Feature point detection method and system for electrocardiosignal and respiratory wave

Abstract

The invention provides a feature point detection method and system for electrocardiosignals and respiratory waves, and the method comprises the steps: obtaining an R wave value according to electrocardiosignal data, first preprocessing data and first threshold data, and obtaining a respiratory wave value according to respiratory wave data, second preprocessing data and second threshold data; whether the R wave value and the respiratory wave value are equal to 0 or not is judged, if not, it is judged that the R wave value is the feature point of the electrocardiosignal, the respiratory wave value is the feature point of the respiratory wave, a synchronizing signal is output to external detection equipment, and if yes, the electrocardiosignal and the respiratory wave are collected again; and according to the first threshold value data and the second threshold value data, respectively performing threshold value updating on the electrocardiosignal and the respiratory wave. The problems that the hardware price is high, the signal acquisition delay is large and the real-time performance is poor due to the fact that an existing detection mode needs a large number of computing resources are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of feature point detection of electrocardiogram signals and respiratory waves, and more specifically, relates to a method and system for detecting feature points of electrocardiogram signals and respiratory waves. Background Art

[0002] In the field of biomedical signal processing technology, ECG signals and respiratory waves are two important physiological signals, and their accurate detection is crucial for many medical applications. For example, in electrocardiogram analysis, the R wave is an important waveform in the QRS complex wave group, and its accurate detection is of great significance for determining heart rate and diagnosing heart disease. Similarly, accurate detection of respiratory waves is also of great value for evaluating respiratory function and diagnosing respiratory diseases. However, due to the complexity and nonlinear characteristics of ECG signals and respiratory waves, as well as discharge interference of pulsed electric field equipment, there are still large technical barriers to the accurate detection of these two signals.

[0003] The detection method adopted in the prior art to break through the above technical barriers is usually to build a set of complex analog amplifier circuits and filter circuits with various parameters, but this method requires a lot of computing resources, resulting in high hardware prices, large delays in collecting signals, and poor real-time performance. Therefore, how to design a method and system that can ensure accurate detection of ECG signals and respiratory waves while reducing the amount of calculation, reducing costs, reducing signal delay time, and having high real-time performance has become a problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0004] The purpose of the present invention is to solve the problems that the existing detection method requires a large amount of computing resources, resulting in high hardware prices, large delay in collecting signals and poor real-time performance.

[0005] In order to achieve the above object, the present invention provides a method and system for detecting feature points of electrocardiogram signals and respiratory waves.

[0006] According to a first aspect of the present invention, there is provided a method for detecting feature points of an electrocardiogram signal and a respiratory wave, comprising:

[0007] Respond to preset periodic acquisition instructions to collect ECG signals and respiratory waves;

[0008] Filtering the electrocardiogram signal and the respiratory wave to obtain electrocardiogram signal data and respiratory wave data;

[0009] Preprocessing the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively;

[0010] Obtaining first threshold data according to the electrocardiogram signal data and the first pre-processed data, and obtaining second threshold data according to the respiratory wave data and the second pre-processed data;

[0011] Obtaining an R wave value according to the electrocardiogram signal data, the first preprocessed data and the first threshold data, and obtaining a respiratory wave value according to the respiratory wave data, the second preprocessed data and the second threshold data;

[0012] respectively determining whether the R wave value and the respiratory wave value are equal to 0,

[0013] If not, the R wave value is determined to be the characteristic point of the electrocardiogram signal, the respiratory wave value is determined to be the characteristic point of the respiratory wave, and a synchronization signal is output to an external detection device.

[0014] If yes, the electrocardiogram signal and the respiratory wave are collected again, and the threshold values ​​of the electrocardiogram signal and the respiratory wave are updated respectively according to the first threshold value data and the second threshold value data.

[0015] Optionally, filtering the electrocardiogram signal and the respiratory wave to obtain electrocardiogram signal data and respiratory wave data includes:

[0016] The electrocardiogram signal and the respiratory wave are filtered to obtain:

[0017] S[n]=b*x[n]+a*S[n-1]

[0018] Where:

[0019] S[n] is the electrocardiogram signal data or the respiratory wave data;

[0020] b is the coefficient of the forward path;

[0021] x[n] is the electrocardiogram signal or the respiratory wave;

[0022] a is the coefficient of the feedback path;

[0023] S[n-1] is the initially collected ECG signal or the respiratory wave, or the ECG signal data or the respiratory wave data acquired last time;

[0024] To obtain the electrocardiogram signal data and the respiratory wave data.

[0025] Optionally, the preprocessing of the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively includes:

[0026] Current slope value acquisition link: According to the ECG signal data or the respiratory wave data, the current slope value is obtained as follows:

[0027] S_D[n]=(-S[n-2]-2*S[n-1]+2*S[n+1]+S[n+2]) / 8

[0028] Where:

[0029] S_D[n] is the current slope value corresponding to the electrocardiogram signal data or the respiratory wave data;

[0030] S[n-2], S[n-1], S[n+1] and S[n+2] are the electrocardiogram signal data or the respiratory wave data of the previous, previous, next and next time respectively;

[0031] Window integral average value acquisition stage: According to the current slope value, obtain:

[0032] S_D1[n]=S_D[n] 2 ,

[0033] WIA[n]=(S_D1[n-(N-1)]+S_D1[n-(N-2)]+…+S_D1[n]) / N

[0034] Where:

[0035] S_D1[n] is the current square value of the current slope value in the window;

[0036] WIA[n] is the window integrated average;

[0037] S_D1[n-(N-1)] is the reciprocal first square value of the current slope value in the window;

[0038] S_D1[n-(N-2)] is the second to last square value of the current slope value in the window;

[0039] N is the number of the electrocardiogram signal data or the respiratory wave data within the integration window width;

[0040] The first pre-processed data includes the current slope value and the window integrated average value corresponding to the electrocardiogram signal data;

[0041] The second pre-processed data includes the current slope value and the window integrated average value corresponding to the respiratory wave data.

[0042] Optionally, obtaining first threshold data according to the electrocardiogram signal data and the first pre-processed data, and obtaining second threshold data according to the respiratory wave data and the second pre-processed data, includes:

[0043] Signal threshold acquisition link: According to the ECG signal data or the respiratory wave data, obtain:

[0044] S_TH[n]=S[n]*0.125+S_TH[n-1]*0.875

[0045] Where:

[0046] S_TH[n-1] is the signal threshold of the previous electrocardiogram signal or the respiratory wave;

[0047] Signal slope threshold acquisition link: According to the current slope value corresponding to the electrocardiogram signal data, or the current slope value corresponding to the respiratory wave data, obtain:

[0048] S_D_TH[n]=S_D[n]*0.125+S_D_TH[n-1]*0.875

[0049] Where:

[0050] S_D_TH[n-1] is the signal slope threshold of the previous electrocardiogram signal or the respiratory wave;

[0051] The first threshold data includes the signal threshold and the signal slope threshold corresponding to the electrocardiogram signal data;

[0052] The second threshold data includes the signal threshold and the signal slope threshold corresponding to the respiratory wave data.

[0053] Optionally, obtaining an R wave value according to the electrocardiogram signal data, the first preprocessed data and the first threshold data, and obtaining a respiratory wave value according to the respiratory wave data, the second preprocessed data and the second threshold data, comprises:

[0054] According to the electrocardiogram signal data and the corresponding current slope value, the window integral average value, the signal threshold and the signal slope threshold value, or according to the respiratory wave data and the corresponding current slope value, the window integral average value, the signal threshold and the signal slope threshold value, obtain:

[0055] R=

[0056] ((S[n]>S_TH[n]*0.75)&&(S_D[n]>S_D_TH[n]*0.75)&&(S_D[n]>WIA[n]*0.75))

[0057] Where:

[0058] R is the R wave value or respiratory wave value.

[0059] According to a second aspect of the present invention, there is also provided a feature point detection system for electrocardiogram signals and respiratory waves, which is used to implement any of the above-mentioned feature point detection methods for electrocardiogram signals and respiratory waves, and is configured to be installed in an external detection device, wherein the feature point detection system includes a main control module and an execution module;

[0060] The main control module is used to output a preset period acquisition instruction;

[0061] The execution module includes a signal acquisition unit, a filtering processing unit, a signal preprocessing unit and a decision output unit.

[0062] The signal acquisition unit is used to respond to a preset period acquisition instruction to acquire the electrocardiogram signal and the respiratory wave.

[0063] The filtering processing unit is used to filter the electrocardiogram signal and the respiratory wave to obtain electrocardiogram signal data and respiratory wave data.

[0064] The signal preprocessing unit is used to preprocess the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively.

[0065] The decision output unit is used to obtain an R wave value according to the electrocardiogram signal data, the first pre-processed data and the first threshold data, and to obtain a respiratory wave value according to the respiratory wave data, the second pre-processed data and the second threshold data.

[0066] respectively determining whether the R wave value and the respiratory wave value are equal to 0,

[0067] If not, the R wave value is determined to be the characteristic point of the electrocardiogram signal, the respiratory wave value is determined to be the characteristic point of the respiratory wave, and a synchronization signal is output to an external detection device.

[0068] If yes, the electrocardiogram signal and the respiratory wave are collected again, and the threshold values ​​of the electrocardiogram signal and the respiratory wave are updated respectively according to the first threshold value data and the second threshold value data.

[0069] Optionally, a signal isolation module for isolating signals between the main control module and the execution module is provided between the two.

[0070] Optionally, the main control module is electrically connected to a power management module for supplying power thereto.

[0071] Optionally, a power isolation module is provided between the power management module and the main control module for isolating power between the two.

[0072] Optionally, the filtering processing unit includes a high-pass filter, a notch filter and a low-pass filter connected in stages.

[0073] The beneficial effects of the present invention are:

[0074] The characteristic point detection method for electrocardiogram signal and respiratory wave proposed by the present invention collects electrocardiogram signal and respiratory wave by responding to preset periodic collection instruction; performs filtering processing on electrocardiogram signal and respiratory wave to obtain electrocardiogram signal data and respiratory wave data; pre-processes the electrocardiogram signal data and respiratory wave data to obtain first pre-processed data and second pre-processed data corresponding to the electrocardiogram signal data and respiratory wave data respectively; obtains first threshold data according to the electrocardiogram signal data and the first pre-processed data, and obtains second threshold data according to the respiratory wave data and the second pre-processed data; obtains R wave value according to the electrocardiogram signal data, the first pre-processed data and the first threshold data, and obtains respiratory wave value according to the respiratory wave data, the second pre-processed data and the second threshold data; judges whether the R wave value and the respiratory wave value are equal to 0 respectively, if not, judges that the R wave value is the characteristic point of the electrocardiogram signal and the respiratory wave value is the characteristic point of the respiratory wave, and outputs a synchronization signal to an external detection device, if yes, collects the electrocardiogram signal and respiratory wave again, and updates the thresholds of the electrocardiogram signal and respiratory wave respectively according to the first threshold data and the second threshold data. According to the above content, compared with the existing detection methods, the feature point detection method of the present invention filters the ECG signal and respiratory wave to effectively filter out the interference signal. By preprocessing the ECG signal data and respiratory wave data, it can reduce the amount of calculation and the hardware construction cost, thereby reducing the signal delay time and improving the real-time transmission, thereby realizing the accurate capture of the feature points of the ECG signal and respiratory wave.

[0075] The feature point detection system for ECG signals and respiratory waves proposed in the present invention belongs to a general inventive concept as does the feature point detection method for ECG signals and respiratory waves described above, and therefore has the same beneficial effects as the feature point detection method for ECG signals and respiratory waves described above, and will not be described in detail herein.

[0076] Based on the above content, it can be seen that the technical solution of the present invention can effectively solve the problems of the existing detection method, such as the complex structure, high hardware price, large acquisition signal delay, large amount of calculation and poor real-time performance.

[0077] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The present invention may be better understood by referring to the following description taken in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to represent the same or similar components.

[0079] Figure 1 A flowchart of a method for detecting feature points of an electrocardiogram signal and a respiratory wave according to an embodiment of the present invention is shown;

[0080] Figure 2 A principle block diagram of a feature point detection system for an electrocardiogram signal and a respiratory wave according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0081] In order to enable those skilled in the art to more fully understand the technical solution of the present invention, the exemplary embodiments of the present invention will be described in more comprehensive and detailed in conjunction with the accompanying drawings below. Obviously, the one or more embodiments of the present invention described below are only one or more of the specific ways in which the technical solution of the present invention can be implemented, and are not exhaustive. It should be understood that other ways belonging to a general inventive concept can be used to implement the technical solution of the present invention, and should not be limited by the exemplary embodiments described. Based on one or more embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0082] Example: Figure 1 The figure shows a flow chart of a method for detecting feature points of an electrocardiogram signal and a respiratory wave according to an embodiment of the present invention.

[0083] Reference Figure 1 , an embodiment of the present invention provides a method for detecting feature points of an electrocardiogram signal and a respiratory wave, comprising:

[0084] Respond to preset periodic acquisition instructions to collect ECG signals and respiratory waves;

[0085] Filtering the ECG signal and respiratory wave to obtain ECG signal data and respiratory wave data;

[0086] Preprocessing the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively;

[0087] Obtaining first threshold data according to the electrocardiogram signal data and the first preprocessed data, and obtaining second threshold data according to the respiratory wave data and the second preprocessed data;

[0088] Obtaining an R wave value according to the electrocardiogram signal data, the first preprocessed data and the first threshold data, and obtaining a respiratory wave value according to the respiratory wave data, the second preprocessed data and the second threshold data;

[0089] Determine whether the R wave value and the respiratory wave value are equal to 0.

[0090] If not, the R wave value is determined to be the characteristic point of the ECG signal, the respiratory wave value is the characteristic point of the respiratory wave, and a synchronization signal is output to the external detection device.

[0091] If yes, the ECG signal and the respiratory wave are collected again, and the thresholds of the ECG signal and the respiratory wave are updated respectively according to the first threshold data and the second threshold data.

[0092] In one embodiment, in response to a preset period acquisition instruction, the electrocardiogram signal and the respiratory wave are acquired, including:

[0093] Cardiac electrodes are used to contact the human body to collect the human body's electrocardiogram signals and respiratory waves.

[0094] Specifically, the working principle of the cardiac electrode sheet is prior art and will not be described in detail here. The preset periodic acquisition instruction is to periodically acquire the ECG signal and respiratory wave at a sampling rate of 500 Hz.

[0095] In one embodiment, filtering the ECG signal and the respiratory wave to obtain ECG signal data and respiratory wave data includes:

[0096] After filtering the ECG signal and respiratory wave, we get:

[0097] S[n]=b*x[n]+a*S[n-1]

[0098] Where:

[0099] S[n] is the ECG signal data or respiratory wave data;

[0100] b is the coefficient of the forward path;

[0101] x[n] is the ECG signal or respiratory wave;

[0102] a is the coefficient of the feedback path;

[0103] S[n-1] is the initially collected ECG signal or respiratory wave, or the ECG signal data or respiratory wave data acquired last time;

[0104] To obtain ECG signal data and respiratory wave data.

[0105] Specifically, the filtering processing mainly adopts a high-pass filter, a notch filter and a low-pass filter which are connected in stages. The cutoff frequency of the high-pass filter is set to 1.5Hz to filter out baseline drift and remove DC components. The center frequency of the notch filter is set to 50Hz to filter out power frequency interference and harmonic components. The cutoff frequency of the low-pass filter is set to 40Hz to filter out electromyographic signals and other high-frequency interference signals, so as to achieve accurate detection of ECG signals and respiratory waves.

[0106] In one embodiment, preprocessing the ECG signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the ECG signal data and the respiratory wave data respectively includes:

[0107] Current slope value acquisition link: According to the ECG signal data or respiratory wave data, the current slope value is obtained as follows:

[0108] S_D[n]=(-S[n-2]-2*S[n-1]+2*S[n+1]+S[n+2]) / 8

[0109] Where:

[0110] S_D[n] is the current slope value corresponding to the ECG signal data or respiratory wave data;

[0111] S[n-2], S[n-1], S[n+1] and S[n+2] are the electrocardiogram signal data or respiratory wave data of the previous, previous, next and next time respectively;

[0112] Window integral average value acquisition stage: According to the current slope value, we get:

[0113] S_D1[n]=S_D[n] 2 ,

[0114] WIA[n]=(S_D1[n-(N-1)]+S_D1[n-(N-2)]+…+S_D1[n]) / N

[0115] Where:

[0116] S_D1[n] is the current square value of the current slope value in the window;

[0117] WIA[n] is the window integrated average;

[0118] S_D1[n-(N-1)] is the reciprocal square value of the current slope value in the window;

[0119] S_D1[n-(N-2)] is the second to last square value of the current slope value in the window;

[0120] N is the number of ECG signal data or respiratory wave data within the integration window width;

[0121] The first preprocessing data includes a current slope value and a window integrated average value corresponding to the electrocardiogram signal data;

[0122] The second pre-processed data includes a current slope value and a window-integrated average value corresponding to the respiratory wave data.

[0123] Specifically, the above algorithm is used to pre-process the ECG signal data and the respiratory wave data, which can increase the feature points in the ECG signal and the respiratory wave, so as to improve the efficiency of capturing the feature points of the ECG signal and the respiratory wave.

[0124] In one embodiment, obtaining first threshold data according to electrocardiogram signal data and first preprocessed data, and obtaining second threshold data according to respiratory wave data and second preprocessed data, comprises:

[0125] Signal threshold acquisition link: According to the ECG signal data or respiratory wave data, obtain:

[0126] S_TH[n]=S[n]*0.125+S_TH[n-1]*0.875

[0127] Where:

[0128] S_TH[n-1] is the signal threshold of the previous ECG signal or respiratory wave;

[0129] Signal slope threshold acquisition link: According to the current slope value corresponding to the ECG signal data, or the current slope value corresponding to the respiratory wave data, obtain:

[0130] S_D_TH[n]=S_D[n]*0.125+S_D_TH[n-1]*0.875

[0131] Where:

[0132] S_D_TH[n-1] is the signal slope threshold of the previous ECG signal or respiratory wave;

[0133] The first threshold data includes a signal threshold and a signal slope threshold corresponding to the electrocardiogram signal data;

[0134] The second threshold data includes a signal threshold and a signal slope threshold corresponding to the respiratory wave data.

[0135] Specifically, when S_TH[n-1] is the signal threshold of the first ECG signal or respiratory wave, and S_D_TH[n-1] is the signal slope threshold of the first ECG signal or respiratory wave, the values ​​of S_TH[n-1] and S_D_TH[n-1] are respectively the maximum value of the signal threshold and the maximum value of the signal slope threshold that can be preset in the present invention.

[0136] In one embodiment, obtaining an R wave value according to the electrocardiogram signal data, the first preprocessed data and the first threshold data, and obtaining a respiratory wave value according to the respiratory wave data, the second preprocessed data and the second threshold data, comprises:

[0137] According to the ECG signal data and its corresponding current slope value, window integral average value, signal threshold and signal slope threshold, or according to the respiratory wave data and its corresponding current slope value, window integral average value, signal threshold and signal slope threshold, the following is obtained:

[0138] R=

[0139] ((S[n]>S_TH[n]*0.75)&&(S_D[n]>S_D_TH[n]*0.75)&&(S_D[n]>WIA[n]*0.75))

[0140] Where:

[0141] R is the R wave value or respiratory wave value.

[0142] Specifically, using the above algorithm to confirm the characteristic points of the electrocardiogram signal and the respiratory wave can reduce the effective amount of calculation and improve the real-time performance of signal transmission.

[0143] The feature point detection method for ECG signals and respiratory waves proposed in the present invention can filter and preprocess the ECG signals and respiratory waves in sequence, and determine the first preprocessing data and the second preprocessing data according to the preprocessing result, and then determine the first threshold data and the second threshold data according to the first preprocessing data and the second preprocessing data to obtain the R wave value of the ECG signal and the respiratory wave value of the respiratory wave. Compared with the existing detection methods, the feature point detection method of the present invention filters the ECG signals and respiratory waves to effectively filter out interference signals, and preprocesses the ECG signal data and respiratory wave data to reduce the amount of calculation, reduce the hardware construction cost, reduce the signal delay time, and improve the real-time transmission, thereby achieving accurate capture of the feature points of the ECG signals and respiratory waves.

[0144] Figure 2 A principle block diagram of a feature point detection system for an electrocardiogram signal and a respiratory wave according to an embodiment of the present invention is shown.

[0145] Accordingly, if Figure 2 As shown, this embodiment also provides a feature point detection system for electrocardiogram signals and respiratory waves, which is used to implement the feature point detection method for electrocardiogram signals and respiratory waves in any of the above embodiments, and is configured to be installed in an external detection device. The feature point detection system includes a main control module and an execution module;

[0146] The main control module is used to output preset period acquisition instructions;

[0147] The execution module includes a signal acquisition unit, a filtering processing unit, a signal preprocessing unit and a decision output unit.

[0148] The signal acquisition unit is used to respond to the preset period acquisition instruction and collect the ECG signal and respiratory wave.

[0149] The filtering processing unit is used to filter the ECG signal and the respiratory wave to obtain ECG signal data and respiratory wave data.

[0150] The signal preprocessing unit is used to preprocess the ECG signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the ECG signal data and the respiratory wave data respectively.

[0151] The decision output unit is used to obtain an R wave value according to the electrocardiogram signal data, the first preprocessing data and the first threshold data, and to obtain a respiratory wave value according to the respiratory wave data, the second preprocessing data and the second threshold data.

[0152] Determine whether the R wave value and the respiratory wave value are equal to 0.

[0153] If not, the R wave value is determined to be the characteristic point of the ECG signal, the respiratory wave value is the characteristic point of the respiratory wave, and a synchronization signal is output to the external detection device.

[0154] If yes, the ECG signal and the respiratory wave are collected again, and the thresholds of the ECG signal and the respiratory wave are updated respectively according to the first threshold data and the second threshold data.

[0155] In a specific embodiment, the main control module is an MCU chip.

[0156] In a specific embodiment, the execution module is an AFE chip.

[0157] Specifically, the AFE chip is an integrated chip circuit with the functions of analog front-end and digital back-end. It can process ECG signals and respiratory waves at the same time, thereby effectively improving the speed and accuracy of ECG signal and respiratory wave processing, while also reducing the cost of system construction.

[0158] In one embodiment, a signal isolation module is provided between the main control module and the execution module for isolating signals between the two.

[0159] In a specific embodiment, the signal isolation module is a digital isolation chip. The digital isolation chip has the functions of avoiding signal interference, reducing electromagnetic interference, preventing the generation of ground loops, and effectively blocking the path of leakage current, etc., and can achieve the protection of the human body and operators while completing the accurate collection of ECG signals and respiratory waves.

[0160] In one embodiment, the main control module is electrically connected to a power management module for supplying power to the main control module.

[0161] In one embodiment, a power isolation module is provided between the power management module and the main control module for isolating the power supply between the two.

[0162] In a specific embodiment, the power isolation module is a DC / DC power module. The DC / DC power module has some power management functions, such as overload protection, overheat protection, short circuit protection and battery charging management, to ensure the safety and reliability between the power supply and the load. The low leakage current and high isolation voltage characteristics of the DC / DC power module can well protect the operator and the human body from dangers such as electric shock.

[0163] In a specific embodiment, the filter processing unit includes a high-pass filter, a notch filter and a low-pass filter connected in stages. Specifically, the high-pass filter, the notch filter and the low-pass filter are all direct type I IIR filters.

[0164] In one embodiment, the main control module includes several communication interfaces for connecting the cardiac electrode sheet and the external detection device. Specifically, the several communication interfaces are pulse output terminals, RS485 interfaces and RS232 interfaces, so as to output synchronization signals of the feature points detected in real time by the feature point detection system to the external detection device.

[0165] The feature point detection system for electrocardiogram signals and respiratory waves proposed in the present invention can realize timely processing of electrocardiogram signals and respiratory wave signals using fewer algorithms by setting a main control module and an execution module, thereby reducing the cost and delay time of the hardware structure and making the use of the feature point detection system more convenient.

[0166] Furthermore, by providing a signal isolation module and a power isolation module, it is possible to achieve accurate collection of electrocardiogram signals and respiratory waves while protecting the human body and operators.

[0167] Furthermore, by setting up a number of communication interfaces, the feature points detected in real time can be output as synchronization signals to external detection equipment to improve the real-time performance of transmission.

[0168] In addition, the external detection device can be any device that requires synchronization of R wave values ​​and respiratory wave values, and the characteristic point detection results and detection instructions of the ECG signal and respiratory wave are displayed and triggered through the display end and control end of the external detection device.

[0169] Although one or more embodiments of the present invention are described above, it should be known to those skilled in the art that the present invention can be implemented in any other form without departing from its subject matter and scope. Therefore, the embodiments described above are illustrative rather than restrictive, and many modifications and substitutions are obvious to those skilled in the art without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for detecting feature points of electrocardiogram signals and respiratory waves, characterized in that: include: Respond to preset periodic acquisition instructions to collect ECG signals and respiratory waves; Filtering the electrocardiogram signal and the respiratory wave to obtain electrocardiogram signal data and respiratory wave data; Preprocessing the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively; Obtaining first threshold data according to the electrocardiogram signal data and the first pre-processed data, and obtaining second threshold data according to the respiratory wave data and the second pre-processed data; Obtaining an R wave value according to the electrocardiogram signal data, the first preprocessed data and the first threshold data, and obtaining a respiratory wave value according to the respiratory wave data, the second preprocessed data and the second threshold data; respectively determining whether the R wave value and the respiratory wave value are equal to 0, If not, the R wave value is determined to be the characteristic point of the electrocardiogram signal, the respiratory wave value is determined to be the characteristic point of the respiratory wave, and a synchronization signal is output to an external detection device. If yes, the electrocardiogram signal and the respiratory wave are collected again, and the threshold values ​​of the electrocardiogram signal and the respiratory wave are updated respectively according to the first threshold value data and the second threshold value data.

2. The feature point detection method for electrocardiogram signal and respiratory wave according to claim 1, characterized in that: The filtering process of the electrocardiogram signal and the respiratory wave to obtain electrocardiogram signal data and respiratory wave data includes: The electrocardiogram signal and the respiratory wave are filtered to obtain: S[n]=b*x[n]+a*S[n-1] Where: S[n] is the electrocardiogram signal data or the respiratory wave data; b is the coefficient of the forward path; x[n] is the electrocardiogram signal or the respiratory wave; a is the coefficient of the feedback path; S[n-1] is the initially collected ECG signal or the respiratory wave, or the ECG signal data or the respiratory wave data acquired last time; To obtain the electrocardiogram signal data and the respiratory wave data.

3. The feature point detection method for electrocardiogram signal and respiratory wave according to claim 2, characterized in that: The preprocessing of the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively includes: Current slope value acquisition link: According to the ECG signal data or the respiratory wave data, the current slope value is obtained as follows: S_D[n]=(-S[n-2]-2*S[n-1]+2*S[n+1]+S[n+2]) / 8 Where: S_D[n] is the current slope value corresponding to the electrocardiogram signal data or the respiratory wave data; S[n-2], S[n-1], S[n+1] and S[n+2] are the electrocardiogram signal data or the respiratory wave data of the previous, previous, next and next time respectively; Window integral average value acquisition stage: According to the current slope value, obtain: S_D1[n]=S_D[n] 2 , WIA[n]=(S_D1[n-(N-1)]+S_D1[n-(N-2)]+…+S_D1[n]) / N Where: S_D1[n] is the current square value of the current slope value in the window; WIA[n] is the window integrated average; S_D1[n-(N-1)] is the reciprocal first square value of the current slope value in the window; S_D1[n-(N-2)] is the second to last square value of the current slope value in the window; N is the number of the electrocardiogram signal data or the respiratory wave data within the integration window width; The first pre-processed data includes the current slope value and the window integrated average value corresponding to the electrocardiogram signal data; The second pre-processed data includes the current slope value and the window integrated average value corresponding to the respiratory wave data.

4. The feature point detection method for electrocardiogram signal and respiratory wave according to claim 3, characterized in that: The step of obtaining first threshold data according to the electrocardiogram signal data and the first pre-processed data, and obtaining second threshold data according to the respiratory wave data and the second pre-processed data, comprises: Signal threshold acquisition link: According to the ECG signal data or the respiratory wave data, obtain: S_TH[n]=S[n]*0.125+S_TH[n-1]*0.875 Where: S_TH[n-1] is the signal threshold of the previous electrocardiogram signal or the respiratory wave; Signal slope threshold acquisition link: According to the current slope value corresponding to the electrocardiogram signal data, or the current slope value corresponding to the respiratory wave data, obtain: S_D_TH[n]=S_D[n]*0.125+S_D_TH[n-1]*0.875 Where: S_D_TH[n-1] is the signal slope threshold of the previous electrocardiogram signal or the respiratory wave; The first threshold data includes the signal threshold and the signal slope threshold corresponding to the electrocardiogram signal data; The second threshold data includes the signal threshold and the signal slope threshold corresponding to the respiratory wave data.

5. The feature point detection method for electrocardiogram signal and respiratory wave according to claim 4, characterized in that: The step of obtaining an R wave value according to the electrocardiogram signal data, the first preprocessed data and the first threshold data, and obtaining a respiratory wave value according to the respiratory wave data, the second preprocessed data and the second threshold data, comprises: According to the electrocardiogram signal data and the corresponding current slope value, the window integral average value, the signal threshold and the signal slope threshold value, or according to the respiratory wave data and the corresponding current slope value, the window integral average value, the signal threshold and the signal slope threshold value, obtain: R＝ ((S[n]>S_TH[n]*0.75)&&(S_D[n]>S_D_TH[n]*0.75)&&(S_D[n]>WIA[n]*0.75)) Where: R is the R wave value or respiratory wave value.

6. A feature point detection system for electrocardiogram signals and respiratory waves, characterized in that: Used to implement the feature point detection method for electrocardiogram signals and respiratory waves described in claims 1-5, and configured to be installed in an external detection device, the feature point detection system includes a main control module and an execution module; The main control module is used to output a preset period acquisition instruction; The execution module includes a signal acquisition unit, a filtering processing unit, a signal preprocessing unit and a decision output unit. The signal acquisition unit is used to respond to a preset period acquisition instruction to acquire the electrocardiogram signal and the respiratory wave. The filtering processing unit is used to filter the electrocardiogram signal and the respiratory wave to obtain electrocardiogram signal data and respiratory wave data. The signal preprocessing unit is used to preprocess the electrocardiogram signal data and the respiratory wave data to obtain first preprocessed data and second preprocessed data corresponding to the electrocardiogram signal data and the respiratory wave data respectively. The decision output unit is used to obtain an R wave value according to the electrocardiogram signal data, the first pre-processed data and the first threshold data, and to obtain a respiratory wave value according to the respiratory wave data, the second pre-processed data and the second threshold data. respectively determining whether the R wave value and the respiratory wave value are equal to 0, If not, the R wave value is determined to be the characteristic point of the electrocardiogram signal, the respiratory wave value is determined to be the characteristic point of the respiratory wave, and a synchronization signal is output to an external detection device. If yes, the electrocardiogram signal and the respiratory wave are collected again, and the threshold values ​​of the electrocardiogram signal and the respiratory wave are updated respectively according to the first threshold value data and the second threshold value data.

7. The feature point detection system for electrocardiogram signals and respiratory waves according to claim 6, characterized in that: A signal isolation module is provided between the main control module and the execution module for isolating signals between the two.

8. The feature point detection system for electrocardiogram signals and respiratory waves according to claim 7, characterized in that: The main control module is electrically connected to a power management module for supplying power to the main control module.

9. The feature point detection system for electrocardiogram signals and respiratory waves according to claim 8, characterized in that: A power isolation module is provided between the power management module and the main control module for isolating the power supply between the two.

10. The feature point detection system for electrocardiogram signals and respiratory waves according to claim 6, characterized in that: The filtering processing unit includes a high-pass filter, a notch filter and a low-pass filter which are connected in stages.

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