A high-frequency electrocardio collection device

Abstract

This invention discloses a high-frequency electrocardiogram (ECG) acquisition device, comprising: a signal acquisition electrode for acquiring raw ECG signals; a low-noise front-end amplification module for amplifying and filtering the raw ECG signals to obtain analog ECG signals; an analog-to-digital conversion module for digitizing the analog ECG signals to obtain digital ECG signals; a signal processing unit for extracting high-frequency features from the digital ECG signals through a combination of adaptive bandpass decomposition and time-frequency envelope analysis, and determining risk indicators corresponding to the raw ECG signals based on the high-frequency features; and a data output module for outputting the risk indicators to a host computer or cloud, thereby achieving highly accurate identification and classification of ECG activity.

Description

Technical Field

[0001] This application relates to the field of electrocardiogram (ECG) acquisition technology, and more specifically, to a high-frequency ECG acquisition device and method. Background Technology

[0002] An electrocardiogram (ECG) is an important physiological signal reflecting the electrical activity of the heart and is widely used in the diagnosis of arrhythmias, myocardial ischemia, and abnormal cardiac function. Traditional ECG acquisition systems mainly focus on low-frequency components (0.05–100 Hz), which correspond to the macroscopic electrical activity of the heart, such as the P wave, QRS complex, and T wave. However, recent studies have shown that ECG signals also contain abundant high-frequency information (150–1000 Hz), which reflects the microscopic electrical activity during myocardial fiber depolarization and is highly sensitive to pathological changes such as early myocardial ischemia, myocardial fibrosis, and arrhythmias.

[0003] Existing high-frequency electrocardiogram (ECG) detection technologies, such as the HyperQ system, primarily detect myocardial ischemia through statistical analysis of the high-frequency energy of the QRS complex. While this method improves the sensitivity of traditional ECG detection to some extent, it still has the following limitations: Hardware limitations: Existing acquisition systems typically have narrow bandwidth (cutoff frequency of about 100Hz) and high system noise (>3 μVpp), making it impossible to effectively capture high-frequency components; Algorithm-level shortcomings: Methods such as HyperQ are mainly based on time-domain energy integration or fixed filtering frequency bands, which makes it difficult to adapt to the dynamic changes in the spectrum of high-frequency signals under different individuals and conditions; Robustness issues: High-frequency signals have weak amplitudes and are easily affected by motion artifacts, electromyographic interference, and power frequency noise. Traditional methods have limited anti-interference capabilities.

[0004] Therefore, there is an urgent need for an ECG acquisition device that simultaneously possesses low-noise broadband acquisition capabilities and adaptive high-frequency signal extraction capabilities, in order to achieve high-resolution and high-sensitivity monitoring of myocardial electrical activity. Summary of the Invention

[0005] This invention provides a high-frequency electrocardiogram (ECG) acquisition device to solve the technical problem in the prior art that it is impossible to accurately identify and classify ECG activity. The high-frequency ECG acquisition device includes: Signal acquisition electrodes are used to acquire raw electrocardiogram (ECG) signals; A low-noise front-end amplification module is used to amplify and filter the original electrocardiogram signal to obtain a simulated electrocardiogram signal; An analog-to-digital converter module is used to digitize the analog electrocardiogram (ECG) signal to obtain a digital ECG signal; The signal processing unit is used to extract high-frequency features from the digital electrocardiogram signal by combining adaptive bandpass decomposition and time-frequency envelope analysis, and to determine the risk index corresponding to the original electrocardiogram signal based on the high-frequency features. The data output module is used to output the risk indicators to the host computer or the cloud.

[0006] In some specific embodiments, the low-noise front-end amplification module specifically includes: A front-end signal amplification circuit is used to amplify the original electrocardiogram signal using a two-stage amplification structure, wherein the two-stage amplification structure includes differential amplification and programmable gain amplification. Anti-aliasing filters are used to limit the bandwidth of the amplified raw ECG signal. The active drive circuit for the right leg is used to counteract common-mode interference by detecting the common-mode voltage of the input channel and feeding its inverted signal back to the human body.

[0007] In some specific embodiments, the differential amplifier module is used to amplify the input differential signal and suppress common-mode interference. The programmable gain amplifier is specifically a programmable gain amplifier, and the overall gain of the pre-amplifier is determined using the following formula: ; in, This represents the overall gain of the front-end amplifier circuit. This is the gain of the preceding differential amplifier stage. The gain of the subsequent programmable gain amplifier. and These are the feedback resistor and input resistor corresponding to the preamplifier circuit. and These are the feedback resistor and input resistor for the subsequent programmable gain amplifier circuit.

[0008] In some specific embodiments, the anti-aliasing filter adopts a second-order Butterworth structure, and the transfer function of the anti-aliasing filter is: ; Where H(s) is the transfer function of the anti-aliasing filter, s is the complex frequency variable in the Laplace transform, and ωc is the cutoff angular frequency of the anti-aliasing filter.

[0009] In some specific embodiments, the output voltage of the active right leg drive circuit specifically satisfies the following formula: ; Wherein, VDRL is the output voltage of the active drive right leg circuit. For feedback gain coefficient, The detected common-mode voltage, This is the reference potential.

[0010] In some specific embodiments, the analog-to-digital conversion module is specifically used for: The analog electrocardiogram (ECG) signal is converted into a digital ECG signal using a sampling rate of not less than 2 kHz and a resolution of not less than 16 bits.

[0011] In some specific embodiments, the signal processing unit specifically includes: An adaptive bandpass decomposition module is used to perform adaptive bandpass decomposition on the digital electrocardiogram signal to obtain modal components corresponding to multiple frequency bands. The time-frequency envelope analysis module is used to select modal components within the target frequency range as high-frequency ECG signals and obtain the instantaneous envelope of the high-frequency ECG signals through Hilbert transform, providing a basis for subsequent time-frequency analysis and feature extraction. The feature recognition module is used to classify and identify the high-frequency features based on the instantaneous envelope and its derived energy and spectral features using a deep neural network model to obtain the risk index.

[0012] In some specific embodiments, the optimization objective of the variational mode decomposition algorithm is specifically determined by the following formula: ; in, For the first Modal signals, Let t be the corresponding center frequency, δ(t) be the Dirac function, and ∂t denote the partial derivative operator with respect to time.

[0013] In some specific embodiments, the time-frequency envelope analysis module extracts the following high-frequency feature parameters by performing Hilbert transform and time-frequency analysis on the high-frequency electrocardiogram modal signal: Instantaneous envelope characteristics are used to characterize the change in amplitude of high-frequency electrocardiogram signals over time; The high-frequency instantaneous energy characteristics are obtained by energy calculation of the instantaneous envelope and are used to characterize the intensity of high-frequency micro-electrical activity in the myocardium. The high-frequency total energy characteristic is obtained by integrating or averaging the instantaneous energy within a preset time window, and is used to reflect the overall energy level of high-frequency electrocardiographic activity. The time-frequency energy distribution characteristics are obtained by performing a short-time Fourier transform on the instantaneous envelope, and are used to describe the distribution of high-frequency ECG energy in the time-frequency domain. The spectral centroid feature, calculated based on the time-frequency energy distribution, is used to characterize the concentrated location of high-frequency energy in the frequency domain; Spectral entropy features, calculated based on the probability distribution of the spectral energy, are used to reflect the complexity and irregularity of high-frequency electrocardiographic activity. The time-frequency envelope analysis module extracts the high-frequency features using the following formula: Analytical signal construction of high-frequency ECG modal signals: ; in, For high-frequency electrocardiogram signals, H{·} represents the Hilbert transform; Instantaneous envelope extraction: ; The instantaneous envelope Characterizing the amplitude variation characteristics of high-frequency electrocardiogram signals; Instantaneous energy is used to characterize the instantaneous energy distribution of high-frequency electrocardiogram signals over time. ; High-frequency energy characteristic calculation: ; in, T To analyze the length of the time window; Time-frequency energy distribution calculation (short-time Fourier transform): ; Where AHF(τ) is the high-frequency electrocardiogram signal to be analyzed, w(t−τ) is a window function centered on time τ, used to realize the local time domain truncation of the signal, and f represents the frequency variable; Power spectral density calculation: ; The power spectral density is used to describe the distribution characteristics of high-frequency electrocardiogram energy in the frequency domain; Spectral centroid calculation: ; Where, f1=150Hz, f2=1000Hz; Spectral entropy calculation: ; ; in, N This represents the number of frequency sampling points.

[0014] In some specific embodiments, the data output module transmits risk indicators and high-frequency electrocardiogram characteristic parameters to a host computer or cloud server in real time via a wired interface or wireless communication.

[0015] By applying the above technical solutions, a high-frequency electrocardiogram (ECG) acquisition device and method are proposed. The high-frequency ECG acquisition device includes: a signal acquisition electrode for acquiring raw ECG signals; a low-noise front-end amplification module for amplifying and filtering the raw ECG signals to obtain analog ECG signals; an analog-to-digital conversion module for digitizing the analog ECG signals to obtain digital ECG signals; a signal processing unit for extracting high-frequency features from the digital ECG signals through a combination of adaptive bandpass decomposition and time-frequency envelope analysis, and determining the risk indicators corresponding to the raw ECG signals based on the high-frequency features; and a data output module for outputting the risk indicators to a host computer or cloud, thereby achieving highly accurate identification and classification of ECG activity. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the structure of a high-frequency electrocardiogram acquisition device provided in an embodiment of this application. Detailed Implementation

[0018] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0019] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0020] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0021] like Figure 1 As shown, this application proposes a high-frequency electrocardiogram (ECG) acquisition device, which includes: Signal acquisition electrodes are used to acquire raw electrocardiogram (ECG) signals; A low-noise front-end amplification module is used to amplify and filter the original electrocardiogram signal to obtain a simulated electrocardiogram signal; An analog-to-digital converter module is used to digitize the analog electrocardiogram (ECG) signal to obtain a digital ECG signal; The signal processing unit is used to extract high-frequency features from the digital electrocardiogram signal by combining adaptive bandpass decomposition and time-frequency envelope analysis, and to determine the risk index corresponding to the original electrocardiogram signal based on the high-frequency features. In some embodiments of this application, the low-noise front-end amplification module specifically includes: A front-end signal amplification circuit is used to amplify the original electrocardiogram signal using a two-stage amplification structure, wherein the two-stage amplification structure includes differential amplification and programmable gain amplification. Anti-aliasing filters are used to limit the bandwidth of the amplified raw ECG signal. The active drive circuit for the right leg is used to counteract common-mode interference by detecting the common-mode voltage of the input channel and feeding its inverted signal back to the human body.

[0022] In some embodiments of this application, the differential amplifier module is used to amplify the input differential signal and suppress common-mode interference. The programmable gain amplifier is specifically a programmable gain amplifier, and the overall gain of the front-end amplification is determined using the following formula: ; in, For total gain, This is the gain of the preceding differential amplifier stage. The gain of the subsequent programmable gain amplifier. and These are the feedback resistor and input resistor corresponding to the preamplifier circuit. and These are the feedback resistor and input resistor for the subsequent programmable gain amplifier circuit.

[0023] In some embodiments of this application, the anti-aliasing filter adopts a second-order Butterworth structure, and the transfer function of the anti-aliasing filter is: ; Where H(s) is the transfer function of the anti-aliasing filter, s is the complex frequency variable in the Laplace transform, and ωc is the cutoff angular frequency of the anti-aliasing filter.

[0024] In some embodiments of this application, the output voltage of the active right leg drive circuit specifically satisfies the following formula: ; Wherein, VDRL is the output voltage of the active drive right leg circuit. For feedback gain coefficient, The detected common-mode voltage, This is the reference potential.

[0025] In this embodiment, the signal acquisition electrodes are used to acquire raw electrocardiogram (ECG) signals from the human body surface. The electrodes can be Ag / AgCl wet electrodes or conductive polymer dry electrode arrays to reduce electrode-skin interface impedance fluctuations and improve signal stability.

[0026] In this embodiment, the low-noise front-end amplification module employs a differential amplification architecture and a low-noise operational amplifier to achieve broadband amplification of microvolt-level potential signals. The total equivalent input noise of the system is less than 1 μVpp, and the bandwidth covers 0.05 Hz to 1000 Hz, thus simultaneously preserving the low-frequency components of traditional ECG and the high-frequency electrical activity information of the myocardium. This module adopts a multi-stage amplification and isolation design, with an instrumentation amplifier at the front end and a programmable gain amplifier (PGA) at the rear end. Its overall gain is determined by the following formula: ; in, and For preamplifier resistors, and This is the amplification resistor for the subsequent stage. The system gain can be adjusted within the range of 20–1000 times, enabling dynamic optimization under different measurement scenarios.

[0027] To further suppress power frequency and environmental interference, this embodiment introduces an active drive right leg circuit (DRL). The DRL circuit detects the common-mode voltage of the input channel. It then feeds its inverted signal back to the human body, thereby canceling common-mode interference. Its output voltage expression is: ; Wherein, VDRL is the output voltage of the active drive right leg circuit. For feedback gain coefficient, This is the reference potential. After optimization, the system's common-mode rejection ratio (CMRR) can reach 110dB. This significantly reduces 50 / 60Hz power supply interference and environmental noise.

[0028] In some embodiments of this application, the analog-to-digital conversion module is specifically used for: The analog electrocardiogram (ECG) signal is converted into a digital ECG signal using a sampling rate of not less than 2 kHz and a resolution of not less than 16 bits.

[0029] Specifically, the analog-to-digital converter (ADC) is responsible for digitizing the analog signal, with a sampling rate of at least 2kHz and a resolution of at least 16 bits, ensuring complete sampling and quantization accuracy of high-frequency ECG signals. A high-performance Σ–Δ ADC (such as the ADS127L01) is preferred to improve dynamic range and linearity.

[0030] The sampling system employs a 16-bit ADC to digitize the amplified analog signal at a sampling rate of 5 kHz. The sampled signal is denoted as x[n]. To remove low-frequency drift and motion artifacts, preprocessing is first performed using a digital high-pass filter with a cutoff frequency of 150 Hz. Its discrete-time filter can be expressed as... ; Where x[n] is the discrete-time input signal and y[n] is the filtered output signal. , , .

[0031] In some embodiments of this application, the signal processing unit specifically includes: An adaptive bandpass decomposition module is used to perform adaptive bandpass decomposition on the digital electrocardiogram signal to obtain modal components corresponding to multiple frequency bands. The time-frequency envelope analysis module is used to select modal components within the target frequency range as high-frequency ECG signals and obtain the instantaneous envelope of the high-frequency ECG signals through Hilbert transform, providing a basis for subsequent time-frequency analysis and feature extraction. The feature recognition module is used to classify and identify the high-frequency features based on the instantaneous envelope and its derived energy and spectral features using a deep neural network model to obtain the risk index.

[0032] In some embodiments of this application, the time-frequency envelope analysis module specifically extracts the following high-frequency feature parameters by performing Hilbert transform and time-frequency analysis on the high-frequency electrocardiogram modal signal: Instantaneous envelope characteristics are used to characterize the change in amplitude of high-frequency electrocardiogram signals over time; The high-frequency instantaneous energy characteristics are obtained by energy calculation of the instantaneous envelope and are used to characterize the intensity of high-frequency micro-electrical activity in the myocardium. The high-frequency total energy characteristic is obtained by integrating or averaging the instantaneous energy within a preset time window, and is used to reflect the overall energy level of high-frequency electrocardiographic activity. The time-frequency energy distribution characteristics are obtained by performing a short-time Fourier transform on the instantaneous envelope, and are used to describe the distribution of high-frequency ECG energy in the time-frequency domain. The spectral centroid feature, calculated based on the time-frequency energy distribution, is used to characterize the concentrated location of high-frequency energy in the frequency domain; Spectral entropy features, calculated based on the probability distribution of the spectral energy, are used to reflect the complexity and irregularity of high-frequency electrocardiographic activity. The time-frequency envelope analysis module extracts the high-frequency features using the following formula: Analytical signal construction of high-frequency ECG modal signals: ; in, For high-frequency electrocardiogram signals, H{·} represents the Hilbert transform; Instantaneous envelope extraction: ; The instantaneous envelope Characterizing the amplitude variation characteristics of high-frequency electrocardiogram signals; Instantaneous energy is used to characterize the instantaneous energy distribution of high-frequency electrocardiogram signals over time.

[0033] ; High-frequency energy characteristic calculation: ; in, T To analyze the length of the time window; Time-frequency energy distribution calculation (short-time Fourier transform): ; Where AHF(τ) is the high-frequency electrocardiogram signal to be analyzed, w(t−τ) is a window function centered on time τ, used to realize the local time domain truncation of the signal, and f represents the frequency variable; Power spectral density calculation: ; The power spectral density is used to describe the distribution characteristics of high-frequency electrocardiogram energy in the frequency domain; Spectral centroid calculation: ; Where, f1=150Hz, f2=1000Hz; Spectral entropy calculation: ; ; in, N This represents the number of frequency sampling points.

[0034] In some embodiments of this application, the data output module transmits risk indicators and high-frequency electrocardiogram characteristic parameters to a host computer or cloud server in real time via a wired interface or wireless communication.

[0035] Specifically, the signal processing unit includes an adaptive bandpass decomposition module, a time-frequency envelope analysis module, and a feature recognition module. The core function of this unit is to perform multi-layer feature extraction and intelligent analysis on the digitized electrocardiogram (ECG) signal to identify high-frequency electrical activity.

[0036] In the adaptive bandpass decomposition module, the Variational Mode Decomposition (VMD) algorithm is used to perform multimodal decomposition of the input signal. Its optimization objective is: ; in, For the first Modal signals, Let δ(t) be the corresponding center frequency, δ(t) be the Dirac function, and ∂t denote the partial derivative operator with respect to time. By constraining the frequency spacing between components, VMD can adaptively distinguish between true high-frequency components and noise artifacts.

[0037] The time-frequency envelope analysis module extracts the following high-frequency characteristic parameters by performing Hilbert transform and time-frequency analysis on high-frequency ECG modal signals.

[0038] Let the high-frequency modal signal be Then the instantaneous envelope can be expressed as: ; in, For high-frequency mode signals within the selected frequency band, Indicates the signal The imaginary part of the analytic signal obtained by performing the Hilbert transform, and the instantaneous envelope, are used to characterize the variation of the amplitude of the high-frequency electrocardiogram signal over time.

[0039] High-frequency instantaneous energy is used to characterize the instantaneous energy distribution of high-frequency electrocardiogram signals over time.

[0040] ; High-frequency total energy characteristics: ; in, T The analysis time window length is obtained by integrating or averaging the instantaneous energy within a preset time window, which reflects the overall energy level of high-frequency electrocardiographic activity. The time-frequency energy distribution function is calculated using the short-time Fourier transform (STFT): ; Where a(τ) is the high-frequency electrocardiogram signal to be analyzed, w(t−τ) is a window function centered at time τ, used to achieve local time-domain truncation of the signal, and f represents the frequency variable. This energy-time spectrum can reflect the dynamic characteristics of myocardial micro-electrical activity in time and frequency.

[0041] Power spectral density calculation: ; The power spectral density is used to describe the distribution characteristics of high-frequency electrocardiogram energy in the frequency domain.

[0042] Spectral centroid calculation: ; Where f1 = 150Hz and f2 = 1000Hz. These values ​​are calculated based on the time-frequency energy distribution and are used to characterize the concentrated location of high-frequency energy in the frequency domain. Spectral entropy calculation: ; ; in, N This represents the number of frequency sampling points. It is calculated based on the probability distribution of the spectral energy and is used to reflect the complexity and irregularity of high-frequency electrocardiographic activity. The feature recognition module uses a deep neural network model to classify and identify the extracted high-frequency energy features. Convolutional neural networks (CNNs) or other temporal network structures can be preferred to achieve the identification and prediction of myocardial ischemia, fibrosis, and arrhythmia risks.

[0043] The data output module is used to output the analysis results and time-frequency characteristic parameters to a host computer or the cloud. The system can transmit data via Wi-Fi or USB interface and supports real-time display and long-term storage.

[0044] In summary, compared with the prior art, the present invention has the following advantages: (1) Ultra-low noise broadband acquisition: The noise of the front-end amplification module is less than 1μVpp, which can effectively distinguish weak high-frequency electrical activity; (2) High-frequency signal integrity preservation: The upper limit of bandwidth is extended to 1000Hz to ensure that high-frequency components are not filtered; (3) Adaptive high-frequency extraction algorithm: Combining variational mode decomposition and time-frequency envelope analysis, adaptive frequency band extraction and artifact suppression are realized, thereby improving the ability to separate signal features.

[0045] By applying the above technical solutions, a high-frequency electrocardiogram (ECG) acquisition device is proposed. The high-frequency ECG acquisition device includes: a signal acquisition electrode for acquiring raw ECG signals; a low-noise front-end amplification module for amplifying and filtering the raw ECG signals to obtain analog ECG signals; an analog-to-digital conversion module for digitizing the analog ECG signals to obtain digital ECG signals; a signal processing unit for extracting high-frequency features from the digital ECG signals through a combination of adaptive bandpass decomposition and time-frequency envelope analysis, and determining risk indicators corresponding to the raw ECG signals based on the high-frequency features; and a data output module for outputting the risk indicators to a host computer or cloud platform, thereby achieving highly accurate identification and classification of ECG activity.

[0046] Furthermore, the present invention also provides a high-frequency electrocardiogram acquisition method, comprising the following steps: (1) Signal acquisition: ECG signals are obtained from the human body surface through signal acquisition electrodes, and then amplified and filtered by a low-noise front-end amplification module to obtain an analog signal with a bandwidth of 0.05–1000 Hz.

[0047] (2) Analog-to-digital conversion: The amplified analog signal is digitized at a sampling rate of at least 2 kHz to obtain a broadband digital electrocardiogram signal. .

[0048] (3) Adaptive decomposition and high-frequency extraction: Variational mode decomposition or adaptive filter bank algorithm is used for high-frequency extraction. Perform frequency band decomposition to extract high-frequency components in the 150–1000 Hz range. .

[0049] (4) Time-frequency envelope analysis: for The instantaneous envelope of the high-frequency signal is obtained by performing a Hilbert transform, and its energy distribution is then subjected to a short-time Fourier transform (STFT) to obtain... .

[0050] (5) Feature recognition and classification: The extracted time-frequency features are input into a deep neural network (CNN) to identify abnormal high-frequency patterns and output risk assessment results.

[0051] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0052] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0053] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A high-frequency electrocardiogram (ECG) acquisition device, characterized in that, The high-frequency electrocardiogram acquisition device includes: Signal acquisition electrodes are used to acquire raw electrocardiogram (ECG) signals; A low-noise front-end amplification module is used to amplify and filter the original electrocardiogram signal to obtain a simulated electrocardiogram signal; An analog-to-digital converter module is used to digitize the analog electrocardiogram (ECG) signal to obtain a digital ECG signal; The signal processing unit is used to extract high-frequency features from the digital electrocardiogram signal by combining adaptive bandpass decomposition and time-frequency envelope analysis, and to determine the risk index corresponding to the original electrocardiogram signal based on the high-frequency features. The data output module is used to output the risk indicators to the host computer or the cloud.

2. The high-frequency electrocardiogram acquisition device according to claim 1, characterized in that, The low-noise front-end amplification module specifically includes: A front-end signal amplification circuit is used to amplify the original electrocardiogram signal using a two-stage amplification structure, wherein the two-stage amplification structure includes differential amplification and programmable gain amplification. Anti-aliasing filters are used to limit the bandwidth of the amplified raw ECG signal. The active drive circuit for the right leg is used to counteract common-mode interference by detecting the common-mode voltage of the input channel and feeding its inverted signal back to the human body.

3. The high-frequency electrocardiogram acquisition device according to claim 1, characterized in that, The differential amplifier module is used to amplify the input differential signal and suppress common-mode interference. The programmable gain amplifier is specifically a programmable gain amplifier, and the overall gain of the front-end amplification is determined by the following formula: ； in, This represents the overall gain of the front-end amplifier circuit. This is the gain of the preceding differential amplifier stage. The gain of the subsequent programmable gain amplifier. and These are the feedback resistor and input resistor corresponding to the preamplifier circuit. and These are the feedback resistor and input resistor for the subsequent programmable gain amplifier circuit.

4. The high-frequency electrocardiogram acquisition device according to claim 1, characterized in that, The anti-aliasing filter adopts a second-order Butterworth structure, and the transfer function of the anti-aliasing filter is: ； Where H(s) is the transfer function of the anti-aliasing filter, s is the complex frequency variable in the Laplace transform, and ωc is the cutoff angular frequency of the anti-aliasing filter.

5. The high-frequency electrocardiogram acquisition device according to claim 2, characterized in that, The output voltage of the active drive right leg circuit specifically satisfies the following formula: ； Wherein, VDRL is the output voltage of the active drive right leg circuit. For feedback gain coefficient, The detected common-mode voltage, This is the reference potential.

6. The high-frequency electrocardiogram acquisition device according to claim 1, characterized in that, The analog-to-digital conversion module is specifically used for: The analog electrocardiogram (ECG) signal is converted into a digital ECG signal using a sampling rate of not less than 2 kHz and a resolution of not less than 16 bits.

7. The high-frequency electrocardiogram acquisition device according to claim 1, characterized in that, The signal processing unit specifically includes: An adaptive bandpass decomposition module is used to perform adaptive bandpass decomposition on the digital electrocardiogram signal to obtain modal components corresponding to multiple frequency bands. The time-frequency envelope analysis module is used to select modal components within the target frequency range as high-frequency ECG signals and obtain the instantaneous envelope of the high-frequency ECG signals through Hilbert transform, providing a basis for subsequent time-frequency analysis and feature extraction. The feature recognition module is used to classify and identify the high-frequency features based on the instantaneous envelope and its derived energy and spectral features using a deep neural network model to obtain the risk index.

8. The high-frequency electrocardiogram acquisition device according to claim 7, characterized in that, The optimization objective of the variational mode decomposition algorithm is specifically determined by the following formula: ； in, For the first Modal signals, Let t be the corresponding center frequency, δ(t) be the Dirac function, and ∂t denote the partial derivative operator with respect to time.

9. The high-frequency electrocardiogram acquisition device as described in claim 1, characterized in that, The time-frequency envelope analysis module specifically extracts the following high-frequency feature parameters by performing Hilbert transform and time-frequency analysis on the high-frequency ECG modal signal: Instantaneous envelope characteristics are used to characterize the change in amplitude of high-frequency electrocardiogram signals over time; The high-frequency instantaneous energy characteristics are obtained by energy calculation of the instantaneous envelope and are used to characterize the intensity of high-frequency micro-electrical activity in the myocardium. The high-frequency total energy characteristic is obtained by integrating or averaging the instantaneous energy within a preset time window, and is used to reflect the overall energy level of high-frequency electrocardiographic activity. The time-frequency energy distribution characteristics are obtained by performing a short-time Fourier transform on the instantaneous envelope, and are used to describe the distribution of high-frequency ECG energy in the time-frequency domain. The spectral centroid feature, calculated based on the time-frequency energy distribution, is used to characterize the concentrated location of high-frequency energy in the frequency domain; Spectral entropy features, calculated based on the probability distribution of the spectral energy, are used to reflect the complexity and irregularity of high-frequency electrocardiographic activity. The time-frequency envelope analysis module extracts the high-frequency features using the following formula: Analytical signal construction of high-frequency ECG modal signals: ； in, For high-frequency electrocardiogram signals, H{·} represents the Hilbert transform; Instantaneous envelope extraction: ； The instantaneous envelope Characterizing the amplitude variation characteristics of high-frequency electrocardiogram signals; Instantaneous energy is used to characterize the instantaneous energy distribution of high-frequency electrocardiogram signals over time. ； High-frequency energy characteristic calculation: ； in, T To analyze the length of the time window; Time-frequency energy distribution calculation (short-time Fourier transform): ； Where AHF(τ) is the high-frequency electrocardiogram signal to be analyzed, w(t−τ) is a window function centered on time τ, used to realize the local time domain truncation of the signal, and f represents the frequency variable; Power spectral density calculation: ； The power spectral density is used to describe the distribution characteristics of high-frequency electrocardiogram energy in the frequency domain; Spectral centroid calculation: ； Where, f1=150Hz, f2=1000Hz; Spectral entropy calculation: ； ； in, N This represents the number of frequency sampling points.

10. The high-frequency electrocardiogram acquisition device as described in claim 1, characterized in that, The data output module transmits risk indicators and high-frequency electrocardiogram characteristic parameters to the host computer or cloud server in real time via a wired interface or wireless communication.

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