WO2020211051A1 - 一种测量心肌组织运动特征的非侵入性方法及系统 - Google Patents

一种测量心肌组织运动特征的非侵入性方法及系统 Download PDF

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WO2020211051A1
WO2020211051A1 PCT/CN2019/083290 CN2019083290W WO2020211051A1 WO 2020211051 A1 WO2020211051 A1 WO 2020211051A1 CN 2019083290 W CN2019083290 W CN 2019083290W WO 2020211051 A1 WO2020211051 A1 WO 2020211051A1
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Prior art keywords
capacitance
heart
tissue
myocardial
resistance
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PCT/CN2019/083290
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English (en)
French (fr)
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王翎
易成
何碧霞
谢鹏
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麦层移动健康管理有限公司
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Priority to US17/594,420 priority Critical patent/US20220183573A1/en
Priority to EP19924783.4A priority patent/EP3957239A4/en
Priority to PCT/CN2019/083290 priority patent/WO2020211051A1/zh
Priority to JP2021549915A priority patent/JP7156739B2/ja
Priority to CN201980095292.2A priority patent/CN113727644A/zh
Publication of WO2020211051A1 publication Critical patent/WO2020211051A1/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
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/7475—User input or interface means, e.g. keyboard, pointing device, joystick

Definitions

  • the present invention relates to a measurement technology for biological tissues, in particular to a non-invasive method and system for measuring the movement characteristics of myocardial tissue.
  • the basic function of the heart is to pump blood, make it circulate in the organism, and provide oxygen and nutrients to the tissues. Therefore, the measurement of cardiac dynamics parameters is extremely important in the medical field.
  • the structural characteristics of cardiomyocytes indicate that they are elastic tissues. Therefore, the movement of myocardial tissue, especially elasticity, should be the main measurement target.
  • the stress-strain relationship of myocardial tissue has been extensively studied, and related applications are mainly realized through ultrasound imaging systems.
  • the heart has four chambers, including two atria and two ventricles. Under normal circumstances, the right atrium collects blood from the superior and inferior vena cava. The blood then enters the right ventricle, where it is pumped into the lungs. The left atrium receives blood from the pulmonary veins and sends it to the left ventricle, which pumps the blood through the aorta to the whole body.
  • the heart wall has a three-layer structure, namely the inner endocardium, the middle myocardium and the outer epicardium. The endocardium is the lining of a single layer of squamous epithelium, covering the heart cavity and valves.
  • the myocardium is the muscle of the heart, a layer of involuntary striated muscle tissue, which is restricted by the framework of collagen, so that myocardial cells are arranged on the curved sheet, forming a spiral structure as a whole.
  • Myocardium is the focus of the present invention.
  • the pericardium is a double-layered sac containing the heart and the roots of large blood vessels.
  • the present invention focuses on the early detection of changes in myocardial tissue and can be used to prevent sudden heart attacks.
  • Heart function There are many ways to measure heart function at different levels, such as organ, tissue, and cell levels.
  • the estimation of ventricular volume can be done through image construction.
  • the stroke volume (SV) and ejection fraction (EF) can also be measured, which represent the overall pump function of the heart. But these parameters do not explain the mechanical properties of the organization.
  • Direct measurement of the strain on the ventricular wall proved to be a very important measurement of myocardial tissue activity, which can indirectly reflect heart function.
  • the measurement is currently mainly done by ultrasound Doppler or ultrasound speckle technology on paired spots. Contracted LV torsion from ultrasound speckle tracking imaging is another technique for assessing cardiac function.
  • the omni-directional longitudinal strain has also proved to be a useful tool for predicting cardiotoxicity in chemotherapy.
  • the present invention proposes a non-invasive method for measuring the movement characteristics of myocardial tissue. Its purpose is to calculate the longitudinal average length of myocardial cells by measuring the overall capacitance of the heart tissue, thereby obtaining the myocardial tissue Sports characteristics.
  • the method is mainly used for information detection for non-treatment purposes.
  • the present invention provides a non-invasive method for measuring the motion characteristics of myocardial tissue, the method includes: transmitting a plurality of generated synchronous orthogonal, different frequency phase controllable and adjustable alternating currents to the living body To generate a plurality of periodic AC voltage signals with different frequencies in synchronization; receive the periodic AC voltage signal modulated by changes in the heart tissue in the organism to obtain the frequency response of the organism; calculate the frequency response according to the frequency response The resistance and capacitance of the cardiac tissue; and the motion characteristics of the myocardial tissue are estimated based on the resistance and capacitance.
  • calculating the resistance and capacitance of the heart tissue according to the frequency response includes obtaining a system transfer function of the organism according to the frequency response, and performing multi-chamber modeling to separate the heart tissue and peripheral tissue.
  • the estimating the movement characteristics of the myocardial tissue according to the resistance and the capacitance includes: calculating the longitudinal average length of the myocardial cell and its change according to the capacitance, and/or calculating the heart pump blood flow according to the resistance; and The longitudinal average length of the cardiomyocytes and their changes and/or the pumping blood flow of the heart obtain the longitudinal elastic state of the heart as a whole.
  • the method further includes estimating the health and working status of the heart and myocardium according to the longitudinal elasticity of the entire heart.
  • the estimation includes analyzing the heart and the myocardium according to the shape of the slope value of the change in the longitudinal elastic state of the heart as a whole, the delay to the R wave, the peak-to-peak value, the longitudinal average length change curve of myocardial cells and their derivatives.
  • the health state and working state of the heart and myocardium include the contraction speed, time, intensity and pattern of the heart tissue, and/or the diastolic speed, time, recovery and pattern of the heart tissue.
  • the obtaining the frequency response of the biological body includes calculating a frequency response estimate value of a specific frequency every 0.25 to 5 milliseconds.
  • calculating the longitudinal average length of the cardiomyocytes and their changes based on the capacitance includes: detecting the longitudinal average length of the cardiomyocytes and their changes over time at a rate of 200 to 4000 times per second; and processing the cardiomyocytes using a digital signal processing method.
  • the digital signal processing method includes digital filtering, fast Fourier transform (FFT), and time domain and frequency domain analysis.
  • the method further includes referring to an electrocardiogram having the same time series to analyze the longitudinal average length change sequence of the cardiomyocytes, and the reference includes comparing the electrocardiogram with the longitudinal average length change sequence of the cardiomyocytes.
  • performing multi-chamber modeling to separate the heart tissue and peripheral tissues includes modeling each chamber through parallel resistance and capacitance modeling, and multiple chambers are connected in series or in parallel.
  • the present invention also provides a system for implementing the above method.
  • the system includes a terminal and at least one processor, wherein the terminal includes a generator for transmitting the generated multiple synchronization signals.
  • AC currents with different frequencies and phases are controllable and adjustable; one or more sensors are used to transmit the periodic AC currents to the organism to generate a plurality of periodic AC voltage signals with different frequencies, and receive The periodic AC voltage signal modulated by changes in the heart tissue in the organism to obtain the frequency response of the organism;
  • the processor is used to calculate the resistance and capacitance of the heart tissue according to the frequency response, and according to the frequency response The resistance and capacitance estimate the movement characteristics of myocardial tissue.
  • the senor is used to collect single or multiple data from different parts.
  • the system may include a database for storing processing results and data of the processor, and the processor may retrieve the database.
  • the processor may be remote, and the remote observation system may work in real-time mode.
  • the terminal further includes a man-machine interface for controlling the system and/or displaying results.
  • the present invention relates to a new technology for detecting myocardial tissue contraction and relaxation at the cellular level. Its advantage is that the present invention provides a continuous, high-sampling rate, non-invasive method to measure myocardial tissue Movement on the entire cell level can detect even more subtle abnormal changes in cardiomyocytes; the present invention avoids the traditional technique of using imaging results for analysis, has faster and standard measurement methods, and lower cost.
  • FIG. 1 is a schematic diagram of a two-dimensional abstract model of simulated cardiomyocytes provided by an embodiment of the present invention
  • Figure 2 is an overall frame diagram of some systems provided by another embodiment of the present invention.
  • Figure 3 is a schematic diagram of the arrangement of transmitting and receiving electrodes according to another embodiment of the present invention.
  • FIG. 4 is a schematic diagram of the structure of a system circuit provided by another embodiment of the present invention.
  • FIGS 5a-5d are flowcharts of methods provided by another embodiment of the present invention.
  • 6a-6d are diagrams of a young man's electrocardiogram, a curve of cardiac resistance and capacitance over time, and a derivative of the capacitance curve according to another embodiment of the present invention.
  • FIGS. 7a-7d are diagrams of the electrocardiogram of a normal middle-aged man, the curve of cardiac resistance and capacitance over time, and the derivative of the capacitance curve according to another embodiment of the present invention.
  • FIGS 8a-8d are diagrams of the electrocardiogram, the curve of cardiac resistance and capacitance over time, and the derivative of the capacitance curve of an elderly woman according to another embodiment of the present invention.
  • 9a-9d are diagrams of the electrocardiogram, the curve of cardiac resistance and capacitance over time, and the derivative of the capacitance curve of an elderly woman according to another embodiment of the present invention.
  • 10a-10d are diagrams of the electrocardiogram, the curve of cardiac resistance and capacitance over time, and the derivative of the capacitance curve of an elderly woman according to another embodiment of the present invention.
  • 11a-11d are diagrams of the electrocardiogram, the curve of cardiac resistance and capacitance over time, and the derivative of the capacitance curve of an elderly woman according to another embodiment of the present invention.
  • 12a-12d are diagrams of the electrocardiogram, the curve of cardiac resistance and capacitance over time, and the derivative of the capacitance curve of an elderly woman according to another embodiment of the present invention.
  • 13a-13d are diagrams of a normal person's electrocardiogram, a curve of changes in cardiac resistance and capacitance over time, and a schematic diagram of the average cell deformation rate (similar to tensor change rate) of a normal person according to another embodiment of the present invention.
  • 14a-14d are diagrams of a normal person's electrocardiogram, a curve of changes in cardiac resistance and capacitance over time, and a schematic diagram of the average cell deformation rate (similar to tensor change rate) of a normal person according to another embodiment of the present invention.
  • 15a-15d are schematic diagrams of a normal person's electrocardiogram, the curve of changes in cardiac resistance and capacitance over time, and the average cell deformation rate (similar to tensor change rate) of a normal person according to another embodiment of the present invention.
  • 16a-16d are diagrams of a normal person's electrocardiogram, a curve of changes in cardiac resistance and capacitance over time, and a schematic diagram of the average cell deformation rate (similar to tensor change rate) of a normal person according to another embodiment of the present invention.
  • 17a-17d are diagrams of an electrocardiogram of a person with abnormal cardiac tissue, curves of changes in cardiac resistance and capacitance over time, and a schematic diagram of the average heart cell deformation rate (similar to tensor change rate) according to another embodiment of the present invention.
  • 18a-18d are diagrams of an electrocardiogram of a person with abnormal cardiac tissue, curves of changes in cardiac resistance and capacitance over time, and a schematic diagram of the average heart cell deformation rate (similar to tensor change rate) according to another embodiment of the present invention.
  • 19a-19d are diagrams of an electrocardiogram, a curve of changes in cardiac resistance and capacitance over time of a person with abnormal cardiac tissue, and a schematic diagram of the average cell deformation rate (similar to tensor change rate) of the heart according to another embodiment of the present invention.
  • 20a-20d are diagrams of an electrocardiogram, a curve of changes in cardiac resistance and capacitance over time of a person with abnormal cardiac tissue, and a schematic diagram of the average cell deformation rate (similar to tensor change rate) of the heart according to another embodiment of the present invention.
  • the present invention relates to a non-invasive technology for detecting the electrical characteristics of tissues in a living body, such as the resistance and capacitance of the tissues and their change patterns. Its goal is to capture changes in body fluids, blood flow, and cardiovascular circulatory tissues, for monitoring the health of organisms, testing and verifying the elasticity of the cardiovascular system, and for information testing for non-therapeutic purposes.
  • heart cells are considered to be equipotential. Therefore, the cell size can be estimated by capacitance measurement.
  • the heart cells When the heart cells are in the normal position, it can be considered that they are arranged in series and parallel at the same time, because the heart structure cells restrict the muscle cells in space.
  • the average geometric scale variables of the cells can be introduced to represent the change process of the cardiomyocytes under the influence of an electromagnetic field.
  • a variable particularly relevant to the present invention is the average longitudinal length of the cardiomyocytes r(t). It is proven to be proportional to the myocardial capacitance measured under the external field. Based on this, the average longitudinal length of the cardiomyocytes and its change can be calculated by measuring the capacitance.
  • the overall longitudinal elasticity of the heart can be described as the relative change rate of myocardial capacitance over time under an external electric field.
  • the most simplified model is to replace the cardiomyocytes with an equivalent sphere in the direction of the applied electromagnetic field.
  • the longitudinal average length r(t) can be regarded as the average contraction radius of the cardiomyocytes.
  • a cell The capacitance of can be estimated with the following formula:
  • r(t) is the equivalent average contraction radius of cardiomyocytes, that is, the average longitudinal length, which is a time variable.
  • ⁇ 0 is the cell permeability.
  • the capacitance is also proportional to the average longitudinal length of the cardiomyocytes, and the proportional coefficient is related to the geometry and the permeability of the cardiomyocytes. For the sake of simplification, an equivalent sphere is used as an illustration below.
  • Fig. 1 is a schematic diagram of a two-dimensional abstract model of simulated cardiomyocytes provided by an embodiment of the present invention, which is supported by multiple cardiomyocyte microstructures.
  • the cardiomyocytes are connected in series and in parallel.
  • M cells connected in series in the longitudinal direction, and a total of L chains are connected in parallel.
  • the longitudinal average length r(t) can be regarded as the average contraction radius of the cardiomyocytes.
  • the capacitance of a cell can be as follows Formula to estimate:
  • r(t) is the equivalent average contraction radius of cardiomyocytes, that is, the average longitudinal length, which is a time variable, and ⁇ 0 is the cell permeability. It can be seen that under normal circumstances, C(t) and r(t) have a linear relationship, that is, the capacitance is proportional to the longitudinal average length of the cardiomyocytes, and the proportional coefficient is related to the geometry and the permeability of the cardiomyocytes. Under abnormal conditions, the position and size of r(t) will change, or abnormal cells have different permeability, which will cause C(t) to change and have different changing patterns.
  • Fig. 2 is an overall frame diagram of a part of the system provided by another embodiment of the present invention.
  • the human or animal body “20" is connected to the collection system “23” through electrodes or contacts "21" and a cable “22".
  • the voltage signal modulated by the human or animal body “20” is transmitted to the collection system "23” through the electrode or contact "21", and the collection system "23” processes the voltage signal and transmits it to the host 24 for further processing Analysis.
  • the host 24 includes a human-computer interaction interface for receiving or transmitting external commands.
  • Fig. 3 is a schematic diagram of the arrangement of transmitting and receiving electrodes according to another embodiment of the present invention.
  • “25” represents the heart tissue in the thoracic cavity of the human or animal body
  • the transmitting electrode "27” and the receiving electrode “26” are both located at the skin directly above the heart tissue "25”.
  • the transmitting electrode "27” includes two pairs of electrodes “T1” and “T2", and “T3” and “T4". Each pair of emitter electrodes are driven in a time-sharing manner and are independent of each other.
  • the electrodes “T1" and “T2” are respectively aligned with the two outer edges of the heart tissue “25” in the longitudinal direction, and the electrodes “T3” and “T4" are respectively aligned with the two outer edges of the heart tissue “25” in the transverse direction.
  • the broadband current signal enters the human or animal body “20" from the transmitting electrode “27”; the receiving electrode “26” includes 3 electrodes “R1", “R2” and “R3”, all aligned with the heart tissue "25” and located Between the transmitting electrodes "27", it is used to detect broadband voltage signals.
  • the electrodes "R1" and “R2” or “R1” and “R3” respectively constitute a longitudinal receiving pair, and the electrodes “R2" and “R3” constitute a horizontal receiving pair.
  • the system may include these two receiving circuit pairs. , Used to detect changes in heart tissue movement in two directions.
  • Fig. 4 is a schematic diagram of a system circuit structure provided by another embodiment of the present invention.
  • the system can not only receive voltage signals, but also transmit current signals to the human or animal body and its tissues.
  • a wideband signal is generated from the frequency domain to the time domain in the integrated circuit (IC) of the microprocessor "1" or the field programmable gate array (FPGA) "2". If the broadband signals are updated infrequently, their time domain signals can be stored in the system, and FPGA “2" can continuously output the signal to the digital-to-analog converter (DAC) "4".
  • the DAC in order to reduce analog distortion, the DAC usually runs at a high speed, for example, more than 16 times the Nyquist rate. The output signal of DAC "4" is amplified to drive the broadband current pump "9".
  • the output of the broadband current pump "9" is connected to the input of the analog switch "11", and the output of "11" is connected to the transmitting electrode pair “T1" and “T2", or "T3" and " T4".
  • the current signal is transmitted to the human or animal body.
  • the two pairs of receiving electrodes "R1, R2" or “R1, R3” can simultaneously or non-simultaneously receive signals in the direction of the long axis of the heart.
  • a pair of receiving electrodes "R2, R3” can receive signals from the short axis of the heart.
  • the voltage signal modulated by the human or animal body is amplified by the preamplifier array "10".
  • the outputs of the preamplifier array “10” are all input to the broadband amplifier array "8", one of the outputs is also connected to a dedicated ECG amplifying collector "7" to obtain an ECG signal, which is sent to the FPGA "2".
  • the broadband amplifier array “8” outputs the signal to the analog-to-digital converter (ADC) "6".
  • ADC analog-to-digital converter
  • This embodiment uses a high-speed and high-resolution analog-to-digital converter. Then the analog-to-digital converter "6" converts the analog signals into digital signals and sends them to FPGA "2".
  • changes in the cardiovascular system of the human body can cause an impedance change of 0.2%, that is, the dynamic range is approximately -54dB. If the result of the received signal requires 1% resolution, the required dynamic range is 94dB, which is about 16 bits. Therefore, the minimum requirement of the digital-to-analog converter used in this embodiment is 16 bits.
  • this embodiment does not use an analog filter, but uses an oversampling DAC. Its high rate will greatly reduce the dependence on the analog filter.
  • the oversampling rate can use 16 times the Nyquist rate or a higher rate. .
  • signal acquisition has higher requirements than signal generation, but oversampling like a DAC requires high hardware performance and resources. If it is a modulated signal, the effect is not obvious. Therefore, the signal acquisition in this embodiment uses a delta-sigma ADC. It needs to be superimposed, and the sampling rate is not high. Specifically, when the sampling speed becomes higher, the bit resolution will decrease. In an alternative embodiment, due to human differences, the dynamic range of ADC needs to be considered. About 3 bits are reserved for this change, while at least one bit is reserved to prevent saturation. In specific implementation, in order to maintain the same dynamic range as the DAC, the ADC will have a minimum of 20 bits, so a full-speed 24-bit sigma delta ADC has a dynamic range of about 20 bits.
  • Figures 5a-5d are flowcharts of methods provided by another embodiment of the present invention, which specifically include signal generation, signal acquisition, and signal processing.
  • the signal generation includes generating a multi-frequency synchronous orthogonal sine wave digital signal S511 from the frequency domain to the time domain, converting the digital signal into an analog signal S512, and amplifying the analog signal to drive the current
  • the pump S513 converts the voltage signal into a current signal S514, and injects the multi-frequency synchronous orthogonal sine wave current into the human or animal body to be tested S515.
  • the signal collection specifically includes receiving an analog voltage signal S521 from the human or animal body and amplifying S522, and converting the analog signal into a digital signal S523.
  • a Fourier transform is performed to convert the signal from the time domain to the frequency domain to obtain a broadband frequency response S531, and these broadband frequency responses are time-varying. Perform frequency correction and filtering on these frequency responses to eliminate distortion and noise S532-S534. These corrected and filtered frequency responses are used to calculate the system transfer function S535, which is also a time-varying sequence.
  • the system transfer function S535 According to the coefficient decomposition of the system transfer function, we can get the heart resistance and capacitance S536. Then filter the resistance and capacitance sequence for the next stage of processing S537. That is, cardiac capacitance is directly related to the size of myocardial cells.
  • this embodiment uses the time derivative of the capacitance divided by the capacitance fluctuation of one cardiac cycle, that is, dc/dt/ ⁇ c.
  • This method is related to specific parameters. For example, in Figure 13d and Figure 14d, after removing the geometric information, only information about the changes in the radius of the cardiomyocytes is left. It represents the changes in cardiomyocytes during the heart cycle. Can do further analysis and machine learning S542 based on this. Cardiac resistance is more complex, which includes the resistance of blood in the ventricular atria and myocardial tissue. However, since the blood changes in the heart dominate, the resistance can be directly used to calculate the blood flow.
  • Fig. 6a-6d are the electrocardiogram of a young man, the curve of the change of cardiac resistance and capacitance over time, and the derivative of the capacitance curve according to another embodiment of the present invention. This is the data of a normal person. Specifically, Fig. 6a is an electrocardiogram (ECG), Fig. 6b is a cardiac resistance curve, Fig. 6c is a myocardial capacitance curve, and Fig. 6d is a curve of myocardial capacitance derivative change.
  • ECG electrocardiogram
  • Fig. 6b is a cardiac resistance curve
  • Fig. 6c is a myocardial capacitance curve
  • Fig. 6d is a curve of myocardial capacitance derivative change.
  • the electrocardiogram is not a standard pattern, and is obtained by simultaneous detection on electrodes that measure the heart voltage signal.
  • Cardiac resistance comes from blood in the chambers and myocardial tissue.
  • the chamber At the end of diastole, the chamber has the largest blood volume and has the smallest electrical resistance.
  • the situation is reversed. This is in full agreement with the actual data, so the displayed cardiac resistance should be dominated by blood resistance.
  • cardiomyocytes relax and have the largest cell volume. Therefore, the capacitance reaches its peak value.
  • myocardial cells have the smallest volume and the smallest capacitance.
  • the capacitance curve does not fully recover to its most diastolic level in this cardiac cycle. There may be two reasons for this. The first is interference; the second is that the diastolic process is also random. Not every cycle is the same, and can be restored to the maximum position, large and small. Looking at the resistance curve of the heart, his heart volume begins to decrease from R wave (contraction), to T wave, and then begins to increase (diastole). It completely coincides with the polarization and depolarization bioelectric activity of the myocardium. Judging from his cardiac capacitance curve, myocardial cells begin to shrink (contract) during the R wave, until the end of the T wave, and begin to grow (diastole).
  • the heart pumping and myocardial work can be estimated, that is, the characteristics of the mechanical activity of biological tissues can be estimated based on the electrical activity of biological tissues.
  • Figures 7a-7d are data of a normal middle-aged male provided by another embodiment of the present invention.
  • Figure 7a is an electrocardiogram (ECG)
  • Figure 7b is a cardiac resistance curve
  • Figure 7c is a myocardial capacitance curve
  • Figure 7d is a myocardial capacitance The derivative of the curve.
  • Figures 8a-8d are data of an elderly woman provided by another embodiment of the present invention, in which Figure 8a is an electrocardiogram (ECG), Figure 8b is a cardiac resistance curve, Figure 8c is a myocardial capacitance curve, and Figure 8d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 8b is a cardiac resistance curve
  • Figure 8c is a myocardial capacitance curve
  • Figure 8d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 8b is a cardiac resistance curve
  • Figure 8c is a myocardial capacitance curve
  • Figure 8d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 8b is a cardiac resistance curve
  • Figure 8c is a myocardial capacitance curve
  • Figure 8d is a myocardial capacitance curve Derivative.
  • the subject's blood pressure is high and premature beat
  • the myocardium had contracted well before the T wave and began to relax, but very slowly, and did not return to the maximum diastolic point.
  • the heart contracts too fast and relaxes slowly. It is speculated that the myocardial tissue is aging.
  • Figures 9a-9d are data of an elderly woman provided by another embodiment of the present invention, in which Figure 9a is an electrocardiogram (ECG), Figure 9b is a cardiac resistance curve, Figure 9c is a myocardial capacitance curve, and Figure 9d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 9b is a cardiac resistance curve
  • Figure 9c is a myocardial capacitance curve
  • Figure 9d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 9b is a cardiac resistance curve
  • Figure 9c is a myocardial capacitance curve
  • Figure 9d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 9b is a cardiac resistance curve
  • Figure 9c is a myocardial capacitance curve
  • Figure 9d is a myocardial capacitance curve Derivative. From the resistance curve, the volumetric contraction of the subject’
  • Figures 10a-10d are data of an elderly woman provided by another embodiment of the present invention, where Figure 10a is an electrocardiogram (ECG), Figure 10b is a cardiac resistance curve, Figure 10c is a myocardial capacitance curve, and Figure 10d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 10b is a cardiac resistance curve
  • Figure 10c is a myocardial capacitance curve
  • Figure 10d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 10b is a cardiac resistance curve
  • Figure 10c is a myocardial capacitance curve
  • Figure 10d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figures 11a-11d are data of an elderly woman provided by another embodiment of the present invention, where Figure 11a is an electrocardiogram (ECG), Figure 11b is a cardiac resistance curve, Figure 11c is a myocardial capacitance curve, and Figure 11d is a myocardial capacitance curve Derivative. From the resistance curve, the contraction of the heart lags slightly and is completed before the T wave peak. Then relax. From the capacitance curve, the starting point of myocardial contraction is normal, but the myocardial contraction is divided into two regions, which is more obvious on the derivative curve of capacitance. Therefore, the condition of cardiomyocytes is not uniform. Cardiomyocytes can also relax and recover. It can be judged that the subject's myocardium is defective.
  • ECG electrocardiogram
  • Figure 11b is a cardiac resistance curve
  • Figure 11c is a myocardial capacitance curve
  • Figure 11d is a myocardial capacitance curve Derivative. From the resistance curve, the contraction of the heart
  • Figures 12a-12d are data of an elderly woman provided by another embodiment of the present invention, where Figure 12a is an electrocardiogram (ECG), Figure 12b is a cardiac resistance curve, Figure 12c is a myocardial capacitance curve, and Figure 12d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 12b is a cardiac resistance curve
  • Figure 12c is a myocardial capacitance curve
  • Figure 12d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figure 12b is a cardiac resistance curve
  • Figure 12c is a myocardial capacitance curve
  • Figure 12d is a myocardial capacitance curve Derivative.
  • ECG electrocardiogram
  • Figures 13a-13d and Figures 14a-14d are data of two persons provided by another embodiment of the present invention.
  • Figure 13a and Figure 14a are electrocardiogram (ECG)
  • Figure 13b and Figure 14b are cardiac resistance curves
  • Figure 13c and Figure 14c are myocardial capacitance curves
  • Figure 13d and Figure 14d are the time curves of the relative change rate of myocardial capacitance.
  • Figure 13d and Figure 14d show the curve of ECG, capacitance and resistance over time, as well as the equivalent deformation rate (S -1 ) of cardiomyocytes, or the relative change rate of capacitance defined as:
  • dc(t)/dt is the time derivative of the capacitance
  • c pp is the peak-to-peak capacitance of this cardiac cycle.
  • ⁇ c(t) is the difference in capacitance at two points in time.
  • Figures 15a-15d and Figures 16a-16d are data of two normal persons provided by another embodiment of the present invention.
  • 15a and 16a are electrocardiograms (ECG)
  • 15b and 16b are cardiac resistance curves
  • 15c and 16c are myocardial capacitance curves
  • 15d and 16d are time curves of the relative change rate of myocardial capacitance.
  • the circle mark is the moment when the heart volume is the smallest
  • the solid dot is the moment when the myocardial cell volume is smallest. In the movement of myocardial tissue in normal people, the circle and the solid point basically overlap.
  • the " ⁇ " in the myocardial capacitance curve is defined as the capacitance value at the moment when the heart volume is minimum minus the minimum capacitance value, and then divided by the peak-to-peak capacitance of this cardiac cycle, as follows:
  • c(t circle ) is the capacitance at the moment when the heart volume is the smallest
  • c(t dot ) is the capacitance at the moment when the myocardial volume is the smallest
  • c pp is the peak-to-peak capacitance of this cardiac cycle.
  • the relative change of myocardial capacitance corresponds to the change of tensor in ultrasound. In ultrasound, when the aortic valve is closed, the same deformation (%) and the same deformation rate (s -1 ) of the tissue are also detected.
  • the time of the minimum value of the heart volume (the maximum value of electrical resistance) can be regarded as the time when the aortic valve is closed.
  • the relative change rate (s -1 ) of the measured capacitance at this moment should be consistent with the equivalent deformation rate (s -1 ) in ultrasonic testing, and both approach zero.
  • the relative change (%) of the measured capacitance at this point in time should be consistent with the tensor deformation in ultrasonic testing, and both approach zero.
  • the maximum equivalent deformation rate (s -1 ) during systole in ultrasound is 1 (s -1 ) for normal people.
  • the relative change in capacitance ( ⁇ ) and the equivalent deformation rate (s -1 ) are both approaching zero.
  • the relative change in capacitance ( ⁇ ) corresponds to the tensor deformation at the time when the aorta is closed in ultrasound Doppler tissue imaging.
  • this embodiment can obtain more information, such as using waveform analysis method, combining P, R, and T waves in the electrocardiogram, combined with statistical models, and resistance and capacitance.
  • the curve characteristics of can be completely analyzed from the perspective of deformation mechanics to analyze the elasticity of the tissue, that is, analyze the change process of the contraction and extension of the longitudinal average length of the cardiomyocytes, such as the speed of contraction and extension, and calculate the elasticity of the myocardium and the ability to do work. .
  • Figures 17a-17d are data of a person with abnormal cardiac tissue provided by another embodiment of the present invention.
  • Fig. 17a is an electrocardiogram (ECG)
  • Fig. 17b is a cardiac resistance curve
  • Fig. 17c is a myocardial capacitance curve
  • Fig. 17d is a time curve of the relative change rate of myocardial capacitance.
  • the circle marked in the figure is the moment when the heart volume is smallest, and the solid dot is the moment when the volume of myocardial cells is smallest. According to calculations, " ⁇ " (27%) and The results of (-5.67) all showed that the heart tissue was abnormal.
  • Figures 18a-18d are data of a person with abnormal cardiac tissue provided by another embodiment of the present invention.
  • Fig. 18a is an electrocardiogram (ECG)
  • Fig. 18b is a cardiac resistance curve
  • Fig. 18c is a myocardial capacitance curve
  • Fig. 18d is a time curve of a relative change rate of myocardial capacitance.
  • the circle marked in the figure is the moment when the heart volume is smallest, and the solid dot is the moment when the volume of myocardial cells is smallest. According to calculations, the result of " ⁇ " (22%) shows that the heart tissue is abnormal, while The result of (-2.6) shows that the heart tissue is slightly abnormal.
  • Figures 19a-19d are data of a person with abnormal cardiac tissue provided by another embodiment of the present invention.
  • Fig. 19a is an electrocardiogram (ECG)
  • Fig. 19b is a cardiac resistance curve
  • Fig. 19c is a myocardial capacitance curve
  • Fig. 19d is a time curve of a relative change rate of myocardial capacitance.
  • the circle marked in the figure is the moment when the heart volume is smallest, and the solid dot is the moment when the volume of myocardial cells is smallest. According to calculations, the result of " ⁇ " (23%) shows that the heart tissue is abnormal, The result of (-0.9) shows that the heart tissue is basically normal.
  • Figures 20a-20d are data of a person with abnormal cardiac tissue provided by another embodiment of the present invention.
  • Fig. 20a is an electrocardiogram (ECG)
  • Fig. 20b is a cardiac resistance curve
  • Fig. 20c is a myocardial capacitance curve
  • Fig. 20d is a time curve of the relative change rate of myocardial capacitance.
  • the circle marked in the figure is the moment when the heart volume is smallest, and the solid dot is the moment when the volume of myocardial cells is smallest. According to calculations, the result of " ⁇ " (17%) shows that the heart tissue is abnormal, The result of (-0.45) shows that the heart tissue is basically normal.

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Abstract

一种测量心肌组织运动特征的非侵入性方法，包括：传输生成的多个同步正交、不同频率的相位可控可调的周期性交流电流至生物体（20）内以产生多个不同频率的周期性交流电压信号；接收由生物体（20）内心脏组织（25）变化调制的交流电压信号，以获取生物体（20）的频率响应；根据频率响应计算心脏组织（25）的电阻和电容；根据电阻和电容估算心肌组织的运动特征。通过引入心肌细胞纵向平均长度，并且根据电容计算心肌细胞的纵向平均长度变化，给出心脏整体的纵向弹性的一种描述方法。提供了连续的、高采样率、非侵入性的方法来测量心肌组织在整个细胞层面上的运动，从而能够检测到心肌细胞更微小的异常变化。

Description

一种测量心肌组织运动特征的非侵入性方法及系统 技术领域
本发明涉及一种对生物体组织的测量技术，特别地涉及一种测量心肌组织运动特征的非侵入性方法和系统。
背景技术
心脏的基本功能是泵送血液，使其在生物体内循环，为组织提供氧气和营养。因此，对心脏动力学参数的测量在医学领域中具有极为重要的意义。心肌细胞的结构特征表明它们是弹性组织。因此心肌组织的运动，尤其是弹性，应是主要的测量目标。目前，心肌组织的应力-应变关系已经得到了广泛研究，相关应用主要通过超声成像系统实现。
心脏有四个腔室，包括两个心房和两个心室。在正常情况下，右心房从上腔静脉和下腔静脉收集血液。然后血液进入右心室，从那里被泵入肺部。左心房接收来自肺静脉的血液，并将其送至左心室，左心室将血液通过主动脉泵送至全身。心脏壁具有三层结构，分别为内心内膜、中心肌和外心外膜。心内膜是单层鳞状上皮的衬里，覆盖心腔和瓣膜。心肌是心脏的肌肉，一层不随意横纹肌组织，其由胶原蛋白的框架限制，使心肌细胞在弯片上排列，整体上形成螺旋结构。心肌是本发明关注的重点。心包膜是一个包含心脏和大血管根部的双层囊。
在病理条件下，最受关注的两个主要情形是高血压和心肌局部缺血。长期高血压最终会导致心室肥厚，甚至心力衰竭。主要由冠状动脉狭窄引起的局部缺血最终会引发心脏病发作，然后是心肌梗塞，并导致心力衰竭。本发明着重 于对心肌组织变化的早期检测，可用于防止突发心脏病。
有许多方法可以在不同层面上测量心脏功能，如器官、组织和细胞等层面上。在器官层面上，对心室容量的估计可以通过图像构建来完成。还可以测量心搏量(SV)和射血分数(EF)，其代表心脏整体的泵功能。但这些参数并没有说明组织的力学特性。直接测量心室壁上的应变被证明是心肌组织活性的一个非常重要的测量，其能够间接反映心脏功能。该测量目前主要是通过成对斑点上的超声多普勒或超声斑点技术完成的。来自超声斑点追踪成像的收缩LV扭转是评估心脏功能的另一种技术。同时，全方位的纵向应变也被证明是预测化疗中心脏毒性的有用工具。
一方面，目前还没有在细胞层面上针对心肌组织健康状态的非侵入性测量方法。另一方面，即使目前的技术可以诊断心肌组织的一些健康状态，但它们有一些缺点。例如，MRI成像方法是一种非常昂贵的技术。而超声成像虽然是一种相对便宜的技术，但仍然受到许多方面的影响。首先，超声成像不能连续地或长期地进行。其次，超声成像结果的分辨率不高，超声成像的结果也依赖于患者，成像结果会因缺少标准化操作而有所不同，超声成像的成本仍然较高。
已经有许多研究人员对动物和人类的侵入性心肌组织表征进行了大量的在先研究。研究表明局部缺血会导致心肌组织的阻抗变化，证明了心脏组织在心搏周期内的阻抗变化。所有该结果都支持本发明。
在许多细胞参数的测量中，需要用细胞尺寸做标准化。在等势细胞里，它依靠电容测量求得细胞的表面积来标准化。这是被广泛利用的技术。这个技术的理论基础是膜电容与细胞表面积成正比。该理论中的膜电容与本发明中的电容存在差异。前者是穿过膜的电容。而本发明中的电容是从膜到无限或地面的电容。它们的物理数学基础是本发明中的电容被证明是与细胞的纵向平均长度 成正比。本发明是该原理在测量心肌组织运动特征的系统中的运用。
发明内容
本发明为解决现有技术中存在的问题，提出了一种测量心肌组织运动特征的非侵入性方法，其目的在于通过测量心脏组织的整体电容来计算心肌细胞的纵向平均长度，从而获得心肌组织的运动特征。所述方法主要用于非治疗目的信息检测。
为实现上述目的，本发明提供了一种测量心肌组织运动特征的非侵入性方法，所述方法包括：传输生成的多个同步正交、不同频率的相位可控可调的交流电流至生物体内以产生多个同步不同频率的周期性交流电压信号；接收由所述生物体内心脏组织变化调制的所述周期性交流电压信号，以获取所述生物体的频率响应；根据所述频率响应计算所述心脏组织的电阻和电容；根据所述电阻和电容估算心肌组织的运动特征。
优选的，根据所述频率响应计算所述心脏组织的电阻和电容包括，根据所述频率响应获取所述生物体的系统传递函数，并进行多室建模以分离所述心脏组织和外围组织。
优选的，所述根据所述电阻和电容估算心肌组织运动特征包括：根据所述电容计算心肌细胞的纵向平均长度及其变化，和/或根据所述电阻计算心脏泵血流量；以及根据所述心肌细胞的纵向平均长度及其变化和/或所述心脏泵血流量，得到心脏整体的纵向弹性状态。
优选的，所述方法还包括，根据所述心脏整体的纵向弹性状态估计心脏和心肌的健康状态和工作状态。
优选的，所述估计包括，根据所述心脏整体的纵向弹性状态的变化的斜率 值、对R波的延迟、峰峰值、心肌细胞纵向平均长度变化曲线及其导数的形状分析所述心脏和心肌的健康状态和工作状态，所述心脏和心肌的健康状态和工作状态包括所述心脏组织的收缩速度、时间、强度和模式，和/或所述心脏组织的舒张速度、时间、恢复和模式。
优选的，所述获取所述生物体频率响应包括，每0.25至5毫秒计算一次特定频率的频率响应估计值。
优选的，根据所述电容计算心肌细胞的纵向平均长度及其变化包括：以每秒200至4000次的速率检测心肌细胞的纵向平均长度及其随时间的变化；使用数字信号处理方法处理所述心肌细胞的纵向平均长度随时间变化的时间序列，所述数字信号处理方法包括数字滤波、快速傅里叶变换(FFT)，以及时域和频域分析。
优选的，所述方法还包括，参考具有相同所述时间序列的心电图以分析所述心肌细胞的纵向平均长度变化序列，所述参考包括比较所述心电图与所述心肌细胞的纵向平均长度变化序列的心搏周期、收缩期和舒张期，和/或所述心搏周期、所述收缩期和舒张期的边界。
优选的，所述进行多室建模以分离所述心脏组织和外围组织包括，每个腔室通过并联的电阻和电容建模，多个腔室之间串联或并联连接。
为实现上述目的，本发明还提供了一种用于实现上述方法的系统，所述系统包括终端和至少一个处理器，其中，所述终端包括：发生器，用于传输生成的多个同步正交不同频率、相位可控可调的周期性交流电流；一个或多个传感器，用于将所述周期性交流电流传输至生物体内以产生多个不同频率的周期性交流电压信号，以及接收由所述生物体内心脏组织变化调制的所述周期性交流电压信号，以获取所述生物体的频率响应；所述处理器用于根据所述频率响应 计算所述心脏组织的电阻和电容，以及根据所述电阻和电容估算心肌组织的运动特征。
优选的，所述传感器用于从不同的部位采集单个或多个数据。
优选的，所述系统可以包括数据库，用于存储所述处理器的处理结果和数据，所述处理器可以检索所述数据库。
优选的，所述处理器可以是远程的，可以远程观察系统在实时模式下工作。
优选的，所述终端还包括人机界面，用于控制系统和/或显示结果。
与现有技术相比，本发明涉及在细胞层面上检测心肌组织收缩和舒张的新技术，其优点在于：本发明提供了连续的、高采样率、非侵入性的方法来测量到心肌组织在整个细胞层面上的运动，从而能够检测到心肌细胞更微小的异常变化；本发明避开了使用成像结果进行分析的传统技术，具有更快捷和标准的测量方法，以及较低的成本。
附图说明
为了更清楚的说明本发明实施例或现有技术的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单的介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明一个实施例提供的模拟心肌细胞二维抽象模型的示意图；
图2是本发明另一个实施例提供的部分系统的总体框架图；
图3是本发明另一个实施例提供的传输和接收电极的设置示意图；
图4是本发明另一个实施例提供的系统电路结构示意图；
图5a-5d是本发明另一个实施例提供的方法流程图；
图6a-6d是本发明另一个实施例提供的一个年轻男性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图7a-7d是本发明另一个实施例提供的一个正常中年男性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图8a-8d是本发明另一个实施例提供的一个老年女性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图9a-9d是本发明另一个实施例提供的一个老年女性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图10a-10d是本发明另一个实施例提供的一个老年女性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图11a-11d是本发明另一个实施例提供的一个老年女性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图12a-12d是本发明另一个实施例提供的一个老年女性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数的示意图；
图13a-13d是本发明另一个实施例提供的一个正常人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图14a-14d是本发明另一个实施例提供的一个正常人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图15a-15d是本发明另一个实施例提供的一个正常人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图16a-16d是本发明另一个实施例提供的一个正常人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图17a-17d是本发明另一个实施例提供的一个心脏组织异常的人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图18a-18d是本发明另一个实施例提供的一个心脏组织异常的人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图19a-19d是本发明另一个实施例提供的一个心脏组织异常的人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图；
图20a-20d是本发明另一个实施例提供的一个心脏组织异常的人的心电图、心脏电阻和电容随时间变化的曲线，以及心脏平均细胞形变率(类似张量变化率)的示意图。
具体实施方式
现在结合附图对本发明实施例作进一步详细的说明。这些附图均为简化的示意图，仅以示意方式说明本发明的基本结构，因此其仅显示与本发明有关的构成。
本发明涉及检测生物体内组织的电特性的无创技术，例如组织的电阻和电容及其变化模式。其目标是捕捉体液变化、血液流动和心血管循环组织的变化，用于生物体的健康状态监测，心血管系统的弹性力学检测验证，也用于非治疗 目的的信息检测。
本发明提供的实施例中，心脏细胞被认为是等势的。因此，可以通过电容测量来估计细胞大小。当心脏细胞处于正常位置时，可以认为它们同时以串联和并联的模式排列，因为心脏结构细胞在空间位置上限制着肌肉细胞。假设正常人的心脏细胞具有相似的体积，可以引入细胞的平均几何尺度变量，表示心肌细胞在外加电磁场影响下的变化过程。与本发明特别相关的变量是心肌细胞纵向平均长度r(t)。它被证明与外场下测量的心肌电容成正比。据此可以通过测量电容，计算心肌细胞的纵向平均长度及其变化，由心肌细胞纵向平均长度的变化，给出心脏整体的纵向弹性的一种描述方法。可以描述心脏整体纵向弹性为外加电场下，心肌电容随时间的相对变化率。
一个最简化的模型，是在外加电磁场方向上，把心肌细胞用一个等效的球体替代，这时纵向平均长度r(t)可以看成心肌细胞的平均收缩半径，在外加电磁场下，一个细胞的电容可以用下述公式来估算：
C(t)＝4πε 0×r(t)
r(t)为心肌细胞的等效平均收缩半径，即纵向平均长度，它是一个时变量。ε 0为细胞磁导率。一般情况下，电容也与心肌细胞纵向平均长度成正比，比例系数与几何形状和心肌细胞磁导率有关。为简化起见，下面以等效球体替代，作为说明。
图1是本发明一个实施例提供的模拟心肌细胞二维抽象模型的示意图，其由多个心肌细胞微结构支持。具体的，当心脏细胞处于正常位置时，由于心脏结构细胞在空间位置上限制着肌肉细胞，可以认为心肌细胞通过串联和并联的方式连接。可选实施例中，假设有M个细胞在纵向方向上串联成链，共有L条链并联连接。同时在外加电磁场方向上，将心肌细胞用一个等效的球体替代， 这时纵向平均长度r(t)可以看成心肌细胞的平均收缩半径，在外加电磁场下，一个细胞的电容可以用下述公式来估算：
Figure PCTCN2019083290-appb-000001
其中，r(t)为心肌细胞的等效平均收缩半径，即纵向平均长度，它是一个时变量，ε 0为细胞磁导率。由此可见，在正常情况下，C(t)和r(t)具有线性关系，即电容与心肌细胞的纵向平均长度成正比，比例系数与几何形状和心肌细胞磁导率有关。在异常状态下，r(t)的位置、大小等会发生变化，或者异常细胞具有不同的磁导率，这将引起C(t)变化，并且具有不同的变化模式。
图2是本发明另一个实施例提供的部分系统的总体框架图。具体的，人体或动物体“20”通过电极或触点“21”和电缆“22”连接至采集系统“23”。可选实施例中，经人体或动物体“20”调制的电压信号通过电极或触点“21”传输至采集系统“23”，采集系统“23”对电压信号处理后传输至主机24做进一步的分析。可选实施例中，主机“24包括人机交互界面，用于接收或传输外部指令。
图3是本发明另一个实施例提供的传输和接收电极的设置示意图。具体的，“25”表示人体或动物体胸腔内的心脏组织，发射电极“27”和接收电极“26”均位于心脏组织“25”的正上方皮肤处。可选实施例中，发射电极“27”包括电极“T1”和“T2”，“T3”和“T4”两对。每对发射电极是分时驱动的，彼此独立。电极“T1”和“T2”分别对准于心脏组织“25”纵向方向的两端外边缘，电极“T3”和“T4”分别对准于心脏组织“25”横向方向的两端外边缘。宽带电流信号由发射电极“27”进入人体或动物体“20”；接收电极“26”包括3个电极“R1”、“R2”和“R3”，均对准心脏组织“25”，并位于发射电极“27”之 间，用于检测宽带电压信号。可选实施例中，电极“R1”和“R2”或“R1”和“R3”分别组成纵向接收对，电极“R2”和“R3”组成横向接收对，系统可以包含这2个接收回路对，用于检测心脏组织在两个方向上的运动变化。
图4是本发明另一个实施例提供的系统电路结构示意图。具体的，该系统不仅可以接收电压信号，还可以将电流信号发射至人体或动物体及其组织中。可选实施例中，在微处理器“1”或现场可编程门阵列(FPGA)“2”的集成电路(IC)中，从频域到时域产生宽带信号。如果宽带信号不频繁地更新，它们的时域信号可以存储在系统中，FPGA“2”可以将信号连续输出到数模转换器(DAC)“4”。可选实施例中，为了减少模拟失真，DAC通常以高速运行，例如超过奈奎斯特(Nyquist)速率的16倍。DAC“4”的输出信号被放大，以驱动宽带电流泵“9”。
可选实施例中，宽带电流泵“9”的输出连接到模拟切换器“11”的输入，“11”的输出分别连到发射电极对“T1”和“T2”，或“T3”和“T4”。由此将电流信号传输至人体或动物体。
可选实施例中，二对接收电极“R1，R2”或“R1，R3”可以同时或非同时接收心脏长轴方向的信号。同时，一对接收电极“R2，R3”可以接收心脏短轴的信号。
可选实施例中，经人体或动物体调制的电压信号由前置放大器阵列“10”放大。前置放大器阵列“10”的输出都输入到宽带放大器阵列“8”，其中一个输出还连接到专用ECG放大采集器“7”，以获得ECG信号，该ECG信号被发送到FPGA“2”。宽带放大器阵列“8”将信号输出到模数转换器(ADC)“6”，本实施例采用一种高速和高分辨率的模数转换器。然后模数转换器“6”将模拟信号转换为数字信号并将它们发送到FPGA“2”。
可选实施例中，人体心血管的变化可导致0.2％的阻抗变化，即动态范围约为-54dB。如果接收信号的结果需要1％的分辨率，则所需的动态范围为94dB，约为16位。因此，本实施例采用的数模转换器的最低要求为16位。
可选实施例中，由于模拟滤波器将改变相位响应，必须进行数字校正以计算人体相位响应。所以本实施例不采用模拟滤波器，而用过采样DAC，它的高速率将极大地降低对模拟滤波器的依赖性，过采样速率可以使用16倍奈奎斯特的速率或更高的速率。
可选实施例中，信号的采集比信号的生成具有更高的要求，但是像DAC那样进行过采样对硬件性能和资源要求很高，如果是调制信号，效果也不明显。因此，本实施例的信号采集采用三角积分ADC。它需要叠加，采样速率不高。具体的，当采样速度变高时，位分辨率会下降。可选实施例中，由于人的差异，需要考虑ADC的动态变化范围。对于该变化保留大约3位，同时保留至少一位余量以防止饱和。具体实现中，为了保持与DAC相同的动态范围，ADC将具有最小的20位，因而全速24位三角积分ADC具有大约20位动态范围。
图5a-5d是本发明另一个实施例提供的方法流程图，具体包括信号产生、信号采集和信号处理。可选实施例中，如图5a所示，信号产生包括从频域到时域产生多频同步正交的正弦波数字信号S511，将数字信号转换为模拟信号S512，放大该模拟信号以驱动电流泵S513，将电压信号转换为电流信号S514，将该多频同步正交的正弦波电流注入待测人体或动物体S515。
可选实施例中，如图5b所示，信号采集具体包括从人体或动物体接收模拟电压信号S521并放大S522，将该模拟信号转换为数字信号S523。
可选实施例中，如图5c所示，在信号采集之后，执行傅里叶变换将信号从时域转换到频域以获得宽带频率响应S531，这些宽带频率响应是时变的。对这 些频率响应进行频率校正和滤波，以消除失真和噪声S532-S534。这些经校正和滤波的频率响应用于计算系统传递函数S535，该函数也是时变序列。根据系统传递函数的系数分解，我们可以获得心脏电阻和电容S536。然后过滤电阻和电容序列以进行下一级处理S537。即心脏电容与心肌细胞大小有直接关系。
可选实施例中，如图5d所示，电容中也有几何信息，应将其除去S541。本实施例在具体实现中使用电容的时间导数除以一个心动周期的电容波动，即dc/dt/Δc，这种方法和具体参量相关。例如在图13d和图14d中，在去除几何信息之后，仅留下关于心肌细胞半径变化的信息。它表示心搏周期中心肌细胞的变化。可以基于此做进一步的分析和机器学习S542。心脏电阻更复杂，其包括心室心房和心肌组织中血液的电阻。但由于心脏内的血液变化占主导，可以直接利用电阻来计算血流量。
图6a-6d是本发明另一个实施例提供的一个年轻男性的心电图、心脏电阻和电容随时间变化的曲线，以及电容曲线的导数。这是一个正常人的数据。具体的，图6a是心电图(ECG)，图6b是心脏电阻曲线，图6c是心肌电容曲线，图6d是心肌电容导数变化的曲线。
可选实施例中，心电图不是标准样式，是在测量心脏电压信号的电极上同时进行检测得到的。心脏电阻来自腔室和心肌组织中的血液。在心脏舒张末期，腔室具有最大血液量，其电阻最小。在收缩结束时，情况则相反。这和实际数据完全吻合，因此显示的心脏电阻应被血液电阻主导。在心脏舒张末期，心肌细胞舒张并具有最大的细胞体积。因此，电容达到峰值。在心脏收缩末期，心肌细胞的体积最小，电容最小。电容曲线在这个心动周期里，没有完全恢复到最舒张的水平。这可能有两个原因，第一是干扰；第二是舒张的过程也有随机性。不是每个周期都是相同的，且都能恢复到最大位置，有大有小。从心脏的 电阻曲线看，他的心脏体积从R波开始变小(收缩)，一直到T波，然后开始变大(舒张)。它完全吻合心肌的极化和去极化生物电活动。从他的心脏电容曲线看，心肌细胞在R波时开始变小(收缩)，直到T波结束，开始变大(舒张)。它没有恢复到舒张的最大点，这是由于心脏舒张的随机性导致的。可以从心脏的体积，以及心肌细胞的体积变化，估计心脏泵血和心肌做功的情况，即根据生物组织的电活动估计其机械活动的特征。
图7a-7d是本发明另一个实施例提供的一个正常中年男性的数据，其中，图7a是心电图(ECG)，图7b是心脏电阻曲线，图7c是心肌电容曲线，图7d是心肌电容曲线的导数。通过比较图7a-7d与图6a-6d可以发现，图7a-7d中心肌细胞体积变大(舒张)的起始点在T波的顶峰，比图6a-6d提前了。由此推测随着年纪的增大，心肌弹性减弱，收缩期变小，心肌舒张起始点越来越提前。被测者的心肌舒张在这个周期里，完全恢复。
图8a-8d是本发明另一个实施例提供的一个老年女性的数据，其中，图8a是心电图(ECG)，图8b是心脏电阻曲线，图8c是心肌电容曲线，图8d是心肌电容曲线的导数。被测者血压偏高，有早搏现象。从电阻曲线看，被测者的心脏收缩正常，但在心肌复极化之前，早早就完成了。复极化之后，在这个周期里，心脏体积没有太大变化，就是没有多少血液充盈。从电容曲线看，心肌在T波之前，早早就收缩完毕，并开始舒张，但非常缓慢，并没有恢复到最大舒张点。心脏的收缩过快，舒张缓慢。由此推测心肌组织老化。
图9a-9d是本发明另一个实施例提供的一个老年女性的数据，其中，图9a是心电图(ECG)，图9b是心脏电阻曲线，图9c是心肌电容曲线，图9d是心肌电容曲线的导数。从电阻曲线看，被测者的心脏体积收缩相对于R波，滞后很多，也就是左心室压力不够，主动脉不能打开，没有血液射出。然后主动脉 打开，心脏血液减少，在T波顶峰稍前完成收缩。然后正常充盈。从电容曲线看，心肌收缩起点正常，但心肌似乎无力，心肌细胞体积变化很小，后期加大。心肌舒张在T波结束。并恢复到最大舒张点。从这里看出，心脏体积的最小点和心肌体积的最小点不一定在同一个时间点。
图10a-10d是本发明另一个实施例提供的一个老年女性的数据，其中，图10a是心电图(ECG)，图10b是心脏电阻曲线，图10c是心肌电容曲线，图10d是心肌电容曲线的导数。从电阻曲线看，被测者的心脏体积收缩相对于R波，有滞后，在T波顶峰稍后完成收缩。然后开始充盈。但充盈严重滞后。从电容曲线看，心肌收缩起点正常，心肌收缩过程基本正常，在T波稍后到达最小。但心肌舒张严重滞后，最后也能基本恢复。
图11a-11d是本发明另一个实施例提供的一个老年女性的数据，其中，图11a是心电图(ECG)，图11b是心脏电阻曲线，图11c是心肌电容曲线，图11d是心肌电容曲线的导数。从电阻曲线看，心脏收缩稍微滞后，在T波峰前完成。然后舒张。从电容曲线看，心肌收缩起点正常，但心肌收缩分成二个区域，在电容的导数曲线上看得更明显。所以心肌细胞的状态不均匀。心肌细胞也能舒张恢复。可以判断被测者心肌有缺陷。
图12a-12d是本发明另一个实施例提供的一个老年女性的数据，其中，图12a是心电图(ECG)，图12b是心脏电阻曲线，图12c是心肌电容曲线，图12d是心肌电容曲线的导数。从电阻曲线看，心脏收缩起始点正常，T波不明显。被测者的心脏收缩分成二部分，这个心动周期没有达到最大收缩。心肌细胞收缩起始点正常。但心肌细胞收缩分成二个阶段，收缩不一致，表明心肌细胞不能协调做功。舒张严重滞后，但还能恢复到最大状态。可以判断被测者具有心脏病。
图13a-13d和图14a-14d分别是本发明另一个实施例提供的两个人的数据。 其中，图13a和图14a是心电图(ECG)，图13b和图14b是心脏电阻曲线，图13c和图14c是心肌电容曲线，图13d和图14d是心肌电容的相对变化率的时间曲线。图13d和图14d显示了ECG、电容和电阻随时间变化曲线，以及心肌细胞的同等形变率(S -1)、或电容的相对变化率
Figure PCTCN2019083290-appb-000002
定义为：
Figure PCTCN2019083290-appb-000003
dc(t)/dt是电容的时间导数，c pp是这个心动周期电容的峰峰值。
或电容的相对变化ε，定义为：
Figure PCTCN2019083290-appb-000004
Δc(t)是两个时间点上的电容差值。
从图中可以看出，正常人的心脏容积变化和心肌细胞体积的变化完全吻合。心肌细胞的同等形变率(s -1)、或本实施例的电容的相对变化率，和电容的相对变化就是二个衡量参数。
图15a-15d和图16a-16d分别是本发明另一个实施例提供的两个正常人的数据。其中，图15a和图16a是心电图(ECG)，图15b和图16b是心脏电阻曲线，图15c和图16c是心肌电容曲线，图15d和图16d是心肌电容的相对变化率的时间曲线。如图所示，圆圈的标记是心脏容积最小的时刻，实心点是心肌细胞体积最小的时刻。在正常人的心肌组织运动中，圆圈和实心点基本重叠。心肌电容曲线中的“ρ”，心肌电容的相对变化，被定义为心脏容积最小时刻的电容值减去电容最小值，然后除以这个心动周期的电容的峰峰值,如下：
Figure PCTCN2019083290-appb-000005
其中，c(t circle)是心脏容积最小的时刻的电容，c(t dot)是心肌体积最小的时刻的电容，c pp是这个心动周期电容的峰峰值。心肌电容的相对变化和超声中的张量变化相对应。超声中在主动脉瓣关闭时，同样检测组织的同等形变(％)和同等形变率(s -1)。本实施例中虽然没有直接的主动脉瓣关闭的信息，但心脏体积的最小值(电阻的最大值)时刻，可以被认为是主动脉瓣关闭的时刻。在这个时刻测得电容的相对变化率(s -1)应该和超声检测中的同等形变率(s -1)吻合，都趋近零。在这个时间点上测得电容的相对变化(％)应该和超声检测中的张量形变吻合，都趋近零。超声中收缩期最大的同等形变率(s -1)对正常人是1(s -1)。也就是说，在心脏体积的最小值或电阻的最大值时刻，电容的相对变化(ρ)和同等形变率(s -1)在都趋近于0。电容的相对变化(ρ)与超声多普勒组织成像中主动脉关闭时刻的张量形变对应。
可选实施例中，借助高采样率和高精度，本实施例能够得到更多的信息，例如利用波形分析法，结合心电图中的P、R、T波，再结合统计模型，以及电阻、电容的曲线特征，可以完全是从形变力学的角度来分析组织的弹性，也就是分析心肌细胞纵向平均长度的收缩和伸张的变化过程，例如收缩的速度和伸张速度，计算心肌的弹性和做功的能力。
图17a-17d是本发明另一个实施例提供的一个心脏组织异常的人的数据。其中，图17a是心电图(ECG)，图17b是心脏电阻曲线，图17c是心肌电容曲线，图17d是心肌电容的相对变化率的时间曲线。图中圆圈的标记是心脏容积最小的时刻，实心点是心肌细胞体积最小的时刻。根据计算，“ρ”(27％)和
Figure PCTCN2019083290-appb-000006
(-5.67)的结果都显示心脏组织出现异常。
图18a-18d是本发明另一个实施例提供的一个心脏组织异常的人的数据。其中，图18a是心电图(ECG)，图18b是心脏电阻曲线，图18c是心肌电容曲线， 图18d是心肌电容的相对变化率的时间曲线。图中圆圈的标记是心脏容积最小的时刻，实心点是心肌细胞体积最小的时刻。根据计算，“ρ”(22％)的结果显示心脏组织出现异常，而
Figure PCTCN2019083290-appb-000007
(-2.6)的结果显示心脏组织稍微异常。
图19a-19d是本发明另一个实施例提供的一个心脏组织异常的人的数据。其中，图19a是心电图(ECG)，图19b是心脏电阻曲线，图19c是心肌电容曲线，图19d是心肌电容的相对变化率的时间曲线。图中圆圈的标记是心脏容积最小的时刻，实心点是心肌细胞体积最小的时刻。根据计算，“ρ”(23％)的结果显示心脏组织出现异常，
Figure PCTCN2019083290-appb-000008
(-0.9)的结果显示心脏组织基本正常。
图20a-20d是本发明另一个实施例提供的一个心脏组织异常的人的数据。其中，图20a是心电图(ECG)，图20b是心脏电阻曲线，图20c是心肌电容曲线，图20d是心肌电容的相对变化率的时间曲线。图中圆圈的标记是心脏容积最小的时刻，实心点是心肌细胞体积最小的时刻。根据计算，“ρ”(17％)的结果显示心脏组织出现异常，
Figure PCTCN2019083290-appb-000009
(-0.45)的结果显示心脏组织基本正常。
上述说明描述了具体实施方式，本领域普通技术人员完全可以在不偏离本项发明技术思想的范围内，进行多样的变更以及修改。本项发明的范围并不局限于说明书上的内容，须根据权利要求的范围来确定。

Claims (14)

  1. 一种测量心肌组织运动特征的非侵入性方法，其特征在于，所述方法包括：
    传输生成的多个同步正交不同频率、相位可控可调的周期性交流电流至生物体内以产生多个同步不同频率的周期性交流电压信号；
    接收由所述生物体内心脏组织变化调制的所述周期性交流电压信号，以获取所述生物体的频率响应；
    根据所述频率响应计算所述心脏组织的电阻和电容；
    根据所述电阻和电容估算心肌组织的运动特征。
  2. 根据权利要求1所述的方法，其特征在于，根据所述频率响应计算所述心脏组织的电阻和电容包括，根据所述频率响应获取所述生物体的系统传递函数，并进行多室建模以分离所述心脏组织和外围组织。
  3. 根据权利要求1所述的方法，其特征在于，根据所述电阻和电容估算心肌组织运动特征包括：
    根据所述电容计算心肌细胞的纵向平均长度及其变化，和/或根据所述电阻计算心脏泵血流量；以及
    根据所述心肌细胞的纵向平均长度及其变化和/或所述心脏泵血流量，得到心脏整体的纵向弹性状态。
  4. 根据权利要求3所述的方法，其特征在于，所述方法还包括，根据所述心脏整体的纵向弹性状态估计心脏和心肌的健康状态和工作状态。
  5. 根据权利要求4所述的方法，其特征在于，所述估计包括，根据所述心脏整体的纵向弹性状态的变化的斜率值、对R波的延迟、峰峰值、心肌细胞纵向平均长度变化曲线及其导数的形状分析所述心脏和心肌的健康状态和工作状态，所述心脏和心肌的健康状态和工作状态包括所述心脏组织的收缩速度、时 间、强度和模式，和/或所述心脏组织的舒张速度、时间、恢复和模式。
  6. 根据权利要求1所述的方法，其特征在于，所述获取所述生物体频率响应包括，每0.25至5毫秒计算一次特定频率的频率响应估计值。
  7. 根据权利要求3所述的方法，其特征在于，所述根据所述电容计算心肌细胞的纵向平均长度及其变化包括：
    以每秒200至4000次的速率检测心肌细胞的纵向平均长度及其随时间的变化；
    使用数字信号处理方法处理所述心肌细胞的纵向平均长度随时间变化的时间序列，所述数字信号处理方法包括数字滤波、快速傅里叶变换(FFT)，以及时域和频域分析。
  8. [根据细则91更正 09.05.2019]　
    根据权利要求7所述的方法，其特征在于，所述方法还包括，参考具有相同所述时间序列的心电图以分析所述心肌细胞的纵向平均长度变化序列，所述参考包括比较所述心电图与所述心肌细胞的纵向平均长度变化序列的心搏周期、收缩期和舒张期，和/或所述心搏周期、所述收缩期和舒张期的边界。
  9. 根据权利要求2所述的方法，其特征在于，所述进行多室建模以分离所述心脏组织和外围组织包括，每个腔室通过并联的电阻和电容建模，多个腔室之间串联或并联连接。
  10. 一种实现上述任一方法的系统，其特征在于，所述系统包括终端和至少一个处理器，其中，所述终端包括：
    发生器，用于生成的多个同步正交不同频率、相位可控可调的周期性交流电流；
    一个或多个传感器，用于将所述周期性交流电流传输至生物体内以产生多个不同频率的周期性交流电压信号，以及接收由所述生物体内心脏组织变化调 制的所述周期性交流电压信号，以获取所述生物体的频率响应；
    所述处理器用于根据所述频率响应计算所述心脏组织的电阻和电容，以及根据所述电阻和电容估算心肌组织的运动特征。
  11. [根据细则91更正 09.05.2019]　
    根据权利要求10所述的系统，其特征在于，所述传感器用于从不同的部位采集单个或多个数据。
  12. [根据细则91更正 09.05.2019]　
    根据权利要求10所述的系统，其特征在于，所述系统还包括数据库，用于存储所述处理器的处理结果和数据，所述处理器可以检索所述数据库。
  13. [根据细则91更正 09.05.2019]　
    如权利要求10所述的系统，其特征在于，所述处理器可以是远程的，可以远程观察系统在实时模式下工作。
  14. [根据细则91更正 09.05.2019]　
    如权利要求10-13任一所述的系统，其特征在于，所述终端还包括人机界面，用于控制系统和/或显示结果。
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