Wearable real-time electrocardiogram monitoring device

Abstract

The utility model relates to the technical field of electrocardiogram monitoring, in particular to a wearable real-time electrocardiogram monitoring device. The device comprises an electrocardio lead interface, an R-wave detector, an electrocardio acquisition module, a microprocessor, a memory and a warning output module, the R-wave detector is used for carrying out R-wave detection on an electrocardiosignal provided by the electrocardiosignal lead interface and outputting an R-wave arrival pulse signal to the microprocessor when the R-wave is detected; the electrocardio acquisition module is used for acquiring an electrocardio signal provided by the electrocardio lead interface and then converting the electrocardio signal into a digital electrocardio signal; the microprocessor is used for sampling the digital electrocardiosignal according to a preset sampling frequency and storing the sampled electrocardiosignal data in the memory; the microprocessor is also used for identifying a real R wave after being triggered by the R wave arrival pulse signal, and executing preset ST elevation or ST low identification after the real R wave is identified, so that the wearable electrocardiogram monitoring equipment can achieve long-range real-time monitoring, and the monitoring capability of sudden heart diseases is improved.

Description

Technical Field

[0001] The utility model relates to the technical field of electrocardiogram monitoring, in particular to a wearable real-time electrocardiogram monitoring device. Background Art

[0002] Myocardial ischemia refers to a state of myocardial hypoxia caused by insufficient blood supply to the coronary arteries. The coronary arteries are the main blood vessels supplying the heart and are responsible for transporting oxygen and nutrients to myocardial cells. When the coronary arteries are narrowed, spasmed, blocked, or have insufficient blood flow, the myocardium cannot get enough oxygen and nutrients, and myocardial ischemia and hypoxia will occur. Acute myocardial ischemia is divided into acute angina pectoris and acute myocardial infarction. Angina-type acute myocardial ischemia refers to sudden coronary artery spasm of the myocardium, insufficient blood supply to the myocardium, and ST segment depression on the electrocardiogram. Continuous angina pectoris may cause acute myocardial infarction.

[0003] Acute myocardial ischemia of the infarction type refers to the acute phase elevation of the ST segment of the electrocardiogram caused by sudden blockage of the coronary artery and lack of myocardial blood supply. Continuous acute myocardial infarction can lead to myocardial necrosis. It is a critical disease that seriously endangers human life and health, and is also one of the important causes of sudden death.

[0004] Electrocardiogram is the main means of detecting acute myocardial ischemia. Existing routine clinical electrocardiogram tests need to be performed in relevant specialized medical institutions. However, acute myocardial ischemia is sudden and unpredictable, and routine clinical electrocardiogram tests cannot effectively adapt to this characteristic.

[0005] Existing conventional portable 24-hour dynamic electrocardiogram recorders need to be replayed on a computer after 24 hours for analysis and recognition before the results can be given for the diagnosis of acute myocardial ischemia, which cannot meet the requirements of real-time warning of acute myocardial ischemia.

[0006] The existing medical-grade wearable dynamic recorder needs to forward the ECG diagnosis of acute myocardial ischemia to the mobile communication device (mobile phone) through wireless communication (such as Bluetooth), and then the mobile phone sends it to the cloud server through the mobile communication network. The cloud server program recognizes and gives the result, and then sends it back to the mobile communication device through the mobile communication network. In this closed loop of signal flow, there are many unpredictable influencing factors, such as:

[0007] (1) The device and the mobile phone cannot communicate in real time: If the user does not bring a mobile phone or the body position changes and the Bluetooth communication between the device and the mobile phone is disconnected, the acute myocardial ischemia data cannot be sent to the cloud in real time via Bluetooth;

[0008] (2) The mobile phone and the WiFi data forwarder have poor communication: For example, due to distance, obstacles, etc., the device data cannot be effectively sent to the mobile phone, and the acute myocardial ischemia data cannot be sent to the cloud in real time;

[0009] (3) Poor mobile communication network: For example, when taking the subway or other means of transportation, or being far away from the mobile communication base station, it is impossible to send acute myocardial ischemia data to the cloud in real time.

[0010] Existing civilian-grade wearable heart rate monitors (such as watches, etc.) can only rely on real-time heart rate to judge common arrhythmias, and cannot bear the application effect of real-time reporting of acute myocardial ischemia.

[0011] Therefore, wearable ECG monitoring equipment with real-time monitoring capabilities and capable of long-term operation has become a technical problem that needs to be solved urgently.

[0012] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Utility Model Content

[0013] In view of this, the purpose of the utility model is to provide a wearable real-time ECG monitoring device, which controls the microprocessor to perform ST segment ECG abnormality detection through the trigger signal of the R wave detector, so that the wearable ECG monitoring equipment can achieve long-term real-time monitoring and improve the monitoring ability of sudden heart disease.

[0014] In order to solve the above technical problems, the embodiment of the utility model provides a wearable real-time ECG monitoring device, including: an ECG lead interface, an R wave detector, an ECG acquisition module, a microprocessor, a memory and an alarm output module;

[0015] The R wave detector is connected to the ECG lead interface and is used to perform R wave detection on the ECG signal provided by the ECG lead interface, and output an R wave arrival pulse signal to the microprocessor when the R wave is detected;

[0016] The ECG acquisition module is connected to the ECG lead interface and is used to acquire the ECG signal provided by the ECG lead interface and convert it into a digital ECG signal;

[0017] The microprocessor is connected to the ECG acquisition module and the memory, and is used to sample the digital ECG signal according to a preset sampling frequency, and store the sampled ECG data in the memory;

[0018] The microprocessor is also used to identify a true R wave after being triggered by the R wave arrival pulse signal, and to perform a preset ST elevation or ST depression identification after identifying the true R wave;

[0019] The microprocessor is connected to the warning output module and is used to control the warning output module to output a corresponding signal according to the ST elevation or depression recognition result.

[0020] As an embodiment, the R wave detector includes: an electrocardiogram amplifier, an R wave filter, an R wave half-wave rectifier, an R wave sample holder, a signal comparator and two monostable triggers;

[0021] The ECG amplifier is connected to the ECG lead interface and is used to amplify the ECG signal and then output it to the R wave filter;

[0022] The R wave filter filters the amplified electrocardiogram signal and outputs the filtered signal to the R wave half-wave rectifier;

[0023] The R wave half-wave rectifier rectifies the filtered ECG signal and outputs it to the input end of the signal comparator and the R wave sample holder;

[0024] The R wave sample and hold device samples and holds the rectified ECG signal and outputs it to the reference end of the signal comparator and one of the monostable triggers;

[0025] The output end of the signal comparator is connected to another monostable trigger;

[0026] The two monostable triggers are used to output R wave arrival pulses to the microprocessor.

[0027] As an embodiment, the microprocessor also includes one or more of the following modules: a baseline correction module, a high-frequency artifact recognition module, a high-frequency filtering module, a low-frequency artifact recognition module, a low-frequency filtering module, a wavelet transform module and a feature extraction module, and the feature extraction module is used to extract Q, R, S, ST, and T features of the QRS complex.

[0028] As an embodiment, the high-frequency filtering module is used for real R-wave arrival detection.

[0029] As an embodiment, the wavelet transform adopts a one-dimensional discrete wavelet transform algorithm.

[0030] As an embodiment, the ECG monitoring device further includes a Bluetooth communication module connected to the microprocessor.

[0031] As an embodiment, the warning output module includes: a sound warning module and / or a vibration warning module.

[0032] As an embodiment, the sampling frequency of the digital ECG signal is 250 Hz.

[0033] Compared with the prior art, the technical solution of the embodiment of the utility model has at least the following advantages and beneficial effects:

[0034] The wearable real-time ECG monitoring device of the embodiment of the utility model detects the R wave arrival signal in real time through the R wave detector hardware circuit and outputs the R wave arrival pulse to the microprocessor. The time cost of the R wave hardware detection circuit is low and can be ignored. Therefore, the microprocessor can correct the R wave arrival signal after being triggered to identify the real R wave arrival signal, and quickly identify ST elevation or depression after identifying the real R wave arrival signal, so as to perform real-time monitoring of sudden diseases such as acute myocardial ischemia. The microprocessor automatically enters sleep after the detection is completed, which can greatly reduce the power consumption of the ECG monitoring device, so that it can perform longer-term monitoring to meet the long-term monitoring needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a structural schematic diagram of a wearable real-time electrocardiogram monitoring device provided by an embodiment of the utility model;

[0036] Figure 2 It is a structural schematic diagram of an R wave detector of a wearable real-time ECG monitoring device provided by an embodiment of the utility model;

[0037] Figure 3 It is a structural schematic diagram of a microprocessor of a wearable real-time electrocardiogram monitoring device provided by an embodiment of the utility model;

[0038] Figure 4 It is a normal electrocardiogram provided by the embodiment of the utility model;

[0039] Figure 5 It is a schematic diagram of ST elevation electrocardiogram provided by an embodiment of the utility model;

[0040] Figure 6 This is a schematic diagram of an ST depression type electrocardiogram provided by an embodiment of the utility model. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the utility model clearer, the various embodiments of the utility model will be described in detail below with reference to the accompanying drawings. However, it can be understood by those skilled in the art that in the various embodiments of the utility model, many technical details are provided in order to enable readers to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical schemes claimed for protection in the claims of the present application can be implemented.

[0042] See also Figure 1 As shown, this embodiment provides a wearable real-time ECG monitoring device that can be used for real-time monitoring of acute myocardial ischemia, etc. Figure 1As shown, the monitoring device 100 includes: an ECG lead interface 101 , an R wave detector 102 , an ECG acquisition module 103 , a microprocessor 104 , a memory 105 and an alarm output module 106 .

[0043] The R wave detector 102 is connected to the ECG lead interface 101 and is used to perform R wave detection on the ECG signal provided by the ECG lead interface 101, and when the R wave is detected, outputs an R wave arrival pulse signal to the microprocessor 104. The ECG lead interface 101 is connected to ECG lead electrodes, which include, for example, LL, RA, LA, and V lead electrodes, which can meet the ECG signal collection required for acute myocardial ischemia identification.

[0044] The ECG acquisition module 103 is connected to the ECG lead interface 101 and is used to acquire the ECG signal provided by the ECG lead interface and convert it into a digital ECG signal. The analog-to-digital conversion of the ECG signal adopts known technology, which will not be described in detail here.

[0045] The microprocessor 104 is connected to the ECG acquisition module 103 and the memory 105, and is used to sample the digital ECG signal according to a preset sampling frequency, and store the sampled ECG data in the memory 105. Exemplarily, the sampling frequency of the digital ECG signal sampled by the microprocessor 104 is 250 Hz, that is, 250 times per second.

[0046] The microprocessor 104 is also used to identify the real R wave after being triggered by the R wave arrival pulse signal, and perform preset ST elevation or ST depression identification after identifying the real R wave. The R wave arrival signal detected by the R wave detector contains a non-real R wave signal. The microprocessor 104 corrects the R wave arrival signal and eliminates the erroneous R wave arrival signal, so that the real R wave arrival signal can be more accurately located.

[0047] The microprocessor 104 is connected to the warning output module 106 and is used to control the warning output module 106 to output a corresponding signal according to the ST elevation or depression recognition result. After the microprocessor 104 recognizes ST elevation or ST depression, it can further identify acute myocardial ischemia, and when acute myocardial ischemia is recognized, it can control the warning output module 106 to output a warning signal to remind the monitored person to take timely treatment measures.

[0048] like Figure 2 As shown, the R-wave detector 102 of this embodiment may include: an ECG amplifier 1021, an R-wave filter 1022, an R-wave half-wave rectifier 1023, an R-wave sample holder 1024, a signal comparator 1025 and two monostable triggers 1026.

[0049] The ECG amplifier 1021 is connected to the ECG lead interface 101 and is used to amplify the ECG signal and then output it to the R wave filter 1022 .

[0050] The R wave filter 1022 filters the amplified ECG signal and outputs it to the R wave half-wave rectifier 1023. The R wave half-wave rectifier 1023 rectifies the filtered ECG signal and outputs it to the input end of the signal comparator 1025 and the R wave sample holder 1024.

[0051] The R wave sample and hold device 1024 samples and holds the rectified ECG signal and outputs it to the reference terminal of the signal comparator 1025 and one of the monostable triggers 102 6 .

[0052] The output end of the signal comparator 1025 is connected to another monostable trigger 1026. The two monostable triggers 1026 are used to output R wave arrival pulses to the microprocessor 104 to trigger the microprocessor 104 to perform a preset monitoring task.

[0053] The R-wave detector is implemented using a hardware circuit, instead of using software to monitor the arrival of the R-wave in the microprocessor. This has a low time cost and can save microprocessor time resources, reserving more sleep time for the microprocessor, greatly reducing the overall power consumption of the device, allowing limited power to achieve longer-term monitoring.

[0054] like Figure 3 As shown, the microprocessor 104 may also include one or more of the following modules: a baseline correction module 1041, an R wave correction module 1042, a high-frequency artifact recognition module 1043, a high-frequency filtering module 1044, a low-frequency artifact recognition module 1045, a low-frequency filtering module 1046, a wavelet transform module 1047 and a feature extraction module 1048. The feature extraction module 1048 is used to extract Q, R, S, ST, and T features of the QRS complex.

[0055] Specifically, the baseline correction module 1041 can be used to correct digital ECG signals. For example, when wearing the device, the impedance change between the electrode and the skin caused by sweating caused by human activity produces an ultra-low frequency baseline drift. The amplitude baseline reference of the parameter characteristics is determined by the baseline correction module. It can be understood that a moving window median filtering method can be used. This method is real-time, fast, occupies little memory, and has a short calculation time.

[0056] The R wave correction module 1042 can implement R wave arrival recognition using known hardware and software, which will not be described in detail here.

[0057] The high-frequency artifact recognition module 1043 can be used to identify and mark high-frequency and high-amplitude artifact signals in digital ECG signals. High-frequency and high-amplitude artifact signals are, for example, sawtooth oscillation waves, which can be specifically identified and marked based on characteristics such as the amplitude and duration of the signal.

[0058] The high-frequency filtering module 1044 adopts a high-pass filter for high-frequency filtering of the digital ECG signal. The high-frequency filtering algorithm adopts a digital algorithm time, which only allows the high-frequency components in the input signal to pass through and removes the low-frequency components. Specifically, it can be used to allow signals above 4HZ in the ECG signal to pass through, so that only the R wave is retained as the component of the digital ECG signal, which can be used to detect the arrival of the R wave.

[0059] The low-frequency artifact recognition module 1045 is used to recognize and mark low-frequency artifact signals of digital ECG signals. When the human body moves while wearing the device, the electrode sheet that picks up the ECG signal rubs against the skin and shifts, generating an oscillation wave turning point with a lower frequency and amplitude. More than 5 consecutive points ≥ the threshold value within 1 second are marked as low-frequency artifacts. This embodiment does not impose excessive restrictions on the type and recognition method of low-frequency artifacts.

[0060] The low-frequency filter module 1046 is used to perform low-pass filtering on the digital ECG signal, which allows ECG signals below 35 Hz to pass through and removes 50 Hz power frequency and other noise signals.

[0061] The wavelet transform module 1047 is used to perform real-time frequency domain conversion on the digital ECG signal. The wavelet transform can perform localized analysis on the time (space) frequency, and gradually perform multi-scale refinement on the signal (function) through telescoping and translation operations, and finally achieve time subdivision at high frequencies and frequency subdivision at low frequencies. It can automatically adapt to the requirements of time-frequency signal analysis, and transform continuous two-point discrete signals into vector curves of different frequencies (orders), so as to focus on any details of the signal. For ECG signals, through the selection of order parameters, the continuous curves of different characteristic frequencies of the Q, R, S, ST, and T periods in the ECG signals are displayed, so as to facilitate the acquisition of the parameter characteristics of Q, R, S, ST, and T. In this embodiment, the wavelet transform module 1047 adopts a one-dimensional discrete wavelet transform, which has a fast operation speed and can meet the requirements of fast processing of real-time signals.

[0062] The feature extraction module 1048 is used to extract the Q, R, S, ST, and T features of the QRS complex to facilitate the identification of ST elevation or depression. The theoretical value of the QT interval is determined based on the RR interval.

[0063] The warning output module 106 may include: a sound warning module and / or a vibration warning module. The sound warning module prompts ECG abnormalities by emitting a warning sound, and the vibration warning module prompts ECG abnormalities by emitting vibrations. Specifically, when the microprocessor detects ST elevation or ST depression, and the duration exceeds 60 seconds, it can be regarded as detecting acute myocardial ischemia, and then controls the warning output module 106 to emit a sound and / or vibration warning. It can be understood that the ECG monitoring device of this embodiment may also include a Bluetooth communication module connected to the microprocessor, which communicates with electronic devices such as a user's mobile phone through the Bluetooth communication module, sends the monitored ECG abnormal events to the user's mobile phone, and warns the user through the mobile phone.

[0064] This embodiment can be applied to real-time monitoring of acute myocardial ischemia. Acute myocardial ischemia has the characteristic of suddenness, that is, there is no predictable time or time period for its occurrence. Acute myocardial ischemia has the characteristic of persistence. After the first acute myocardial ischemia ECG signal pattern is determined, it must last for more than 60 seconds and all ECG patterns have the same characteristics. Acute myocardial ischemia refers to ST depression and ST elevation changes caused by typical coronary artery blood supply obstruction, and does not include ST changes caused by variant angina. Figures 4 to 6 As shown, they are normal electrocardiogram, ST elevation acute myocardial ischemia electrocardiogram and ST depression acute myocardial ischemia. ST elevation or ST depression can be identified by using known identification algorithms, and a strong real-time identification algorithm is used as much as possible.

[0065] In the embodiment of the utility model, except for the identification of ST elevation or ST depression, all other steps and algorithms adopt general, simple, known, streamlined, and time-saving calculations, which can ensure the pipeline completion of each algorithm while the microprocessor samples in real time, and can make full use of the microprocessor running time resources to centrally process the identification of acute myocardial ischemia when the R wave arrives. Since the R wave arrival is detected by hardware, it not only saves time for the microprocessor, but also improves the accuracy of the R wave arrival detection.

[0066] Compared with the prior art, this embodiment detects the R wave arrival signal in real time through the R wave detector hardware circuit and outputs the R wave arrival pulse to the microprocessor. The time cost of the R wave hardware detection circuit is low and can be ignored. Therefore, after the microprocessor is triggered, it can correct the R wave arrival signal to identify the real R wave arrival signal, and quickly identify ST elevation or depression after identifying the real R wave arrival signal, so as to perform real-time monitoring of sudden diseases such as acute myocardial ischemia. The microprocessor automatically enters sleep mode after the detection is completed, which can greatly reduce the power consumption of the ECG monitoring device, so that it can perform longer-term monitoring to meet long-term monitoring needs.

[0067] Those skilled in the art will appreciate that the above-mentioned embodiments are specific examples for implementing the present invention, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A wearable real-time electrocardiogram monitoring device, characterized in that: include: ECG lead interface, R wave detector, ECG acquisition module, microprocessor, memory and warning output module; The R wave detector is connected to the ECG lead interface and is used to perform R wave detection on the ECG signal provided by the ECG lead interface, and output an R wave arrival pulse signal to the microprocessor when the R wave is detected; The ECG acquisition module is connected to the ECG lead interface and is used to acquire the ECG signal provided by the ECG lead interface and convert it into a digital ECG signal; The microprocessor is connected to the ECG acquisition module and the memory, and is used to sample the digital ECG signal according to a preset sampling frequency, and store the sampled ECG data in the memory; The microprocessor is also used to identify a true R wave after being triggered by the R wave arrival pulse signal, and to perform a preset ST elevation or ST depression identification after identifying the true R wave; The microprocessor is connected to the warning output module and is used to control the warning output module to output a corresponding signal according to the ST elevation or depression recognition result.

2. The wearable real-time ECG monitoring device according to claim 1, characterized in that: The R wave detector comprises: an electrocardiogram amplifier, an R wave filter, an R wave half-wave rectifier, an R wave sample holder, a signal comparator and two monostable triggers; The ECG amplifier is connected to the ECG lead interface and is used to amplify the ECG signal and then output it to the R wave filter; The R wave filter filters the amplified ECG signal and outputs it to the R wave half-wave rectifier; The R wave half-wave rectifier rectifies the filtered ECG signal and outputs it to the input end of the signal comparator and the R wave sample holder; The R wave sample and hold device samples and holds the rectified ECG signal and outputs it to the reference end of the signal comparator and one of the monostable triggers; The output end of the signal comparator is connected to another monostable trigger; The two monostable triggers are used to output R wave arrival pulses to the microprocessor.

3. The wearable real-time ECG monitoring device according to claim 1, characterized in that: The microprocessor also includes one or more of the following modules: a baseline correction module, a high-frequency artifact recognition module, a high-frequency filtering module, a low-frequency artifact recognition module, a low-frequency filtering module, a wavelet transform module and a feature extraction module, wherein the feature extraction module is used to extract the Q, R, S, ST, and T features of the QRS complex.

4. The wearable real-time ECG monitoring device according to claim 3, characterized in that: The high-frequency filtering module is used for real R-wave arrival detection.

5. The wearable real-time ECG monitoring device according to claim 3, characterized in that: The wavelet transform adopts a one-dimensional discrete wavelet transform algorithm.

6. The wearable real-time ECG monitoring device according to claim 1, characterized in that: The electrocardiogram monitoring device also includes a Bluetooth communication module connected to the microprocessor.

7. The wearable real-time ECG monitoring device according to claim 1, characterized in that: The warning output module includes: a sound warning module and / or a vibration warning module.

8. The wearable real-time ECG monitoring device according to claim 1, characterized in that: The sampling frequency of the digital ECG signal is 250 Hz.

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