HRV derived time domain index of data serials similarity

to measure anesthetic depth based on ensemble neural network

Quan Liu1, 2, Li Ma1, 2, Ren-Chun Chiu5, Shou-Zen Fan3, Maysam F. Abbod4, Jiann-Shing Shieh 5, 6,*

1 School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; 2 Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of

Education, Wuhan 430070, ChinaE-Mail: quanliu@whut.edu.cn (Q.L.) ; excellentmary @whut .com (L.M.)

3 Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan; E-Mail: shouzen@gmail.com;

4 Department of Electronic and Computer Engineering, Brunel University London, UB8 3PH, UK;E-Mail: maysam.abbod@brunel.ac.uk;

5 Department of Mechanical Engineering, and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li 32003, Taiwan;Email: q99246538@gmail.com

6 Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan.

* Author to whom correspondence should be addressed; E-Mail: jsshieh@saturn.yzu.edu.tw; Tel.: +886-3-4638800 ext. 2470; Fax: +886-3-4558013.

Abstract: Evaluation of depth of anesthesia (DoA) accurately is always critical in clinical

surgery. Traditionally, index derived from electroencephalogram (EEG) plays the dominant

role to measure DoA. For lack of ideal approach to quantify the consciousness level when

drugs are used like ketamine, nitrous oxide and so on, much many efforts are devoted to

optimize the DoA measurement methods. In this study, 110 cases of physiological data are

analyzed to predict DoA. We propose a short term index generated by heart rate variability

(HRV) of electrocardiogram (ECG) called similarity index (SI). It represents the data

difference complexity by observing two consecutive 32s HRV data segments. Compared with

expert assessment of consciousness level (EACL) of DoA, it shows strong correlation with

anesthetic depth. In order to optimize measure thise effect, artificial neural network (ANN)

models are constructed to fit model SI. We also conduct tThe ANN model is developed absed

on blind cross validation to overcome the random error of neural network. The results show

that Furthermore, the ensemble ANN (EANN) presents better capability accuracy of DoA

1

assessment. Our This research shows thatis HRV related SI parameter can be another an

effective method for DoA evaluation. We It is believed that it is possible and meaningful to

incorporate the SI to measure the DoA with other methods together if suitablywhen

conditions allow.

Keywords: HRV, DoA, similarity index, artificial neural network, EACL

1. Introduction

Anesthesia has been the essentially important procedure in almost all surgeries [1, 2]. General

anesthesia is a kind of “artificial sleep” — actually a drug-induced, reversible condition that

shows specific behavioral and physiological features like unconsciousness, analgesia and

akinesia. Clinically and practically, the routine observations, for example, heart rate,

respiration, blood pressure, lacrimation, sweating and so on, mainly assist the doctors to

control the anesthetic management smoothly and safely. Nevertheless, clinical post-operation

challenges including airway and oxygenation problems, emergence delirium [3] and cognitive

dysfunction [4] still exist when patients recover from general anesthesia, especially for the

elderly with risk of even stroke and heart attack [5]. Therefore, accurate monitoring of depth

of anesthesia (DoA) can contribute to improvements in safety and quality of anesthesia, thus

fulfilling patients’ satisfactions.

Because the state of general anesthesia is aroused by the anesthetics functioning in the spinal

cord, stem and cortex in brain [6, 7], it is reasonable to get inspiration from

electroencephalogram (EEG) patterns [8]. Two main indices derived from EEG are the

bispectral index (BIS) (Aspect Medical Systems, Newton, MA, USA) [9] and Entropy family

(GE Healthcare, Helsinki, Finland) [10]. The former one is obtained by calculating the

adjusted weights on the spectral power, the burst suppression features and the bispectrum of

the EEG data, while the latter index is constructed by relating the data degree of disorder

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(entropy) with consciousness state of patients [10, 11]. Although the EEG-based indices have

been applied commercially for nearly 20 years, it is still not yet part the standard

anesthesiology practice [12]. Reasons for this situation may be as follows. Firstly, these

indices are developed from adult patient cohorts resulting in less accuracy in infants or the

younger [13]. Secondly, it cannot give anthe accurate DoA for some specific drug occasions,

especially when patients are induced by ketamine and nitrous dioxide [14]. Finally, the EEG

signal is sensitive to noise and weak to be acquired purely for real-time computing. Besides,

the electrode sensors for EEG data is much more expensive for patients, which may be

another main reason to keep it from becoming the regular tool in surgery.

Since EEG has so many disadvantages above [15], all features above pose an urgent need of

new ideas to compensate the mainstream methods. Actually, electrocardiogram (ECG) is

another very important kind of clinical physiological signal for patients and it is highly

recommended to be continuously monitored as international standards for a safe practice of

anesthesia [16]. Differential anesthetics affect QT interval of ECG during anesthetic induction

[17]. Rhythmic-to-non-rhythmic observations from ECG can also provide the anesthetic

information [18]. Due to the heart rate variability (HRV) which varies with the anesthetic

procedure [19, 20], HRV is associated with autonomic regulation and highly influenced by

general anesthesia [21]. Heartbeat dynamics is significantly correlated with loss of

consciousness [22]. As we know, the ECG data has more stability than EEG signal, and it

means more resistant to noise with even cheap electrode sensors. So HRV can be one of

potential indicator for DoA. Moreover, the inter-individual variations occur normally among

people influenced by age, weight, life habits, etc. This effect makes the ECG derived analysis

index reflect the individual anesthetic state more specifically, not like EEG-based indices

assuming that same index value indicates the same consciousness level for all anesthetics and

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patients [12]. So it is much worthwhile to undertake some DoA research based on the HRV.

As it iswe known, artificial neural network is a very significant modeling tool in statistics,

machine learning and cognitive science [23, 24]. It is similar to biological neural networks in

the performing by its units of functions collectively and in parallel, rather than by a clear

delineation of subtasks to which individual units are assigned. Neural network models which

command the central nervous system and the rest of the brain are part of theoretical

neuroscience and computational neuroscience, thus making it optimal for non-linear,

distributed, parallel and local processing and adaptation. Ensemble artificial neural network

(EANN) is the process of creating multiple models and combining them to produce a desired

output, as opposed to creating just one model [25-27]. Normally, an ensemble of models

performs better than any individual model because of the ensemble modelsformer average

effects [28, 29]. In summary, neural network is a powerful and effective method for data

regression and model optimization for non-stationary data. In biomedical fields, it plays an

important role for the complex physiological data analysis [30].

In this study, we optimize an indicator index named similarity index (SI) derived from HRV

[31]. This time domain index is calculated by evaluating the similarity of the statistical

distribution of R–R interval measurements in consecutive data blocks. And we use it to do the

comparisons with expert assessment of consciousness level (EACL), which are determined by

five expert anesthetists’ average evaluation after their observation for patients. Also,

optimization is conducted by applying the EANN to build a model for estimation of DoA.

Through the time domain SI extraction and EANN modeling by targeting the EACL, we find

the result can predict the DoA of whole surgery. The rest of this paper is divided into four

sections. Section 2 describes the general anesthesia knowledge and patients participants as

well as the data analysis methods. Section 3 presents the processing results and some

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comparisons with EACL. Section 4 gives the discussion and limitations. TheAnd conclusion

is givenprovided in Section 5.

2. Materials and methods

2.1. Ethnic statement

All studies were approved by the Institutional Review Board within ethics committee from

the participated hospitals and written informed consent was also obtained from the permission

of the patients. All other efforts were made to keep the regular hospital surgeries running

smoothly.

2.2. Standard anesthetic procedure

For surgeries, anesthesia is essential and significant. Anesthetic procedures were as described

below [32] and are outlined in Fig. 1. It simply utilizes the end-tide gas concentration vs. time

to show the anesthesia steps. The routine anesthetic practice consist of four stages: awake,

induction, maintenance, emergence (recovery) [33]. General anesthesia performs all stages

with monitoring the physiological signals like EEG, ECG, photoplethysmography (PPG) and

also the intermittent vital signs of blood pressure (BP), heart rate (HR), pulse rate (PR),

oxyhemoglobin saturation by pulse oximetry (SPO2), etc. After electrodes are placed, the

medical data can be collected for the whole operation. For analysis of data, data segments of

different stages can be obtained to undertake the analysis.

2.3. Data recording

ECG data acquired in this study is collected from patients through chest mounted electrode

sensors by MP60 anesthetic monitor machine (Philip, IntelliVue, US). This machine is

connected with a recording computer within a real-time software developed by our research

team using Borland C++ Builder 6 developing environment kit (Borland company, C++

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version 6) to collect the data with sampling rate of 500 Hz. The recording rate of intermittent

vital signs like heart rate and blood pressure is one point every 5 seconds. Patients who

provide the electrophysiological data come from National Taiwan University Hospital

(NTUH) in Taiwan.

2.4. Clinical data collection

Patients’ demographics and clinical information, including height, weight, age, gender,

operation time, surgical procedure and anesthetic management are acquired from the

anesthetic recording sheets by hospital staff. And also some issues related with the research

procedure like body movement, electrotome operation are recorded by research team. Before

doing these information collection, the research team will ask the signature consent from the

patients. Both the hospital regular recording and the research specific notes are integrated to

serve as auxiliary clinical information. Large data mining can be conducted through these

valuable referenced data.

2.5. Patient participants

Patients who were scheduled for an elective surgical procedure were recruited from the pre-

operative clinic at NTUH in 2015. Eligibility criteria consisted of age, personal willingness

and specific operation type. People were not eligible for inclusion in the study if they were (1)

under 22 years old, (2) diagnosed with a neurological or cardiovascular disorder, (3) under the

local anesthesia, not general. The selective method isare shown in Fig. 2. According to the

criteria, more than 122 patients were eligible. However, dozens of cases were unexpectedly

not able to be collected. Here, it should be pointed out that when it comes to the failure

collection, the cases arewe just ignored them, leading to the number value loss of

unsuccessful collection cases. So 122 cases of data were obtained finally. And then 12 cases

data were abandoned in next data preprocessing part. All the patients have 7-8 hours of

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limosis before operations. The general parameters information covers all the 110 patients, but

the anesthetic management drugs for individual differ practically. For example, the propofol

and fentanyl induction are implemented for patients mostly (n = 100). Details characteristics

of subjects are provided in Table 1.

2.6. ECG data preprocessing

(1) Data conditioning

Data conditioning is critical in signal analysis for the DoA, mostly called preprocessing.

Because it can overcome the compatibility and non-analysis trouble in advance. It generally

consists of data format conversion, artefactartifact removal, data rearrangement. Due to the

data collection storage limitation, we employ the txt file format. Before applying the

following algorithm analysis, we transformed all the patients’ data type available to

MATLAB analysis with appropriate specific name. Moreover, the intact data series needs to

be checked visually. It means some cases may have technique failure and clinic problems for

data recording. Besides, during the induction stage of the anesthesia, the noise might be

strong enough to be impossible to extract the R peak of ECG wave in some cases. 12 cases

like this are rejected. All the data rearrangement work should be undertaken to promote the

analysis.

(2) Expert Assessment of Consciousness Level

As a whole, the EACL means the patients’ DoA quantifictation derived from anesthetic

experts with rich clinical anesthetic experiences. It is well known that there is no absolutely

accurate standard index for symbolizing the patients’ anesthetic state and in clinical surgery

the anesthetist usually control the anesthesia procedure based on the experience by observing

the ECG, BP, HR, SPO2, etc. So we discuss to accept the assessment quantifictation from the

experienced anesthesiologists. The procedure in Fig. 3 is described as follows: Firstly, while

acquiring the physiological data, research nurses keep observing the state of patients to record

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the events and signs in detail and carefully, which possibly have relationship with ‘the state of

anesthetic depth, for example, time of the induction and extubation, drugs administered time

and their dose, minimum alveolar concentration (MAC) values and so on. Then, five

experienced anesthesiologists need to make a continuous curve individually and

independently to draw the changes of ‘the state of anesthetic depth’ of patients for the whole

duration of operation based on the hospital formal anesthesia sheets and our research extra

records mentioned previously. In order to be consistent with BIS, the curve is predefined the

range 0 - 100 from unconsciousbrain death to fully awake (40 - 60 represents an appropriate

anesthesia level during surgery). Finally, because the original curve was completed by hand

drawing, it is digitalized by a web-software (ANSYS, webplotdigtilzer, US) [34] and

resampled with a frequency of 0.2 Hz with MATLAB interpolation method to be same to BIS

index. Thus, the result should be treated as expert assessment of conscious level. However,

each anesthesiologist with different experience may have a different standard on EACL,

therefore, in order to eliminate consciousness level error to the least, the assessmentwe can

average the data from five anesthesiologists is averaged. Fig. 4 gives aone case example of

EACL from five doctors. It is more convinced to use the mean value as a real gold standard of

DoA.

2.7. Data analysis

A. Similarity Index Algorithm of HRV

I. Similarity index protocol

The principle of SI comes from HRV of ECG data. It is a time domain parameter index

representing the similarity degree of consecutive data segments by computing their statistical

distribution of R-R interval variability difference. Fig. 5 shows the entire procedure details of

SI computation from ECG recordings. All steps are explained in the followings:.

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Step 1. Extract the R peak of ECG signal to get the instantaneous R-R intervals R (n ).

Resample the data with commonly used algorithm of Berger to 4 Hz [35].

Step 2. Calculate the difference between two consecutive heartbeat intervals:

D (n )=|R (n+1 )−R (n )|n=1,2,3 … (1)

Step 3. Choose any time point t, and then select a data block. Let us define the length of the

block with M data points. Compare the statistical distribution consecutive blocks, one from

D(t−M +1) to D(t ), the other from D(t +1) to D(t +M ). The distribution histograms of

both data blocks are generated with the same cell size. The probability of D (n ) value

belonging to ith cell of the histogram is denoted as P1(i) for the first data block while the

P2(i) for the second one. The determination of the cell number is described later. For

example, forin data block One, the range of data value is 0 to 0.5 s, if we choose 100 cells

arechosen, and the cell width should be set as 0.005 s. It means the P1(1) denotes the

possibility between 0 to 0.005, i.e. P1 (1 )=¿ Probability (0<D (n )<0.005), P1 (2 )=¿

Probability (0.005<D (n )<0.010) , etc. This is the same to data block Two.

Step 4. After multiplying the probability of corresponding cells in the histograms of both data

blocks, the sum of the product value in all cells will be the SI in equation (2)

SI=¿ (2)

wWhere n is the number of cells, P1(i) and, P2(i) areis the probability of each cell in the

histograms of data block One and Two respectively. Multiplying the sum by 100 aims to get

an index value from 0 to 100 to be consistent with the consciousness level we recognize

clinically like BIS value ranging from 0 (deep coma) to 100 (awake state), thus making it

easier for us to judge the DoA.

II. Implication of similarity index

Mathematically, SI is a kind of similarity evaluation of the statistical distribution between two

consecutive data segments. If the heart rate pattern is more stable, the consecutive data

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segment should be more similar. It means the histograms will present the consistent

distribution, i.e. P1 (i ) and P2 (i ) fluctuate simultaneously. In equation (2), the SI will be lower

under this condition. So a higher SI can symbolize a much more variable heart rate. In other

words, it happens more when the patient is awake or less anesthetizedc.

B. ECG analysis 110 cases data are analyzed to get the SI. In every case, SI is calculated through the whole

operation procedure including the awake state, induction state, maintenance state and the

recovery state. Also all the data is obtained under different kinds of anesthetics to guarantee

the compatibility. The parameters are selected empirically. Because the D (n ) range is from 0

ms to 0.5 ms, we take it as the length of histogram. We tested the number of cells are selected

aswith 100, 200… 500, and selected the best performance value 250. To divide the data range

into 250 cells, the width of cell should be set 0.002. TWe set the data block M is set toas 128.

The sample frequency of D (n ) is set to 4 Hz, which means one data block needs 32 s. At one

time, 64 s data (two 32 s data blocks) should be used to calculate the SI.

2.8. Statistical analysis

Statistical analysis is undertaken by comparing the properties with EACL. Taking the

EACAL as the gold standard, we compute the correlation coefficient and mean absolute error

(MAE) are calculated for intact 110 sets of cases data without the 12 cases damaged for

technique reasons and unexpected procedure mentioned in Section 2.6. Their statistical

distributions (histogram) are made to show the clear results overall. In addition, we use the

mean ± 2*standard deviation as the threshold [36] to do the receiver operating characteristic

(ROC) curve analysis and calculate the area under curve (AUC) to assess the neural network

fitting index prediction ability besides the correlation coefficient and MAE.

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In order to measure the DoA more accurately, the regression analysis should be conducted to

make a model. AWe employ the artificial neural network (ANN) is utilized to bridge the

relationship between the SI and the EACL, thus generating the output to evaluate more

accurately. An ANN consists of three parts: input layer, hidden layer and output layer. Here,

we choose tthe feedback propagation ANN type is selected, which is most widely used in

machine learning. In previous studies [36-38] about non-linear and non-stationary medical

data analysis, this back propagation network is set four layers: the input layer, one hidden

layer with 17 neuron nodes, the other hidden layer with 10 neuron nodes and the output layer.

As we know, the number of nodes and layers affect the performance of ANN including fitting

effect, time elapse, etc. From engineering perspective, 3 to 4 layers are mostly used [26, 27].

Furthermore,We also test some sample cases for node number selection is tested. It isWe

found that the performance differsbehaves differently under condition of fixed layers number.

Choosing the ANN structure parameters above is also a balance between the modeling

accuracy effect and the calculation time. Obviously, the SI data series are regarded as the

inputs, while the target is the EACL. It is necessary that the SI should be consistent with

EACL of the same case. Since five doctors complete the EACL, it is inevitable for individual

difference for some cases, thus resulting in the low correlation coefficient different values.

Here, we choose 105 sets of data from 110 cases whose correlation coefficient higher than 0.3

(most value distribution much higher than 0.3 in Fig. 8) for ANN regression. 85 sets of cases

data are separated for modeling construction, of which 70% are for training, 15% for

validation and 15% for testing. Using so many sets of data for ANN modeling hopes to

guarantee its fitting ability. After ANN model generated, 20 sets of data left are used for the

pure testing for the ANN models to check its performance.

Furthermore, we practically repeat the modeling procedure is repeated 10 times to generate 10

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ANN models. E. And then each model iswill be used to test the 20 sets of data left to get the

effectiveness. Afterwards, parameters are calculated to see the variability of all the ANN

models. It is clear that every time the ANN training construction from the beginning will be

different with weights and bias even though the same input data and proportion distribution

for training, validation and test. TheSo this cross validation iswe conduct aimeds at blind test

to prove the regression result does not change regardless of input samples. Besides, we

ensemble the 10 ANN models are aimed to validate the 20 cases to optimize the regression

effect, thus developing better DoA prediction results in Fig. 6. All the data analysis are

performed in MATLAB (Mathworks R2014b, US).

3. Results

3.1. A typical demonstration of SI pattern

Fig. 7 gives a typical trend of SI from a patient in the top panel. The lower panel below gives

the corresponding EACL averaged by five experienced and professional anesthetists. It can be

obviously be seen that the DoA changes with the ongoing operation. It should be noted that

the higher the value is, the lower the level of consciousness is. After induction, the value falls

sharply although with some variation in the maintenance period. When it refers to the

emergingence from the maintenance period, the trend increase dramatically. Generally, it

shows the similar fluctuation compared with EACL. The statistical details are proposed in the

following partssubsections.

3.2. Statistical distribution of correlation coefficient

In order to view the coefficient distribution characteristic of all the 110 sets of data, we put all

the correlation coefficient value in the histogram seen in Fig. 8, the cell width of which is set

0.1. Obviously, most the data value is located in the range from 0.6 to 0.8 with 078±0.16

(mean±std), which can reflect the strong relationship with EACL, in other words, it has some

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potential to symbolize the DoA.

3.3. Performance comparisons between the original SI and the fitting SI

with ANN

As explained abovebefore, we establish the ANN model is established to improveoptimize the

model regression performance. The 20 sets of data left are used to quantify the output result.

Here, the original SI obtained from the same group data are made comparison with the ones

fitted by ANN (ANN output). The correlation coefficient between gold standard EACL and

both original SI and ANN-derived SI from case 1 to case 20 are presented in Table 2. It can

be distinguished that the latter one has improved the correlation with EACL except case 13

and case 15 show opposite effect. From the mean value statistics in the left panel of Fig. 9, we

it can be seen that the ANN-derived SI show has better performance. Furthermore, Table 3

gives the MAE comparisons like correlation coefficient. Based on quantification, we can

obviously see the regression effect of ANN. It shortens the much difference from the EACL

by showing the statistical result in middle panel of Fig. 9. In addition, we calculate the AUC

is calculated forof both original SI and ANN derived SI for the tested 20 cases in Table 4 and

ROC curves in shown in Fig. 10, which can prove that the optimized SI have better capability

for consciousness level evaluation. The right panel in Fig. 9 shows that the AUC of ANN-

derived SI is 0.95±0.05, which is much higher than the original one. P < 0.05 means the t test

statistically significant. It is discernible that from the relationship and value difference, ANN-

derived SI can measure the DoA more accurately from Tables 2 to 4.

Moreover, we show oneone typical ANN derived SI of the Fig. 7 case isto be demonstrated in

Fig. 11. Clearly, the ANN fitting SI is more related with the gold standard. Especially, the

original SI varies sharply at induction stage, while the ANN fitting one basically is consistent

with EACL. And Furthermore, in early maintenance period, the original SI reaches the

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bottom zero line, which is definitely not reasonable. Generally, the ANN fitting SI show

better correlation with EACL. We can sayIt can be seen that the ANN function to better the

DoA prediction results.

3.4. Blind cross validation for ANN

Apparently, ANN model improves the SI performance. However, it is just one ANN model

test. Considering the compatibility question, we conduct the blind cross validation to check

the ANN model method efficiency further. The results are presented in Table 5, from which

we can be seen that all the 10 ANN models for the 20 cases have good and similar results. It

also shows the same validation for MAE in Table 6. It can be proved that the sample selection

does not affect the ANN construction and effectiveness.

3.5. Ensemble artificial neural network

In order to better the regression performance of ANN, we employ the EANN to predict the

DoA. Table 7 gives the quantification results with correlation coefficient and MAE of EANN.

From this, it is not hard to findIt is clear that the EANN produceswith better accuracy

resultscapability. In comparison with 10 single ANN results, the mean of correlation

coefficient of 20 cases is higher with lower standard deviation, while the MAE also prove this

effect with lower mean and standard deviation, meaning that the EANN perform better than

just one single ANN.

In Fig. 12, top panel shows that the ANN has little fluctuation difference regardless of input

training data in terms of correlation coefficient. The EANN has the highest correlation with

lowest standard deviation to prove the better performance of EANN. MAE distribution is

given in bottom panel. As to individual ANN, they have similar ability, not significantly.

Same to the result of correlation coefficient, EANN has almost produced the lowest MAE.

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4. Discussion

As we it is known, the doctors practically use the observations and physiological signs to

evaluate the consciousness level for clinical operation. These medical parameters are usually

referred to heart rate, blood pressure, photoplethysmography, etc [16]. However, it is actually

not ideally close to DoA. So some new methods were proposed years ago. The auditory

evoked potential (AEP) and EEG related indices like BIS or Entropy are employed to

quantify the DoA [9, 38, 39]. And they are powerful and effective to some extent. Due to the

reasons for EEG derived evaluation mentioned in section one, we proposed the SI method for

measuring DoA, which comes from ECG. It can reflect the strong relationship with HRV,

which is correlated to autonomic nervous system (ANS) function seriously affected by

anesthesia [21, 40]. This is widely accepted in anesthetic fields. That is the reason we why

deeper research is conducted ondeepen the ECG based DoA assessment research with ECG

[41].

Considering the commercial EEG derived index, our SI just aims at the EACL. From all the

results above, it is undoubted that SI could measure the DoA. However, the comparison

between SI and BIS, AEP or Entropy is still unclear for us. In order to show its advantage, the

difference should be checked later. Maybe, EEG derived indices and ECG derived ones

indicies have specific features and superiority of their own, respectively. This is one part we

that should be conducted and analyzedmake the statistic clear.

Besides, the ANN regression modeling part is predefined the framework of initial neural

network based on our previous engineering research experiences [36, 38, 42]. Actually, it is

worthwhile to study the ANN parameters like numbers of layers, number of each layers’

nodes and the type of neural network [43]. Also the weights and the expiration criteria should

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also be discussed to optimize the performance to the largest extent.

Mathematically, SI is not the just the heart rate or HRV. It is a difference quantification of

two consecutive data blocks. The relationship is clearly explained in Section 2. The higher the

difference is, the higher the SI value is. Obviously, the people are awake, and the ANS has the

regulation function affecting the ECG. It must be claimed that some kind of heart disease will

influence the HRV [20], probably it did to SI too. It is urgent for us to check and optimize this

potential side effect though the regression results seem not bad. It cannot be suitable for all

occasions for 100% correctness. But, efforts to check and fix the algorithm are

unquestionable.

Although more than 100 sets of cases data are collected to build the SI and the results show

good performance, it should also be admitted that most of our data cases come from middle

aged patients. Commercial devices provide the children and adults option for doctors to

choose for specific surgery patients. It is also common for doctors to make decision for

anesthetic drug selection before surgery. It is essential to get more young samples’ data to

check and update the model [32, 44]. Moreover, we acquire the regular patients’ data in

hospitals. It means we must obey the rules for surgery operation. At any time, patients’ safety

come first. We did notNo specific cases were picked to fomulate the specific case to get the

data. As a results, the anesthetic drugs for patients are all chosen based on totally the

experience and habits of anesthetics although sometimes other kinds of drugs also can fulfill

the achievements of successful operations[1, 45]. For theIn maintenance stageperiod, almost

all the selected data we have are related with propofol, sevoflurane and desflurane as

indicated in Table 1. We alsoMore data is required for patients associated with need to get

data availability with the others kinds of drugs like dextroamphet, isoflurane, nitrous oxide,

etc [12, 46], which can enhance the system generalizationour compatibility definitely.

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Furthermore, it is we hoped to study the single specific subjects to get the performance details

if possiblein the near future.

5. Conclusion

In this study, 110 cases of physiological data are undertaken to prove the availability of SI

algorithm to evaluate the DoA. We use ECG is utilized to derived SI to get the differences of

HRV, demonstrating its DoA measurement availability. In order to optimize the prediction

effectaccuracy, ANN models are constructed and blind cross validations are conducted to do

the regression test. In addition, EANN are employed to overcome the random error of neural

networks. Our This research proves that HRV can be a supportingnother effective method for

DoA evaluation than its application for this area currently. Due to the lack the ideal

measurement methods for consciousness level of patients, we it is believed that it is possible

to incorporate the SI to quantify the DoA with other methods together. Combining the all

physiological medical signals related to anesthesia together like ECG, EEG is obviously

meaningful and helpful to improve the accuracy.

Acknowledgments

The authors wish to thank the exchange student program of Yuan Ze University, in Taiwan

and Wuhan University of Technology, in China for supporting this research. This research is

supported by the Center for Dynamical Biomarkers and Translational Medicine, National

Central University, Taiwan, which is sponsored by National Science Council (Grant Number:

NSC102-2911-I-008-001). Also, it is sponsored by Wuhan University of Technology

international exchange program (Grant Number: 2015-JL-012) and China National Nature

Science Foundation (Grant Number: 51475342).

Conflict of Interests

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The authors declare no conflict of interests.

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21

Figure. 1. Anesthetic procedure

22

Figure 2. Study Protocol. In fact, more than 122 patients during this collection periods were consulted

and eligible, 122 of them finally are successfully collected. 12 were excluded for analysis due to

technical and clinical reasons. The 110 left subjects are intact for four stages analysis to evaluate DoA.

Their demography information is shown in Table 1.

23

Figure 3. Flowchart design of EACL. Recordings are clinically related BP, HR, SPO2 and drug

administration records; Assessment are done by five experienced experts by plotting the DoA curves

with range from 0 to100; After using ANSYS to digitalize the curve value, we obtained the final gold

standard by averaging the five doctors’ assessment.

24

Figure 4. One example of EACL. From top to bottom, it is the five doctors’ assessment respectively,

the final one is the gold standard: EACL. Red solid line is the mean value, the two dashed line is the

mean±std.

Figure 5. Similarity index protocol. ECG is a sample of step 1, R (n) means step 2. Step 3 includes D

(n) and histogram. The histogram distribution are used for SI computation.

Figure 6. The flow chart of EANN model construction

25

Figure 7. One case demo of SI. The top panel shows one SI curve derived from a case ECG data, the

lower one is the corresponding EACL, in which the blue thick line is average of other five doctors’ thin

lines.

Figure 8. Histogram distribution of correlation coefficient between SI and EACL. Except one in

negative correlation, others are positive values, of which most are located at high value section from

0.6 to 0.9.

26

Figure 9. Difference between the original SI and fitting SI for correlation coefficient, MAE and AUC.

All of them indicate the fitting SI has better performance.

27

Figure 10. The ROC of original SI and ANN derived one. Both shows the prediction of DoA features

(AUC>0.5). The ANN fitting SI (blue curve) has larger AUC than the original SI (red one), indicating

better ability to predict DoA.

Figure 11. One typical demo of the ANN regression effect for SI. The blue line represents the ANN

derived output, it has more similar fluctuation rhythm with EACL (black line). Relatively, the original

SI (red line) shows weaker relationship.

28

Figure 12. The mean value and standard deviation statistics of ANNs and the EANN. Top panel shows

that the ANN has little fluctuation difference regardless of input training data in terms of correlation

coefficient .The EANN has the highest correlation with lowest standard deviation to prove the better

performance of EANN. MAE distribution is given in bottom panel. As to individual ANN, they have

similar ability, not significantly. Same to the result of correlation coefficient, EANN has almost the

lowest MAE.

29

Table 1. Patients clinical characteristics and demographics. Values are means (sd). Some eligible

subjects are excluded by reasons described in Figure 2.

Parameters

Age(year) 49.0(12.5)

Male gender (%) 16.4%,n=18

Height(cm) 158.7(7.6)

Weight(kg) 59.4(12.7)

BMI(kg·m-2) 23.6(4.9)

Median duration of surgery(min) 120 (CI:113.9~138.9)

Anesthetic management

Propofol induction(mg) 115.6(34.3),n=100

Fentanyl induction(mg) 95.5(41.4) ,n=100

Lidocaine induction(mg) 48.1(6.5) ,n=60

Glycopymolfe induction (mg) 0.2(0.04) ,n=64

Nimbex induction (mg) 9.5(1.7) ,n=50

Xylocaine induction (mg) 44.5(9.0),n=33

Rubine induction (mg) 0.2(0.06),n=32

Maintenance drugs infusion rate –—

Sevoflurane maintenance (%) 53.4%,n=59

Desflurane maintenance (%) 35.5%,n=39

Propofol maintenance (%) 29.1%,n=32

Additional drugs administrated when approaching the end of

surgery

Morphine (mg) 4.5(2.3),n=47

Ketamine (mg) 29.8(7.3),n=25

Atropine (mg) 1.1(0.4),n=49

Vagostin (mg) 2.4(0.2),n=48

30

Table 2. The correlation coefficient comparison between EACL and both original SI and ANN fitting

SI of 20 cases. The latter one has better performance except few cases. From p value (unpaired t test),

the two groups are considered statistically significant. (P<0.05 means statistically significant)

Case Original SI &EACL Fitting SI & EACL

1 0.7456 0.8478

2 0.8263 0.8799

3 0.8756 0.9570

4 0.8812 0.9661

5 0.7752 0.8857

6 0.6732 0.7146

7 0.7078 0.7197

8 0.7818 0.7976

9 0.7764 0.8880

10 0.8400 0.9401

11 0.8397 0.8815

12 0.5817 0.6448

13 0.7833 0.7330

14 0.8585 0.9199

15 0.9073 0.8764

16 0.8445 0.8718

17 0.6938 0.7565

18 0.7736 0.8939

19 0.8994 0.9198

20 0.7902 0.922

Mean±std 0.7928±0.0830 0.8508±0.0913

p-value 0.0420

31

Table 3. The MAE between EACL and both original SI and ANN fitting SI of 20 cases. The latter one

shows better performance except few cases. From p value (unpaired t test), the two groups are

considered statistically significant. (P<0.05 means statistically significant)

Case Original SI &EACL Fitting SI & EACL

1 25.3235 2.9221

2 24.4898 3.1145

3 24.4483 8.9847

4 21.6974 4.6953

5 38.0500 6.3051

6 8.6140 9.0382

7 46.8434 11.4393

8 30.7200 4.5732

9 23.8712 6.0356

10 41.8986 14.1500

11 36.0559 3.1404

12 35.9865 3.5006

13 33.9785 5.3338

14 28.5371 5.0643

15 33.0614 9.2370

16 22.8827 4.0254

17 33.6476 7.6811

18 29.6125 9.1065

19 19.8529 3.5845

20 36.3620 4.3487

Mean±std 29.7967±8.7180 6.314±3.1201

p-value 9.2214e-14

32

Table 4. The AUC between EACL and both original SI and ANN fitting SI of 20 cases. P value

(unpaired t test) show two group are significantly different. The latter one has higher mean value and

lower standard deviation. (p<0.05 means statistically significant)

Case Original SI &EACL Fitting SI & EACL

1 0.9493 0.9985

2 0.8805 0.9771

3 0.8992 0.9973

4 0.9013 0.9999

5 0.8272 0.9229

6 0.6574 0.8843

7 0.7386 0.8800

8 0.5786 0.8181

9 0.9691 0.9692

10 0.9781 0.9878

11 0.9926 0.9557

12 0.9990 0.9213

13 0.9575 0.9120

14 0.8326 0.9892

15 0.7216 0.9141

16 0.9059 0.9520

17 0.9876 0.9874

18 0.8992 0.9993

19 0.8508 0.9921

20 0.9408 0.9924

Mean±std 0.8733±0.1176 0.9525±0.0510

p-value 0.0088

33

Table 5. The correlation coefficient between 10 group ANNs fitting SI and EACL of 20 cases. From

the mean value comparison, it prove the ANN performance regardless of different input case data.

Mean

±std

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 case

0.8508

±0.0913

0.9220

0.9198

0.8939

0.7565

0.8718

0.8764

0.9199

0.7330

0.6448

0.8815

0.9401

0.888

0.7976

0.7197

0.7146

0.8857

0.9661

0.957

0.8799

0.8478

1

0.8346

±0.0952

0.9296

0.9218

0.8935

0.7552

0.8708

0.8260

0.8887

0.7409

0.6538

0.8827

0.9126

0.8903

0.7694

0.6222

0.7070

0.8619

0.9242

0.9498

0.861

0.8299

2

0.8417

±0.1025

0.9059

0.9085

0.8920

0.7377

0.8569

0.8891

0.8995

0.7769

0.6869

0.8798

0.9304

0.8738

0.7832

0.5645

0.6931

0.8936

0.9602

0.9494

0.8913

0.8608

3

0.8378

±0.0972

0.9024

0.9003

0.8735

0.7223

0.8602

0.8905

0.8981

0.7500

0.6438

0.8804

0.9301

0.7839

0.7677

0.6537

0.7093

0.9055

0.9584

0.9414

0.8832

0.9024

4

0.8398

±0.0945

0.9136

0.9059

0.9060

0.7132

0.8390

0.8693

0.8854

0.7528

0.6926

0.8829

0.9176

0.8732

0.7724

0.6193

0.7069

0.8924

0.9389

0.9389

0.9003

0.8764

5

0.8459

±0.0933

0.9117

0.9231

0.8610

0.7309

0.8612

0.9115

0.9068

0.7566

0.6719

0.8889

0.9439

0.8594

0.800

0.6506

0.7046

0.8989

0.9391

0.9434

0.8898

0.8644

6

0.8448

±0.0921

0.9158

0.9086

0.9159

0.7236

0.8448

0.8920

0.8975

0.7576

0.6731

0.8795

0.9199

0.8777

0.7849

0.6613

0.7074

0.8796

0.9575

0.9416

0.8955

0.8628

7

0.8158

±0.0976

0.9054

0.8731

0.8454

0.6900

0.8167

0.8581

0.8902

0.7298

0.6070

0.8392

0.8591

0.8586

0.7422

0.6357

0.7069

0.8834

0.9391

0.9420

0.8850

0.8085

8

34

0.8340

±0.0959

0.9092

0.9081

0.8754

0.6855

0.8321

0.8682

0.8937

0.7499

0.6947

0.8732

0.9092

0.8755

0.7662

0.6206

0.7037

0.8896

0.9477

0.9487

0.8926

0.8361

9

0.8507

±0.0899

0.9185

0.9199

0.9046

0.7243

0.8508

0.8900

0.9095

0.7717

0.6857

0.8800

0.9291

0.8949

0.7997

0.6897

0.6990

0.8914

0.9593

0.9531

0.8995

0.8431

10

Table 6. The MAE between 10 group ANNs fitting SI and EACL of 20 cases.

Mean

±std

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 case

6.314

±3.1201

4.3487

3.5845

9.1065

7.6811

4.0254

9.2370

5.0643

5.3338

3.5006

3.1404

14.1500

6.0356

4.5732

11.4393

9.0382

6.3051

4.6953

8.9847

3.1145

2.9221

1

4.8873

±1.929

2.3737

2.5905

3.5516

6.4034

5.7468

4.9801

6.5182

4.9892

4.1154

3.1757

2.6385

4.4438

5.0209

8.3731

8.9417

4.0933

4.0625

7.6880

5.3564

2.6827

2

5.8552

±2.631

3.1353

3.8629

3.5772

9.4121

7.3108

3.7547

9.6654

5.8334

5.1184

3.4592

3.1447

6.1772

5.0497

10.8584

9.2390

7.1507

5.6870

9.0495

2.8042

2.8137

3

5.1737

±2.258

2.5801

2.9363

3.9215

8.3961

6.3420

3.4935

8.1039

5.4610

5.1514

3.1550

2.5335

6.2669

4.7504

10.2631

6.9730

6.4972

3.6287

7.2403

2.9131

2.8665

4

4.9005

±2.177

2.3523

2.8535

3.5387

7.8236

6.1320

3.9909

7.3870

4.7757

3.9650

2.9789

2.2605

5.2391

4.5744

9.2659

8.4902

5.0852

3.8159

7.5716

3.6013

2.3074

5

4.9101

±2.128

2.5495

2.8403

4.4469

7.6367

6.1956

3.3965

6.9913

5.0247

4.4831

2.8974

2.2825

5.0096

4.2593

9.8139

8.6548

5.5718

3.9208

6.1523

3.6292

2.4450

6

4.8997

±2.236

2.2327

2.6773

3.3120

8.1453

5.9867

3.5113

7.9014

4.9836

4.4783

2.9637

2.2341

5.1407

4.6167

9.4114

7.7064

5.4500

4.1241

7.6846

3.0344

2.3994

7

6.0248

±2.505

3.3063

5.3862

4.1753

8.8473

7.9073

3.8171

8.9179

5.7844

5.9349

4.0945

3.4031

6.4486

5.5799

12.2275

8.8494

7.0734

4.8074

8.0653

2.9126

2.9568

8

35

5.4458

± 2.4640

2.5920

3.2275

3.8506

8.9173

6.9204

3.6162

8.7615

5.4467

4.9116

3.2646

2.7433

5.8773

4.7853

10.4042

8.6022

6.5485

4.6977

8.2014

2.8609

2.6864

9

5.5916

±2.519

3.2067

4.1902

3.3847

8.8923

7.3476

3.6204

9.0214

5.1118

5.1229

3.4686

3.0011

6.0386

4.5787

11.3041

7.9568

6.4348

4.5093

8.8773

2.9610

2.8044

10

Table 7. The correlation coefficient and MAE value between EACL and EANN fitting SI of 20 cases.

Compared with all single ANN performance in Table 4 and Table 5, the mean of correlation coefficient

of 20 cases here is higher with lower standard deviation, while the MAE also prove this with lower

mean and standard deviation, meaning that the EANN perform better than just one single ANN.

Case Correlation coefficient MAE

1 0.8413 2.1975

2 0.8871 3.1593

3 0.9497 6.8287

4 0.8994 4.6681

5 0.8404 6.1740

6 0.8081 4.3851

7 0.7286 8.0616

8 0.8704 3.4809

9 0.8799 3.1161

10 0.9411 2.3909

11 0.8477 2.9354

12 0.7722 4.9511

13 0.7716 4.7145

14 0.9041 3.4764

15 0.8736 6.4892

16 0.8848 8.3562

17 0.7667 3.5179

18 0.8385 6.5030

19 0.9127 2.4303

20 0.9145 3.1895

Mean±std 0.8566±0.0612 4.5513±1.9049

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