A Critical Care Monitoring System for Depth of Anaesthesia Analysis Based on Entropy Analysis and Physiological Information Database

A Critical Care Monitoring System for Depth of Anaesthesia Analysis Based on Entropy Analysis and

Physiological Information Database

Qin Wei

, Yang Li

, Shou-Zen Fan

, Quan Liu

, Maysam F. Abbod

, Cheng-Wei Lu

,

Tzu-Yu Lin

, Kuo-Kuang Jen

, Shang-Ju Wu

, Jiann-Shing Shieh

*

1

School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China; E-Mail: [email protected];

2

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; E-Mail: [email protected];

3

Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan; E-Mail: [email protected];

4

School of Engineering and Design, Brunel University, London UB8 3PH, UK;

E-Mail: [email protected];

5

Dept. of Anesthesiology, Far Eastern Memorial Hospital, Ban-Chiao, Taiwan;

[email protected]; [email protected];

6

Department of Mechanical Engineering, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li 32003, Taiwan;

7

Chung-Shan Institute of Science and Technology, Taoyuan, Longtan Taiwan;

[email protected]; [email protected];

8

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

* Author to whom correspondence should be addressed; E-Mail:

[email protected];

Tel.: +886-3-4638800 ext. 2470; Fax: +886-3-4558013.

Abstract

Diagnosis of depth of anaesthesia (DoA) plays an important role for treatment

and drug usage in operating theatre and intensive care unit. With the flourishing

development of analysis methods and monitoring devices for DoA, a small amount of

physiological data had been stored and shared for further researches. In this paper, a

critical care monitoring (CCM) system for DoA monitoring and analysis were

designed and developed, which includes two main components: the DoA analysis

subsystem and the physiologic information database. In the analysis of DoA,

according to our previous studies on approximate entropy (ApEn), sample entropy

(SampEn) and multi-scale entropy (MSE), the SampEn and MSE were integrated into

the system for indicating the state of patients underwent surgeries at real time because

of their stability. Moreover, a physiologic information database including biologic

data and clinical information was constructed through a browser and sever (BS)

model so as to provide a safe and open platform for storage, share and further study of

clinical anaesthesia information. Therefore, this CCM system not only supplies the

original biological data and information collected from operation room, but also

shares our studies for improvement and creation on the research of DoA.

Key words: Depth of anaesthesia, approximate entropy, sample entropy, multi-scale entropy, physiologic information database

1. Introduction

Depth of anaesthesia (DoA) monitoring during surgery is a kind of protection for

patients’ live and recovery, therefore a lot of methods with regard to detect the DoA

were presented and developed for clinical application in operating theatre and

intensive care unit (ICU), like Bispectral Index (BIS) [1-3], response entropy (RE)

and state entropy (SE) [4, 5], and so on. Both BIS and RE/SE indexes are acquired

through the analysis of the electroencephalograph (EEG) detected from patients

undergoing surgeries. In the operation room, apart from EEG, other physiological

signals such as electrocardiograph (ECG), respiration (Resp), blood pressure (BP),

pulse oxygen saturation (SpO

), there are also the important responses of patient for

indication of DoA. However, the BIS Module in MP60 and Entropy Module in Datex-

Ohmeda S/5 pays more attention to online analysis of DoA but neglect of the storage

and sharing of the all physiologic data. In addition, the module and devices of BIS

and Entropy are too expensive to be widely available to most of operation rooms and

hospitals. A critical care monitoring (CCM) system for DoA based on other entropy

indexes and a physiologic database is presented in this paper, which include two main components: the DoA analysis subsystem and the physiologic information database

(PID).

In our previous studies on DoA analysis, the entropy family plays an important role in measuring the complexity of complex time series. Especially, the approximate entropy (ApEn) is able to be an index of degree of conscious state or DoA of patients [6]. But ApEn is more adaptive to long-term time series compare to the sample entropy (SampEn), and is less sensitive to the change of complexity than SampEn [7].

Furthermore, the distribution of complexity in different time scales is capable of

describing the different states of patients during operation by using multi-scale

entropy (MSE) [8]. Therefore, the SampEn and MSE were integrated into the DoA

analysis subsystem of the service system for DoA in order to detect the status of

patients and provide the new indexes. Then, a PID was constructed to store and

manage the data and information in operation. The structure of the PID is based on

browser and server (BS) model with classical three-tier architecture, by which the cost

can be greatly reduced in the load of client computer, system update, and routine

maintenance. The service system provides the data access via the web browser, so that

the PID has a small part of the business logic in the front (web browser) and the main

business logic in the application server. Due to the nature of the model, it is

convenient to manage and maintain the system, and is easy to access and share physiological data and information in PID for authorized users from different places

and through different ways, such as the LAN, the WAN, the Internet / Intranet, etc.

The paper is organized as follows. section II introduces simply the complexity analysis methods for DOA monitoring, such as ApEn, SampEn and MSE. The third section describes the CCM system for DoA in components, functions and examples.

The practical application is illustrated in the fourth section and the last section presents the conclusion and discuss the future work.

2. Method for DOA Analysis

2.1 Approximate Entropy

Entropy family as widely used methods in nonlinear dynamic analysis has already been applied in analysis of physiological time series. ApEn is one of the methods which has been employed in clinical cardiovascular studies [9-11] and irregularity of EEG signals under general anesthesia [12, 13]. It is referred to as a measure of the complexity of the sequence and statistical quantitative rules [14], and its computation is as follows:

(1)

where N is the length of time series, a tolerance r is the threshold to identify whether a selected sequence matches the given pattern in the same length m or not, and Ф

(r) is

( , , )

( )

( )

ApEn m N r   r  

r

the matching number of time series within parameters m and r.

But there are two shortcomings [15-17] in application of ApEn:

(1) The length of time series affects ApEn heavily, and it gets lower uniformly

than expected for short data;

(2) It lacks of the relative consistency.

Thereby, the SampEn was proposed to improve the ApEn, which does not count

self-matches in the computation.

2.2 Sample Entropy

Richman and Moorman [18] developed an improvement of the ApEn algorithm, named SampEn, in order to remove some of the deficiencies mentioned above. Its parameters are the same as ApEn, so it is also expressed as SampEn(m, r, N). The differences with respect to ApEn are: 1) self-matching is deleted; 2) the first N-m vectors of the length are only chosen in time series; and 3) the conditional probabilities are not estimated in a template manner. Thus, the probability measure is computed directly as the logarithm of conditional probability instead of the ratio of the logarithmic sums, which is described as follow:

(2)

Even though the application of ApEn to clinical recordings often obtains good results, SampEn is more suitable in real time analysis of physiological signals and acquisition of general information about the regularity and the persistence of those

( , , ) ln

( )

( )

SampEn m N r     A r B r  

signals. In comparison among SEF95%, RE, SE and SampEn in DoA monitoring, firstly EEG data were collected from twenty patients undergoing urological surgery in Far Eastern Memorial Hospital of Taiwan. Age of patients ranges from 15 to 60 years.

The used anesthetic agent in the operation is propofol. The device monitoring EEG is Datex-Ohmeda S/5

Compact Anaesthesia Monitor, in which response entropy (RE) and state entropy (SE) are displayed as an index of DOA for anesthetists. The one compared result of twenty cases is depicted in Figure 1. On account of the urological surgery, the two stages of pre-operation and recovery were in a short time. In addition, the frontal electromyogram (FEMG) mixed with EEG in the two stages, so the SampEn is disturbed badly in these two stages. But it has the same trend with RE and SE and is better than SEF 95% during intubation and maintenance, shown in Figure 1. In order to decrease impact of noises and describe the state of patient during a short time, MSE is employed to give the pattern of complexity in different scale

factors.

2.3 Multi-scale Entropy

Costa et al. [19, 20] proposed the MSE to observe the complexity of physiologic

time series in different time scales. Its calculation consists of two procedures: at first,

a “coarse-graining” process is used to construct the new sequence of the entire time

series in different time scales. For a given time series, multiple coarse-grained time

series are constructed by averaging the data points within non-overlapping windows

of increasing length τ. Each element of the coarse-grained time series, is

calculated according to the equation:

(3)

where τ represents the scale factor and 1≤j≤N/τ. The length of each coarse-grained time series is N/τ. Moreover, the coarse-grained time series is original time series if τ= 1. For multiple coarse-grained time series, SampEn is secondly calculated for each of them to indicate the complexity in different scale factor τ, which presents the distribution of complexity from small to big time scale

3. A CCM System for DoA Analysis

A CCM system for DoA Analysis was developed to provide a platform for real time DoA analysis based on entropy family and the collection and share of physiological data and operation information. Its structure is shown as Figure 2. In the operation room, physiological data detected from the patient are transmitted from monitoring device to our DoA analysis subsystem for signal processing and collection. Then, the data and information of operation will be uploaded to the server and stored into PID through the internet for the available services supplied from

application server.

3.1 DOA analysis subsystem

In Figure 2, the DoA analysis subsystem includes two functions in its program,

( )

y

( ) 1

( j 1) 1 j

j i i

y





x

one is online analysis using traditional methods, SampEn and MSE; the other one is offline analysis for anesthesiologists to have postoperative studies. Therefore, anesthesiologists could not only use indexes to diagnose the DOA while online analysis of EEG, but also process those recordings of signals and indexes according to

their ideas after operation.

3.1.1 Online Analysis

For the online analysis of DoA, EEG signals detected at real time are calculated as complexity index of activity of central nervous system. Thanks to the relationship between N and m, the average sum of minimum 10

and maximum 30

(when m = 2) is chosen. Thereby, when SampEn and MSE are computed online, the selected N is equal to the scale factor τ multiplied to 500. For example, as scale factor τ is 6, the N is 3000. Due to sample rate of EEG in Datex-Ohmeda is 100 samples per second, thus, the computation of MSE from 1 to 6 needs EEG samples in 30 seconds collected at first. Under this condition, it is indispensable to use a sliding window for entropy

index at every second.

In Figure 3, the main interface of this program is displayed, in which the

communication, the signals transmitted from physiological monitor, the operation

event and some indexes are integrated together. The RS232 communication function,

selection of analyzed physiologic signal, input of operation events are located at

upside of waveform display of detected signals and analyzed results. There are totally

nine figures lined in three rows. On the first line, Figures 3(a-c) are the waveforms of EEG, ECG and plethysmography from left to right. Physiological indexes, such as RE and SE, heart rate (HR) and SpO

are displayed on the left side of each panel. EEG signals are collected for 30 seconds at first for the MSE calculation, and then its analysis and computation carry out at every second with sliding window. The MSE from scale factor τ =1 to 6 is shown in Figure 3(d), its sum of each scale factor is plotted as a blue line in Figure 3(g), and its ratio of scale factor 6 to 1 represented as a percentage is displayed with red line. This ratio indicates the change of complexity of time series in big time scale compared to that of original time series, which is sensitive to the slow rhythm of EEG, like delta wave in EEG [7]. Figure 3(e) shows indexes of RE and SE from monitor device to compare with SampEn in Figure 3(h).

The adjusted value of sample entropy is the product of sample entropy and 10 for clarity. Moreover, the spectrum of EEG computed by fast Fourier transform (FFT) is shown in Figure 3(f), and Figure 3(i) provides the SEF 95% and percentage of delta, theta, alpha, beta bands in frequency domain. All the results are stored in text files at

every half an hour for offline analysis.

Those traditional indexes like SEF 95% and percentage of four bands are used to

compare with our entropy indexes SampEn, MSE and its complexity at the same time

in online analysis. Furthermore, as entropy was proved to be a valid indicator of the

hypnotic effect of some anesthetic drugs [21], it is more optimal than spectrum in describing the irregularity or unpredictability of a signal. So the online analysis of

EEG based on entorpy is significant in measure DOA.

3.1.2 Offline Analysis

There have been many commercial programs for clinician to analyze physiological signals in time and frequency domains and statistics. For validation of our own program, MSE results are compared to the commercial software Visual Signal, and the same results are shown in Figures 4 and 5. The average sum and ratio of MSE are computed as well. Parameters m and r are able to be different for other trials. Figure 6 shows the result of fast Fourier transform of EEG signals, accuracy of

which has already been proved by Matlab.

Even though the EEG signals are the direct responses of cerebral cortex, however,

some drugs affect brain stem internally, so that it is insufficient to detect DOA only

through EEG. At this time, ECG, SpO

and respiration of patients should be

integrated into analysis and measurement of DOA. Therefore, the ECG signals are

involved in our offline analysis firstly. The R peak can be extracted from ECG

waveform displayed in Figure 7. In next step, the interval of R peak is calculated and

shown in the list below. SampEn of the HR is shown on the right side if quantity of

data is adequate. As m is taken as 2, the minimum for calculation is fixed at 120,

which is bigger than 10

[15, 22].

3.2. The Physiological Information Database

The PID was established to store, manage and share the collected and analyzed data and operation information, which has classic three-tier architecture shown in Figure 2. The database is in charge of data and information storage, the application server provides authority, services, and file management, and all services and data access are on the basis of web browser and server model. Thus, a variety of devices can obtain the service in different and remote access. The realization of this platform depends on the popular web server software Apacha [23], the relational database management system MySQL and the hypertext preprocesser PHP, all of which is

widely used in server architecture [24].

3.2.1 The functions of PID

Figure 8 displays the main functions in the PID. First of all, all members should

be authorized so as to have further data access and services safely. The authority

includes member ID check, random code verification, service acceptance and

permission. Then members are permitted to login, upload and download data, view

and print, and further self-analysis. The principle function of the PID system is a file

management system [25]. Due to different member assigned by the different

authorities, the application server must identify the identification and authority of

member who requires the particular services, and then give him the corresponding

treatment and service. In order to manage the data in PID easily, each dataset has its

own describing information. Furthermore, the database will give the new data list of

database for members after new dataset is uploaded.

3.2.2 The Services in PID

1. Type of physiological information

There are three types of physiologic information about the operation information uploaded. First one is the physiological data such as ECG, EEG, BP, and SpO

and so on. The second type is event recording during operation, like drug injection, movement, and other general operation during surgeries. The last one is the patient anesthesia record that is a form filled by anesthetist. The three types of information should be converted into uniform format respectively for recycled use and storage.

The physiological data and online and offline analysis results are transformed into

*.MAT file using Matlab. Because the MAT format has the advantage of less storage space, and Matlab supplies simulation and processing platform for a further research of DoA. The operation events in Chinese and English are combined with the patient anesthesia record into a *. JPG file. For the patient privacy, the picture hides the name

of patient.

When all conversion and format verification has been done, the physiological

data and information can be uploaded into PID by the authorized members. There are

three kinds of authorized members : administrator, member and guest, they have their

own service and permission in our PID, shown in Table 1. Administrator has all

permission to the services in PID, members in our corporation group are only restricted to delete data and information, and however, the guests can just upload their

own physiological data and information and look over in the PID.

2. Login

Figure 9 is the home page of PID website [26], on which the sponsors and creators of PID for DoA analysis name the PID as YZU (Yuan Ze University) and NUTH (National University of Taiwan Hospital) databank. Members can login and logout through it, the login interface is shown in Figure 10. After entering the account, password and the random verification code, members are able to enjoy the

service of PID.

3. Databank

The main menu of PID consists of Introduction, Databank, Publication, File management and Patient information, shown in Figure 11. The Databank is the data pool for physiological data collected at real time from operation room. When you click the submenu Operation Room Date and the physiological signal you want, a data list is shown on the right. The name, type, description and size of each dataset are

involved and illustrated clearly, displayed in Figure 12.

4. Data Upload and download

The File management takes charge of data uploaded and downloaded. While

uploading data, some information must input, such as the type of data, the last name

and age of the patient, and the type of operation. Then, the physiological data and patient information can be uploaded together, and the uploaded interface is displayed in Figure 13. It is simple to download the data as long as click the file name of data you want, a information window will ask you whether to save data or not, as shown in

Figure 14.

5. Patient Information

Patient information is a service for members to check the stored data, information of patient and anesthetic process. This service is available by the last option Info on the right side of each dataset in the data list. In Figure 15, the information about patient, data, and operation event and anesthesia record are all shown in one page for members download and look over. However, due to patient

privacy, anesthesia record in Figure 15 were not included.

4. Clinical Applications for CCM system

So far, the PID has stored the physiologic signals and information of 146 patients

through our collection from the National Taiwan University Hospital and the Far

Eastern Memorial Hospital for nearly three years. All data were acquired after

obtaining the approval of Hospital Ethics Committee and the written informed

consent from the patients. According to amount of data, we presented some DoA

indexes via offline analysis based on entropy family [7, 8, 27]. The previous studies

indicate that the SampEn is more effective than ApEn in DoA monitoring while compared to the RE/SE indexes of Datex-Ohmeda S5. Moreover, multivariate empirical mode decomposition (MEMD) has the advantage of extracting the EEG from the disturbance of various noises among empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD) and MEMD [7]. Besides, the SampEn of de-noised EEG by using MEMD has high correlation with BIS index of Philip MP60 after regression by using artificial neural network (ANN) [27]. Therefore, the MSE was also used to detect the DoA, then an improvement on coarse-graining process was

proposed to elevate the computation efficiency and sensitivity in small time scales [8].

Consequently, the DoA analysis subsystem has been tested in real time for

monitoring the status of 20 patients undergoing surgeries and anesthesia in the Far

Eastern Memorial Hospital in the consideration of the DoA new entropy indexes

based on our previous offline analysis. Therefore, the various physiological signals,

the real time analyzed results and operation information have been stored and

managed as a part of 146 dataset in the databank of our PID. The general data in

monitoring DoA were detected by the commercial devices: Datex–Ohmeda

AS5Anesthesia Monitor and the Philip InterliVue MP60, shown in Figure 11. The

particular physiological signals from one patient are drawn in Figure 16, which

include EEG, ECG, Photoplethysmography (PPG), mean value of non-invasive BP

(NBP), HR, SpO

and BIS.

At present, anesthesiologists provide assistance for us to collect data and information about patients and anesthesia, and give requirements for the CCM system. In our next step of study, a DoA score diagnosed by these sophisticated anesthesiologists will be employed to improve our entropy indexes, which includes not only the objective response of patients but also the subjective diagnosis of trained doctors. Therefore, our CCM system can be evaluated by the clinical application totally.

5. Discussion and Conclusion

In this paper, results of CCM system for DoA analysis were presented, the

first one is a DoA analysis subsystem that is able to collect and process the

physiological signals detected from patients underwent operations at real time

according to our previous studies on entropy family; the second one is a PID

based on web browser and server model to store, manage and share useful clinic

data for members who devote to study on the DoA. The two components are

combined together as a service system for DoA analysis, which was developed by

Taiwan Yuan Ze University and National University of Taiwan Hospital. Through

the debugging and testing, the SampEn and MSE indexes integrated in the

subsystem are effective to indicate the state of patients. Moreover, due to continuous collection and analysis, the PID has already stored managed 146 dataset of operation data and information in the Databank. In the future, the

number of data will be greater.

Due to entropy is better than traditional spectrum analysis in describing internal changes of EEG signals, it is more effective to help clinician to measure DOA of patients. However, there are some technical limitations in practical application of those methods. It is significant to consider the relationship between sampling time and various scales of EEG signals while using MSE so as to avoid spurious interpretations [28]. Besides, a full description of physiologically relevant nonlinear dynamics in EEG signals is still unclear [29], so it is difficult to know the priori parameters (e.g., time scale and frequency), on that all relevant brain dynamics and pathophysiologic processes are adequately captured.

Therefore, clinical practice is the best and direct method to find the adaptive parameters for not only MSE computation but also our system in analysis of DOA. In the PID, we will continue to collect more physiological data, enrich the content of each physiological data, so that the researchers can obtain more valuable and reliable clinical data and information, and a further study on DoA.

When the database is bigger, it is important to search the data wanted precisely

and quickly. Therefore, the Search function is an important part that we will add in the database right away, and the one and only remark will provide the

physiological data and information of Databank as soon as possible.

Nowadays, a system for multimodal neuro-intensive care monitoring (ICM) is called as ICM+ that was developed and adopted by the Cambridge Neurosurgical Unit, UK [30]. Except for monitoring of multiple variables about head injured, it also describes the dynamics of the studied pathology and presents some useful secondary indices for clinical neurosciences. In additional, the PhysioNet created and established by Harvard University US supplies free web access to large collection of recorded physiologic signals and related open-source software [31]. Both of them are popular in physiological researchers. However, the former one only has function of online analysis on kinds of head injuries, the latter one concentrates on the big databank and offline analysis by various methods. Our CCM system combines the advantages of them to integrate the online analysis with physiological information database for not only the DoA analysis in the operation theatre recently, but also for the intensive care monitoring in the future, and give a complete service for doctors and researchers.

Acknowledgements

The authors wish to thank the exchange master student program of Yuan Ze

University in Taiwan and Wuhan University of Technology in China for supporting

this research. This research was supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (Grant Number: NSC101-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science & Technology in Taiwan (Grant Numbers: CSIST-095-V201 and CSIST-095-V202). Moreover, this work was supported by the National High Technology Research and Development Program (863 Program) of China under Grant no. 2012AA041203.

References

[1] V. Billard, P. L. Gambus, N. chamoun, D. R. Stanski, S. L. shafer, A comparison of spectral edge, delta power, and bispectral index as EEG measures of alfentanil,

porpofol, and midazolam drug effect, Clin, Pharmacol. Ther., 61, 1997,45-58.

[2] M. Struys, L. Versichelen, G. Byttebier, E. Mortier, A. Moerman, G. Rolly, Clinical usefulness of the bispectral index for titrating propofol effectsite

concentration, Anaesthesia, 53,1998, 4-12.

[3] C. De Deyne, M. Struys, J. Decruyenaere, et al., Use of continuous bispectral EEG monitoring to assess depth of sedation in ICU, Intensive Care Med., 24,1998,1294-1298.

[4] A. L. G. Vanluchene, H. Vereecke, O. Thas, et al., Spectral entropy as an

electroencephalographic measure of anesthetic drug effect, Anesthesiology,

101,2004,34-42.

[5] P. F. White, J. Tang, G.F. Romero, et al., A comparison of state and response entropy versus bispectral index values during the perioperative period, Anesthesia

& Analgesia , 102(1), 2006, 160-167.

[6] S. Z. Fan, J. R. Yeh, B. C. Chen, J. S. Shieh, Comparison of EEG Approximate Entropy and Complexity Measures of Depth of Anaesthesia during Inhalational General Anaesthesia, Journal of Medical and Biological Engineering , 31(5),

2011, 359-366.

[7] Wei Q, Liu Q, Fan S-Z, Lu C-W, Lin T-Y, Abbod MF, Shieh J-S., Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia

Based on Sample Entropy. Entropy. 2013; 15(9):3458-3470.

[8] Liu Q, Wei Q, Fan S-Z, Lu C-W, Lin T-Y, Abbod MF, Shieh J-S. Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for

Monitoring Depth of Anesthesia During Surgery. Entropy. 2012; 14(6):978-992.

[9] L. A. Fleisher, J. A. DiPietro, T. R. Johnson, and S. Pincus, Complementary and noncoincident increases in heart rate variability and irregularity during fetal

development, Clin. Sci., 92, 1997, 345–349.

[10] L. A. Fleisher, S. M. Pincus, and S. H. Rosenbaum, Approximate entropy of

heart rate as a correlate of postoperative ventricular dysfunction, Anesthesiology,

78, 1993,683–692.

[11] A. L. Goldberger, J. E. Mietus, D. R. Rigney, M. L .Wood, and S. M. Fortney, Effects of head-down bed rest on complex heart rate variability: response to

LBNP testing, J Appl Physiol , 77, 1994, 2863–2869.

[12] V. K. Yeragania, R. Pohla, M. Mallavarapub, R. Balona, Approximate entropy of symptoms of mood: an effective technique to quatify regularity of mood, Bipolar

Disorders, 5, 2003, 279-286

[13] L. Diambra, J. C. Bastos de Figueiredo, C. P. Malta, Epileptic activity

recognition in EEG recording, Elsevier Physica. A., 273, 1999, 495-505.

[14] S. M. Pincus, “Approximate Entropy: A Complexity Measure For Biological Time Series Data,” Bioengineering Conference, 1991, Proceedings of the 1991

IEEE Seventeenth Annual Northeast, pp. 35 –36, 1991.

[15] S. M. Pincus, and A. L. Goldberger, Physiological time-series analysis: what

does regularity quantify?, Am J Physiol Heart Circ Physiol, 266, 1994, 1643-1656.

[16] S. M. Pincus, Approximate entropy (ApEn) as a complexity measure, Chaos, 5,

1995,110-117.

[17] S. M. Pincus, Quantifying complexity and regularity of neurobiological systems, Methods Neurosci, 28, 1995, 336-363.

[18 ] Richman, J.; Moorman, J. Physiological time series analysis using approximate entropy and sample entropy. Am. J. Physiol. 278, 2000, H2039–H2049.

[19] M.Costa, A. L. Goldberger, C.-K. Peng, Multiscale entropy analysis of biological

signals, Phys Rev E ,71, 2005, 021906.

[20] M. Costa, A. L. Goldberger, C.-K. Peng, Multiscale entropy analysis of

physiologic time series, Phys Rev Lett, 89, 2002, 062102.

[21] A. Vakkuri, A. Yli-Hankala, P. Talja, et al, Time-freqency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system duringsevoflurane, propofol, and thiopental anesthesia, Acta Anaesthesiol Scand, 48, 2004, 145-153.

[22] S. M. Pincus, I. M. Gladstone, R. A. Ehrenkranz, A regularity statistic for

medical data analysis, J Clin Monit , 7, 1991, 335-45.

[23] Vukotic, Aleksa ， Goodwill, James. “Apache Tomcat 7”. Apress. ISBN ：

9781430237235. 2011.9.

[24] Yank, Kevin. “Build Your Own Database Driven Web Site Using PHP &

Mysql”. Sitepoint Pty Ltd. ISBN：9780987153081. 2012.5

[25] R. Ramakrishnan ， J. Gehrke. Database Management System, Elsevier, Third

Version. DrMaster Press, ISBN：9789861578651. 2012.5.

[26] http://140.112.126.218/yuanze/Databank/

[27] Huang J-R, Fan S-Z, Abbod MF, Jen K-K, Wu J-F, Shieh J-S. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia.

Entropy. 2013; 15(9):3325-3339.

[28] R. A. Thuraisingham, G. A. Gottwald, On multiscale entropy analysis for physiological data, Physica A: Statistical Mechanics and its Applications, 366,

2006, 323.

[29] C. J. Stam, Nonlinear dynamical analysis of EEG and MEG: review of an

emerging field, Clin. Neurophysiol., 116, 2005, 2266-2301.

[30] http://www.neurosurg.cam.ac.uk/pages/ICM/

[31] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG,

Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and

PhysioNet: Components of a New Research Resource for Complex Physiologic

Signals. Circulation 101(23):e215-e220.

Figure 1. The result of SEF95%, RE, SE and SampEn of EEG signals in urological

surgery.

Figure 2. The structure of a service system for DoA analysis.

Figure 3. Real time data mining of DOA analyzing on EEG signals. (a) waveform of electroencephalography and value of RE and SE; (b) waveform of electrocardiography and value of heart rate; (c) waveform of plethysmography and value of SpO

; (d) MSE of EEG waveform collected during 30s; (e) RE and SE from device; (f) spectrum of EEG with range from 0 to 50 Hz; (g) complexity index and ratio of MSE; (h) adjusted sample entropy (scale 1 in MSE multiplied by 10) of EEG;

(i) SEF 95% and percentage of delta, theta, alpha, beta bands of EEG signals in

frequency domain.

Figure 4. Data Viewer of MSE in Visual signal.

Figure 5. MSE in offline data mining.

Figure 6. Fast Fourier transform of EEG signals.

Figure 7. RR interval of heart beat in offline data mining and its value of SampEn.

Figure 8. The Functions in PID

Figure 9. The homepage of PID.

Figure 10. The login interface.

Figure 11. The introduction of operation room data.

Figure 12. The Data List of operation room data.

Figure 13. The Data upload.

Figure 14. The data download.

Figure 15. Operation events and anesthesia records.

0 1 2 3 4 5 6 7 8 9 10 0

2000 4000

EEG

Physiological Data and Index

0 1 2 3 4 5 6 7 8 9 10

-1 0 1

ECG

0 1 2 3 4 5 6 7 8 9 10

0 2000 4000

Time(s)

PPG

0 10 20 30 40 50 60 70 80 90

8090 100 SPO 2

0 10 20 30 40 50 60 70 80 90

-200 0 200

HR

0 10 20 30 40 50 60 70 80 90

60 80 100

NBP

0 10 20 30 40 50 60 70 80 90

0 50 100

Time(min)

BIS

Figure 16. Physiological signals and index collected in a case.

Table1. The three kinds of service permission in PID.

Authority Download Upload Delete

Administrators √ √ √

Members √ √ ×

Guests √ × ×