Electrical Impedance Tomography (EIT) is one of the major research topics in biomedical engineering today. It is a new generation of effective non-invasive functional imaging technology that emerged in the 1980s after morphological and structural imaging. This chapter mainly introduces the theory of electrical impedance imaging technology, the principle of bioelectrical impedance measurement system and the theory of weak signal detection technology in bioelectrical impedance measurement system. It analyzes the commonly used weak signal detection methods and introduces the noise source and processing of weak signal detection in impedance measurement. method.
Electrical impedance imaging is a new medical imaging technology based on the traditional CT idea, which is based on the distribution of internal electrical resistivity (conductivity). It is an inexpensive non-invasive detection technology. It does not use radioactive sources and is harmless to the human body. It can be used as a medical monitoring technique for long-term, continuous monitoring of patients. According to the electrical characteristics (resistivity, permittivity) of human tissues and organs, the excitation current or voltage is applied through the surface electrode array, and the boundary voltage or current signal is measured to obtain the internal electrical characteristic parameter distribution of the object, thereby reconstructing the internal structure and functional characteristics of the object. image. Because the electrical properties of different tissues and organs of the human body are different, this EIT image not only contains rich anatomical information, but also can obtain physiological, pathological state and functional information corresponding to the electrical characteristics of tissues and organs, and study the functions of human tissues and organs. There are important clinical implications for changes and disease diagnosis.
The main research issues of EIT are: EIT positive problem, inverse problem calculation and hardware system design. The hardware system design is mainly composed of three parts, electrode array sensor, data acquisition and image reconstruction computer. The sensor converts the multiphase fluid distribution into an output electrode, and the data acquisition system converts the electrode value into a digital quantity and transmits it to a computer, and the computer reconstructs the medium distribution of the measured object according to the image reconstruction algorithm.
The goal of electrical impedance tomography EIT is to detect differences in tissue electrical properties to produce tomographic images. The information on the electrical characteristics of the tissue detected by the EIT is not detectable by other imaging means, which may reveal important clinical medical information that other detection means cannot provide. The amplitude and frequency of the current injected into the human body are relatively low. Compared with other detection methods such as x-ray, CT, and nuclear magnetic resonance (MRI), it has no harm to the human body, low equipment cost, fast imaging speed, safe use and convenient carrying. The advantages.
The human body can be seen as a complex conductor containing a large number of tissue structures with different electrical properties and different spatial distributions. Different tissues of different organs in different parts of the human body have different constituent characteristics and composition components, and exhibit corresponding impedance characteristics, and the difference between them is also very obvious. Moreover, the impedance of the tissue is directly related to the frequency of the applied signal. Tables 2.1 and 2.2 give the resistivity and dielectric constant of different ex vivo tissues at different frequencies.
As can be seen from Table 2.1 and Table 2.2, the higher the frequency, the greater the conductivity of the biological tissue and the smaller the relative dielectric constant. EIT technology is also developing at high speed and high precision. The sampling speed and accuracy in the system are the most critical factors.
2.2 weak signal detection methodWeak signal detection is the front-end processing technology of the EIT system. The front-end detection pre-processing does not drown the useful signal in the noise, which is more conducive to obtaining useful signals. The accuracy of the entire EIT system is improved, and the performance is also greatly improved.
2.2.1 Principle of a typical impedance measurement systemAfter nearly 20 years of development, EIT has become a research field with a certain foundation. Research institutes have also launched their own research methods and systems. Most research institutes use the electrodes around the imaging site of the human body, apply the signal source to the imaging site of the human body through the current, measure the boundary voltage or current, and obtain or store the measured transmission complex impedance data through various methods. The data is further processed by the reconstruction algorithm. The principle is shown in Figure 2.1.
The control and signal detection acquisition unit in the system is often the core of hardware design. This key factor directly affects the later algorithm reconstruction and imaging results. Providing a good front-end signal for signal detection and acquisition is one of the focuses of this paper.
2.2.2 Principle of a typical front-end weak signal detection system
The typical hardware circuit for front-end signal acquisition mainly includes an acquisition electrode, an amplification filter circuit, and an AD conversion. According to the electrical characteristics of the biological body and the basic requirements of the collection technology, the electrical signals fed back by the biological body are very weak, distributed in the mV level and the μV level. Therefore, it is necessary to amplify the weak signal to achieve the requirements of the AD acquisition unit after amplification. The typical weak signal detection and conditioning composition block diagram is shown in Figure 2.2. The design of the front-end detection pre-processing module includes the following parts: electrode sensor, voltage/current measurement amplification, preamplifier, ADC conversion circuit, etc.
Data acquisition technology is a key technology in the process of information acquisition, and it is a bridge to simulate the world to the digital world.
As an important part of information technology, data acquisition technology has been widely used in various fields of national economy and national defense construction. With the development of science and technology, especially the development and popularization of computer technology, data acquisition technology has broad development prospects.
As the front end of data acquisition, weak signal detection directly affects the sampling accuracy and sampling speed of data acquisition. In bioelectrical impedance measurement, data acquisition has its particularity. Since the biological body itself is an electrical conductor, power frequency interference and external electric field and magnetic field induction will form measurement noise and interference signal detection in the human body. Therefore, weak signal detection It has become one of the focuses of the entire system research.
In the process of detecting biological weak signals, since the actual working conditions are not ideal, the detection signals often contain strong background noise. These noises include bioelectric noise such as myoelectric noise, respiratory wave noise, brain electrical noise, and electrocardiogram noise, as well as electrical noise such as power frequency noise, internal noise of the board, and common mode noise.
Noise for weak signal detection is almost everywhere, it always coexists with the signal. In the traditional detection method, the useful signal is always detected by suppressing the noise. However, while suppressing the noise, it is inevitable that the signal will be attenuated and lost. Therefore, how to deal with this contradiction is often a problem that the researcher needs to solve.
It is generally believed that the signal is deterministic and a function of time, and we can calculate and measure its value at a certain time in advance. In actual signal detection, the signal is often accompanied by noise or other interference.
Due to the special nature of biomedicine, the output signal of the sensor is usually very weak and submerged in the background of strong noise, so noise becomes the main problem of signal detection. The purpose of weak signal detection is to distinguish the signal from the noise and recover the signal extracted from the flood and noise. In signal detection systems, the highest signal level that can be processed is limited by the characteristics of the circuit, but the minimum detectable level depends on the noise, ie the noise limits the dynamic range of the system and the resolution of the sensor. This is especially true for bioelectrical impedance measurement systems. The noise in bioelectrical impedance measurement consists of two parts, one is the interference caused by the characteristics of the biological body's own impedance, which forms noise through the sensor, electrode contact surface and individual individual differences; the other part is caused by random disturbance in the circuit. The electrical noise generated.
The power excitation signal has a small amplitude and strong noise. Therefore, whether it is effective to remove the noise in the weak power excitation signal and extract its characteristic information is of great significance for the research and clinical application of the power excitation signal. The brain's EEG, ECG, and myoelectricity are all causes of noise. EEG is a combined result of postsynaptic potentials in human brain neurons, and is an overall reflection of the electrophysiological activity of cranial nerve cells in the cerebral cortex or scalp surface [20]. In a natural state free from external stimuli, the spontaneous EEG signals generated by the human brain are generally regarded as non-stationary and relatively prominent random signals. The electrocardiogram and myoelectric signals of the organism itself are similar periodic signals generated by the human body in a natural state, and also cause noise.
2.2.3.2 Electrical noise The electrical noise in the detection is mainly caused by the internal noise of the detection system, which is generated by the resistor and various devices. The vast majority of electrical noise is a continuous random variable, which is a kind of independent random stationary process. At any moment, its amplitude, phase and waveform are random, but it is subject to a certain statistical distribution law.
Resistance thermal noise is generated by the thermal motion of free electrons inside the resistor. The undulating current, the charged particles (free electrons) in the resistor are thermally excited at a certain temperature, and collide with each other inside the conductor (thermal turbulence) to collide with each other. When the collision occurs between the two collisions, a duration is generated. Very short pulse current. The combination of such pulse currents produced by many such random thermal turbulence electrons creates an irregular current inside the resistor. For a sufficiently long period of time, the average value of its current is equal to zero, and the instantaneous value varies above and below the average value. When the actual circuit contains multiple resistors, each resistor will introduce a noise source. Generally, if there are multiple resistors connected in parallel, the total noise current is equal to the mean square value of the noise current generated by each conductance.
Biological weak signals are characterized by small amplitudes that are often submerged in noise. In order to detect weak signals covered by background noise, people have carried out long-term research work, analyze the causes and laws of noise, study the characteristics, correlation and statistical characteristics of the measured signals to find useful signals from background noise. Methods. Commonly used weak signal detection methods are: coherent detection method, weak signal detection based on chaotic vibrator, synchronous accumulation method, two-way denoising method, narrow-band filtering method, and the like.
2.3.1 Coherent detection methodThe related receiving technology is a feature of applying signal periodicity and randomness of noise, and adopting autocorrelation or cross-correlation operation to achieve a technique of removing noise and detecting signals [13][15]. Since the signal and noise are independent processes, according to the correlation function and the cross-correlation function definition, the signal is only related to the signal itself, and is not related to noise, and the noise is generally not related.
2.3.1.1 Autocorrelation detectionThe block diagram of the implementation of autocorrelation detection [13] is shown in Figure 2.3.
Let input xi(t) consist of measured signal si(t) and noise ni(t), namely:
Xi(t) is simultaneously input to the two channels of the relevant receiver, one of which will pass through the delayer, causing it to delay for a period of time Ï„. Both the delayed xi(t-Ï„) and the undelayed xi(t) are sent to the multiplier, and then the product is integrated, and then the average value is output, thereby obtaining the correlation value of a point on the correlation function. If the delay time Ï„ is changed, the above calculation can be repeated to obtain the correlation curve of the correlation function R xx(Ï„) and Ï„, that is, the output obtained from the correlation is:
According to the nature of the cross-correlation function, since the signal s(t) is not correlated with the noise n(t), and the average value of the noise is zero, R sn(τ)=0, R ns(τ)=0, then R xx( τ)= R ss(τ) +R nn(τ). As τ increases, R nn(τ) → 0, then for a sufficiently large τ, R xx(τ) = R ss(τ) can be obtained. This results in the autocorrelation function R xx(τ) of the signal si(t), which will contain some of the information carried by si(t).
As the time Ï„ increases, the autocorrelation function of the noise decays rapidly, and the autocorrelation function of the signal is a periodic function of small attenuation, so that a useful signal can be detected.
If the repetition period or frequency of the transmitted signal is known, a local signal having the same repetition period as the transmitted signal can be issued at the receiving end to cross-correlate the local signal with the noise-mixed input signal. Figure 2.4 is a block diagram showing the principle of cross-correlation detection. Let the input x(t) be: x (t ) = s (t ) +n (t )
s(t) is the signal to be tested, n(t) is the noise mixed in the signal s(t), and y(t) is the known reference signal. If y(t) is related to the signal s(t), There is no correlation with the noise n(t). After the input is delayed, multiplied, integrated and averaged, the cross-correlation output R xy(Ï„) is obtained.
Since the reference signal y(t) has some correlation with the signal s(t), and y(t) has no correlation with the noise n(t), and the average value of the noise is zero, there is R ny(Ï„)= 0, ie:
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