WO2022088186A1 - 一种食道压信号的滤波方法及滤波装置 - Google Patents

一种食道压信号的滤波方法及滤波装置 Download PDF

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WO2022088186A1
WO2022088186A1 PCT/CN2020/125948 CN2020125948W WO2022088186A1 WO 2022088186 A1 WO2022088186 A1 WO 2022088186A1 CN 2020125948 W CN2020125948 W CN 2020125948W WO 2022088186 A1 WO2022088186 A1 WO 2022088186A1
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signal
esophageal pressure
heartbeat
notch
pressure signal
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PCT/CN2020/125948
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English (en)
French (fr)
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黄志文
刘京雷
周小勇
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深圳迈瑞生物医疗电子股份有限公司
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Priority to PCT/CN2020/125948 priority Critical patent/WO2022088186A1/zh
Priority to CN202080106800.5A priority patent/CN116528755A/zh
Publication of WO2022088186A1 publication Critical patent/WO2022088186A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs

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  • the invention relates to the field of medical technology, in particular to a filtering method and a filtering device for an esophageal pressure signal.
  • esophageal pressure As an approximate surrogate for intra plural pressure (Ppl) has been widely recognized by physicians around the world. Doctors can approximate the patient's pleural pressure through the above method, so as to calculate the patient's transpulmonary pressure, chest wall compliance, lung compliance, transpulmonary driving pressure and other parameters. effects on the chest wall, and can further describe and evaluate the patient's ventilation process.
  • the esophageal pressure catheter To obtain an accurate esophageal pressure waveform through an esophageal pressure catheter, it is first necessary to place the esophageal pressure catheter in the correct position in the patient's esophagus. After the esophageal pressure catheter is placed, because the position of the balloon in the esophagus is close to the heart, the patient's heart beat will also affect the pressure measured by the esophageal pressure catheter. Therefore, the esophageal pressure waveform is often disturbed by the patient's heartbeat, which is usually called the heartbeat. Notch or heartbeat artifact (cardiogenic oscillation).
  • the existence of the heartbeat notch interferes with the obtained esophageal pressure value to a certain extent, especially when the patient's heartbeat notch amplitude is large, the noise signal brought by the heartbeat notch may also cover the patient's own esophageal pressure signal, resulting in the esophageal pressure value. Inaccurate, affecting the subsequent further calculation of the patient's lung physiological parameters.
  • the existence of the heartbeat notch will affect the doctor's judgment, making it difficult for the doctor to accurately assess the patient's inspiratory effort, as well as identify and analyze the patient's human-machine confrontation, etc.
  • the esophageal pressure signal will be interfered by the heartbeat notch signal, so esophageal pressure filtering is very necessary and important.
  • Schuessler uses high-pass filtered esophageal pressure signals and R-wave signals extracted from ECG (electrocardiogram, electrocardiogram) to construct an adaptive noise canceler.
  • ECG electrocardiogram, electrocardiogram
  • Gra ⁇ hoff adopts the template subtraction method, which needs to introduce ECG/EMG (electromyogram, electromyogram) signal to analyze the noise template, but both methods require a monitor beside the ventilator, which is inconvenient to use in clinical practice.
  • the present invention mainly provides a filtering method for the esophageal pressure signal, which can filter the heartbeat notch signal in the esophageal pressure signal in real time without connecting the ventilation equipment (such as ventilator, anesthesia machine, etc.) Therefore, the filtering requirement of the esophageal pressure signal in the ventilation equipment is satisfied, and the present invention also provides a corresponding filtering device.
  • an embodiment provides a method for filtering an esophageal pressure signal, comprising the steps of:
  • the filtering parameters of the adaptive filter are updated according to the reference signal to obtain the updated adaptive filter
  • the heartbeat notch signal in the esophageal pressure signal is filtered out according to the updated adaptive filter.
  • an embodiment provides a method for filtering an esophageal pressure signal, comprising the steps of:
  • the filtering parameters of the adaptive filter are updated according to the reference signal to obtain the updated adaptive filter
  • the heartbeat notch signal in the esophageal pressure signal is filtered out according to the updated adaptive filter.
  • an embodiment provides a method for filtering physiological signals, comprising the steps of:
  • the filtering parameters of the adaptive filter are updated according to the reference signal to obtain the updated adaptive filter
  • the noise signal in the physiological signal is filtered out according to the updated adaptive filter.
  • an embodiment provides an apparatus for filtering an esophageal pressure signal, including:
  • a sensor interface which receives the patient's esophageal pressure value monitored by the pressure sensor
  • the processor is connected to the sensor interface signal, receives the esophageal pressure value and generates an esophageal pressure signal, and constructs a reference signal according to the esophageal pressure signal, and updates the filtering parameters of the adaptive filter according to the reference signal to obtain the updated An adaptive filter, filtering out the heartbeat notch signal in the esophageal pressure signal according to the updated adaptive filter.
  • an embodiment provides a ventilator, comprising:
  • Air source interface connect external air source
  • the breathing circuit connects the air source interface with the patient's breathing system, so as to input the gas provided by the air source to the patient and receive the gas exhaled by the patient;
  • Respiratory assistance device which provides respiratory support power to control the output of gas provided by the gas source to the patient, and collects and reuses or discharges the gas exhaled by the patient to the external environment
  • the respiratory assistance device includes a machine-controlled ventilation module and/or manual ventilation modules
  • the filtering device of the esophageal pressure signal in the above fourth aspect is the filtering device of the esophageal pressure signal in the above fourth aspect.
  • an embodiment provides a ventilation device, comprising:
  • a processor for implementing the method described in the first aspect by executing a program stored in the memory.
  • an embodiment provides a medical device, comprising:
  • a processor configured to implement the method described in the second aspect or the third aspect by executing a program stored in the memory.
  • an embodiment provides a computer-readable storage medium including a program executable by a processor to implement the methods described in the first to third aspects.
  • a reference signal is constructed according to the esophageal pressure signal itself during the ventilation process, and the esophageal pressure signal is filtered according to the reference signal and the adaptive filter. Only the esophageal pressure signal needs to be collected, so in the process of filtering the esophageal pressure signal, there is no need to introduce other external equipment except the ventilation equipment used for mechanical ventilation. The signal has a good filtering effect.
  • FIG. 1 is a schematic structural diagram of an adaptive noise canceller
  • FIG. 2 is a schematic diagram of the structural composition of a ventilator according to an embodiment
  • FIG. 3 is a waveform diagram of an esophageal pressure signal according to an embodiment
  • FIG. 4 is a schematic diagram of the time interval between peaks in one cycle in the esophageal pressure signal according to an embodiment
  • FIG. 5 is a spectrogram of an esophageal pressure signal according to an embodiment
  • FIG. 6 is a spectrogram of a reference signal according to an embodiment
  • FIG. 7 is a spectrum diagram of a reference signal according to another embodiment.
  • FIG. 8 is a schematic structural diagram of a filtering process of an adaptive filter according to an embodiment
  • FIG. 9 is a waveform diagram and a spectrogram of a filtered esophageal pressure signal according to an embodiment
  • FIG. 10 is a flowchart of a filtering method for an esophageal pressure signal according to an embodiment
  • FIG. 11 is a flowchart of a method for updating an adaptive filter according to an embodiment
  • connection and “connection” mentioned in this application, unless otherwise specified, include both direct and indirect connections (connections).
  • the adaptive filter referred to in the present invention refers to an algorithm or a device that automatically adjusts the filter coefficients by adopting a specific algorithm based on the estimation of the statistical characteristics of the input and output signals to achieve the best filtering characteristics .
  • a typical structure of an adaptive noise canceller with adaptive filtering method is shown in Figure 1. The structure needs to input two signals: (1) the basic signal s+n, the basic signal s+n is considered as the target The superposition of the signal s+n and the noise signal n; (2) the reference signal n 0 , the reference signal n 0 is correlated with the noise signal n, but not correlated with the target signal s.
  • the function of the denoiser is to obtain an output signal approximate to the target signal s from the input basic signal s+n
  • the specific filtering process is as follows:
  • the adaptive filtering algorithm Input the reference signal n 0 to the adaptive filter to get the output signal of the filter Input the basic signal s+n to the denoiser, and subtract the output signal of the filter from the basic signal s+n In this way, the output signal of the noise canceler is obtained At the same time, the basic signal s+n and the output signal of the filter are obtained.
  • Commonly used adaptive filtering algorithms include the least mean square (Least Mean Square, LMS) algorithm, recursive least squares algorithm, Kalman filter algorithm (Recursive Least Square, RLS) and neural network algorithms, and derived from these algorithms.
  • the improved algorithms such as normalized LMS, variable step size LMS, fast RLS, square root RLS and RLS algorithm based on QR decomposition, etc.
  • the filter automatically optimizes the filter parameters and/or structure according to the filtering results at the previous moment to adapt to the unpredictable and time-varying statistical characteristics of signals and noise.
  • the final effect is to make Filter output signal is the optimal estimate of the noise signal n under this rule, and the output signal Also approximates the target signal s.
  • the adaptive filtering algorithm does not need to know the prior information of the signal and noise, the calculation amount is small, it is suitable for real-time processing, and it is widely used in many engineering problems.
  • the fundamental wave referred to in the present invention means that other waveforms can be obtained by superimposing and changing several types of waveforms, and these types of waveforms are called fundamental waves.
  • the fundamental frequency referred to in the present invention also referred to as the fundamental frequency, refers to the lowest frequency in the complex wave.
  • any periodic waveform can be decomposed into a fundamental frequency sine wave plus many high frequency sine waves, the high frequency is an integral multiple of the fundamental frequency (N, can only be an integer), and the high frequency sine wave can be Considered to be what the present invention calls harmonics.
  • FIG. 2 shows a schematic diagram of the structure of a ventilator (or may also be referred to as a ventilation device).
  • the ventilator includes an air source interface 10, a breathing assistance device 20, a breathing circuit 30, a sensor interface 40, and a memory. 50 , processor 60 and display 70 .
  • the gas source interface 10 is used for connecting with a gas source (not shown in the figure), and the gas source is used for supplying gas. Oxygen, air, etc. can be used as the gas.
  • the air source can be a compressed air cylinder or a central air supply source, and the ventilator is supplied with air through the air source interface 10, and the air supply types include oxygen O2 and air.
  • the gas source interface 10 may include conventional components such as a pressure gauge, a pressure regulator, a flow meter, a pressure reducing valve, and a proportional control protection device, which are respectively used to control the flow of various gases (eg, oxygen and air).
  • the gas input from the gas source interface 10 enters the breathing circuit 30 and forms a mixed gas with the original gas in the breathing circuit 30 .
  • the breathing assistance device 20 is used to provide power for the non-spontaneous breathing of the patient 80 to maintain airway patency, that is, to drive the gas input from the air source interface 10 and the mixed gas in the breathing circuit 30 to the breathing system of the patient 80, and to exhale the patient 80
  • the gas is drained into the breathing circuit 30, thereby improving ventilation and oxygenation, preventing hypoxia of the patient 80 and accumulation of carbon dioxide in the patient 80.
  • the breathing assistance device 20 generally includes a machine-controlled ventilation module, and the airflow conduit of the machine-controlled ventilation module communicates with the breathing circuit 30 . When the patient 80 does not resume spontaneous breathing during the operation, the machine-controlled ventilation module is used to provide the patient 80 with power to breathe.
  • the breathing assistance device 20 further includes a manual ventilation module, and the air flow conduit of the manual ventilation module communicates with the breathing circuit 30 .
  • a manual ventilation module During the induction phase before intubation of the patient 80 during the procedure, it is usually necessary to use a manual ventilation module to assist the patient 80 in breathing.
  • the breathing assistance device 20 includes both a machine-controlled ventilation module and a manual ventilation module, the machine-controlled or manual ventilation mode can be switched through a machine-controlled or manual switch (eg, a three-way valve), so that the machine-controlled ventilation module or the manual ventilation mode can be switched
  • the module communicates with the breathing circuit 30 to control the breathing of the patient 80 .
  • the ventilator may only include a machine-controlled ventilation module or a manual ventilation module.
  • the breathing circuit 30 includes an inspiratory passage 30a, an expiratory passage 30b and a carbon dioxide absorber 31.
  • the inspiratory passage 30a and the expiratory passage 30b communicate to form a closed circuit, and the carbon dioxide absorber 31 is arranged on the pipeline of the expiratory passage 30b.
  • the mixed gas of fresh air introduced by the air source interface 10 is input through the inlet of the inspiratory passage 30a, and provided to the patient 80 through the patient interface 33 provided at the outlet of the inspiratory passage 30a.
  • the patient interface 33 may be a mask, nasal cannula, or endotracheal cannula.
  • the inhalation passage 30a is provided with a one-way valve 32, and the one-way valve 32 is opened in the inhalation phase and closed in the expiratory phase.
  • the expiratory passage 30b is also provided with a one-way valve 32, and the one-way valve 32 is closed during the inspiratory phase and opened during the expiratory phase.
  • the inlet of the exhalation passage 30b is communicated with the patient interface 33.
  • the breathing circuit 30 is further provided with a flow sensor and/or a pressure sensor, which are respectively used to detect the gas flow and/or the pressure in the pipeline.
  • the sensor interface 40 is used to receive various breathing information of the patient 80 collected by the sensor, such as the esophageal pressure value of the patient 80 in the machine-assisted ventilation state collected by the sensor.
  • the sensor interface 40 is connected to the signal output end of the pressure sensor.
  • the sensor interface 40 may simply serve as a connector between the sensor output and subsequent circuitry (eg, the processor 60 ), without signal processing.
  • the sensor interface 40 may also be integrated into the processor 60 as an interface of the processor 60 for accessing signals.
  • the sensor interface 40 may include an amplifying circuit, a filtering circuit and an A/D conversion circuit for respectively amplifying, filtering and analog-digital conversion processing of the input analog signal.
  • the connection relationship among the amplifier circuit, the filter circuit and the A/D conversion circuit can be changed according to the specific design of the circuit, or a certain circuit can be reduced, for example, the amplifier circuit or the filter circuit can be reduced, thereby reducing the its corresponding function.
  • the sensor interface 40 can also access information collected by other sensors, such as flow information and/or pressure information of the breathing circuit 30 .
  • the pressure sensor used to monitor the esophageal pressure value can be a part of the ventilator, or it can be a peripheral accessory independent of the ventilator.
  • the memory 50 can be used for storing data or programs, for example, for storing data collected by each sensor, data generated by the processor 60 through calculation, or an image frame generated by the processor 60, and the image frame can be a 2D or 3D image, Alternatively, memory 50 may store a graphical user interface, one or more default image display settings, programming instructions for processor 60 .
  • the memory 50 may be a tangible and non-transitory computer-readable medium, such as flash memory, RAM, ROM, EEPROM, and the like.
  • the processor 60 is used to execute instructions or programs to control various control valves in the breathing assistance device 20, the air source interface 10 and/or the breathing circuit 30, or to process the received data to generate the required calculations or judgments As a result, visual data or graphs are generated, and the visual data or graphs are output to the display 70 for display.
  • the processor 60 is signal-connected with the sensor interface 40, so as to obtain the esophageal pressure signal of the patient 80, and construct a reference signal according to the esophageal pressure signal. The reason for constructing a reference signal with close frequency domain distribution or time domain distribution is to obtain a signal close to the energy distribution of the heartbeat notch signal.
  • the construction process of the reference signal is described below by taking the construction of a reference signal close to the frequency domain distribution of the heartbeat notch as an example:
  • the fundamental frequency of the heartbeat notch signal in the esophageal pressure signal is obtained, and two ways to obtain the fundamental frequency are described below.
  • the first way is to identify the time domain feature and/or frequency domain feature of the heartbeat notch signal from the esophageal pressure signal, and calculate the fundamental frequency of the heartbeat notch signal.
  • FIG. 3 is a waveform diagram of a typical esophageal pressure signal.
  • the processor 60 can identify the waveform in at least one cycle of the esophageal pressure signal. For example, the embodiment shown in FIG. 3 has multiple cycles, and the processor 60 can select one cycle. , calculate the time interval between two adjacent peaks in the period, the time interval is the period of the heartbeat notch signal, and the inverse of the time interval can be used as the fundamental frequency of the heartbeat notch signal.
  • the average number of time intervals between multiple consecutive wave peaks in one cycle can also be calculated (for example, the dotted line in FIG. 4 represents the time interval between three consecutive wave peaks in one cycle), and the average number is taken as The average value of the period of the heartbeat notch signal, and then the fundamental frequency of the heartbeat notch signal can be obtained according to the average value. Therefore, the calculation of the heartbeat notch signal is more accurate.
  • the second method is to obtain the frequency distribution information of the heartbeat notch signal according to the short-term frequency spectrum of the calculated esophageal pressure signal, so as to obtain the fundamental frequency of the heartbeat notch signal.
  • Fig. 5 shows a spectrum diagram of a typical esophageal pressure signal.
  • the processor 60 can recognize that the frequency of the heartbeat notch signal is mainly distributed at the abscissas 1, 2, 3, 4 and 5, so 1hz can be used as the heartbeat cutoff.
  • the fundamental frequency of the trace signal is to obtain the frequency distribution information of the heartbeat notch signal according to the short-term frequency spectrum of the calculated esophageal pressure signal, so as to obtain the fundamental frequency of the heartbeat notch signal.
  • the fundamental frequency of the heartbeat notch signal can be obtained in the above two manners, and either one or a combination of the two can be used. In other embodiments, other feasible manners can also be used.
  • an external device may be introduced to verify the fundamental frequency of the heartbeat notch signal.
  • the pulse parameter and/or the heart rate parameter of the patient 80 are obtained by using a monitor or an electrocardiogram device, and the fundamental frequency of the heartbeat notch signal is verified according to the heart rate parameter and/or the pulse rate parameter, for example, when the difference between the two is not When the preset threshold is exceeded, it can be considered that the fundamental frequency of the heartbeat notch signal obtained by the ventilator is credible.
  • the blood oxygen signal of the patient 80 may also be obtained, for example, the pulse parameter and/or the heart rate parameter of the patient 80 may be obtained through a monitor or an electrocardiogram device, and then the pulse parameter and/or the heart rate parameter may be used as the heartbeat notch signal the fundamental frequency.
  • this method also requires the use of other medical equipment or monitoring equipment.
  • a related reference signal is constructed according to the fundamental frequency, and the reference signal can be obtained by constructing a fundamental wave whose frequency is the same as the fundamental frequency of the heartbeat notch signal. It is any one of sine wave, triangle wave, square wave, sawtooth wave and Gaussian function.
  • a reference signal can be obtained from the fundamental wave, for example, the fundamental wave can be directly used as a reference signal.
  • the following takes a sine wave as an example to illustrate the construction process of the fundamental wave.
  • the construction method is as follows:
  • Noise_ref A*sin(2* ⁇ *HeartRate/(SamplingRate*60)*t+B).
  • Noise_ref is the reference signal
  • HeartRate is the heart rate of the patient 80
  • Samplingrate is the sampling rate of the above-mentioned ventilator when acquiring the esophageal pressure signal of the patient 80
  • A is the amplitude
  • B is the phase
  • the frequency of the sine wave can be adjusted to meet the construction requirements.
  • a sine wave whose frequency is equal to the fundamental frequency of the heartbeat notch signal can be constructed.
  • a reference signal is constructed according to the fundamental frequency of the heartbeat notch signal obtained from the esophageal pressure signal in FIG. 3
  • FIG. 6 is a Fourier-transformed signal spectrum of the constructed reference signal.
  • the reference signal may include signal components at respective harmonic frequencies of the heartbeat notch in addition to the signal components of the fundamental frequency energy of the heartbeat notch.
  • the reference signal can be constructed in the following way:
  • Noise_ref A 1 *sin(2* ⁇ *HeartRate/(SamplingRate*60)*t+B 1 )+
  • This formula can indicate that the reference signal Noise_ref is obtained by superimposing the fundamental wave and its 2nd to Nth harmonics, 2 ⁇ N.
  • a 2 *sin(2* ⁇ *HeartRate*2/(SamplingRate*60)*t+B 2 ) contains the energy of the heartbeat notch signal at the second harmonic.
  • FIG. 7 is a signal spectrum diagram after Fourier transform of the reference signal constructed according to the above formula.
  • the harmonic energy of the heartbeat notch signal can be filtered out, so as to better filter the esophageal pressure.
  • the constructed reference signal includes the harmonic energy of the first order to the heartbeat notch signal, which can be determined according to actual use requirements.
  • the reference signal can be fixedly constructed to contain the third harmonic energy to the heart beat notch signal, or to contain the fifth harmonic energy to the heart beat notch signal, to meet the needs of most filtering occasions.
  • the processor 60 After obtaining the reference signal, the processor 60 updates the filtering parameters of the adaptive filter according to the obtained reference signal, thereby obtaining the updated adaptive filter.
  • the classic denoiser mentioned above is a way to update the filtering parameters of the adaptive filter by using the reference signal. The parameters are updated, as described below.
  • the esophageal pressure signal is used as the basic signal, the reference signal is superimposed on this basis, the superimposed signal is used as the first input signal, and the first input signal is filtered by an adaptive filter to obtain the output signal , and then calculate the error signal between the esophageal pressure signal and the output signal.
  • the adaptive algorithm is used to update the filtering parameters of the adaptive filter. For example, the step size, order and coefficient of the adaptive filter can be updated. Any one or several of them are updated to obtain an updated adaptive filter.
  • the first input signal is the result of superimposing noise on the basis of the basic signal, so the result of adaptive filtering is to make the adaptive filter in The attenuated gain coefficient is obtained at the noise frequency, so that the noise signal in the first input signal can be reduced.
  • the method of updating the filtering parameters of the adaptive filter after the error signal is obtained can use the various algorithms mentioned above when introducing the noise canceller, so as to minimize the error signal.
  • the adaptive filter can be used to filter the esophageal pressure signal.
  • One of the filtering methods is shown in Figure 8.
  • the esophageal pressure signal is used as the second input signal, and the updated adaptive filter is used to filter the esophageal pressure signal.
  • the second input signal is filtered, so as to filter out the heartbeat notch signal in the esophageal pressure signal.
  • the filtering results are shown in Figure 9, where the dark solid line is the spectrum and waveform after filtering, and the light solid line is the spectrum and waveform before filtering. It can be clearly seen that in the esophageal pressure signal The noise signal is effectively filtered out.
  • FIG. 10 is a flowchart of an esophageal pressure filtering method according to an embodiment, including steps:
  • Step 100 acquiring the esophageal pressure signal of the patient 80 .
  • a ventilation device eg, an anesthesia machine and/or a ventilator
  • the esophageal pressure signal of the patient 80 is acquired through a sensor of the ventilation device itself.
  • Step 200 construct a reference signal according to the esophageal pressure signal.
  • the reference signal can be close to the frequency domain distribution of the heartbeat notch signal in the esophageal pressure signal, or it can be close to the time domain distribution of the heartbeat notch signal in the esophageal pressure signal.
  • the signal is to obtain a signal close to the energy distribution of the heartbeat notch signal.
  • the esophageal pressure signal is used as the basic signal to construct a reference signal close to the frequency domain distribution of the heartbeat notch signal, which specifically includes the following steps:
  • Step 210 Obtain the fundamental frequency of the heartbeat notch signal in the esophageal pressure signal according to the esophageal pressure signal.
  • the time domain feature and/or the frequency domain feature of the heartbeat notch signal is identified from the esophageal pressure signal, and the fundamental frequency of the heartbeat notch signal is calculated.
  • Fig. 3 is a waveform diagram of a typical esophageal pressure signal.
  • the esophageal pressure signal can be identified with the help of a ventilator in at least one cycle.
  • the embodiment shown in Fig. 3 has multiple cycles. For a cycle, calculate the time interval between two adjacent peaks in the cycle, the time interval is the cycle of the heartbeat notch signal, and the inverse of the time interval can be used as the fundamental frequency of the heartbeat notch signal.
  • the average number of time intervals between multiple consecutive wave peaks in one cycle can also be calculated (for example, the dotted line in FIG. 4 represents the time interval between three consecutive wave peaks in one cycle), and the average number is taken as The average value of the period of the heartbeat notch signal, and then the fundamental frequency of the heartbeat notch signal can be obtained according to the average value. Therefore, the calculation of the heartbeat notch signal is more accurate.
  • the frequency distribution information of the heartbeat notch signal can also be obtained by calculating the short-term spectrum of the esophageal pressure signal, so as to obtain the fundamental frequency of the heartbeat notch signal.
  • Figure 5 shows the spectrogram of a typical esophageal pressure signal.
  • the fundamental frequency of the heartbeat notch signal can be obtained in the above two manners, and either one or a combination of the two can be used. In other embodiments, other feasible manners can also be used.
  • an external device may be introduced to verify the fundamental frequency of the heartbeat notch signal.
  • the pulse parameter and/or the heart rate parameter of the patient 80 are obtained by using a monitor or an electrocardiogram device, and the fundamental frequency of the heartbeat notch signal is verified according to the heart rate parameter and/or the pulse rate parameter, for example, when the difference between the two is not When the preset threshold is exceeded, it can be considered that the fundamental frequency of the heartbeat notch signal obtained by the ventilator is credible.
  • Step 220 Construct a reference signal according to the fundamental frequency of the heartbeat notch signal.
  • the reference signal can be obtained by constructing a fundamental wave whose frequency is the same as the fundamental frequency of the heartbeat notch signal, and the fundamental wave can be any one of a sine wave, a triangular wave, a square wave, a sawtooth wave and a Gaussian function .
  • a reference signal can be obtained from the fundamental wave, for example, the fundamental wave can be directly used as a reference signal.
  • the following takes a sine wave as an example to illustrate the construction process of the fundamental wave.
  • the construction method is as follows:
  • Noise_ref A*sin(2* ⁇ *HeartRate/(SamplingRate*60)*t+B).
  • Noise_ref is the reference signal
  • HeartRate is the heart rate of the patient 80
  • Samplingrate is the sampling rate of the above-mentioned ventilator when acquiring the esophageal pressure signal of the patient 80
  • A is the amplitude
  • B is the phase
  • the frequency of the sine wave can be adjusted to meet the construction requirements.
  • a sine wave whose frequency is equal to the fundamental frequency of the heartbeat notch signal can be constructed.
  • a reference signal is constructed according to the fundamental frequency of the heartbeat notch signal obtained from the esophageal pressure signal in FIG. 3
  • FIG. 6 is a Fourier-transformed signal spectrum of the constructed reference signal.
  • the reference signal may include signal components at respective harmonic frequencies of the heartbeat notch in addition to the signal components of the fundamental frequency energy of the heartbeat notch.
  • the reference signal can be constructed in the following way:
  • Noise_ref A 1 *sin(2* ⁇ *HeartRate/(SamplingRate*60)*t+B 1 )+
  • This formula can indicate that the reference signal Noise_ref is obtained by superimposing the fundamental wave and its 2nd to Nth harmonics, 2 ⁇ N.
  • a 2 *sin(2* ⁇ *HeartRate*2/(SamplingRate*60)*t+B 2 ) contains the energy of the heartbeat notch signal at the second harmonic.
  • FIG. 7 is a signal spectrum diagram after Fourier transform of the reference signal constructed according to the above formula.
  • the harmonic energy of the heartbeat notch signal can be filtered out, so as to better filter the esophageal pressure.
  • the constructed reference signal includes the harmonic energy of the first order to the heartbeat notch signal, which can be determined according to actual use requirements.
  • the reference signal can be fixedly constructed to contain the third harmonic energy to the heart beat notch signal, or to contain the fifth harmonic energy to the heart beat notch signal, to meet the needs of most filtering occasions.
  • Step 300 Update the filtering parameters of the adaptive filter according to the reference signal to obtain an updated adaptive filter.
  • the classic denoiser mentioned above is a way to update the filtering parameters of the adaptive filter by using the reference signal. parameters are updated, including steps:
  • Step 310 superimpose the esophageal pressure signal and the reference signal, and use the superimposed signal as the first input signal.
  • Step 320 using an adaptive filter to filter the first input signal to obtain an output signal.
  • Step 330 Calculate the error signal between the esophageal pressure signal and the output signal.
  • Step 340 according to the error signal, use an adaptive algorithm to update the filtering parameters of the adaptive filter to obtain an updated adaptive filter.
  • Step 340 is to change the filtering parameters of the adaptive filter in the direction that minimizes the error signal according to the above-mentioned various algorithms, for example, to change any one or more of the step size, order and coefficient of the adaptive filter, thereby Get the updated adaptive filter.
  • Step 400 Filter out the heartbeat notch signal in the esophageal pressure signal according to the updated adaptive filter.
  • the esophageal pressure signal is used as the second input signal
  • the updated adaptive filter is used to filter the second input signal, so as to filter out the heartbeat notch signal in the esophageal pressure signal.
  • the filtering results are shown in Figure 9, where the dark solid line is the spectrum and waveform after filtering, and the light solid line is the spectrum and waveform before filtering. It can be clearly seen that in the esophageal pressure signal The noise signal is effectively filtered out.
  • other physiological signals can also filter out noise signals in a similar manner to the above. Specifically, first obtain the physiological signals of the patient 80 that change with time; The reference signal whose domain distribution is close or the time domain distribution is close; then update the filtering parameters of the adaptive filter according to the reference signal to obtain the updated adaptive filter; finally filter out the physiological signal according to the updated adaptive filter noise signal.
  • the user can decide when to enable and stop the filtering method of the adaptive filter, and does not need to use the filtering method of the adaptive filter all the time, and can also use other filtering methods in combination at the same time. filtering method.
  • the frequency domain distribution of the reference signal of the adaptive filter is close to the heartbeat notch signal, and the frequency domain energy distribution is cut from the heartbeat.
  • the trace is consistent, and the real-time filtering effect is better.

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Abstract

本发明提供了一种食道压信号的滤波方法以及食道压滤波装置,该方法包括步骤:获取患者的食道压信号；根据所述食道压信号构造参考信号；根据所述参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器；根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。上述食道压信号的滤波方法能够有效滤除食道压信号中的噪声信号。

Description

一种食道压信号的滤波方法及滤波装置 技术领域
本发明涉及医疗技术领域，具体涉及一种食道压信号的滤波方法及滤波装置。
背景技术
在临床机械通气过程中，医生可通过一个特殊的、带气囊的食道压导管伸入到患者食道当中，从而获取患者食道内压力变化。将食道压(esophageal pressure，Pes)作为胸腔膜压力(intra plural pressure，Ppl)的近似替代，其有效性已经得到了全世界医生的广泛认可。医生可以通过上述方法近似获取患者胸腔膜压力，从而计算患者跨肺压、胸壁顺应性、肺顺应性、跨肺驱动压等参数，至此，可以准确地得出机械通气时送气压力对患者肺与胸壁的影响，并可进一步描述与评价患者的通气过程。
要通过食道压导管获取准确的食道压波形，首先需要将食道压导管放置在患者食道的正确位置。食道压导管放置完成后，由于气囊在食管中的位置邻近心脏，患者的心脏搏动同样会影响食道压导管所测的压力，因此食道压波形往往会受到患者心跳的干扰，通常称这个干扰为心跳切迹或心跳伪影(cardiogenic oscillation)。心跳切迹的存在对获取到的食道压数值有一定干扰，尤其是当患者心跳切迹幅度很大时，心跳切迹带来的噪声信号还可能遮盖患者自身的食道压信号，导致食道压数值不准确，影响后续进一步计算患者肺生理参数。此外，当医生需要通过食道压的摆动来评估、识别患者吸气努力时，心跳切迹的存在会影响医生的判断，导致医生很难准确评估患者吸气努力大小，以及识别、分析患者人机对抗情况等。
综上，食道压信号会受到心跳切迹信号的干扰，因此，食道压滤波是非常必要且重要的。
目前，学者们对食道压滤波方法的研究并不多。例如，Schuessler用经过高通滤波后的食道压信号，与从ECG(electrocardiogram，心电图)中提取的R波信号来构造自适应消噪器。Graβhoff采用模板相减法， 需要引入ECG/EMG(electromyogram，肌电图)信号来分析噪声模板，但是这两种方法都要求呼吸机旁边配备一个监护仪，在临床中使用不便。
发明内容
本发明主要提供一种食道压信号的滤波方法，该滤波方法能够在通气设备(例如呼吸机、麻醉机等)不外接其他设备的情况下，对食道压信号中的心跳切迹信号实时滤波，从而满足通气设备中对食道压信号的滤波需求，本发明还提供了一种相应的滤波装置。
根据第一方面，一种实施例提供一种食道压信号的滤波方法,包括步骤:
获取患者的食道压信号；
根据所述食道压信号构造参考信号；
根据所述参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器；
根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。
根据第二方面，一种实施例提供一种食道压信号的滤波方法,包括步骤:
获取患者的食道压信号和血氧信号；
根据所述血氧信号构造参考信号；
根据所述参考信号对自适应滤波器的滤波参数进行更新,得到更新后的自适应滤波器；
根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。
根据第三方面，一种实施例提供一种生理信号的滤波方法,包括步骤：
获取患者随时间变化的生理信号；
根据所述生理信号构造出与所述生理信号中噪声信号的频域分布接近或时域分布接近的参考信号；
根据所述参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器；
根据更新后的自适应滤波器滤除所述生理信号中的噪声信号。
根据第四方面，一种实施例提供一种食道压信号的滤波装置，包括：
传感器接口，其接收压力传感器监测的患者的食道压力值；
处理器，与传感器接口信号连接，接收食道压力值并生成食道压信号，以及根据所述食道压信号构造参考信号，根据所述参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器，根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。
根据第五方面，一种实施例提供一种呼吸机，包括：
气源接口，连接外部气源；
呼吸回路，将气源接口和患者的呼吸系统连通，以将气源提供的气体输入给患者，接收患者呼出的气体；
呼吸辅助装置，提供呼吸支持动力，以控制将气源提供的气体输出给患者，将患者呼出的气体收集重复利用或排到外部环境，所述呼吸辅助装置包括机控通气模块和/或手动通气模块；以及
上述第四方面中的食道压信号的滤波装置。
根据第六方面，一种实施例提供一种通气设备，包括：
存储器，用于存储程序；
处理器，用于通过执行所述存储器存储的程序以实现第一方面中所述的方法。
根据第七方面，一种实施例提供一种医疗设备，包括：
存储器，用于存储程序；
处理器，用于通过执行所述存储器存储的程序以实现第二方面或第三方面中所述的方法。
根据第八方面，一种实施例提供一种计算机可读存储介质，包括程序，所述程序能够被处理器执行以实现第一方面至第三方面中所述的方法。
上述食道压信号的滤波方法，在通气的过程中根据食道压信号本身构造出参考信号，并根据该参考信号以及自适应滤波器对食道压信号进行滤波。需要采集的只有食道压信号，故在对食道压信号进行滤波的过程中，除了用于机械通气的通气设备外，不需要再引入其他外接设备，并且，采用了自适应滤波方式，对食道压信号有很好的滤波效果。
附图说明
图1为一种自适应消噪器的结构示意图；
图2为一种实施例的呼吸机的结构组成示意图；
图3为一种实施例的食道压信号的波形图；
图4为一种实施例的食道压信号中一个周期内波峰间时间间隔的示意图；
图5为一种实施例的食道压信号的频谱图；
图6为一种实施例的参考信号的频谱图；
图7为另一种实施例的参考信号的频谱图；
图8为一种实施例的自适应滤波器的滤波过程的结构示意图；
图9为一种实施例的经过滤波的食道压信号的波形图和频谱图；
图10为一种实施例的食道压信号的滤波方法的流程图；
图11为一种实施例的自适应滤波器的更新方法的流程图；
10、气源接口；
20、呼吸辅助装置；
30、呼吸回路；
30a、吸气通路；30b、呼气通路；31、二氧化碳吸收器；32、单向阀；33、患者接口；
40、传感器接口；
50、存储器；
60、处理器；
70、显示器；
80、患者。
具体实施方式
下面通过具体实施方式结合附图对本发明作进一步详细说明。其中不同实施方式中类似元件采用了相关联的类似的元件标号。在以下的实施方式中，很多细节描述是为了使得本申请能被更好的理解。然而，本领域技术人员可以毫不费力的认识到，其中部分特征在不同情况下是可以省略的，或者可以由其他元件、材料、方法所替代。在某些情况下，本申请相关的一些操作并没有在说明书中显示或者描述，这是为了避免 本申请的核心部分被过多的描述所淹没，而对于本领域技术人员而言，详细描述这些相关操作并不是必要的，他们根据说明书中的描述以及本领域的一般技术知识即可完整了解相关操作。
另外，说明书中所描述的特点、操作或者特征可以以任意适当的方式结合形成各种实施方式。同时，方法描述中的各步骤或者动作也可以按照本领域技术人员所能显而易见的方式进行顺序调换或调整。因此，说明书和附图中的各种顺序只是为了清楚描述某一个实施例，并不意味着是必须的顺序，除非另有说明其中某个顺序是必须遵循的。
本文中为部件所编序号本身，例如“第一”、“第二”等，仅用于区分所描述的对象，不具有任何顺序或技术含义。而本申请所说“连接”、“联接”，如无特别说明，均包括直接和间接连接(联接)。
本发明中所称的自适应滤波器，指的是以输入和输出信号的统计特性的估计为依据，采取特定算法自动地调整滤波器系数，使其达到最佳滤波特性的一种算法或装置。一种典型的应用了自适应滤波方法的自适应消噪器的结构如图1所示，该结构需要输入两个信号：(1)基本信号s+n，基本信号s+n被认为是目标信号s+n与噪声信号n的叠加；(2)参考信号n 0，参考信号n 0与噪声信号n相关联，但与目标信号s不相关。该消噪器的作用在于从输入的基本信号s+n中，得到一个近似于目标信号s的输出信号
Figure PCTCN2020125948-appb-000001
具体的滤波过程如下：
向自适应滤波器输入参考信号n 0，得到滤波器的输出信号
Figure PCTCN2020125948-appb-000002
向消噪器输入基本信号s+n，并用基本信号s+n减去滤波器的输出信号
Figure PCTCN2020125948-appb-000003
这样就得到消噪器的输出信号
Figure PCTCN2020125948-appb-000004
同时，得到基本信号s+n与滤波器的输出信号
Figure PCTCN2020125948-appb-000005
之间的误差信号ε，然后根据自适应滤波算法通过优化自适应滤波器的参数等方法不断减小误差信号ε。常用的自适应滤波算法包括最小均方(Least Mean Square,LMS)算法、递归最小二乘算法、卡尔曼滤波器算法(Recursive Least Square,RLS)和神经网络算法等，以及从这些算法衍伸出来的改进算法，如归一化LMS、变步长LMS、快速RLS、平方根RLS和基于QR分解的RLS算法等。在上述滤波器算法的规则下，滤波器根据前一时刻的滤波结果，自动优化滤波器参数和/或结构，以适应信号与噪声的不可预测、随时间变化的统计特性，最终的效果是使得滤波器输出信号
Figure PCTCN2020125948-appb-000006
是对噪声信号n在此规则下的最优估计，输出信 号
Figure PCTCN2020125948-appb-000007
也近似于目标信号s。自适应滤波算法不需要知道信号与噪声的先验信息，计算量小，适合实时处理，在很多工程问题中都被广泛应用。
本发明中所称的基础波，指的是可由几类波形叠加变化得到其他波形，这几类波形就被称为基础波。
本发明中所称的基础频率，又被称为基频，指的是复合波中的最低频率。而任何周期性波形均可分解为一个基频正弦波加上许多高次频率的正弦波，高次频率是基频的整倍数(N，只能为整数)，高次频率的正弦波可被认为是本发明所称的谐波。
请参照图2，图2所示为一种呼吸机(或者也可以称为通气设备)的结构组成示意图，呼吸机包括气源接口10、呼吸辅助装置20、呼吸回路30、传感器接口40、存储器50、处理器60和显示器70。
气源接口10用于与气源(图中未示出)连接，气源用以提供气体。该气体通常可采用氧气和空气等。一些实施例中，该气源可以采用压缩气瓶或中心供气源，通过气源接口10为呼吸机供气，供气种类有氧气O2和空气等。气源接口10中可以包括压力表、压力调节器、流量计、减压阀和比例调控保护装置等常规组件，分别用于控制各种气体(例如氧气和空气)的流量。气源接口10输入的气体进入呼吸回路30中，和呼吸回路30中原有的气体组成混合气体。
呼吸辅助装置20用于为患者80的非自主呼吸提供动力，维持气道通畅，即将气源接口10输入的气体和呼吸回路30中的混合气体驱动到患者80的呼吸系统，并将患者80呼出的气体引流到呼吸回路30中，从而改善通气和氧合，防止患者80机体缺氧和二氧化碳在患者80体内蓄积。在具体实施例中，呼吸辅助装置20通常包括机控通气模块，机控通气模块的气流管道和呼吸回路30连通。在手术过程中的患者80未恢复自主呼吸的状态下，采用机控通气模块为患者80提供呼吸的动力。在有的实施例中，呼吸辅助装置20还包括手动通气模块，手动通气模块的气流管道和呼吸回路30连通。在手术过程中对患者80插管之前的诱导阶段，通常需要采用手动通气模块对患者80进行呼吸辅助。当呼吸辅助装置20同时包括机控通气模块和手动通气模块时，可通过机控或手控开关(例如一个三通阀)来切换机控或手动通气模式，以便将机控通气模块或手动通气模块和呼吸回路30连通，从而控制患者80的呼吸。本领域技术人员应当理解，可以根据具体的需要，呼吸机中可以只包括机控通 气模块或手动通气模块。
呼吸回路30包括吸气通路30a、呼气通路30b和二氧化碳吸收器31，吸气通路30a和呼气通路30b连通构成一闭合回路，二氧化碳吸收器31设置在呼气通路30b的管路上。气源接口10引入的新鲜空气的混合气体由吸气通路30a的入口输入，通过设置在吸气通路30a的出口处的患者接口33提供给患者80。患者接口33可以是面罩、鼻插管或气管插管。在较佳的实施例中，吸气通路30a上设置有单向阀32，该单向阀32在吸气相时打开，在呼气相时关闭。呼气通路30b也上设置有单向阀32，该单向阀32在吸气相时关闭，在呼气相时打开。呼气通路30b的入口和患者接口33连通，当患者80呼气时，呼出的气体经呼气通路30b进入二氧化碳吸收器31中，呼出的气体中的二氧化碳被二氧化碳吸收器31中的物质滤除，滤除二氧化碳后的气体再循环进入吸气通路30a中。在有的实施例中，在呼吸回路30中还设置有流量传感器和/或压力传感器，分别用于检测气体流量和/或管路中的压力。
传感器接口40用于接收传感器采集的患者80的各种呼吸信息，例如传感器采集的患者80在机器协助通气状态下的食道压力值，具体而言，传感器接口40连接压力传感器的信号输出端。在一种实施例中，传感器接口40可以只是作为传感器输出端和后续电路(例如处理器60)的一个连接器，不对信号进行处理。传感器接口40还可以作为处理器60的用于接入信号的接口而集成到处理器60中。在另一种实施例中，传感器接口40可以包括放大电路、滤波电路和A/D转换电路，用于对输入的模拟信号分别进行放大、滤波和模数转换处理。当然，技术人员应当理解，放大电路、滤波电路和A/D转换电路三者的连接关系可以根据电路的具体设计而变化，也可以减少某一个电路，例如可以减少放大电路或滤波电路，从而减少其相应的功能。另外，传感器接口40还可以接入其他传感器所采集的信息，例如呼吸回路30的流量信息和/或压力信息。
作为具体的产品，用于监测食道压力值的压力传感器可以是属于呼吸机的一部分，也可以是独立于呼吸机之外的外设附件。
存储器50可以用于存储数据或者程序，例如用于存储各传感器所采集的数据、处理器60经计算所生成的数据或处理器60所生成的图像帧，该图像帧可以是2D或3D图像，或者存储器50可以存储图形用户界面、 一个或多个默认图像显示设置、用于处理器60的编程指令。存储器50可以是有形且非暂态的计算机可读介质，例如闪存、RAM、ROM、EEPROM等。
处理器60用于执行指令或程序，对呼吸辅助装置20、气源接口10和/或呼吸回路30中的各种控制阀进行控制，或对接收的数据进行处理，生成所需要的计算或判断结果，或者生成可视化数据或图形，并将可视化数据或图形输出给显示器70进行显示。本实施例中，处理器60与传感器接口40信号连接，从而获取患者80的食道压信号，并根据食道压信号构造参考信号，例如，将食道压信号作为基本信号，构造与心跳切迹信号频域分布或时域分布接近的参考信号，之所以要构造频域分布或时域分布接近的参考信号，是要得到与心跳切迹信号能量分布接近的信号。
下面以构造与心跳切迹频域分布接近的参考信号为例，对参考信号的构造过程进行说明：
首先，获得食道压信号中心跳切迹信号的基频频率，下面举两种得到基频频率的方式进行说明。
第一种方式是从食道压信号中识别心跳切迹信号的时域特征和/或频域特征，计算得到心跳切迹信号的基频频率。图3所示为典型食道压信号的波形图，处理器60可识别食道压信号的至少一个周期内的波形，例如图3中所示实施例具有多个周期，处理器60可选中其中一个周期，计算该周期内两个相邻波峰之间的时间间隔，该时间间隔为心跳切迹信号的周期，可将该时间间隔的倒数作为心跳切迹信号的基频频率。
在一些实施例中，还可以计算一个周期内多个连续波峰之间时间间隔的平均数(例如图4中的虚线表示一个周期内三个连续波峰之间的时间间隔)，将该平均数作为心跳切迹信号的周期的平均值，再根据该平均值可得到心跳切迹信号的基频频率。从而使得心跳切迹信号的计算更加准确。
第二种方式是根据计算食道压信号的短时频谱，来获取心跳切迹信号的频率分布信息，从而得到心跳切迹信号的基频频率。例如，图5所示为典型食道压信号的频谱图，处理器60能够识别到心跳切迹信号的频率主要分布在横坐标1、2、3、4和5处，故可将1hz作为心跳切迹信号的基频频率。
上述两种方式均可得到心跳切迹信号的基频频率，可使用两者任意一种或者两者的组合，在其他实施例中，也可以采用其他可行的方式。
在一些实施例中，在得到心跳切迹信号的基频频率后，还可以引入外部设备对该心跳切迹信号的基频频率进行验证。例如，使用监护仪或者心电设备获取患者80的脉搏参数和/或心率参数，根据该心率参数和/或脉率参数对心跳切迹信号的基频频率进行验证，例如，当两者差别未超过预设的阈值时，可以认为呼吸机得到的心跳切迹信号的基频频率是可信的。
在一些实施例中，还可以获取患者80的血氧信号，例如通过监护仪或者心电设备获取患者80的脉搏参数和/或心率参数，然后将脉搏参数和/或心率参数作为心跳切迹信号的基频频率。该方式除了呼吸机外，还需要用到其他医疗设备或监护设备。
确定好心跳切迹信号的基频频率后，根据该基频频率构造相关的参考信号，可以通过构造频率与心跳切迹信号的基频频率相同的基础波的方式，得到参考信号，基础波可以是正弦波、三角波、方波、锯齿波和高斯函数中的任意一种。根据该基础波可以得到参考信号，例如，可以将该基础波直接作为参考信号。下面以正弦波为例对基础波的构造过程进行举例说明，构造的方法如下：
Noise_ref＝A*sin(2*π*HeartRate/(SamplingRate*60)*t+B)。
其中，Noise_ref为参考信号，HeartRate为患者80的心率，Samplingrate为上述呼吸机在获取患者80的食道压信号时的采样率，A为幅值，B为相位，上述幅值、相位以及等右侧的正弦波的频率均可调，以满足构造要求。
可依据上述公式，构造出一个频率等于心跳切迹信号的基频频率的正弦波。例如，根据从图3的食道压信号中获取到的心跳切迹信号的基频频率，构造出了参考信号，图6为对构造出的参考信号所做的傅里叶变换后的信号频谱图，可以看出，该信号的频域能量主要集中在心跳切迹信号的基频频率上，故上述方式成功构造出了合适的参考信号。
需要说明的是，上述构造参考信号的过程是对本发明的一种实施方式的解释，本发明构造参考信号的方法并不限于所举例子。
在一些实施例中，参考信号除了包含心跳切迹基频能量的信号成分，还可以包含心跳切迹各自谐波频率处的信号成分。例如，可以以下列的 方式构造参考信号：
Noise_ref＝A 1*sin(2*π*HeartRate/(SamplingRate*60)*t+B 1)+
A 2*sin(2*π*HeartRate*2/(SamplingRate*60)*t+B 2)+
A 3*sin(2*π*HeartRate*3/(SamplingRate*60)*t+B 3)+...。
该公式可表示基础波与其第2至N次谐波进行叠加得到参考信号Noise_ref，2≤N。
其中，A 2*sin(2*π*HeartRate*2/(SamplingRate*60)*t+B 2)包含了心跳切迹信号在二次谐波处的能量。
A 3*sin(2*π*HeartRate*3/(SamplingRate*60)*t+B 3)包含了心跳切迹信号在三次谐波处的能量，本式中各符号的含义在上文已作解释，在此不赘述。图7为根据上述公式构造出的参考信号所做的傅里叶变换后的信号频谱图。
通过上述构造方式，能够在对食道压信号进行滤波时，滤除心跳切迹信号的谐波能量，从而更好地进行食道压滤波。
构造的参考信号包括到心跳切迹信号的第几次谐波能量，可根据实际使用需求决定。例如，可以固定将参考信号构造到包含到心跳切迹信号的三次谐波能量，或者构造到包含到心跳切迹信号的五次谐波能量，以满足大部分滤波场合的需求。
在得到参考信号后，处理器60根据得到的参考信号对自适应滤波器的滤波参数更新，从而得到更新后的自适应滤波器。上文中所提及的经典消噪器是利用参考信号对自适应滤波器的滤波参数进行更新的一种方式，除此之外，还可以以图8所示的方式对自适应滤波器的滤波参数进行更新，下面做具体说明。
如图8所示，将食道压信号作为基本信号，在此基础上叠加上参考信号，将叠加后的信号作为第一输入信号，采用自适应滤波器对第一输入信号进行滤波，得到输出信号，然后计算食道压信号与输出信号之间的误差信号，最后根据误差信号，采用自适应算法对自适应滤波器的滤波参数进行更新，例如可以对自适应滤波器的步长、阶数和系数中的任意一个或几个进行更新，从而得到更新后的自适应滤波器，第一输入信号是在基本信号的基础上叠加了噪声的结果，故自适应滤波的结果是使得自适应滤波器在噪声频率处获取衰减的增益系数，从而可以减小第一 输入信号中的噪声信号。得到误差信号后对自适应滤波器的滤波参数进行更新的方法可采用上述在介绍消噪器时所提及的各类算法，使误差信号最小。
得到自适应滤波器后，可用自适应滤波器对食道压信号进行滤波，其中一种滤波方式如图8所示，将食道压信号作为第二输入信号，采用更新后的自适应滤波器对第二输入信号进行滤波，从而滤除食道压信号中的心跳切迹信号。滤波结果如图9所示，其中，深色实线部分为滤波后的频谱图与波形图，浅色实线为滤波前的频谱图和波形图，可以很明显的看出，食道压信号中的噪声信号被有效地滤除了。
基于上述呼吸机，下面对食道压信号的滤波方法进行说明。
如图10所示为一种实施例的食道压滤波方法的流程图，包括步骤：
步骤100，获取患者80的食道压信号。
例如，在通气设备(例如麻醉机和/或呼吸机)向患者80通气时，通过通气设备自身的传感器获取患者80的食道压信号。
步骤200，根据食道压信号构造参考信号。
该参考信号可以与食道压信号中心跳切迹信号的频域分布接近，也可以与食道压信号中心跳切迹信号的时域分布接近，之所以要构造频域分布或时域分布接近的参考信号，是要得到与心跳切迹信号能量分布接近的信号。
在一些实施例中，将食道压信号作为基本信号，构造与心跳切迹信号频域分布接近的参考信号，具体包括以下步骤：
步骤210，根据食道压信号得到食道压信号中心跳切迹信号的基频频率。
例如，从食道压信号中识别心跳切迹信号的时域特征和/或频域特征，计算得到心跳切迹信号的基频频率。图3所示为典型食道压信号的波形图，可借助获取食道压信号的呼吸机，识别食道压信号的至少一个周期内的波形，例如图3中所示实施例具有多个周期，选中其中一个周期，计算该周期内两个相邻波峰之间的时间间隔，该时间间隔为心跳切迹信号的周期，可将该时间间隔的倒数作为心跳切迹信号的基频频率。
在一些实施例中，还可以计算一个周期内多个连续波峰之间时间间隔的平均数(例如图4中的虚线表示一个周期内三个连续波峰之间的时 间间隔)，将该平均数作为心跳切迹信号的周期的平均值，再根据该平均值可得到心跳切迹信号的基频频率。从而使得心跳切迹信号的计算更加准确。
在一些实施例中，还可根据计算食道压信号的短时频谱，来获取心跳切迹信号的频率分布信息，从而得到心跳切迹信号的基频频率。例如，图5所示为典型食道压信号的频谱图，借助外部机器能够识别到心跳切迹信号的频率主要分布在横坐标1、2、3、4和5处，其中，故可将1hz作为心跳切迹信号的基频频率。
上述两种方式均可得到心跳切迹信号的基频频率，可使用两者任意一种或者两者的组合，在其他实施例中，也可以采用其他可行的方式。
在一些实施例中，在得到心跳切迹信号的基频频率后，还可以引入外部设备对该心跳切迹信号的基频频率进行验证。例如，使用监护仪或者心电设备获取患者80的脉搏参数和/或心率参数，根据该心率参数和/或脉率参数对心跳切迹信号的基频频率进行验证，例如，当两者差别未超过预设的阈值时，可以认为呼吸机得到的心跳切迹信号的基频频率是可信的。
步骤220，根据心跳切迹信号的基频频率，构造参考信号。
本步骤中，可通过构造频率与心跳切迹信号的基频频率相同的基础波的方式，得到参考信号，基础波可以是正弦波、三角波、方波、锯齿波和高斯函数中的任意一种。根据该基础波可以得到参考信号，例如，可以将该基础波直接作为参考信号。下面以正弦波为例对基础波的构造过程进行举例说明，构造的方法如下：
Noise_ref＝A*sin(2*π*HeartRate/(SamplingRate*60)*t+B)。
其中，Noise_ref为参考信号，HeartRate为患者80的心率，Samplingrate为上述呼吸机在获取患者80的食道压信号时的采样率，A为幅值，B为相位，上述幅值、相位以及等右侧的正弦波的频率均可调，以满足构造要求。
可依据上述公式，构造出一个频率等于心跳切迹信号的基频频率的正弦波。例如，根据从图3的食道压信号中获取到的心跳切迹信号的基频频率，构造出了参考信号，图6为对构造出的参考信号所做的傅里叶变换后的信号频谱图，可以看出，该信号的频域能量主要集中在心跳切迹信号的基频频率上，故上述方式成功构造出了合适的参考信号。
需要说明的是，上述构造参考信号的过程是对本发明的一种实施方式的解释，本发明构造参考信号的方法并不限于所举例子。
在一些实施例中，参考信号除了包含心跳切迹基频能量的信号成分，还可以包含心跳切迹各自谐波频率处的信号成分。例如，可以以下列的方式构造参考信号：
Noise_ref＝A 1*sin(2*π*HeartRate/(SamplingRate*60)*t+B 1)+
A 2*sin(2*π*HeartRate*2/(SamplingRate*60)*t+B 2)+
A 3*sin(2*π*HeartRate*3/(SamplingRate*60)*t+B 3)+...
该公式可表示基础波与其第2至N次谐波进行叠加得到参考信号Noise_ref，2≤N。
其中，A 2*sin(2*π*HeartRate*2/(SamplingRate*60)*t+B 2)包含了心跳切迹信号在二次谐波处的能量。
A 3*sin(2*π*HeartRate*3/(SamplingRate*60)*t+B 3)包含了心跳切迹信号在三次谐波处的能量，本式中各符号的含义在上文已作解释，在此不赘述。图7为根据上述公式构造出的参考信号所做的傅里叶变换后的信号频谱图。
通过上述构造方式，能够在对食道压信号进行滤波时，滤除心跳切迹信号的谐波能量，从而更好地进行食道压滤波。
构造的参考信号包括到心跳切迹信号的第几次谐波能量，可根据实际使用需求决定。例如，可以固定将参考信号构造到包含到心跳切迹信号的三次谐波能量，或者构造到包含到心跳切迹信号的五次谐波能量，以满足大部分滤波场合的需求。
步骤300，根据参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器。
上文中所提及的经典消噪器是利用参考信号对自适应滤波器的滤波参数进行更新的一种方式，除此之外，还可以以图11所示的流程对自适应滤波器的滤波参数进行更新，包括步骤：
步骤310，将食道压信号与参考信号进行叠加，将叠加后的信号作为第一输入信号。
步骤320，采用自适应滤波器对第一输入信号进行滤波，得到输出信号。
步骤330，计算食道压信号与输出信号之间的误差信号。
步骤340，根据误差信号，采用自适应算法对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器。
步骤340就是依照上述的各种算法，按照使误差信号最小的方向改变自适应滤波器的滤波参数，例如，改变自适应滤波器的步长、阶数和系数中的任意一个或几个，从而得到更新后的自适应滤波器。
步骤400，根据更新后的自适应滤波器滤除食道压信号中的心跳切迹信号。
在一些实施例中，将食道压信号作为第二输入信号，采用更新后的自适应滤波器对第二输入信号进行滤波，从而滤除食道压信号中的心跳切迹信号。滤波结果如图9所示，其中，深色实线部分为滤波后的频谱图与波形图，浅色实线为滤波前的频谱图和波形图，可以很明显的看出，食道压信号中的噪声信号被有效地滤除了。
除了食道压信号外，其他生理信号也可以采用类似上述方式，滤除噪声信号，具体来说，首先获取患者80随时间变化的生理信号；然后根据生理信号构造出与生理信号中噪声信号的频域分布接近或时域分布接近的参考信号；接着根据参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器；最后根据更新后的自适应滤波器滤除生理信号中的噪声信号。应理解的是，在本发明各实施例中，用户可以自行决定何时启用和停止该自适应滤波器的滤波方法，并无需始终使用该自适应滤波器的滤波方法，还可以同时结合使用其他的滤波方法。
上述实施例中，除了用于机械通气的呼吸设备外，不需要再引入其他外接设备；且自适应滤波器的参考信号的频域分布与心跳切迹信号接近，频域能量分布上与心跳切迹相符合，实时滤波效果更好。
以上应用了具体个例对本发明进行阐述，只是用于帮助理解本发明，并不用以限制本发明。对于本领域的一般技术人员，依据本发明的思想，可以对上述具体实施方式进行变化。

Claims (36)

  1. 一种食道压信号的滤波方法,其特征在于,包括步骤:
    获取患者的食道压信号；
    根据所述食道压信号构造参考信号；
    根据所述参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器；
    根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。
  2. 如权利要求1所述的滤波方法，其特征在于，所述根据所述食道压信号构造参考信号包括：
    将所述食道压信号作为基本信号，构造与心跳切迹信号频域分布接近的参考信号。
  3. 如权利要求1所述的滤波方法，其特征在于，所述根据所述食道压信号构造参考信号包括：
    将所述食道压信号作为基本信号，构造与心跳切迹信号时域分布接近的参考信号。
  4. 如权利要求2所述的方法，其特征在于，构造所述参考信号，包括:
    根据所述食道压信号得到所述食道压信号中心跳切迹信号的基频频率；
    根据所述心跳切迹信号的基频频率，构造所述参考信号。
  5. 如权利要求4所述的方法，其特征在于，得到所述食道压信号中心跳切迹信号的基频频率,包括以下至少一种方式:
    从所述食道压信号中识别心跳切迹信号的时域特征和/或频域特征，计算得到心跳切迹信号的基频频率；
    根据计算所述食道压信号的短时频谱，来获取心跳切迹信号的频率分布信息，从而得到心跳切迹信号的基频频率。
  6. 如权利要求5所述的方法，其特征在于，所述计算得到心跳切迹信号的基频频率，包括：
    识别所述食道压信号的至少一个周期内的波形；
    计算所述食道压信号的至少一个周期中一个周期内两个相邻波峰之 间的时间间隔；
    根据所述时间间隔计算得到心跳切迹信号的基频频率。
  7. 如权利要求5所述的方法，其特征在于，所述计算得到心跳切迹信号的基频频率，包括：
    识别所述食道压信号的至少一个周期内的波形；
    计算所述食道压信号的至少一个周期内多个相邻波峰之间的至少两个时间间隔；
    根据至少两个时间间隔的均值计算得到心跳切迹信号的基频频率。
  8. 如权利要求4所述的方法，其特征在于，所述方法还包括：
    获取患者的脉率参数和/或心率参数，根据所述心率参数和/或脉率参数对所述心跳切迹信号的基频频率进行验证。
  9. 如权利要求4所述的方法，其特征在于，所述根据所述心跳切迹信号的基频频率，构造所述参考信号，包括：
    构造频率与所述基频频率相同的基础波，所述基础波包括正弦波、三角波、方波、锯齿波和高斯函数中的任意一种；
    根据所述基础波得到参考信号。
  10. 如权利要求9所述的方法，其特征在于，所述根据所述基础波得到参考信号，包括：
    采用所述基础波作为参考信号；或者
    计算得到所述基础波的第2至N次谐波；
    将所述基础波与其第2至N次谐波进行叠加，得到参考信号，其中N大于等于2。
  11. 如权利要求10所述的方法，其特征在于，N等于3或5。
  12. 如权利要求1所述的方法，其特征在于，对自适应滤波器的滤波参数进行更新的方法，包括：
    将所述食道压信号与参考信号进行叠加，将叠加后的信号作为第一输入信号；
    采用所述自适应滤波器对第一输入信号进行滤波，得到输出信号；
    计算所述食道压信号与输出信号之间的误差信号；
    根据所述误差信号，采用自适应算法对所述自适应滤波器的滤波参数进行更新，得到所述更新后的自适应滤波器。
  13. 如权利要求12所述的方法，其特征在于，所述采用自适应算 法对所述自适应滤波器的滤波参数进行更新，包括：
    对自适应滤波器的步长、阶数和系数中的至少一个进行更新。
  14. 如权利要求1所述的方法，其特征在于，所述根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号，包括：
    将所述食道压信号作为第二输入信号；
    采用所述更新后的自适应滤波器对所述第二输入信号进行滤波，从而滤除所述食道压信号中的心跳切迹信号。
  15. 一种食道压信号的滤波方法,其特征在于,包括步骤:
    获取患者的食道压信号和血氧信号；
    根据所述血氧信号构造参考信号；
    根据所述参考信号对自适应滤波器的滤波参数进行更新,得到更新后的自适应滤波器；
    根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。
  16. 如权利要求15所述的滤波方法，其特征在于，所述根据血氧信号构造参考信号，包括：
    将所述血氧信号作为基本信号，构造出与心跳切迹信号频域分布接近的参考信号。
  17. 如权利要求16所述的滤波方法，其特征在于，构造所述参考信号,包括：
    将所述血氧信号中的脉率参数作为心跳切迹信号的基频频率。
  18. 一种生理信号的滤波方法,其特征在于，包括步骤：
    获取患者随时间变化的生理信号；
    根据所述生理信号构造出与所述生理信号中噪声信号的频域分布接近或时域分布接近的参考信号；
    根据所述参考信号对自适应滤波器的滤波参数进行更新，得到更新后的自适应滤波器；
    根据更新后的自适应滤波器滤除所述生理信号中的噪声信号。
  19. 一种食道压信号的滤波装置，其特征在于，包括：
    传感器接口，其接收压力传感器监测的患者的食道压力值；
    处理器，与传感器接口信号连接，接收食道压力值并生成食道压信号，以及根据所述食道压信号构造参考信号，根据所述参考信号对自适 应滤波器的滤波参数进行更新，得到更新后的自适应滤波器，根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号。
  20. 如权利要求19所述的食道压信号的滤波装置，其特征在于，所述根据所述食道压信号构造参考信号包括:
    将所述食道压信号作为基本信号，构造与心跳切迹信号频域分布接近的参考信号。
  21. 如权利要求19所述的食道压信号的滤波装置，其特征在于，所述根据所述食道压信号构造参考信号包括：
    将所述食道压信号作为基本信号，构造与心跳切迹信号时域分布接近的参考信号。
  22. 如权利要求20所述的食道压信号的滤波装置，其特征在于，构造所述参考信号的方法，包括：
    根据所述食道压信号得到所述食道压信号中心跳切迹信号的基频频率；
    根据所述心跳切迹信号的基频频率，构造所述参考信号。
  23. 如权利要求22所述的食道压信号的滤波装置，其特征在于，得到所述食道压信号中心跳切迹信号的基频频率,包括以下至少一种方式：
    根据所述食道压信号的时域特征和/或频域特征计算得到心跳切迹信号的基频频率；
    从所述食道压信号中识别心跳切迹信号的时域特征和/或频域特征，计算得到心跳切迹信号的基频频率；
    根据计算所述食道压信号的短时频谱，来获取心跳切迹信号的频率分布信息，从而得到心跳切迹信号的基频频率。
  24. 如权利要求23所述的食道压信号的滤波装置，其特征在于，所述计算得到心跳切迹信号的基频频率，包括：
    识别所述食道压信号的至少一个周期内的波形；
    计算所述食道压信号的至少一个周期中一个周期内两个相邻波峰之间的时间间隔；
    根据所述时间间隔计算得到心跳切迹信号的基频频率。
  25. 如权利要求23所述的食道压信号的滤波装置，其特征在于，所述计算得到心跳切迹信号的基频频率，包括：
    识别所述食道压信号的至少一个周期内的波形；
    计算所述食道压信号的至少一个周期内多个相邻波峰之间的至少两个时间间隔；
    根据至少两个时间间隔的均值计算得到心跳切迹信号的基频频率。
  26. 如权利要求22所述的食道压信号的滤波装置，其特征在于，还包括：
    获取患者的脉率参数和/或心率参数，根据所述心率参数和/或脉率参数对所述心跳切迹信号的基频频率进行验证。
  27. 如权利要求22所述的食道压信号的滤波装置，其特征在于，所述根据所述心跳切迹信号的基频频率，构造所述参考信号，包括:
    构造频率与所述基频频率相同的基础波，所述基础波包括正弦波、三角波、方波、锯齿波和高斯函数中的任意一种；
    根据所述基础波得到参考信号。
  28. 如权利要求27所述的食道压信号的滤波装置，其特征在于，所述根据所述基础波得到参考信号，包括：
    采用所述基础波作为参考信号；或者
    计算得到所述基础波的第2至N次谐波；
    将所述基础波与其第2至N次谐波进行叠加，得到参考信号，其中N大于等于2。
  29. 如权利要求28所述的食道压信号的滤波装置，其特征在于，N等于3或5。
  30. 如权利要求19所述的食道压信号的滤波装置，其特征在于，对自适应滤波器的滤波参数进行更新的方法，包括：
    将所述食道压信号与参考信号进行叠加，将叠加后的信号作为第一输入信号；
    采用所述自适应滤波器对第一输入信号进行滤波，得到输出信号；
    计算所述食道压信号与输出信号之间的误差信号；
    根据所述误差信号，采用自适应算法对所述自适应滤波器的滤波参数进行更新，得到所述更新后的自适应滤波器。
  31. 如权利要求30所述的食道压信号的滤波装置，其特征在于，所述采用自适应算法对所述自适应滤波器的滤波参数进行更新，包括：
    对自适应滤波器的步长、阶数和系数中的至少一个进行更新。
  32. 如权利要求19所述的食道压信号的滤波装置，其特征在于，所述根据更新后的自适应滤波器滤除所述食道压信号中的心跳切迹信号，包括：
    将所述食道压信号作为第二输入信号；
    采用所述更新后的自适应滤波器对所述第二输入信号进行滤波，从而滤除所述食道压信号中的心跳切迹信号。
  33. 一种呼吸机，其特征在于，包括：
    气源接口，连接外部气源；
    呼吸回路，将气源接口和患者的呼吸系统连通，以将气源提供的气体输入给患者，接收患者呼出的气体；
    呼吸辅助装置，提供呼吸支持动力，以控制将气源提供的气体输出给患者，将患者呼出的气体收集重复利用或排到外部环境，所述呼吸辅助装置包括机控通气模块和/或手动通气模块；以及
    如权利要求19至32中任一项所述的食道压信号的滤波装置。
  34. 一种通气设备，其特征在于，包括：
    存储器，用于存储程序；
    处理器，用于通过执行所述存储器存储的程序以实现如权利要求1-14中任一项所述的方法。
  35. 一种医疗设备，其特征在于，包括：
    存储器，用于存储程序；
    处理器，用于通过执行所述存储器存储的程序以实现如权利要求15-18任一项所述的方法。
  36. 一种计算机可读存储介质，其特征在于，包括程序，所述程序能够被处理器执行以实现如权利要求1-19中任一项所述的方法。
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0694285A2 (en) * 1994-07-27 1996-01-31 Hewlett-Packard Company Real-time artifact removal from waveforms using a dynamic filter having a fixed delay
US20160278711A1 (en) * 2015-03-24 2016-09-29 Draeger Medical Systems, Inc. Adaptive removal of the cardiac artifact in respiration waveform
US20190274586A1 (en) * 2017-09-08 2019-09-12 Nox Medical Ehf System and method for non-invasively determining an internal component of respiratory effort
CN111543999A (zh) * 2019-02-11 2020-08-18 律维施泰因医学技术股份有限公司 用于感测病人的呼吸努力的系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0694285A2 (en) * 1994-07-27 1996-01-31 Hewlett-Packard Company Real-time artifact removal from waveforms using a dynamic filter having a fixed delay
US20160278711A1 (en) * 2015-03-24 2016-09-29 Draeger Medical Systems, Inc. Adaptive removal of the cardiac artifact in respiration waveform
US20190274586A1 (en) * 2017-09-08 2019-09-12 Nox Medical Ehf System and method for non-invasively determining an internal component of respiratory effort
CN111543999A (zh) * 2019-02-11 2020-08-18 律维施泰因医学技术股份有限公司 用于感测病人的呼吸努力的系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YU MING, CHEN FENG; ZHANG GUANG; LI LIANGZHE; WANG CHUNCHEN; WANG DAN; ZHAN NINGBO; GU BIAO; WU TAIHU: "Adaptive Cardiopulmonary Resuscitation Artifacts Elimination Algorithm Based on Empirical Mode Decomposition and Independent Component Analysis", SHENGWU YIXUE GONGCHENGXUE ZAZHI = JOURNAL OF BIOMEDICAL ENGINEERING, SICHUAN DAXUE HUAXI YIYUAN, CN, vol. 33, no. 5, 1 October 2016 (2016-10-01), CN , pages 834 - 841, XP055925662, ISSN: 1001-5515, DOI: 10.7507/1001-5515.201600135 *

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