A Critical Care Monitoring System for Depth of
Anaesthesia Analysis Based on Entropy Analysis and
Physiological Information Database
Qin Wei1, Yang Li2, Shou-Zen Fan3, Quan Liu2, Maysam F. Abbod4, Cheng-Wei Lu5,6,
Tzu-Yu Lin5,6, Kuo-Kuang Jen7, Shang-Ju Wu7,
Jiann-Shing Shieh 6,8,*
1
2
3
4
5
6
7
8
School of Mechanical and Electronic Engineering, Wuhan University of
Technology, Wuhan 430070, China; E-Mail: zivaw2010@hotmail.com;
School of Information Engineering, Wuhan University of Technology, Wuhan
430070, China; E-Mail: quanliu@whut.edu.cn;
Department of Anesthesiology, College of Medicine, National Taiwan University,
Taipei 100, Taiwan; E-Mail: shouzen@gmail.com;
School of Engineering and Design, Brunel University, London UB8 3PH, UK;
E-Mail: maysam.abbod@brunel.ac.uk;
Dept. of Anesthesiology, Far Eastern Memorial Hospital, Ban-Chiao, Taiwan;
drluchengwei@gmail.com; drlin1971@gmail.com;
Department of Mechanical Engineering, Yuan Ze University, 135, Yuan-Tung
Road, Chung-Li 32003, Taiwan;
Chung-Shan Institute of Science and Technology, Taoyuan, Longtan Taiwan;
rgg@ms.hinet.net; shanglu111@gmail.com;
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.
1
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
2
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 (SpO2), 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
3
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
4
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:
ApEn(m, N, r)  m (r) m1(r)
(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 Фm(r)
is the matching number of time series within parameters m and r.
But there are two shortcomings [15-17] in application of ApEn:
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(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:
SampEn(m, N, r)   ln  Am (r) Bm (r)
(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
signals. In comparison among SEF95%, RE, SE and SampEn in DoA monitoring,
firstly EEG data were collected from twenty patients undergoing urological surgery in
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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/5TM 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, y (j ) is
calculated according to the equation:
7
y (j ) 
1


j
i ( j1) 1
xi
(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,
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
8
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 10m and maximum 30m (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 SpO2 are displayed on the left side of each panel. EEG
9
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.
10
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, SpO2 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 102 [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
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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
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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 SpO2 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
13
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
14
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
15
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
16
(NBP), HR, SpO2 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
17
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
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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
19
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.
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24
Pre-oper
ation
Induc
tion
Reco
very
Mainten
ance
Figure 1. The result of SEF95%, RE, SE and SampEn of EEG signals in urological
surgery.
Physiological
monitor
Physiological Information Database
DoA Analysis
Subsystem
Internet
Online analysis
Offline analysis
Aenesthetist
monitors the index
at real time
Application Sever
Services
Doctor does
postoperative
examination
Self-Analysis
Display
Figure 2. The structure of a service system for DoA analysis.
25
Print
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 SpO2; (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.
26
Figure 4. Data Viewer of MSE in Visual signal.
Figure 5. MSE in offline data mining.
27
Figure 6. Fast Fourier transform of EEG signals.
Figure 7. RR interval of heart beat in offline data mining and its value of SampEn.
28
Authority
Service
File Management
1. ID Check;
2. Code Verification;
3. Service Acceptance;
4. Service Permission.
1. Login/out;
2. Upload/Download/Delete;
3. Display/ Print;
4. Self-analysis.
1. User Information Management;
2. Data Management;
3. Data Information Management;
4. Data List Update.
Figure 8. The Functions in PID
Figure 9. The homepage of PID.
Figure 10. The login interface.
29
Figure 11. The introduction of operation room data.
Figure 12. The Data List of operation room data.
Figure 13. The Data upload.
30
Figure 14. The data download.
Figure 15. Operation events and anesthesia records.
31
BIS
NBP
HR
SPO
2
PPG
ECG
EEG
Physiological Data and Index
4000
2000
0
1
0
-1
4000
2000
0
100
90
80
200
0
-200
100
80
60
100
50
0
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
Time(s)
6
7
8
9
10
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
Time(min)
60
70
80
90
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
√
×
×
32