源产生的干扰数据也是多种多样;同时考虑到干扰数据虽然多样性布较广,但与正常数据的差异很大,所以在收集干扰的训练数据时也是尽可能的保证多样性,同时采取移动窗口滑动采样,尽可能增加干扰 数据的多样性,以使模型对干扰数据更加鲁棒,即使未来的干扰数据不同于以往任何的干扰,但相比于正常数据,其与干扰的相似度也会大于正常数据,从而使模型识别干扰数据的能力增强。
本步骤中采用的干扰识别二分类模型可以如图3所示,网络首先使用2层卷积层,卷积核大小是1x5,每层后加上一个最大值池化。卷积核数目从128开始,每经过一次最大池化层,卷积核数目翻倍。卷积层之后是两个全连接层和一个softmax分类器。由于该模型的分类数为2,所以softmax有两个输出单元,依次对应相应类别,采用交叉熵做为损失函数。
对于该模型的训练,我们采用了来源于30万病人近400万精确标注的数据片段。标注分为两类:正常心电图信号或者是有明显干扰的心电图信号片段。我们通过定制开发的工具进行片段标注,然后以自定义标准数据格式保存干扰片段信息。
在训练过程,使用两台GPU服务器进行几十次轮循训练。在一个具体的例子中,采样率是200Hz,数据长度是300个心电图电压值(毫伏)的一个片段D[300],输入数据是:InputData(i,j),其中,i是第i个导联,j是导联i第j个片段D。输入数据全部经过随机打散才开始训练,保证了训练过程收敛,同时,控制从同一个病人的心电图数据中收集太多的样本,提高模型的泛化能力,既真实场景下的准确率。训练收敛后,使用100万独立的测试数据进行测试,准确率可以到达99.3%。另有具体测试数据如下表1。
  干扰 正常
敏感率(Sensitivity) 99.14% 99.32%
阳性预测率(Positive Predicitivity) 96.44% 99.84%
表1
步骤135,根据检测置信度确定心搏数据的有效性,并且,根据确定有效的心搏数据的导联参数和心搏数据,基于干扰识别的结果和时间规则合并生成心搏时间序列数据,并根据心搏时间序列数据生成心搏分析数据;
具体的,由于心电图信号的复杂性以及每个导联可能受到不同程度的干扰 影响,依靠单个导联检测心搏会存在多检和漏检的情况,不同导联检测到心搏结果的时间表征数据没有对齐,所以需要对所有导联的心搏数据根据干扰识别结果和时间规则进行合并,生成一个完整的心搏时间序列数据,统一所有导联心搏数据的时间表征数据。其中,时间表征数据用于表示每个数据点在心电图数据信号时间轴上的时间信息。根据这个统一的心搏时间序列数据,在后续的分析计算时,可以使用预先设置好的阀值,对各导联心搏数据进行切割,从而生成具体分析需要的各导联的心搏分析数据。
上述每个导联的心搏数据在合并前,需要根据步骤133中获得的检测置信度确定心搏数据的有效性。
具体的,导联心搏合并模块执行的心搏数据合并过程如下:根据心电图基本规律参考数据的不应期获取不同导联心搏数据的时间表征数据组合,丢弃其中偏差较大的心搏数据,对上述时间表征数据组合投票产生合并心搏位置,将合并心搏位置加入合并心搏时间序列,移动到下一组待处理的心搏数据,循环执行直至完成所有心搏数据的合并。
其中,心电图活动不应期可以优选在200毫秒至280毫秒之间。获取的不同导联心搏数据的时间表征数据组合应满足以下条件:心搏数据的时间表征数据组合中每个导联最多包含一个心搏数据的时间表征数据。在对心搏数据的时间表征数据组合进行投票时,使用检出心搏数据的导联数占有效导联数的百分比来决定;若心搏数据的时间表征数据对应导联的位置为低电压段、干扰段以及电极脱落时认为该导联对此心搏数据为无效导联。在计算合并心搏具体位置时,可以采用心搏数据的时间表征数据平均值得到。在合并过程中,本方法设置了一个不应期来避免错误合并。
在本步骤中,通过合并操作输出一个统一的心搏时间序列数据。该步骤同时能够降低心搏的多检率和漏检率,有效的提高心搏检测的敏感度和阳性预测率。
步骤136,根据心搏分类模型对心搏分析数据进行幅值和时间表征数据的 特征提取和分析,得到心搏分析数据的一次分类信息;
不同心电监测设备在信号测量、采集或者输出的导联数据等方面存在的差异,因此可以根据具体情况,使用简单的单导联分类方法,或者是多导联分类方法。多导联分类方法又包括导联投票决策分类方法和导联同步关联分类方法两种。导联投票决策分类方法是基于各导联的心搏分析数据进行导联独立分类,再把结果投票融合确定分类结果的投票决策方法;导联同步关联分类方法则采用对各导联的心搏分析数据进行同步关联分析的方法。单导联分类方法就是对单导联设备的心搏分析数据,直接使用对应导联模型进行分类,没有投票决策过程。下面对以上所述几种分类方法分别进行说明。
单导联分类方法包括:
根据心搏时间序列数据,将单导联心搏数据进行切割生成单导联的心搏分析数据,并输入到训练得到的对应该导联的心搏分类模型进行幅值和时间表征数据的特征提取和分析,得到单导联的一次分类信息。
导联投票决策分类方法可以具体包括:
第一步、根据心搏时间序列数据,对各导联心搏数据进行切割,从而生成各导联的心搏分析数据;
第二步、根据训练得到的各导联对应的心搏分类模型对各导联的心搏分析数据进行幅值和时间表征数据的特征提取和分析,得到各导联的分类信息;
第三步、根据各导联的分类信息和导联权重值参考系数进行分类投票决策计算,得到所述一次分类信息。具体的,导联权重值参考系数是基于心电数据贝叶斯统计分析得到各导联对不同心搏分类的投票权重系数。
导联同步关联分类方法可以具体包括:
根据心搏时间序列数据,对各导联心搏数据进行切割,从而生成各导联的心搏分析数据;然后根据训练得到的多导联同步关联分类模型对各导联的心搏分析数据进行同步幅值和时间表征数据的特征提取和分析,得到心搏分析数据的一次分类信息。
心搏数据的同步关联分类方法输入是动态心电图设备所有导联数据,按照心搏分析数据统一的心搏位点,截取各导联上相同位置和一定长度的数据点,同步输送给经过训练的人工智能深度学习模型进行计算分析,输出是每个心搏位置点综合考虑了所有导联心电图信号特征,以及心搏在时间上前后关联的心律特征的准确心搏分类。
本方法充分考虑了心电图不同导联数据实际上就是测量了心脏电信号在不同的心电轴向量方向传递的信息流,把心电图信号在时间和空间上传递的多维度数字特征进行综合分析,极大地改进了传统方法仅仅依靠单个导联独立分析,然后把结果汇总进行一些统计学的投票方式而比较容易得出的分类错误的缺陷,极大地提高了心搏分类的准确率。
本步骤中采用的心搏分类模型可以如图4所示,具体可以为基于人工智能深度学习的卷积神经网络AlexNet,VGG16,Inception等模型启发的端对端多标签分类模型。具体的讲,该模型的网络是一个7层的卷积网络,每个卷积之后紧跟一个激活函数。第一层是两个不同尺度的卷积层,之后是六个卷积层。七层卷积的卷积核分别是96,256,256,384,384,384,256。除第一层卷积核有两个尺度分别是5和11外,其他层卷积核尺度为5。第三、五、六、七层卷积层后是池化层。最后跟着两个全连接层。
本步骤中的心搏分类模型,我们采用了训练集包含30万病人的1700万数据样本进行训练。这些样本是根据动态心电图分析诊断的要求对数据进行准确的标注产生的,标注主要是针对常见心律失常,传导阻滞以及ST段和T波改变,可满足不同应用场景的模型训练。具体以预设标准数据格式保存标注的信息。在训练数据的预处理上,为增加模型的泛化能力,对于样本量较少的分类做了小幅的滑动来扩增数据,具体的说,就是以每个心搏为基础,按照一定步长(比如10-50个数据点)移动2次,这样就可以增加2倍的数据,提高了对这些数据量比较少的分类样本的识别准确率。经过实际结果验证,泛化能力也得到了改善。
在一个实际训练过程使用了两台GPU服务器进行几十次轮循训练,训练收敛后,使用500万独立的测试数据进行测试,准确率可以到达91.92%。
其中,训练数据的截取的长度,可以是1秒到10秒。比如采样率是200Hz,以2.5s为采样长度,取得的数据长度是500个心电图电压值(毫伏)的一个片段D[500],输入数据是:InputData(i,j),其中,i是第i个导联,j是导联i第j个片段D。输入数据全部经过随机打散才开始训练,保证了训练过程收敛,同时,控制从同一个病人的心电图数据中收集太多的样本,提高模型的泛化能力,既真实场景下的准确率。训练时候,同步输入所有导联对应的片段数据D,按照图像分析的多通道分析方法,对每个时间位置的多个空间维度(不同心电轴向量)的导联数据进行同步学习,从而得到一个比常规算法更准确的分类结果。
步骤137,对一次分类信息结果中的特定心搏的心搏分析数据输入到ST段和T波改变模型进行识别,确定ST段和T波评价信息;
ST段和T波评价信息具体为心搏分析数据对应的ST段和T波发生改变的导联位置信息。因为临床诊断要求对于ST段和T波的改变定位到具体的导联。
其中,一次分类信息的特定心搏数据是指包含窦性心搏(N)和其它可能包含ST改变的心搏类型的心搏分析数据。
ST段和T波改变导联定位模块将一次分类信息的特定心搏数据,按照每个导联依次输入到一个为识别ST段和T波改变的人工智能深度学习训练模型,进行计算分析,输出的结果说明导联片段的特征是否符合ST段和T波改变的结论,这样就可以确定ST段和T波改变发生的在具体那些导联的信息,即ST段和T波评价信息。具体方法可以是:把一次分类信息中结果是窦性心搏的各导联心搏分析数据,输入给ST段和T波改变模型,对窦性心搏分析数据进行逐一识别判断,以确定窦性心搏分析数据是否存在ST段和T波改变特征以及发生的具体导联位置信息,确定ST段和T波评价信息。
本步骤中采用的ST段和T波改变模型可以如图5所示,具体可以为基于人工智能深度学习的卷积神经网络AlexNet和VGG16等模型启发的端对端分类模型。具体的讲,该模型是一个7层的网络,模型包含了7个卷积,5个池化和2个全连接。卷积使用的卷积核均为1x5,每层卷积的滤波器个数各不相同。第1层卷积滤波器个数为96;第2层卷积和第3层卷积连用,滤波器个数为256;第4层卷积和第5层卷积连用,滤波器个数为384;第6层卷积滤波器个数为384;第7层卷积滤波器个数为256;第1、3、5、6、7层卷积层后是池化。随后是两个全连接,最后还采用Softmax分类器将结果分为两类。为了增加模型的非线性,提取数据更高维度的特征,故采用两个卷积连用的模式。
因为带有ST段和T波改变的心搏在所有心搏中的占比较低,为了兼顾训练数据的多样性及各个类别数据量的均衡性,选取无ST段和T波改变以及有ST段和T波改变的训练数据比例约为2:1,保证了模型在分类过程中良好的泛化能力且不出现对训练数据占比较多一类的倾向性。由于心搏的形态多种多样,不同个体表现的形态不尽相同,因此,为了模型更好估计各分类的分布,能有效提取特征,训练样本从不同年龄,体重,性别和居住地区的个体收集;另外,因为单个个体在同一时间段内的心电图数据往往是高度相似的,所以为了避免过度学习,在获取单个个体的数据时,从所有数据中随机选取不同时间段的少量样本;最后,由于患者的心搏形态存在个体间差异大,而个体内相似度高的特点,因而在划分训练、测试集时,把不同的患者分到不同的数据集,避免同一个体的数据同时出现在训练集与测试集中,由此,所得模型测试结果最接近真实应用场景,保证了模型的可靠性和普适性。
步骤138,根据心搏时间序列数据,对心搏分析数据进行P波和T波特征检测,确定每个心搏中P波和T波的详细特征信息;
具体的,详细特征信息包括幅值、方向、形态和起止时间的数据;在对心搏信号的分析中,P波、T波以及QRS波中的各项特征也是心电图分析中的重 要依据。
在P波和T波特征检测模块中,通过计算QRS波群中切分点位置,以及P波和T波的切分点位置,来提取P波、T波以及QRS波群中的各项特征。可以分别通过QRS波群切分点检测、单导联PT检测算法和多导联PT检测算法来实现。
QRS波群切分点检测:根据QRS波群检测算法提供的QRS波群段功率最大点以及起止点,寻找单个导联中QRS波群的R点,R’点,S点以及S’点。在存在多导联数据时,计算各个切分点的中位数作为最后的切分点位置。
单导联P波、T波检测算法:P波和T波相对QRS波群幅度低、信号平缓,容易淹没在低频噪声中,是检测中的难点。本方法依据QRS波群检测的结果,在消除QRS波群对低频频段的影响后,使用低通滤波器对信号进行第三滤波,使PT波相对幅度增高。之后通过峰值检测的方法在两个QRS波群之间寻找T波。因为T波是心室复极产生的波群,因此T波和QRS波群之间有明确的锁时关系。以检测到的QRS波群为基准,在每个QRS波群到下一个QRS波群间期取中点(比如限制在第一个QRS波群后400ms到600ms之间的范围)作为T波检测结束点,在此区间内选取最大的峰作为T波。再在剩余的峰值内选择幅度最大的峰为P波。同时也根据P波和T波的峰值与位置数据,确定P波和T波的方向和形态特征。优选的,低通滤波的截止频率设置为10-30Hz之间。
多导联P波、T波检测算法:在多导联的情况中,由于心搏中各个波的产生时间相同,空间分布不同,而噪声的时间空间分布不同,可以通过溯源算法来进行P、T波的检测。首先对信号进行QRS波群消除处理并使用低通滤波器对信号进行第三滤波以去除干扰。之后通过独立成分分析算法计算原始波形中的各个独立成分。在分离出的各个独立成分中,依据其峰值的分布特征以及QRS波群位置,选取相应的成分作为P波和T波信号,同时确定P波和T波的方向和形态特征。
步骤139,对心搏分析数据在一次分类信息下根据心电图基本规律参考数据、P波和T波的详细特征信息以及ST段和T波评价信息进行二次分类处理,得到心搏分类信息;对心搏分类信息进行分析匹配,生成心电图事件数据。
具体的,心电图基本规律参考数据是遵循权威心电图教科书中对心肌细胞电生理活动和心电图临床诊断的基本规则描述生成的,比如两个心搏之间最小的时间间隔,P波与R波的最小间隔等等,用于将心搏分类后的一次分类信息再进行细分;主要根据是心搏间RR间期以及不同心搏信号在各导联上的医学显著性;心搏审核模块依据心电图基本规律参考数据结合一定连续多个心搏分析数据的分类识别,以及P波和T波的详细特征信息将室性心搏分类拆分更细的心搏分类,包括:室性早搏(V)、室性逸搏(VE)、加速性室性早搏(VT),将室上性类心搏细分为室上性早搏(S)、房性逸搏(SE)、交界性逸搏(JE)和房性加速性早搏(AT)等等。
此外,通过二次分类处理,还可以纠正一次分类中发生的不符合心电图基本规律参考数据的错误分类识别。将细分后的心搏分类按照心电图基本规律参考数据进行模式匹配,找到不符合心电图基本规律参考数据的分类识别,根据RR间期及前后分类标识纠正为合理的分类。
具体的,经过二次分类处理,可以输出多种心搏分类,比如:正常窦性心搏(N)、完全性右束支阻滞(N_CRB)、完全性左束支阻滞(N_CLB)、室内阻滞(N_VB)、一度房室传导阻滞(N_B1)、预激(N_PS)、室性早搏(V)、室性逸搏(VE)、加速性室性早搏(VT)、室上性早搏(S)、房性逸搏(SE)、交界性逸搏(JE)、加速性房性早搏(AT)、房扑房颤(AF)、伪差(A)等分类结果。
通过本步骤,还可以完成基础心率参数的计算。其中基础计算的心率参数包括:RR间期、心率、QT时间、QTc时间等参数。
随后,根据心搏二次分类结果,按照心电图基本规律参考数据进行模式匹配,可以得到分类对应于心电图事件数据的以下这些典型的心电图事件,包括但不限于:
室上性早搏
室上性早搏成对
室上性早搏二联律
室上性早搏三联律
房性逸搏
房性逸搏心律
交界性逸搏
交界性逸搏心律
非阵发性室上性心动过速
最快室上性心动过速
最长室上性心动过速
室上性心动过速
短阵室上性心动过速
心房扑动-心房颤动
室性早搏
室性早搏成对
室性早搏二联律
室性早搏三联律
室性逸搏
室性逸搏心律
加速性室性自主心律
最快室性心动过速
最长室性心动过速
室性心动过速
短阵室性心动过速
二度I型窦房传导阻滞
二度II型窦房传导阻滞
一度房室传导阻滞
二度I型房室传导阻滞
二度II型房室传导阻滞
二度II型(2:1)房室传导阻滞
高度房室传导阻滞
完全性左束支阻滞
完全性右束支阻滞
室内阻滞
预激综合症
ST段和T波改变
最长RR间期
将心搏分析数据根据心搏分类信息和心电图基本规律参考数据生成心电图事件数据。心电事件数据包括动态监护设备的设备ID信息。
步骤140,动态监护设备根据心电图事件数据确定对应的心电图事件信息,并确定心电图事件信息是否为心电异常事件信息;
具体的,在得到心电图事件数据之后,可以通过人工智能学习得到的心电图事件数据与心电图时间信息的对应关系,对应得到相应的心电事件信息,比如,心电图事件数据对应的心电事件信息为窦性心搏事件、室性早搏事件等。这其中仅有部分为需要产生报警的心电异常事件。
上述的数据处理过程均为实时的,因此动态监护设备会不断有心电图事件信息的产生。在实际应用中也可以合理设定心电图事件信息的输出间隔,既减小数据运算量,又避免漏检的情况。
在得到心电图事件信息后,与动态监护设备中记录的心电异常事件信息进行匹配,当为心电异常事件信息时,执行步骤150,否则继续执行步骤120,对被测对象继续进行监护数据采集。
步骤150,输出报警信息;
具体的,当确定心电图事件信息为心电异常事件信息时,动态监测设备根据心电异常事件信息产生相应的报警信息。
报警信息能够在动态监测设备本地进行输出,可以简单的为预定声音的报警、光电报警,也可以是根据心电异常时间信息输出的语音报警、信息显示报警等。
通过产生报警,提示被监测对象其当前的心电信号异常,从而能够迅速的引起被监测对象的警惕。
为了对异常状况进行有效记录,优选的,在确定为心电异常事件信息的时候,动态监护设备将该心电图事件信息的心电图数据、心电图事件数据、报警信息,以及产生该报警信息的心电图数据的数据采集时间(即为报警时间信息对应时间)的前后预设时段内的多个心电图数据生成的异常事件记录数据。
该异常事件记录数据被存储在动态监护设备中,用以进行心电异常分析时追溯使用。
本发明除了能够实现本地的报警输出,提示用户发生了心电异常事件外,还可以通过与服务器之间的数据交互,将心电异常事件进行上报。通过上报还可以实现远程监控,从而使得通过服务器端接入的监护人员能够做出相应的反应,如派出医疗救护人员或者远程指导用户进行应急处理、指导就医等。
为实现上述目的,本发明的心电信息动态监护方法可以如图6所示,包括如下步骤:
步骤610,动态监护设备接收用户输入或者服务器下发的监测基准数据;
步骤620,动态监护设备对被测对象进行监护数据采集,得到被测对象的心电图数据;
步骤630,动态监护设备对心电图数据进行波群特征识别,得到心电图数据的特征信号,根据特征信号对心电图数据进行心搏分类,结合心电图基本 规律参考数据得到心搏分类信息,并生成心电图事件数据;
步骤640,动态监护设备根据心电图事件数据确定对应的心电图事件信息,并确定心电图事件信息是否为心电异常事件信息;
当为心电异常事件信息时,执行步骤650,否则继续执行步骤620,对被测对象继续进行监护数据采集。
步骤650,输出报警信息;
上述步骤同前述实施例步骤110-150,此处不再展开赘述。
步骤660,动态监护设备将心电图数据、心电图事件数据、报警信息,以及根据报警时间信息对应时间的前后预设时段内的多个心电图数据生成的异常事件记录数据中的一个或多个发送至服务器。
具体的,动态监护设备可以通过有线方式或无线网络与服务器进行连接进行数据传输。优选的在进行实时数据传输时采用无线网络实现数据传输。
其中无线网络包括无线但不限于基于IEEE 802.11b标准的无线局域网(WIFI),蓝牙,3G/4G/5G移动通信网络,物联网等方式。
动态监护设备在输出报警信息后或者在输出报警信息的同时,就可以将心电图数据、心电图事件数据、报警信息,以及根据报警时间信息对应时间的前后预设时段内的多个心电图数据生成的异常事件记录数据中的一个或多个通过上述有线方式或无线网络发送至服务器。
服务器根据接收到的信息可以实现远程监控,还可以通过服务器对上述信息进行进一步处理,比如发送给相关医疗机构的终端设备,根据上述信息确定后续的处理程序,派出救护人员或者远程指导用户进行应急处理、指导就医等。
在优选的方案中,动态监护设备可以通过网络进行定位,在发送的报警信息中能够上报被监测对象当前的位置信息,使得服务器能够获取被监测对象的实时位置的信息。
步骤670,动态监护设备接收并输出服务器发送的对应心电图事件数据和/ 或报警信息的报警反馈信息。
在优选的方案中,在服务器对接收到的信息产生后续处理时,还可以向动态监护设备进行反馈,比如当根据报警信息向被监测用户派出救护人员时,生成告知信息发送给动态监护设备进行输出,通知被监测对象已派出救护人员;或者当根据报警信息产生应急处理信息时,将应急处理信息发送给动态监护设备并输出,指导被监测对象采取相应的动作,如原地静坐、平躺、立即服用药物或迅速就医等。输出方式包括但不限于语音播放或显示输出。
此外,动态监护设备还能够支持用户主动触发事件记录,当用户自觉身体状况不适的情况下,能够通过动态监护设备对自身症状进行语音、文字等方式的输入,动态监护设备根据用户输入的信息生成主动报警事件记录,并且发送给服务器。
其具体实现过程可以如图7所示,包括如下步骤:
步骤710,动态监护设备接收被测对象输入的主动报警指令;
具体的,为便于用户使用,该主动报警指令的触发可以是通过硬件按键设计在动态监护设备上,采用一键式触发方式,也可以是在动态监护设备的人机交互界面上的一个功能选项,通过用户操作实现主动报警指令的输入。
步骤720,根据主动报警指令接收被测对象输入的主动报警信息;
具体的,当接收到主动报警指令时,动态监护设备产生输入设备启动指令,启动动态监护设备上的输入设备,可以是语音输入设备、视音频输入设备、文字信息输入设备等。具体可以是麦克风、摄像头或者软键盘、触摸屏等。
在启动输入设备后,对用户的输入进行监听,将监听到的信息记录为主动报警信息。
步骤730,根据主动报警指令获取当前时刻前后预设时段内的心电图数据;
具体的,在接收到主动报警指令时,动态监护设备还对主动报警指令产生时刻前后预设时段的心电图数据进行记录,用以进行分析,确认用户自觉不适的原因。
步骤730与步骤720可以同步执行。
步骤740,根据主动报警指令和当前时刻的时间信息生成主动报警事件记录信息,并且根据主动报警信息和当前时刻前后预设时段内的心电图数据生成主动报警事件记录数据;
具体的,当产生主动报警指令时,动态监护设备对当前时间进行记录,生成主动报警事件记录信息,用以指示产生了用户主动报警的事件。并且,根据前述步骤720记录的主动报警信息和步骤730记录得到的心电图数据生成主动报警事件记录数据。主动报警事件记录信息和主动报警事件记录数据具有关联关系,通过主动报警时间记录信息可以获取到主动报警事件记录数据。
步骤750,将主动报警事件记录信息和主动报警事件记录数据发送给服务器。
具体的,动态监护设备在记录完成后自动将主动报警事件记录信息和主动报警事件记录数据上传服务器。在上传之前,动态监护设备首先确定与服务器之间的通信连接状态,如果是处于正常连接状态,则可直接将数据上传,当然也可以同步存储在本地。如果处于连接断开状态,则可以将主动报警事件记录信息和主动报警事件记录数据缓存在动态监护设备的本地存储器中,并按预设时间间隔发起与服务器之间的通信连接请求,当完成连接后再自动进行主动报警事件记录信息和主动报警事件记录数据的上传。
相应的,图8为本发明实施例提供的一种动态监护系统的结构示意图,该动态监护系统包括一个或多个动态监护设备1和服务器2。动态监护设备1包括:处理器11和存储器12。存储器12可通过总线13与处理器11连接。存储器12可以是非易失存储器,例如硬盘驱动器和闪存,存储器12中存储有软件程序和设备驱动程序。软件程序能够执行本发明实施例提供的上述方法的各种功能;设备驱动程序可以是网络和接口驱动程序。处理器11用于执行软件程序,该软件程序被执行时,能够实现本发明实施例提供的方法。
需要说明的是,本发明实施例还提供了一种计算机可读存储介质。该计算机可读存储介质上存储有计算机程序,该计算机程序被处理器执行时,能够实现本发明实施例提供的方法。
本发明实施例还提供了一种包含指令的计算机程序产品。当该计算机程序产品在计算机上运行时,使得处理器执行上述方法。
本发明实施例提供的心电信息动态监护方法和动态监护系统,通过动态监护设备对心电图数据进行完整快速的自动分析,及时发现异常并产生报警信息,同时支持用户在自觉异常时主动上报报警。对于产生异常报警的情况进行数据记录和存储,以便能够迅速分析异常发生的原因,并具有可追溯性。
在上述实施例中,心电信息动态监护方法主要执行于动态监护设备中,事实上,本发明的心电信息动态监护方法亦可执行于服务器中,动态监护设备主要执行心电图数据的采集、传输,报警信息的输出和主动报警的触发。下面以一个实施例进行简要说明,各具体处理过程与上述实施例中重复的部分均不再赘述。
结合图9所示的心电信息动态监护方法的流程图,对本发明的心电信息动态监护方法进行详述。在本实施例中心电信息动态监护方法主要执行于服务器中。如图9所示,本发明的心电信息动态监护方法主要包括如下步骤:
步骤910,动态监护设备对被测对象进行体征监护数据采集,得到被测对象的心电图数据并获取被测对象信息,将心电图数据和被测对象信息发送给服务器;
心电图数据具有时间属性信息和动态监护设备的设备ID信息;
步骤920,服务器对心电图数据进行波群特征识别,得到心电图数据的特征信号,根据特征信号对心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据;
该步骤的具体执行过程与前述步骤120相同,只是执行主体变为服务器。
步骤930,服务器根据被测对象信息确定监测基准数据;
服务器根据动态监护设备发送的被测对象信息确定相适应的监测基准数据,具体可以结合被测对象信息中的年龄、性别、病史等来确定,优选的还可以根据被测对象信息获取其历史监测数据,结合历史监测数据确定面向被测对象的个性化的监测基准数据。监测基准数据至少应当包括被测对象信息对应的心电异常事件信息。
步骤940,服务器根据心电图事件数据确定对应的心电图事件信息,并确定心电图事件信息是否为心电异常事件信息;
本步骤的具体执行过程与上述步骤140相同,区别仅在于执行主体不同。
当为心电异常事件信息时,执行步骤950,否则继续执行步骤910。
步骤950,生成报警信息;
具体的,服务器确定心电图事件信息为心电异常事件信息时,根据心电异常事件信息产生相应的报警信息。
报警信息包括心电异常事件信息、报警时间信息和动态监护设备的设备ID信息。
同时,在确定为心电异常事件信息的时候,服务器将该心电图事件信息的心电图数据、心电图事件数据、报警信息,以及产生该报警信息的心电图数据的数据采集时间(即为报警时间信息对应时间)的前后预设时段内的多个心电图数据生成的异常事件记录数据。该异常事件记录数据被存储在服务器中,可在用以进行心电异常分析时追溯使用。
步骤960,服务器根据动态监护设备的设备ID信息将报警信息发送至动态监护设备,使动态监护设备根据报警信息产生相应的报警输出信号。
具体的,服务器根据设备ID确定动态监护设备,并通过与动态监护设备之间的通信连接将报警信息发送给动态监护设备。
动态监护设备根据接收到的报警信息产生报警输出信号。具体可以简单的为预定声音的报警、光电报警,也可以是根据心电异常时间信息输出的语音报警、信息显示报警等。通过产生报警,提示被监测对象其当前的心电信 号异常,从而能够迅速的引起被监测对象的警惕。
同样的,服务器能够执行如上述实施例中所述的远程监控,从而使得通过服务器端接入的监护人员能够做出相应的反应,如派出医疗救护人员或者远程指导用户进行应急处理、指导就医等。具体可以通过网络进行定位,确定动态监护设备当前的位置信息,使得服务器能够获取被监测对象的实时位置的信息。
同样的,服务器还可以向动态监护设备发送反馈信息,比如当根据报警信息向被监测用户派出救护人员时,生成告知信息发送给动态监护设备进行输出,通知被监测对象已派出救护人员;或者当根据报警信息产生应急处理信息时,将应急处理信息发送给动态监护设备并输出,指导被监测对象采取相应的动作,如原地静坐、平躺、立即服用药物或迅速就医等。输出方式包括但不限于语音播放或显示输出。
用户主动触发事件记录在本实施例中也可实现,其处理过程与前述实施例相同,此处不再赘述。
相应的,图10为本发明实施例提供的一种动态监护系统的结构示意图,该动态监护系统包括一个或多个动态监护设备2和服务器1。服务器1包括:处理器11和存储器12。存储器12可通过总线13与处理器11连接。存储器12可以是非易失存储器,例如硬盘驱动器和闪存,存储器12中存储有软件程序和设备驱动程序。软件程序能够执行本发明实施例提供的上述方法的各种功能;设备驱动程序可以是网络和接口驱动程序。处理器11用于执行软件程序,该软件程序被执行时,能够实现本发明实施例提供的方法。
需要说明的是,本发明实施例还提供了一种计算机可读存储介质。该计算机可读存储介质上存储有计算机程序,该计算机程序被处理器执行时,能够实现本发明实施例提供的方法。
本发明实施例还提供了一种包含指令的计算机程序产品。当该计算机程序产品在计算机上运行时,使得处理器执行上述方法。
本发明实施例提供的心电信息动态监护方法和动态监护系统,通过动态监护设备对心电图数据进行采集,上传服务器进行完整快速的自动分析,及时发现异常并产生报警信息下发给动态监护设备,同时动态监护设备支持用户在自觉异常时主动上报报警。服务器对于产生异常报警的情况进行数据记录和存储,以便能够迅速分析异常发生的原因,并具有可追溯性。
专业人员应该还可以进一步意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,为了清楚地说明硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本发明的范围。
结合本文中所公开的实施例描述的方法或算法的步骤可以用硬件、处理器执行的软件模块,或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
以上所述的具体实施方式,对本发明的目的、技术方案和有益效果进行了进一步详细说明,所应理解的是,以上所述仅为本发明的具体实施方式而已,并不用于限定本发明的保护范围,凡在本发明的精神和原则之内,所做的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。

Claims (15)

  1. 一种心电信息动态监护方法,其特征在于,所述方法包括:
    动态监护设备接收用户输入或者服务器下发的监测基准数据;所述监测基准数据包括被测对象信息和心电异常事件信息;
    所述动态监护设备对被测对象进行监护数据采集,得到所述被测对象的心电图数据;
    所述动态监护设备对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据;所述心电事件数据包括所述动态监护设备的设备ID信息;
    所述动态监护设备根据所述心电图事件数据确定对应的心电图事件信息,并确定所述心电图事件信息是否为所述心电异常事件信息;当为所述心电异常事件信息时,输出报警信息。
  2. 根据权利要求1所述的心电信息动态监护方法,其特征在于,所述方法还包括:
    所述动态监护设备接收被测对象输入的主动报警指令;
    根据所述主动报警指令接收所述被测对象输入的主动报警信息;
    根据所述主动报警指令获取当前时刻前后预设时段内的心电图数据;
    根据所述主动报警指令和所述当前时刻的时间信息生成主动报警事件记录信息,并且根据所述主动报警信息和所述当前时刻前后预设时段内的心电图数据生成主动报警事件记录数据;
    将所述主动报警事件记录信息和所述主动报警事件记录数据发送给所述服务器。
  3. 根据权利要求1所述的心电信息动态监护方法,其特征在于,所述报警信息包括所述心电异常事件信息、报警时间信息和所述动态监护设备的设备ID信息,所述方法还包括:
    所述动态监护设备将所述心电图数据、心电图事件数据、报警信息,以及根据所述报警时间信息对应时间的前后预设时段内的多个心电图数据生成的异常事件记录数据中的一个或多个发送至所述服务器。
  4. 根据权利要求3所述的心电信息动态监护方法,其特征在于,所述方法还包括:
    所述动态监护设备接收并输出所述服务器发送的对应所述心电图事件数据和/或报警信息的报警反馈信息。
  5. 根据权利要求1所述的心电信息动态监护方法,其特征在于,所述对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据具体包括:
    将所述心电图数据的数据格式经过重采样转换为预设标准数据格式,并对转换后的预设标准数据格式的心电图数据进行第一滤波处理;
    对所述第一滤波处理后的心电图数据进行心搏检测处理,识别所述心电图数据包括的多个心搏数据,每个所述心搏数据对应一个心搏周期,包括相应的P波、QRS波群、T波的幅值和起止时间数据;
    根据所述心搏数据确定每个心搏的检测置信度;
    根据干扰识别二分类模型对所述心搏数据进行干扰识别,得到心搏数据是否存在干扰噪音,以及用于判断干扰噪音的一个概率值;
    根据所述检测置信度确定心搏数据的有效性,并且,根据确定有效的心搏数据的导联参数和心搏数据,基于所述干扰识别的结果和时间规则合并生成心搏时间序列数据;根据所述心搏时间序列数据生成心搏分析数据;
    根据心搏分类模型对所述心搏分析数据进行幅值和时间表征数据的特征提取和分析,得到所述心搏分析数据的一次分类信息;
    对所述一次分类信息结果中的特定心搏的心搏分析数据输入到ST段和T波改变模型进行识别,确定ST段和T波评价信息;
    根据所述心搏时间序列数据,对所述心搏分析数据进行P波和T波特征检测,确定每个心搏中P波和T波的详细特征信息,详细特征信息包括幅值、方向、形态和起止时间的数据;
    对所述心搏分析数据在所述一次分类信息下根据所述心电图基本规律参考数据、所述P波和T波的详细特征信息以及所述ST段和T波评价信息进行二次分类处理,得到心搏分类信息;
    对所述心搏分类信息进行分析匹配,生成所述心电图事件数据。
  6. 一种心电信息动态监护方法,其特征在于,所述方法包括:
    动态监护设备对被测对象进行体征监护数据采集,得到所述被测对象的心电图数据并获取被测对象信息,将所述心电图数据和被测对象信息发送给服务器;所述心电图数据具有时间属性信息和动态监护设备的设备ID信息;
    所述服务器对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据;所述心电事件数据包括所述动态监护设备的设备ID信息;
    所述服务器根据所述被测对象信息确定监测基准数据;所述监测基准数据包括所述被测对象信息对应的心电异常事件信息;
    所述服务器根据所述心电图事件数据确定对应的心电图事件信息,并确定所述心电图事件信息是否为心电异常事件信息;当为心电异常事件信息时,生成报警信息;所述报警信息包括所述心电异常事件信息、报警时间信息和所述动态监护设备的设备ID信息;
    所述服务器根据所述动态监护设备的设备ID信息将所述报警信息发送至所述动态监护设备,使所述动态监护设备根据所述报警信息产生相应的报警输出信号。
  7. 根据权利要求6所述的心电信息动态监护方法,其特征在于,所述方法还包括:
    所述动态监护设备接收被测对象输入的主动报警指令;
    根据所述主动报警指令接收所述被测对象输入的主动报警信息;
    根据所述主动报警指令获取当前时刻前后预设时段内的心电图数据;
    根据所述主动报警指令和所述当前时刻的时间信息生成主动报警事件记录信息,并且根据所述主动报警信息和所述当前时刻前后预设时段内的心电图数据生成主动报警事件记录数据;
    将所述主动报警事件记录信息和所述主动报警事件记录数据发送给所述服务器。
  8. 根据权利要求6所述的心电信息动态监护方法,其特征在于,所述方法还包括:
    当为心电异常事件信息时,所述服务器根据所述时间属性信息获取所述心电图数据对应时间的前后预设时段内的多个心电图数据,生成异常事件记录数据;
    所述服务器生成所述异常事件记录数据与所述报警信息的关联信息。
  9. 根据权利要求6所述的心电信息动态监护方法,其特征在于,所述对所述心电图数据进行波群特征识别,得到所述心电图数据的特征信号,根据所述特征信号对所述心电图数据进行心搏分类,结合心电图基本规律参考数据得到心搏分类信息,并生成心电图事件数据具体包括:
    将所述心电图数据的数据格式经过重采样转换为预设标准数据格式,并对转换后的预设标准数据格式的心电图数据进行第一滤波处理;
    对所述第一滤波处理后的心电图数据进行心搏检测处理,识别所述心电图数据包括的多个心搏数据,每个所述心搏数据对应一个心搏周期,包括相应的P波、QRS波群、T波的幅值和起止时间数据;
    根据所述心搏数据确定每个心搏的检测置信度;
    根据干扰识别二分类模型对所述心搏数据进行干扰识别,得到心搏数据是否存在干扰噪音,以及用于判断干扰噪音的一个概率值;
    根据所述检测置信度确定心搏数据的有效性,并且,根据确定有效的心搏数据的导联参数和心搏数据,基于所述干扰识别的结果和时间规则合并生成心搏时间序列数据;根据所述心搏时间序列数据生成心搏分析数据;
    根据心搏分类模型对所述心搏分析数据进行幅值和时间表征数据的特征提取和分析,得到所述心搏分析数据的一次分类信息;
    对所述一次分类信息结果中的特定心搏的心搏分析数据输入到ST段和T波改变模型进行识别,确定ST段和T波评价信息;
    根据所述心搏时间序列数据,对所述心搏分析数据进行P波和T波特征检测,确定每个心搏中P波和T波的详细特征信息,详细特征信息包括幅值、方向、形态和起止时间的数据;
    对所述心搏分析数据在所述一次分类信息下根据所述心电图基本规律参考数据、所述P波和T波的详细特征信息以及所述ST段和T波评价信息进行二次分类处理,得到心搏分类信息;
    对所述心搏分类信息进行分析匹配,生成所述心电图事件数据。
  10. 一种动态监护系统,其特征在于,所述动态监护系统包括权利要求1-5任一项所述的一个或多个动态监护设备和服务器;
    所述动态监护设备包括:存储器和处理器;所述存储器用于存储程序,所述处理器用于执行如权利要求1至5任一项所述的方法。
  11. 一种包含指令的计算机程序产品,当其在计算机上运行时,使得计算机执行如权利要求1至5任一项所述的方法。
  12. 一种计算机可读存储介质,包括指令,当所述指令在计算机上运行时,使所述计算机执行根据权利要求1至5任一项所述的方法。
  13. 一种动态监护系统,其特征在于,所述动态监护系统包括权利要求6-9所述的服务器和一个或多个动态监护设备;
    所述服务器包括:存储器和处理器;所述存储器用于存储程序,所述处理器用于执行如权利要求6至9任一项所述的方法。
  14. 一种包含指令的计算机程序产品,当其在计算机上运行时,使得计算机执行如权利要求6-9任一项所述的方法。
  15. 一种计算机可读存储介质,包括指令,当所述指令在计算机上运行时,使所述计算机执行根据权利要求6-9任一项所述的方法。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113768516A (zh) * 2021-09-27 2021-12-10 牛海成 一种基于人工智能的心电图异常程度检测方法及系统
CN114072045A (zh) * 2019-10-29 2022-02-18 深圳迈瑞生物医疗电子股份有限公司 自适应报警系统、方法、装置、及物联网系统
CN114550411A (zh) * 2020-11-26 2022-05-27 深圳市科瑞康实业有限公司 一种对异常心搏类型数据进行预警的方法

Families Citing this family (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110897629A (zh) * 2018-09-14 2020-03-24 杭州脉流科技有限公司 基于深度学习算法的心电特征提取方法、装置、系统、设备和分类方法
CN109620181A (zh) * 2019-01-16 2019-04-16 贝骨新材料科技(上海)有限公司 一种心率与心率变异性监测的方法及护理型监护设备
CN109875546B (zh) * 2019-01-24 2020-07-28 西安交通大学 一种面向心电图数据的深度模型分类结果可视化方法
CN109875521A (zh) * 2019-04-18 2019-06-14 厦门纳龙科技有限公司 一种心电图数据分析以及系统
CN110403596A (zh) * 2019-07-23 2019-11-05 北京大学深圳医院 基于物联网的心电监护系统及其方法
EP3981321A4 (en) * 2019-07-29 2022-08-03 Cardio Intelligence Inc. ELECTROCARDIOGRAM DISPLAY DEVICE, ELECTROCARDIOGRAM DISPLAY METHOD AND PROGRAM
CN110495872B (zh) * 2019-08-27 2022-03-15 中科麦迪人工智能研究院(苏州)有限公司 基于图片及心搏信息的心电分析方法、装置、设备及介质
CN111182045B (zh) * 2020-03-09 2022-04-26 上海乐普云智科技股份有限公司 一种心电采集模块的数据传输方法
CN113647903B (zh) * 2020-05-12 2022-08-26 深圳市科瑞康实业有限公司 一种报警切换方法
US20220015711A1 (en) * 2020-07-20 2022-01-20 Board Of Regents, The University Of Texas System System and method for automated analysis and detection of cardiac arrhythmias from electrocardiograms
CN112244855B (zh) * 2020-11-13 2022-08-26 上海乐普云智科技股份有限公司 一种面向心电数据的数据处理系统
CN112842347A (zh) * 2020-12-23 2021-05-28 深圳酷派技术有限公司 心电测量方法、装置、可穿戴设备及存储介质
CN113509186B (zh) * 2021-06-30 2022-10-25 重庆理工大学 基于深度卷积神经网络的ecg分类系统与方法
CN113545765B (zh) * 2021-07-16 2024-04-09 厦门硅田系统工程有限公司 一种心率测量装置心率连续输出方法和心率测量装置
CN113749666B (zh) * 2021-09-10 2023-10-27 郑州大学 基于融合心室规则特征与XGBoost的心肌梗死分类方法
WO2023211724A1 (en) * 2022-04-25 2023-11-02 Cardiac Pacemakers, Inc. Ai-based detection of physiologic events
WO2023212207A1 (en) * 2022-04-27 2023-11-02 Prima Medical, Inc. Systems and methods for feature state change detection and uses thereof
US20240057864A1 (en) * 2022-04-27 2024-02-22 Preventice Solutions, Inc. Beat reclassification
WO2023212194A1 (en) * 2022-04-28 2023-11-02 Preventice Solutions, Inc. Beat and rhythm reclassification
CN117257324B (zh) * 2023-11-22 2024-01-30 齐鲁工业大学(山东省科学院) 基于卷积神经网络和ecg信号的房颤检测方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN205433651U (zh) * 2016-02-23 2016-08-10 济宁中科大象医疗电子科技有限公司 可穿戴式运动及心电信号实时采集及监护设备
US20170087371A1 (en) * 2015-09-30 2017-03-30 Zoll Medical Corporation Medical Device Operational Modes
CN106821366A (zh) * 2015-10-14 2017-06-13 张胜国 智能心电监护系统
CN107440709A (zh) * 2017-09-18 2017-12-08 山东正心医疗科技有限公司 智能穿戴式心电监护系统
CN206792389U (zh) * 2017-07-25 2017-12-26 青岛科技大学 基于NB‑IoT的可穿戴式监护系统
CN107714023A (zh) * 2017-11-27 2018-02-23 乐普(北京)医疗器械股份有限公司 基于人工智能自学习的静态心电图分析方法和装置

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6416471B1 (en) 1999-04-15 2002-07-09 Nexan Limited Portable remote patient telemonitoring system
CA2842420C (en) 2003-11-18 2016-10-11 Adidas Ag Method and system for processing data from ambulatory physiological monitoring
US8831732B2 (en) 2010-04-29 2014-09-09 Cyberonics, Inc. Method, apparatus and system for validating and quantifying cardiac beat data quality
US9107571B2 (en) * 2012-04-24 2015-08-18 Cardimetrix, Llc ECG acquisition and treatment-response system for treating abnormal cardiac function
KR20140063100A (ko) 2012-11-16 2014-05-27 삼성전자주식회사 원격 심질환 관리 장치 및 방법
US11039764B2 (en) * 2016-03-31 2021-06-22 Zoll Medical Corporation Biometric identification in medical devices
US20170372026A1 (en) 2016-06-28 2017-12-28 Alodeep Sanyal Non-Invasive continuous and adaptive health monitoring eco-system
US10912478B2 (en) * 2017-02-28 2021-02-09 Zoll Medical Corporation Configuring a cardiac monitoring device

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170087371A1 (en) * 2015-09-30 2017-03-30 Zoll Medical Corporation Medical Device Operational Modes
CN106821366A (zh) * 2015-10-14 2017-06-13 张胜国 智能心电监护系统
CN205433651U (zh) * 2016-02-23 2016-08-10 济宁中科大象医疗电子科技有限公司 可穿戴式运动及心电信号实时采集及监护设备
CN206792389U (zh) * 2017-07-25 2017-12-26 青岛科技大学 基于NB‑IoT的可穿戴式监护系统
CN107440709A (zh) * 2017-09-18 2017-12-08 山东正心医疗科技有限公司 智能穿戴式心电监护系统
CN107714023A (zh) * 2017-11-27 2018-02-23 乐普(北京)医疗器械股份有限公司 基于人工智能自学习的静态心电图分析方法和装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3698707A4 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114072045A (zh) * 2019-10-29 2022-02-18 深圳迈瑞生物医疗电子股份有限公司 自适应报警系统、方法、装置、及物联网系统
CN114072045B (zh) * 2019-10-29 2024-03-22 深圳迈瑞生物医疗电子股份有限公司 自适应报警系统、方法、装置、及物联网系统
CN114550411A (zh) * 2020-11-26 2022-05-27 深圳市科瑞康实业有限公司 一种对异常心搏类型数据进行预警的方法
CN114550411B (zh) * 2020-11-26 2023-09-29 深圳市科瑞康实业有限公司 一种对异常心搏类型数据进行预警的方法
CN113768516A (zh) * 2021-09-27 2021-12-10 牛海成 一种基于人工智能的心电图异常程度检测方法及系统
CN113768516B (zh) * 2021-09-27 2024-05-14 吉林省辰一科技有限公司 一种基于人工智能的心电图异常程度检测方法及系统

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