=Paper=
{{Paper
|id=Vol-2300/Paper14
|storemode=property
|title=Methods and Tools for Electrophysiological Monitoring of Recurrent Laryngeal Nerve Monitoring During Surgery on Neck Organs
|pdfUrl=https://ceur-ws.org/Vol-2300/Paper14.pdf
|volume=Vol-2300
|authors=Mykola Dyvak,Volodymyr Tymets,Andriy Dyvak,Oksana Huhul
|dblpUrl=https://dblp.org/rec/conf/acit4/DyvakTDH18
}}
==Methods and Tools for Electrophysiological Monitoring of Recurrent Laryngeal Nerve Monitoring During Surgery on Neck Organs==
https://ceur-ws.org/Vol-2300/Paper14.pdf
54
Methods and Tools for Electrophysiological
Monitoring of Recurrent Laryngeal Nerve Monitoring
During Surgery on Neck Organs
Mykola Dyvak1, Volodymyr Tymets1, Andriy Dyvak2, Oksana Huhul1,
1. Faculty of Computer Information Technologies, Ternopil National Economic University, UKRAINE, Ternopil, 8
Chekhova str., e-mail: mdy@tneu.edu.ua, volodymyrtymets@gmail.com, oksansggg@i.ua
2. Department of Surgery with Urology №1 by L.Ya. Kovalchuk, I. Horbachevsky Ternopil State Medical University,
UKRAINE, Ternopil, 1 Maidan Voli, e-mail: dyvak_anmy@tdmu.edu.ua
Abstract – Methods and tools for electrophysiological II. TASK STATEMENT
monitoring and identification of recurrent laryngeal
nerve (RLN) are considered in the paper. The method Let’s consider the functionality principles of existing
hardware solution for RLN identification. The scheme of
of identification and tools for stimulation of surgical
the device is shown in Fig.1 [7].
wound tissues during surgery on neck organs are
represented. Improved information technology of RLN
identification and results of its application are shown.
Keywords– neck organs surgery, recurrent laryngeal
nerve, single-board computer, multi-functional electro-
stimulator.
I. INTRODUCTION
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
[1-5]. The main problem that arises during this process is
the proper choice of stimulation methods. In [1] and [4],
the latest results of researches related to RLN neuro-
monitoring are represented.
Other electrophysiological method of RLN stimulation 1 is respiratory tube, 2 is larynx, 3 is sound sensor, 4 are vocal
and monitoring requires the alternating current with fixed cords, 5 is probe, 6 is surgical wound, 7 is multifunctional block
frequency [7]. The methods and mathematical models for for RLN stimulation and visualize of RLN
solving this problem are considered in [6-7]. Fig. 1. Scheme of device and method for RLN identification
The main problem of the mentioned methods is a high
risk of RLN damage. This risk is mainly caused by the In respiratory tube 1 that inserted into larynx 2, the
accuracy of output information signal (result of surgery sound sensor 3 implemented and positioned above vocal
wound tissues stimulation) processing. Methods of cords 4.
spectral analysis of this signal [7] give an opportunity to Probe 5 connected to alternator. It performs the
select the main spectral component and to classify the function of a current generator controlled by the single-
surgery wound tissues. Methods of RLN location board computer 7. Surgical wound tissues are stimulated
visualisation based on building the model of distribution by the alternating current with the fixed frequency via
of amplitude of the information signal on the surgery probe. As a result, vocal cords 4 are stretched.
wound allow to detect the high-risky area [7]. The
Flow of air that passes through patient’s larynx, is
combination of these method in one information
modulated by stretched vocal cords. The result is
technology of processing of signal (reaction on the
surgery wound tissues stimulation) is an important task. registered by voice sensor 3. Obtained signal is amplified
The paper is dedicated to this task. and is processed by single-board computer 7.
The method of RLN monitoring is based on the task of For processing of obtained signal, special software is
its stimulation as the first sub-task. Other aspect of the installed on a single-board computer. The main functions
task is the processing of reaction on RLN stimulation. of the software are:
After the processing, a conclusion about the RLN location - segmentation of information signal based on analysis
in the surgery area is made. of its amplitude;
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
55
- analysis of amplitude spectrum using Fourier- main spectral component in Fig. 2 a). Fig. 2 b) reflects the
transform; result of stimulation of the muscle tissue at a distance of
- calculation of a spectral component with a maximal not more than 3 mm, with a specific distinguished main
amplitude (let’s call it “the main spectral component” spectral component with a small amplitude value. Finally,
further); the result of RLN stimulation with a specific main spectral
- classification of tissues of surgical environment at component with a sufficiently high normalized amplitude
the points of stimulation using threshold method. (6 times higher than in the previous case) is illustrated in
Software for changing the frequency of RLN Fig. 2 c).
stimulation is written in programming language Node.js.
Node was created by Ryan Dahl. Now Node.js is a The described properties give a possibility to develop
trademark of Joyent, Inc. and is used with its permission the method, mathematical model and to improve the RLN
and maintained by the Node.js Foundation [8-9]. Node is identification technology in general.
open-source platform and located on Github. III. MATHEMATICAL MODEL FOR RLN
Block of processing and displaying information is
IDENTIFICATION
developed based on single-board computer Raspberry Pі 3
[10]. Let’s represent obtained set of stimulated points in
Raspberry Pi 3 was selected because of two upgrades such form:
made to it. The first one is a next generation Quad Core
− +
Broadcom BCM2837 64-bit ARMv8 processor making �𝑧𝑧𝑖𝑖,𝑗𝑗 � = �𝑧𝑧𝑖𝑖,𝑗𝑗 , 𝑧𝑧𝑖𝑖,𝑗𝑗 �, 𝑖𝑖 = 1, … , 𝐼𝐼, 𝑗𝑗 = 1, … , 𝐽𝐽, (1)
the processor speed increased from 900 MHz in the Pi 2 to
up to 1.2GHz in the Pi 3. where [ z i , j ] is an interval estimation of the normalized
The second one is addition of the built-in BCM43143 Wi- amplitude of main spectral component; i, j are indices of
fi chip. There is also Bluetooth Low Energy (BLE) discrete increments of coordinate values on X and Y axes
module implemented on the board. relatively to some initially given point. Interval estimation
At the same time, such an approach does not ensure a of the amplitude [ z i , j ] is caused by the fact that different
significant decrease in the RLN damage risk. For
values of main spectral component amplitude z i , j may be
detection of high-risky area of surgery, it is expedient to
build a mathematical model for RLN identification. obtained for equal values of i and j. In addition, there is
For these purposes, we use the properties of surgical some error of detecting the point with coordinates i, j.
wound tissues which are characterized by a different A mathematical model for RLN identification is
reaction on the stimulation by alternating current with a considered as a discrete difference model (DDM), that is,
fixed frequency. In Fig. 2, the fragments of amplified the difference scheme in such form [1, 7]:
information signal obtained from the sound sensor and
fragments of their spectral characteristics are shown. −
�𝑣𝑣�𝑖𝑖+1,𝑗𝑗+1 � = �𝑣𝑣�𝑖𝑖+1,𝑗𝑗+1 +
; 𝑣𝑣�𝑖𝑖+1,𝑗𝑗+1 � = 𝑓𝑓⃗𝑇𝑇 (�𝑣𝑣�0,0 �, …,
�𝑣𝑣�0,𝑗𝑗 �, … , �𝑣𝑣�𝑖𝑖,0 �, … , �𝑣𝑣�𝑖𝑖,𝑗𝑗 � ∗ 𝑔𝑔�⃗,
𝑖𝑖 = 𝑑𝑑 + 1, … , 𝐼𝐼, 𝑗𝑗 = 𝑑𝑑 + 1, … , 𝐽𝐽, (2)
where f T (•) is a vector of unknown basis functions that
define the structure of DDM; vi , j is a predicted value of
main spectral component amplitude in the point with
discrete specified spatial coordinates i, j; g is a vector of
unknown parameters of DDM; d is order of DDM. Further,
the model (2) will be called an interval discrete difference
model (IDDM).
To identify this model, the results of measurement of the
main spectral component amplitude in the points of
stimulation are used.
Based on the requirements of ensuring the accuracy of
the model within the accuracy of the experiment, the
setting of IDDM (2) will be realized using such criterion
[2]:
− + − +
�𝑣𝑣�𝑖𝑖,𝑗𝑗 ; 𝑣𝑣�𝑖𝑖,𝑗𝑗 � ⊂ �𝑧𝑧̂𝑖𝑖,𝑗𝑗 ; 𝑧𝑧̂𝑖𝑖,𝑗𝑗 �, ∀𝑖𝑖 = 1, … , ∀𝑗𝑗 =
1, … , 𝐽𝐽. (3)
Fig. 2. Result of stimulation of RLN by alternating current with By substituting in the expression (3), the recurrent
frequency of 300 Hz. − +
expression (2) instead of the interval estimations [vi , j ; vi , j ] ,
We can see the result of stimulation of the muscle together with the defined initial interval values, we obtain
tissue at a distance of more than 1 cm from RLN, with a the interval system of non-linear algebraic equations
specific blurred spectrum, without a clearly distinguished (ISNAE) [12]:
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
56
�𝑣𝑣� − ; 𝑣𝑣� + � ⊆ �𝑧𝑧̂0,0− +
; 𝑧𝑧̂0,0 �;
⎧ 0,0 0,0
⎪ − ⋮
+ − +
⎪ �𝑣𝑣�𝑖𝑖−2,𝑗𝑗−2 ; 𝑣𝑣�𝑖𝑖−2,𝑗𝑗−2 � ⊆ �𝑧𝑧̂𝑖𝑖−2,𝑗𝑗−2 ; 𝑧𝑧̂𝑖𝑖−2,𝑗𝑗−2 �;
⎪ ⃗𝑇𝑇
⎪ �𝑣𝑣�𝑖𝑖−1,𝑗𝑗−1 � = 𝑓𝑓 (�𝑣𝑣�0,0 �, … , �𝑣𝑣�𝑖𝑖−2,𝑗𝑗−2 �,
⎪ 𝑢𝑢
�⃗𝑜𝑜 ) ∗ 𝑔𝑔�⃗ ;
− ⃗𝑇𝑇
⎨ 𝑧𝑧𝑖𝑖,𝑗𝑗 ≤ 𝑓𝑓 ��𝑣𝑣�0,0 �, … , �𝑣𝑣�𝑖𝑖−1,𝑗𝑗−1 �, 𝑢𝑢�⃗𝑖𝑖,𝑗𝑗 , … , 𝑢𝑢
�⃗𝑖𝑖,𝑗𝑗 � ∗
⎪ 𝑔𝑔�⃗ ≤ 𝑧𝑧𝑖𝑖,𝑗𝑗
+
Fig.4. Scheme of information technology for information signal
⎪ (as a result of stimulation of surgical wound of tissues)
−
⎪ 𝑧𝑧𝑖𝑖+1,𝑗𝑗 ≤ 𝑓𝑓⃗𝑇𝑇 ��𝑣𝑣�0,0 �, … , �𝑣𝑣�𝑖𝑖−1,𝑗𝑗−1 �, 𝑢𝑢
�⃗𝑖𝑖,𝑗𝑗 , … , 𝑢𝑢
�⃗𝑖𝑖,𝑗𝑗 � ∗ processing.
⎪ Step 1. Obtaining of information signal (as result of
⎪ 𝑔𝑔�⃗ ≤ 𝑧𝑧𝑖𝑖+1,𝑗𝑗
+
stimulation of surgery wound tissues).
⎩𝑖𝑖 = 2, … 𝐼𝐼, 𝑗𝑗 = 2 At this step, the multifunctional block for surgical
The solution of the obtained system is a vector of wound tissues stimulation is used. The result of
stimulation is recorded by the sound sensor. The obtained
unknown parameters of IDDM g . Methods of solving of
this system are described in the papers [11-12]. information signal is digitized and processed by the
The obtained model in the form of IDDM (2) is single-board computer Raspberry Pi 3. The stimulation
graphically represented in Fig. 3. results are processed in a real-time mode. Also, they may
be stored on the external data storages.
Step 2. Segmentation of information signal.
This step is needed to highlight the patient reaction on
tissues stimulation from sound signal taken during inhale
or exhale.
In Fig. 4, the information signal is displayed and the
segmentation principle is visually represented. As we can
see from the Fig. 2, there are two segments. Each of them
is result of the stimulation of RLN. The intervals between
the segments represent delay of a patient’s breath. These
intervals are not informative for tissue classification
method and named as the noise.
Unlike existing method of segmentation
“synchronization with the appearance of the current of
stimulation”, main approach of automatic segmentation is
based on the principle of threshold choice of an
informative segment.
Fig. 3. Visualization of RLN location using IDDM (2).
As we see in Fig. 3, the distribution of values of Fig. 5 Illustration of the information signal segmentation.
amplitude of the main spectral component of signals as
the reaction on RLN stimulation and surgical wound Because information signal is represented in digital
tissues stimulation is marked with grey color. The RLN form, for determining of segment beginning to estimate
location is marked by black colored line on the plane. the energy threshold of current, n countdowns are
proposed:
IV. METHODS AND TOOLS OF
ELECRTOPHYSIOLOGICAL MOMITORING RLN 𝐸𝐸 = ∑𝑛𝑛𝑖𝑖=1 𝑢𝑢𝑖𝑖2 , (4)
where 𝑢𝑢𝑖𝑖 is і-th countdown of information signal.
For RLN location identification in the surgery area
during surgery on neck organs, we improved the If this energy exceeds the threshold, then, this is the
information technology. This technology consists of 4 beginning of the segment:
main steps, a detailed description of which is given below.
The scheme of the information technology is shown in 𝐸𝐸 ≥ 𝐸𝐸𝑡𝑡𝑡𝑡 , then, 𝑢𝑢𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 𝑢𝑢𝑛𝑛 . (5)
Fig. 4. If the energy of n counts is less than the threshold,
then, this is the end of the segment:
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
57
𝐸𝐸 ≤ 𝐸𝐸𝑡𝑡𝑡𝑡 , then, 𝑢𝑢𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 𝑢𝑢𝑛𝑛 .
(6) tissues in surgical wound during surgery on the neck
So, the resulting segment consists of a set of organs” (0117U000410).
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ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic