KR102592050B1 - A portable ECG electrode and an ECG measurement system for small animals - Google Patents

Abstract

The present invention collects data by simultaneously measuring acceleration/angular velocity according to movement while collecting ECG signals of small animals, applies FIR BPF and adaptive filter to the measured ECG signal, and R by QRS complex (QRS complex) It relates to a portable electrocardiogram electrode and electrocardiogram measurement system for small animals that detect peaks and extract heart rate, comprising: an electrocardiogram electrode attached to a small animal; An electrocardiogram measurement module that receives electrocardiogram signals of small animals from the electrocardiogram electrodes; A data collection system that collects ECG data from the ECG measurement module and has a built-in 3-axis acceleration sensor and angular velocity (gyroscope) sensor to measure acceleration/angular velocity according to movement; And, a configuration including a signal processing unit that applies FIR BPF and an adaptive filter to the measured ECG signal, detects the QRS complex, and finally detects the R peak to extract the heart rate.
By using the above system, ECG signals of small animals are collected and data is collected by measuring acceleration/angular velocity according to movement at the same time, so the ECG can be measured more accurately even when small animals engage in various activities, and FIR BPF and adaptation By applying a type filter and detecting the QRS complex, it is possible to remove various types of noise and have robust performance against various noises.

Description

{ A portable ECG electrode and an ECG measurement system for small animals }

The present invention collects data by simultaneously measuring acceleration/angular velocity according to movement while collecting ECG signals of small animals, applies FIR BPF and adaptive filter to the measured ECG signal, and R by QRS complex (QRS complex) It relates to a portable electrocardiogram electrode and electrocardiogram measurement system for small animals that detect peaks and extract heart rate.

Recently, the pet-related market is showing rapid growth every year. Factors driving the growth of the pet-related market include social factors and changes in awareness about pets. Social factors are mainly emerging as an aging population structure, an increase in single households, and a social structure with increasing stress. There is a trend to recognize pets as family members, companions, or partners.

As the number of people raising pets increases, the pet industry is also growing rapidly. Dog-only beauty salons, dog funeral homes, dog-only hotels, and dog cafes are rapidly emerging as new blue ocean businesses. Recently, the premium trend centered on ‘care’ and ‘well-being’ is gaining momentum in the pet industry. In other words, as the level of awareness about companion animals increases, a premium trend centered on care and well-being is blowing in the companion animal industry.

In addition, not only pet food and animal supplies but also medical services are becoming premium. For example, services that provide one-stop pet-related services such as specialized veterinary clinics with specialized treatment areas, hotels, kindergartens, and beauty salons are emerging. Some large animal hospitals are equipped with state-of-the-art MRI equipment for accurate diagnosis of companion animals. Diabetes monitors specifically designed for animals to check diabetes in pets are also gaining popularity in the market.

In addition, pet medical devices are priced 2-3 times higher than artificial medical devices, making them a high-value industry with high sales margins. In addition, the aging of companion animals, especially dogs, is rapidly progressing, and as a result, the number of patients with geriatric diseases such as heart disease or neoplastic disease is rapidly increasing.

The biggest problem with current veterinary medical devices is that medical devices made for human use are simply changed in appearance and sold as veterinary medical devices. Therefore, human values are used for various parameters, resulting in serious errors in the accuracy and reliability of the test. is occurring.

An electrocardiogram test is not only a basic test for heart disease patients, but is also a test that must be performed before anesthesia for surgery. It is essential for real-time examination of hospitalized patients even after surgery.

However, in the case of domestic and foreign animal hospitals, veterinarians/nurses have no choice but to watch from the side to check the condition of hospitalized animals, and a device that tests vital signs of patients hospitalized after surgery transmits signals to a smartphone app. Therefore, a system is needed to notify specialists of the alarm.

For this purpose, technologies for measuring electrocardiograms by attaching them to companion animals, etc. are being proposed [Patent Documents 1, 2].

Korea Patent Publication No. 10-2017-0101358 (published on September 6, 2017) Korean Patent Publication No. 10-2005-0111088 (published on November 24, 2005)

Pan, Jiapu, and Willis J. Tompkins. “A real-time QRS detection algorithm.” IEEE transactions on biomedical engineering 3 (1985): 230-236.

The purpose of the present invention is to solve the problems described above, and the data collection device is equipped with a sensor that measures the electrocardiogram from electrocardiogram electrodes, a 3-axis acceleration sensor, and an angular velocity (gyroscope) sensor, so as to collect the ECG signal of a small animal. The aim is to provide a portable electrocardiogram electrode and electrocardiogram measurement system for small animals that collects data by simultaneously measuring acceleration/angular velocity according to movement.

In addition, the purpose of the present invention is to apply FIR BPF and adaptive filter to the measured ECG signal, detect the QRS complex, and finally detect the R peak to extract the heart rate, a portable ECG electrode for small animals. and providing an electrocardiogram measurement system.

In order to achieve the above object, the present invention relates to a portable electrocardiogram electrode and electrocardiogram measurement system for small animals, comprising: an electrocardiogram electrode attached to a small animal; An electrocardiogram measurement module that receives electrocardiogram signals of small animals from the electrocardiogram electrodes; A data collection system that collects ECG data from the ECG measurement module and has a built-in 3-axis acceleration sensor and angular velocity (gyroscope) sensor to measure acceleration/angular velocity according to movement; And, it is characterized by including a signal processing unit that applies FIR BPF and adaptive filter to the measured ECG signal, detects QRS complex, and finally detects R peak to extract heart rate.

In addition, the present invention relates to a portable electrocardiogram electrode and electrocardiogram measurement system for small animals, wherein the electrocardiogram electrode includes a fabric base composed of a fabric woven three-dimensionally with highly elastic stretch yarn, an adhesive composed of an elastomer and an adhesive resin, and a release paper using a medium-release type. It is characterized by having an adhesive pad for fixing the electrode pattern consisting of.

In addition, the present invention relates to a portable electrocardiogram electrode and electrocardiogram measurement system for small animals, wherein the data collection system is connected to a control module functioning as a main control device and electrodes/cables for detecting ECG biosignals to form an analog front. It is characterized by being composed of an AD module, which is responsible for the module's functions, and a motion sensor board that detects the movement of the animal.

In addition, the present invention relates to a portable electrocardiogram electrode and electrocardiogram measurement system for small animals, in which the coefficients of the adaptive filter are calculated by normalized LMS (NLMS) and then calculated using the following [Equation 1]. It is characterized by

[Formula 1]

However, u(n) is the input signal, e(n) is the error signal, the difference between the desired signal d(n) and the filter output signal y(n), w is the filter coefficients, and μ is the learning coefficient, and the filter The output signal y(n) = wT(n)u(n), and u(n) = [u(n)… u(n-N+1)] and N is the filter length parameter.

As described above, according to the portable electrocardiogram electrode and electrocardiogram measurement system for small animals according to the present invention, data is collected by collecting ECG signals of small animals and simultaneously measuring acceleration/angular velocity according to movement, thereby allowing small animals to participate in various activities. Even so, the effect of being able to measure the electrocardiogram more accurately is obtained.

According to the portable ECG electrode and ECG measurement system for small animals according to the present invention, various types of noise are removed by applying FIR BPF and adaptive filter for ECG signal measurement and detecting QRS complex. The effect of having robust performance is obtained even against .

1 is a block diagram of the overall system configuration according to the present invention.
Figure 2 is a circuit diagram of an AD module according to an embodiment of the present invention.
Figure 3 is a timing diagram of the continuous conversion mode of the AD module according to an embodiment of the present invention.
Figure 4 is a graph showing ECG signal waveforms for various sampling frequencies (125Hz, 250Hz, 500Hz) according to the experiment of the present invention.
Figure 5 is a graph showing the ECG, acceleration, and angular velocity values of Subject 1 when lying still according to the experiment of the present invention.
Figure 6 is a graph showing ECG, acceleration, and angular velocity values when Subject 1 rolls left and right according to the experiment of the present invention.
Figure 7 is a graph showing the ECG, acceleration, and angular velocity values of Subject 1 when walking according to the experiment of the present invention.
Figure 8 is a flowchart illustrating a method for detecting a QRS complex according to an embodiment of the present invention.
Figure 9 is a block diagram of the configuration of a 128-tap FIR filter according to an embodiment of the present invention.
Figure 10 is a graph of the frequency characteristics of an FIR BPF designed according to an embodiment of the present invention.
Figure 11 is a graph (500Hz sampling frequency) before and after application of the FIR filter according to the experiment of the present invention.
Figure 12 is a block diagram of the configuration of an adaptive filter according to an embodiment of the present invention.
13 is a flowchart illustrating a QRS detection method according to an embodiment of the present invention.
Figure 14 is a table showing the frequency range of factors and noise affecting the ECG signal according to an embodiment of the present invention.
15 is a noisy ECG signal according to an experiment of the present invention, a graph of various noise waveforms and an ideal ECG signal.
16 is a graph of FIR BPF input and output signal waveforms (detailed waveforms) according to the experiment of the present invention.
Figure 17 is a graph showing the performance results of the adaptive filter according to the experiment of the present invention.
Figure 18 is a graph showing the results of applying FIR BPF + adaptive filter according to the experiment of the present invention.
Figure 19 is a graph showing the results of R peak detection by the signal processing method according to the present invention, according to the experiment of the present invention.

Hereinafter, specific details for implementing the present invention will be described with reference to the drawings.

In addition, in explaining the present invention, like parts are given the same reference numerals, and repeated description thereof is omitted.

First, a portable electrocardiogram measurement module according to an embodiment of the present invention will be described. The basic design specifications of the portable ECG measurement module for small animals according to the present invention are designed as portable ECG hardware for small animals.

Next, the configuration of a portable electrocardiogram electrode according to an embodiment of the present invention will be described.

The portable electrocardiogram electrode according to the present invention consists of a painless ECG patch electrode suitable for small animals such as pets.

Painless patches for small objects are largely divided into electrocardiogram electrode patterns and electrocardiogram electrode patches (hereinafter referred to as adhesive pads for fixing sensors).

The adhesive pad for fixing the electrode pattern consists of a fabric base, adhesive, and release paper.

First, the fabric base uses a three-dimensional woven fabric with high elasticity and elasticity to sufficiently protect and secure the electrode pattern.

Next, medical adhesives are generally composed of elastomers and tackifying resins, and the principle is basically the same as that of industrial adhesives. As a material, a copolymer containing acrylic acid ester as a main ingredient and a combination of natural rubber or synthetic rubber and tackifying resin are used.

Next, the release paper is a commonly used release paper and is made of a medium-release type.

Next, a method for detecting HR in an animal electrocardiogram according to an embodiment of the present invention will be described.

First, the electrocardiogram data collection system for small animals will be described.

The ECG data acquisition system for small animals consists of three main modules. An evaluation board that functions as the main control device (hereinafter, this board is referred to as a control module), an AD module that is connected to electrodes/cables for ECG bio-signal detection and functions as an analog front module, and animal movement. It consists of three boards, including a motion sensor board (hereinafter referred to as motion module) that detects.

As shown in Figure 1, the operation and connection information of the entire system are as shown in Figure 3. When the measured data is sent to the virtual COM port at a certain sampling time to the PC connected to the collection system and the USB interface, the PC It operates by receiving and storing values.

Next, the AD module for measuring ECG signals will be described.

The AD module has a built-in amplifier suitable for ECG biosignals and has the function of performing AD conversion of these signals and sending the results to the MCU through SPI communication.

This AD module (evaluation board) has the function of transmitting ECG data obtained by performing SPI communication with the built-in controller (MCU) using 4 electrodes connected through an RS-232C connector to a PC connected through USB (virtual serial port). Perform.

However, the above program acquires a certain number of samples and post-processes the acquired data, so it is not suitable for the present invention, which requires real-time signal processing. Therefore, in this study, the entire system is constructed by connecting appropriate signal lines from this evaluation board to the main control module.

Next, SPI communication between the control module and AD module will be explained.

The AD module transmits the converted data to the MCU. Continuous conversion mode uses a hardware timer built into the AD module to perform AD conversion at certain sampling times and continuously transmits the result through a communication channel. It means transmitting. The collection system according to the present invention uses a continuous conversion mode.

Figure 3 shows a timing diagram of the continuous conversion mode. When the control module outputs the START signal line as H, the actual conversion operation is continuously performed. When conversion is completed, the /DRDY (Data Ready negative) signal line becomes L, and when this signal is read by the control module (actually processed through an interrupt method), it means that a new sampling time has arrived. Afterwards, the /CS (chip select negative) signal line is set to L and at the same time, the SPI communicator is activated to send a clock signal and read data from the AD module. The data at this time is the status register and 2-channel data. Using the Status Register value, you can check the connection status of the electrode and whether the data packet brought to a specific bit is correct data. Of the two channels, the first channel is data related to respiration, and the second channel is data related to electrocardiogram.

Next, the motion module will be explained.

This motion module basically sends measured data (acceleration and angular velocity measurements) to the MCU using ^I2C communication.

The motion module has a built-in 3-axis acceleration sensor and angular velocity (gyroscope) sensor, converts each measurement value into 16-bit AD, processes it into a signal to remove noise, etc. (it has a built-in motion processor responsible for this) and It has the function of providing the result to the MCU through I2C communication.

It is connected to the control module of the developed acquisition system through I2C communication, and as described above, the I2C communicator is activated at every sampling time created by the AD module to read acceleration and angular velocity data from the motion module. The control module sends the collected data to the PC using a serial communicator in a certain order. The actual control module has a virtual serial communication port using a serial-USB conversion device and transmits data through a USB connector connected to the PC. The serial communication protocol uses the 8-N-1 (8bit data, No parity, 1 stop bit) method at a speed of 115200bps.

Next, experiments on the data collection system according to the present invention and its effects will be described.

To verify the performance of the collection system according to the present invention, an experiment was performed on humans. There are two types of ECG electrodes: patch type and clip type. In the present invention, patch type electrodes were used in the same way as animal patch type electrodes.

First, experiments were conducted on several sampling cycles.

For data analysis, the AD module was set to sampling frequencies of 125, 250, and 500 Hz and a basic data collection experiment was conducted. The results are depicted in Figure 4a. As shown in Figure 4, it can be seen that the larger the sampling frequency, the larger the 60Hz power noise.

Next, the collection performance of the motion sensor's output was examined. As mentioned above, the main function of the collection system is to simultaneously transmit and record data collected from the AD module and motion sensor to a PC.

This experiment used patch-type electrodes attached to the chest in the same way as above, and data was collected while the subject engaged in various activities (lying still, rolling over, walking, etc.). Motion data has three values (x, y, and z axes) of acceleration and angular velocity, and since changes in their size well reflect changes in activity, the norm value (i.e. the size of the vector) of the three values is calculated. This is shown.

First, Figure 5 shows the measured values when Subject 1 was lying still. As the acceleration and angular velocity vector values show, there is a certain amount of change during the initial lying down motion, but then only very small values are shown. This reflects well that it does not move still.

Next, Figure 6 is a graph of the results when the subject turns the body left and right while lying down. First, it shows that the angular velocity value changes to a large value. Through this, it is possible to understand how the subject moves. In the case of ECG data, there is no significant change, but it shows some fluctuating changes.

Finally, Figure 7 shows data collected when the subject walks a certain distance. Unlike the previous sideways roll, the acceleration vector also shows a significant change, and it can be seen that the amount of change in ECG data has also increased.

In conclusion, the developed collection system demonstrated the ability to measure and collect ECG data relatively accurately even under conditions where the subject moves a lot.

Next, a method for detecting an animal ECG QRS complex (QRS complex) according to an embodiment of the present invention will be described. In other words, a method of detecting the QRS complex (QRS complex) using data collected by the data collection system will be explained.

As shown in Figure 8, after removing noise using two types of filters, a traditional signal processing method is applied, and the final result is configured to find the peak using the R peak detection algorithm and calculate HR.

The configuration of the FIR Band Pass Filter will be described.

In the present invention, a digital filter in the form of FIR (Finite Impulse Response) was used. The reason is that it has the following advantages compared to IIR type filters.

- Simple structure

- Has linear phase characteristics

- Safety is always guaranteed

- The values of all coefficients are 1 order of magnitude smaller, so faster calculation performance can be achieved when using DSP.

In the present invention, an FIR BPF with 128 taps was designed. It was decided to use a 128-tap FIR filter as shown in Figure 9. In other words, it is a filter that uses 128 filter coefficients and past data (delay) up to the 128th. The filter coefficient was calculated using WinFilter, a filter design program.

Figure 10 is a graph showing the frequency response and phase response of the filter. The design parameters were designed to have a cut-off frequency of 4Hz to 50Hz using the Butterworth method.

The FIR BPF designed in this way was implemented in the collection system described above, and the experimental results of applying it were collected. This result is shown in Figure 11.

As shown in Figure 11, the 60Hz power supply voltage noise is completely removed by this filter. The first graph (red) is the input signal with noise, and the second graph (blue) is the filtered result signal.

Figure 3-18 Graph before and after application of FIR filter (500Hz sampling frequency)

Next, the adaptive filter according to the present invention is described. First, an adaptive filter is a filter that has the ability to remove noise that changes over time. The configuration of a general adaptive filter is shown in Figure 12.

The coefficients of adaptive filters are usually calculated using the least mean square (LMS) method. This algorithm is described in the following equation.

[Equation 1]

Here, u(n) is the input signal, e(n) is the error signal, the difference between the desired signal d(n) and the filter output signal y(n), w is the filter coefficients, and μ is the learning coefficient. Filter output signal y(n) = wT(n)u(n) where u(n) = [u(n)… u(n-N+1)] and N is the filter length parameter.

However, this LMS method has the disadvantage of being sensitive to the size of the input signal, so a method called normalized LMS (NLMS) was proposed, and this method is used in the present invention. This method is as follows:

[Equation 2]

In the present invention, the filter order was set to 22, the tap number was set to 23, and the learning coefficient was set to 0.1. The performance of this filter will be described in detail below.

Next, the QRS detection algorithm will be explained.

This very well-known method consists of a step-by-step signal processing method as shown in the following figure. In the present invention, a program was developed to process this method using the Python language. In the present invention, the signal was processed according to the steps shown in FIG. 13 according to [Non-patent Document 1].

Next, the performance evaluation of the signal processing algorithm according to the experiment of the present invention will be described. The performance evaluation results of the previously described signal processing algorithm are described.

First, the computer simulation environment for performance evaluation is described.

In particular, the generation of ideal ECG signals and appropriate noise signals is explained.

To create an appropriate noise-mixed ECG signal, random noise with the following frequency components was added to the ideal ECG signal.

The ideal ECG signal was created manually based on the typical signal waveform contained in the textbook and copied in multiple ways to match the appropriate average heart rate.

Additionally, regarding noise signal generation, there are several factors that create noise in the ECG signal. In Figure 14, the cause of this noise and the frequency characteristics of the resulting noise are described.

The noisy ECG signal generated for computer simulation is shown in Figure 15. Explaining the graph shown in FIG. 15, clockwise from the top left, number 1 is the ideal ECG signal, number 2 is noise due to breathing, number 3 is noise due to device movement, number 4 is power noise, number 5 is the final ECG signal, and number 6 is Noise caused by muscle changes, number 7 is a graph of noise caused by movement.

Next, the FIR BPF performance evaluation with 128 taps will be described.

16 is a graph of the input and output signal waveforms of the filter. First, the waveform over a long time range is shown in Figure 16. As a result of filtering, it can be seen that most low-frequency components have been removed. Figure 16 shows the waveform for a short period of time, and it can be seen that the waveform of the low-frequency component is more clearly removed.

Next, let's look at the performance of the adaptive filter described above. Figure 17 shows the waveforms of the input and output signals of the adaptive filter. As before, the noisy ECG signal was set as the input. As the figure shows, the adaptive filter also performs filtering appropriately.

Next, the results of applying FIR BPF + Adaptive filter 2nd stage are explained.

As explained in Figure 9, the proposed signal processing system removes noise by successively using BPF and adaptive filter. The results are shown in Figure 18. The graphs in Figure 18 show the input signal (ECG + noise) in the first graph in the left column, the BPF output signal in the first graph in the right column, the adaptive filter output in the second graph on the left, and the two-stage filter ( BPF + Adaptive Filter) output, and finally, the bottom left shows the error signal between the input signal and the final output signal. As the error graph shows, there is a very small error.

Next, performance evaluation using average error and SNR evaluation indices will be described.

To quantitatively evaluate the performance of the above filter, the following two indicators are introduced.

First, it is the mean squared error (MSE). Performance is compared by calculating the mean square error value of the error value with the actual ideal ECG signal.

The noise signal appears as MSE=424.61, and the filtered signal appears as MSE=93.26. It is approximately 21.9% of the noise signal error value.

Next, the signal to noise ratio (SNR) is usually expressed in dB units. This results in SNR = 1.55 dB vs. noise signal. The SNR of the filtered signal appears to be 2.31.

Next, the R peak detection performance evaluation will be described.

The QRS complex detection algorithm ultimately seeks to find the exact heart rate (HR) by detecting the R peak as accurately as possible. The R peak detection performance using the signal processing method according to the present invention is as follows.

As shown in Figure 19, for the 120 R peaks, which are the true values in the ideal signal, the R peak detection result for the noisy signal was 105, showing 15 errors (12.5%), but for the filtered signal, it was 119, which was 1. Only errors (0.83%) were described.

Although the invention made by the present inventor has been described in detail according to the above-mentioned embodiments, the present invention is not limited to the above-described embodiments and can of course be changed in various ways without departing from the gist of the invention.

10: ECG electrode 20: ECG measurement module
30: data collection system 40: signal processing unit

Claims (3)

In a portable electrocardiogram electrode and electrocardiogram measurement system for small animals,
Electrocardiogram electrodes attached to small animals;
An electrocardiogram measurement module that receives electrocardiogram signals of small animals from the electrocardiogram electrodes;
A data collection system that collects ECG data from the ECG measurement module and has a built-in 3-axis acceleration sensor and angular velocity (gyroscope) sensor to measure acceleration/angular velocity according to movement; and,
A signal processing unit applies a FIR bandpass filter (BPF) and an adaptive filter to the measured ECG signal, detects the QRS complex, and finally detects the R peak to extract the heart rate,
The data collection system includes a control module that functions as the main control device, an AD module that is connected to electrodes/cables for ECG biosignal detection and functions as an analog front module, and a motion sensor that detects animal movement. It consists of a sensor board,
The FIR bandpass filter is designed to have 128 taps and consists of 128 filter coefficients and a filter that uses past data up to the 128th,
A portable electrocardiogram electrode and electrocardiogram measurement system for small animals, characterized in that the coefficients of the adaptive filter are calculated by normalized LMS (NLMS) and then calculated using the following [Equation 1].
[Formula 1]

However, u(n) is the input signal, e(n) is the error signal, the difference between the desired signal d(n) and the filter output signal y(n), w is the filter coefficients, and μ is the learning coefficient, and the filter The output signal y(n) = wT(n)u(n), and u(n) = [u(n)… u(n-N+1)] and N is the filter length parameter.
According to paragraph 1,
The electrocardiogram electrode is for small animals, characterized in that it is provided with a fabric base made of a three-dimensional woven fabric with high elasticity stretch yarn, an adhesive made of an elastomer and an adhesive resin, and an adhesive pad for fixing the electrode pattern made of a release paper using a medium release type. Portable electrocardiogram electrodes and electrocardiogram measurement system.
delete