The main principles of monitoring and identification of recurrent laryngeal nerve (RLN) are considered in the paper. The steps of identification and tools for stimulation of surgical wound tissues during surgery on neck organs are represented. Improved information technology of RLN.
Recurrent laryngeal nerve (RLN) monitoring is very important procedure during the
neck surgery. For this purpose, special neuro monitors are used. They work based on
the principle of surgical wound tissues stimulation and estimation of results of such
stimulation [
The alternating current with fixed frequency is required for other
electrophysiological method of RLN stimulation and monitoring. We have reviewed the mathematical
models and methods of dealing with this problem in [
However, the mentioned methods can lead to the RLN damage. The reason of this risk is mostly the accuracy of output information signal processing (the result of stimulation of surgery wound tissues).
With the help of the method of this signal spectral analysis, we have an opportunity to choose the major spectral components and classify the surgery wound tissues. The high risky area can be detected by the methods of RLN location visualization that incorporate the model of information signal amplitude on the surgery wound.
It’s essential to combine all of these methods into one signal processing (its reaction on the stimulation of the surgery wound tissues) technology. In the paper, we concentrate on exactly this task.
The method of RLN monitoring is based on the task of its stimulation as the first sub-task. Other aspect of the task is the processing of reaction on RLN stimulation. After the processing, a conclusion about the RLN location in the surgery area is made.
Let’s review the principles of functioning of the existing hardware solution designed to
identify RLN [
Larynx 2 with respiratory probe 1 inside and inside of this probe the sound sensor 3 placed vocal cords 4.
We have invented the new algorithm of RLN allocation algorithm in the area of surgical intervention. This algorithm consists of four main steps. A detailed description of all steps of our algorithm is given below. The figure depicts the visualization of our algorithm steps sequence.
Step 1. Record the sound signal of the reaction to stimulation of the area of surgical intervention
As it’s known, the resistivity of surgery wound tissues with different structure may range from 0m to 1kOm. Concerning the nerve tissues, their resistance depends on their thickness. Moreover, the method of signal transmission in these issues is significantly different from current transmission in conductor (electron motion at selected voltage difference) The picture 2 illustrates the method of charge propagation in the nerve tissues, including RLN. Let’s inspect this method in more detail.
RLN is the set of nerve fibers wrapped in the medullary sheath with the isolator electronic properties. Medullary sheath covers the axon lengthwise, however it is absent at the point of projection discharge from the neuroсyton in the areas of the axon divarication and gaps, that are called Node of Ranvier. The areas of these nodes contain ion channels with positively-charged sodions. There is no voltage difference between the neighbor nodes. When applying the voltage with the difference in any area, this area is stimulated. Because of the natrium channels opening and penetration of sodions into the tissue, the stimulated node becomes negatively-charged in comparison with contiguous, not stimulated node.
The result of the voltage difference between these areas is the ion flow through the tissue fluid in order to set an electrical equilibrium as shown in the figure 3.
In such a way, we will receive reaction on the RLN stimulation. In the process of stimulation it is necessary to provide the relevant reaction on the surgery wounds stimulation. It is done to avoid the nerve fiber damage because of the current intensity and provide the traction of muscles, that stretch vocal cords.
This block should provide not only formation of direct current, alternating current and stimulation current in the form of rectangular impulses, but also the corresponding parameters of this current. The last turned out to be a difficult enough task. Schematic of impulse process in the myelin nerve.
The main functions of electrostimulator are: generating of direct current with strength in range from 0.5 to 2 mA independently on the type and structure of the surgical wound tissue; generating of alternating current with the frequency range from 1 to 1000 Hz with the strength from 0.5 to 2 mA independently on the type and structure of the surgical wound tissue; generating of impulses with regulated duration from 1s to 1 ms, frequency range from 1 to 1000 Hz and regulated current strength from 0.5 to 2mA. will get the wrong result in future steps. where is і-th countdown of information signal.
If this energy exceeds the threshold, then, this is the beginning of the segment: ≥ then, =
If the energy of n counts is less than the threshold, then, this is the end of the segment: ≤ then,
= = { ∈ [ ; ]}
So, the resulting segment consists of a set of countdowns: where [ ;
] is interval of countdowns of determined signal.
Step 3. Spectral transformation of sound signal.
This step is necessary for transforming the sound wave into the spectral form. The famous method of sound signal processing Fourier transform is used for this purpose.
Step 4. Prediction of spreading spread amplitude of spectrum.
A mathematical model for recurrent laryngeal nerve identification is considered as
an interval discrete dynamic model. For prediction, we used the method of structural
and parametric identification based on the behavioral model of artificial bee colony
[1314]. Behavioral model of artificial bee colony imitates the foraging behavior of the
honeybee colony [
As result, we have developed a device that recognizes the recurrent laryngeal nerve. This device implements a logic described above and is demonstrated below. 1 is sound sensor 2 is respiratory tube, 3 is negative needle clip, 4 is positive probe, 5 is power block, 6 is sound card, 7 is single-board computer, 8 is current stabilizing analog circuit
The device was used during the operations under the doctor’s supervision. It allowed us to get the results of stimulation represented by sound files. We have processed these files and demonstrated the results in the next section. 4
We have conducted a number of operations with the device for RLN monitoring. These operations allowed us to analyze how different types of tissues respond to the stimulation. This response was recorded and processed. We have outlined the spectral components of more than 200 stimulation points: 134 of them are nerve stimulation and 80 muscle tissue. We have also outlined and calculated the energy of each stimulation point. It allowed us to track the difference of reaction on different types of tissues.
Number of point Stimulation 1 2 3 4 5 6
Normalized amplitude of the main spectral component 0,460823977
Calculated energy of the main spectral component
0,971944721 0,546804881 0,522947842 0,174415538 0,288416452 0,216996177 8 0,286873816 1,076928574 nerve 9 0,36834112 0,909430922 nerve 10 0,368404502 0,884671695 nerve 11 0,35624509 0,90006498 nerve 12 0,178152121 0,484689097 nerve 13 0,211462636 0,540572562 nerve 14 0,470992005 1,120013139 nerve 15 0,414762358 0,783916271 nerve 16 0,244265797 0,596207752 nerve 17 0,271379822 0,663615117 nerve 18 0,216705181 0,861875304 nerve 19 0,206413221 0,62733553 nerve 20 0,19285882 0,504861419 nerve 21 0,255752665 0,551960348 nerve 22 0,206541859 0,738761809 nerve
Table 1 provides the main spectral components of the nerve tissue stimulation. Below, we have graphically demonstrated the spectral stimulation points of the nerve.
The table 2 provides the main spectral components of the muscle tissue stimulation. Below are the graphical spectral components of muscle tissue stimulation points.
As you can see from the table 1 and 2 - maximal spectra of nerve tissue are much higher than the maximum spectra of the muscle tissue. We have calculated the average indexes of the spectral components of all stimulation points of two tissue types.
As we can see from the image below, the average maximum index is higher than the average component of the muscle tissue.
Taking into account the foregoing, we counted the energy of all points and calculated their maximum and minimum thresholds. 0,15
0,1 0,05
0 0,5
Energy of Signal Nerve
Muscle
As you can see on the next figure, the maximum threshold of the muscle tissue doesn’t reach the maximum threshold of the nerve. It allows us to determine the type of the stimulated tissue. The further research is dedicated to the improvement of the RLN identification method.
For realizing of the proposed methods and tools for electrophysiological RLN monitoring and identification we used small device named as Raspberry Pi 3. After probation method of RLN identification in real patients we have got very optimistic results. We made surgery on sample of 200 points of simulations for many patients. And in this simple have shown for as the in 80 % cases we can correctly detect then main location of recurrent laryngeal nerve. As results we can decrease the risk of malignant interference with nerve activity from 21 % to 16%