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P2020-012 IBM SPSS Data Collection Technical champion Mastery v1

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P2020-012 exam Dumps Source : IBM SPSS Data Collection Technical champion Mastery v1

Test Code : P2020-012
Test cognomen : IBM SPSS Data Collection Technical champion Mastery v1
Vendor cognomen : IBM
exam questions : 60 existent Questions

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IBM Watson Studio: Product Overview and insight | existent Questions and Pass4sure dumps

download the authoritative e-book: Cloud Computing 2019: using the Cloud for aggressive expertise

See the whole record of desktop learning SolutionsSee consumer reviews of IBM Watson Studio

final analysis

Watson is an umbrella for whole IBM profound studying and synthetic intelligence, as well as desktop learning. The enterprise become a pioneer in introducing AI applied sciences to the enterprise world. What this capability for consumers: Watson Studio is a suitable contender for any organization looking to set up machine learning and profound researching technologies.

The platform offers huge tackle and technologies for data scientists, builders and matter recall specialists that exigency to explore data, construct models, and coach and set up machine discovering models at scale. The solution comprises tackle to partake visualizations and effects with others. Watson Studio supports cloud, computer and local deployment frameworks.

The latter resides in the back of an organization’s firewall or as a SaaS solution running in an IBM inner most cloud. IBM Watson Studio is ranked as a “leader” in the Forrester Wave. It turned into a purchasers’ altenative 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio depends on a group of IBM tools and technologies to build powerful laptop studying applications and features. This contains IBM Cloud pretrained machine learning fashions comparable to visual focus, Watson herbal Language Classifier, and others. The ambiance uses Jupyter Notebooks along with other open source tools and scripting languages to enrich developed-in collaborative mission aspects.;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i

The result is an environment that enables quick and powerful computer gaining information of progress and first-class tuning of models. statistics scientists and others can select from a lot of capacities of Anaconda, Spark and GPU environments.

Watson Studio supports superior visible modeling via a drag-and-drop interface offered through IBM’s SPSS Modeler. furthermore, it contains automated profound studying using a drag-and-drop, no-code interface in Neural network Modeler.

Overview and contours person Base

facts scientists, developers and matter reckon consultants.


Graphical drag-and-drop and command line.

Scripting Languages/codecs Supported

helps Anaconda and Apache Spark. The latter presents Scala, Python and R interfaces.

codecs Supported

Most primary data and file codecs are supported through open supply Jupyter Notebooks.


IBM Watson Studio connects a pair of IBM items, including SPSS Modeler and statistics Science event (DSX) together with open source tools, with the purpose to bring a stalwart Predictive Analytics and laptop getting to know (PAML) answer.

The environment comprises open records units through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version features interactivity with laptop servers and R Studio, along with Python, R., and Scala coder for records scientists.

Reporting and Visualization

Visualization through SPSS Modeler. robust logging and reporting services are developed into the product.


IBM has adopted a pay-as-you-go model. Watson Studio Cloud – regular expenses $ninety nine per 30 days with 50 capacity unit hours per 30 days covered. Watson Studio Cloud - commercial enterprise runs $6,000 per thirty days with 5,000 capacity unit hours. Watson Studio computer expenses $199 per thirty days with unlimited modeling. Watson Studio autochthonous – for industry information science teams N/A.

IBM Watson Studio Overview and lines at a glance:

dealer and contours

IBM Watson Studio

ML focus of attention

wide records science focal point with cloud and computer ML structures.

Key aspects and capabilities

strong visible recognition and herbal classification equipment. bendy mode that incorporates open supply tools. Connects to IBM SPSS Modeler.

person feedback

tremendously rated for points and capabilities. Some complaints revolving round the employ of notebooks.

Pricing and licensing

Tiered mannequin from $99 monthly per consumer to $6,000 monthly per user or greater at commercial enterprise level.

My Highlights from IBM believe 2018: information Science, SPSS, Augmented verity and the consumer event | existent Questions and Pass4sure dumps

I attended IBM’s inaugural feel adventure in Las Vegas final week. This adventure, IBM’s biggest (estimated 30,000+ attendees!), concentrated on making your industry smarter and blanketed keynotes and periods on such issues as artificial intelligence, facts science, blockchain, quantum computing and cryptography. i used to exist invited by artery of IBM as a visitor to partake some insights from the point of view of a data scientist. under are just a few highlights of the adventure.

information Science the usage of IBM SPSS SPSS at 50

50 Years of SPSS Innovation. click image to magnify.

IBM SPSS is IBM’s set of predictive analytics products that tackle the complete analytical technique, from planning to information assortment to analysis, reporting and deployment. IBM celebrated the 50th anniversary of IBM SPSS with their recent beta liberate of IBM SPSS facts 25, the largest beta unlock in its heritage. The up-to-date edition contains recent developments love ebook-in a position charts, MS workplace integration, Bayesian facts and advanced records. additionally, they delivered a brand recent consumer interface which is fairly slick.

i was added to SPSS data in faculty and hold used it for each of my analysis initiatives since then. To exist sincere, SPSS records has aged more desirable than I actually have! I actually hold already begun the employ of the recent version and am pretty excited in regards to the recent aspects and user interface. i will exist able to record about journey in a later result up. check out SPSS with a free 14-day trial.

improving the consumer event

recent experiences hold estimated that 45% of dealers are expected to multiply using artificial intelligence for customer journey within the subsequent three years, and fifty five% of agents are focused on optimizing the customer journey to raise consumer loyalty. additionally, eighty five% of whole customer interactions with a industry will exist managed devoid of human interplay via 2020.

customer experience administration (CXM) is the system of figuring out and managing valued clientele’ interactions with and perceptions about the enterprise/company. IBM knows that enhancing the client event is increasingly becoming facts-intensive undertaking, and using the combined energy of statistics and nowadays’s processing capabilities can alleviate groups mannequin the tactics that strike the consumer event. I attended a pair of sessions to learn about how IBM is leveraging the vigor of IBM Watson to assist their clients with Watson Commerce and Watson customer event Analytics solutions. These options employ the power of artificial intelligence (e.g., predictive analytics) to ameliorate how corporations can better manipulate customer relationships to multiply client loyalty and circulation their company forward.

data Science Meets greater Analytics and Augmented fact

These information authorities from Aginity, IBM Analytics, and IBM Immersive Insights are improving how you entangle from information to insights.

I saw a superb demonstration of the intersection of information science, stronger analytics and augmented fact. Getting from data to insights is the goal of records science efforts and, as facts sources continue to grow, they will want better how you can entangle to these insights. Aginity is working with to exhibit the prerogative artery to ameliorate your predictions via augmenting public statistics with enhanced records (with derived attributes) and stronger analytics to build more suitable predictions. the employ of baseball statistics, Ari Kaplan of Aginity brought up that the improvements in predictive models might translate into millions of dollars per participant. whereas his demo focused on the employ of these applied sciences in baseball information, the principles are generalizable to any industry vertical, including finance, healthcare and media.

on the equal demonstration station, Alfredo Ruiz, lead of the Augmented verity software at IBM Analytics, showed me how his group (IBM Immersive Insights) is incorporating augmented verity into facts Science journey to alleviate agencies superior snitch into account their ever-expanding data units. I’m anticipating seeing how his efforts in marrying augmented reality and statistics science development.

I had the privilege of interviewing Ari Kaplan of Aginity who talked concerning the labor he is doing to enrich how Aginity and is improving the facts science process. snitch a notice at what he has to lisp beneath.

Don’t pass over this interview with Ari Kaplan, a exact “Moneyball” and smartly common around foremost League Baseball, as he talks concerning the latest machine studying technologies powering nowadays’s baseball choices, and snitch a notice at the considerable demo.

Posted by artery of IBM records Science on Thursday, March 22, 2018

data Science is a group recreation

Bob, Al and Dez. photograph via Dez Blanchfield

I had the opening to talk with with many industry specialists who arrive to statistics science from a distinct standpoint than I do. while I focus of attention basically on the records and mathematics features of data science, many of my information friends mode facts science from a technological and programming perspective. really, for an upcoming podcast, Dez Blanchfield and i hold been interviewed by means of Al Martin of IBM Analytics to focus on their respective roles in information science. This dialog became a energetic one, and that i am longing for reliving that evening as soon as the podcast is released. The final analysis is that records science requires such a various capacity set that you actually exigency to labor with different individuals who can complement your knowledge.

I’m with facts pros (and actors) Trisha Mahoney, Ryan Arbow and Shadi Copty.

This notion that information science is a group game turned into placed on complete monitor in an pleasing session through which a couples therapist (Trisha Mahoney) helped resolve an dispute between an information science leader (Shadi Copty) and IT chief (Ryan Arbow). Asking probing questions, the counselor revealed that the statistics science and IT chief were at odds due to a want of communique. She delivered them to IBM’s statistics Science adventure, an industry statistics science platform that allows them to comfortably collaborate, employ excellent open source tackle and entangle their models into creation faster.

Analytics: Your aggressive competencies

For me, IBM feel 2018 was whole about making your company smarter via analytics. really, analysis indicates that corporations that are improved in a position to carry the power of analytics to suffer on their industry issues can exist in a far better residence to outperform their analytics-challenged competitors. This thought become illustrated via keynotes, sessions and conversations. by artery of bringing distinctive statistics science authorities collectively to leverage the tools and methods of AI and computer/deep discovering will alleviate you flood your industry ahead. if you were unable to attend the event, you could watch replays of most of the keynotes prerogative here.

(Disclosure: IBM assisted me with commute prices to IBM suppose 2018.)

a glance on the IBM SPSS Modeler and IBM SPSS statistics analytics tools | existent Questions and Pass4sure dumps

IBM's SPSS predictive analytics tackle embrace IBM SPSS Modeler and IBM SPSS records. SPSS Modeler gives statistics mining and text analysis application, whereas SPSS facts is an integrated family unit of items. each tools enable users to build predictive models and execute other analytics tasks.

The IBM SPSS Modeler ambitions clients who've puny or no programming expertise. clients are provided with a drag-and-drop consumer interface, enabling them to construct predictive models and operate other records analytics. Modeler can commemorate diverse strategies and algorithms to champion the person discover assistance hidden in the statistics. The device can furthermore aid in integrating and consolidating whole kinds of information units from dispersed facts sources throughout the corporation.

The IBM SPSS statistics suite is an built-in set of products geared towards extra professional records analysts. SPSS statistics addresses the complete analytical process, from planning to statistics assortment, analysis, reporting and deployment.

IBM SPSS Modeler aspects

edition 18 gives here aspects:

  • more than 30 base desktop getting to know algorithms.
  • Extensions that supply persisted improvements to exist used with open source products, similar to R and Python.
  • superior aid for several multithreaded analytical algorithms, together with Random timber, Tree-AS, Generalized Linear Engine, Linear-AS, Linear pilot Vector computer and Two-Step-AS clustering.
  • The potential to elude lots of Python and Spark computer discovering, in addition to different Python analytics libraries natively in Modeler without requiring the employ of the Analytic Server, as become required within the outdated edition.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is purchasable as a cloud offering. The client front conclusion of SPSS Modeler runs under windows and macOS, whereas the server component runs on Unix, Linux and home windows.

    IBM SPSS Modeler offers here versions:

  • SPSS Modeler own: A single-person laptop product.
  • SPSS Modeler professional: A computing device product that works with IBM SPSS Analytic Server, providing enhanced scalability and performance and enabling purposes for employ throughout a company.
  • SPSS Modeler top class: This edition includes advanced algorithms and capabilities, similar to textual content analytics, entity analytics and sociable network analysis, that raise mannequin accuracy with unstructured statistics.
  • SPSS Modeler Gold: This version gives analytical determination administration, collaboration and deployment capabilities. SPSS Modeler Gold is additionally accessible as a cloud providing.
  • IBM SPSS facts elements

    SPSS statistics edition 24 contains prerogative here recent facets:

  • The capacity to access greater than a hundred extensions, enabling clients to snitch skills of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, download, replace, eradicate and generally manage extensions.
  • a immense ameliorate to the customized Dialog Builder, enabling users to greater without problems build and deploy their personal extensions. Enhancements encompass recent controls and recent homes for existing controls and a few other advancements to the person interface.
  • improvements that enable clients to more easily and without retard import and export information into SPPS records.
  • advancements to the custom Tables module, together with recent statistical performance and client-requested features.
  • IBM SPSS information presents prerogative here three editions (every with further modules):

  • SPSS data customary tools deliver superior statistical procedures that champion linear and nonlinear statistical fashions, in addition to predictive simulation modeling, which bills for unclear inputs, geospatial analytics and customized tables.
  • SPSS records expert tackle pilot facts education, lacking values and statistics validity, determination bushes, and forecasting.
  • SPSS facts top class adds advanced analytical recommendations, together with structural equation modeling, in-depth sampling assessment and checking out. This bundle additionally contains processes that target direct advertising and high-conclusion charts and graphs.
  • Pricing for the SPSS Modeler and SPSS information predictive analytics tools vary depending on the bundle alternate options, the number of clients and the license period. SPSS facts is now purchasable as a subscription alternative or a perpetual license. IBM offers free trials of both IBM SPSS Modeler and IBM SPSS records.

    subsequent Steps

    Why the term unstructured information is a misnomer

    How immense information is changing statistics modeling ideas

    massive information methods pose recent challenges to records governance

    linked elements View more

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    HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia | existent questions and Pass4sure dumps


    Anaesthesia is a significantly primary procedure used in almost whole surgery (Lan et al., 2012; Schwartz et al., 2010). generic anaesthesia is a drug-induced and reversible condition that has specific behavioural and physiological effects such as unconsciousness, analgesia, and akinesia. Clinically and practically, routine observations such as those of heart rate, respiration, blood pressure, lacrimation, and sweating are used to assist doctors in smoothly controlling and safely managing anaesthesia. Nevertheless, patients recovering from generic anaesthesia can experience significant clinical challenges, including airway and oxygenation problems, emergence delirium (Lepouse et al., 2006), cognitive dysfunction (Saczynski et al., 2012), and delayed emergence, and the venerable are particularly at risk of stroke and heart assail (Neumar et al., 2008). Accurate monitoring of the depth of anaesthesia (DoA) would thus contribute to improvements in the safety and property of anaesthesia employ and would provide a superior experience for patients.

    A condition of generic anaesthesia is produced by anaesthetics that act on the spinal cord and the originate and cortex of the brain (Brown, Purdon & Van Dort, 2011; Ching & Brown, 2014); monitoring of electroencephalogram (EEG) patterns is therefore useful (Niedermeyer & Da Silva, 2005). The two main indices derived from an EEG pattern are the bispectral index (BIS) (Aspect Medical Systems, Newton, MA, USA) (Rosow & Manberg, 2001) and entropy (GE Healthcare, Helsinki, Finland) (Viertiö-Oja et al., 2004); the former is obtained by calculating adjustable weights on the power spectrum, the burst suppression pattern, and the bispectrum of EEG data, whereas the latter is constructed by associating the data degree of disorder (entropy) with the consciousness condition of patients (Liang et al., 2015; Viertiö-Oja et al., 2004). Although EEG-based spectral indices hold been applied commercially for nearly 20 years, they are quiet not fragment of yardstick anaesthesiology drill (Purdon et al., 2015), and the reasons for this are complex. First, these indices were developed from adult patient cohorts, and are not strictly pertinent to infants or younger patients, thereby providing lower accuracy (Samarkandi, 2006), and second, the indices cannot generate precise DoA measurements for inevitable drugs, especially when ketamine and nitrous dioxide are used (Avidan et al., 2008; Sleigh & Barnard, 2004). In addition, EEG signals are sensitive to noise, and therefore more complicated algorithms and resources for babel filtering are required. Moreover, using disposable EEG electrodes is much more expensive than using other physiological signal sensors.

    To overcome some of these disadvantages and provide alternatives to EEG-based solutions (Ahmed et al., 2011), it is crucial to pursue recent ideas to champion mainstream methods. In this respect, the electrocardiogram (ECG) provides primary clinical physiological signals and is highly recommended for continuous monitoring and ensuring international standards for the safe drill of anaesthesia (Merry et al., 2010). Different anaesthetics strike the QT interval of an ECG during anaesthetic induction (Oji et al., 2012), and rhythmic-to-non-rhythmic observations from the ECG can provide anaesthetic information (Lin , 2015). In addition, heart rate variability (HRV), related to autonomic regulation, is strongly affected by generic anaesthesia (Hsu et al., 2012) and varies with respect to differing anaesthetic procedures used (Billman, 2013; Mazzeo et al., 2011); therefore, heartbeat dynamics are highly correlated with a loss of consciousness (Citi et al., 2012). Furthermore, ECG signals are more stable than EEG signals, which means that ECG is more resistant to babel even when cheap electrode sensors are used. HRV analysis thus can exist used to rate DoA. Moreover, interindividual variation is customary and is influenced by age, weight, and life habits, which means that the ECG-derived index more specifically reflects an individual’s anaesthetic condition than EEG-based indices that assume one index value indicates the identical consciousness flat for whole anaesthetics and patients (Purdon et al., 2015). Performing DoA research based on the HRV is thus valuable. However, it is primary to guarantee that the ECG is free of artefacts and the ECG waveform (Q R S T waveform) is accurately recognised; otherwise, incorrect variation properties may ultimately exist obtained, resulting in an incorrect R–R interval distribution.

    An artificial neural network (ANN) is an advanced modelling instrument used in statistics, machine learning, and cognitive science (Alpaydin, 2014; Kriegeskorte, 2015). This bio-inspired mode supports self-learning from complicated data by organizing training pattern set and resultant errors between the preferred output and the subsequent network output. It has the considerable capacity of non-linear, distributed, local, and parallel processing and adaptation and one of the most often used models in engineering applications. An ensemble artificial neural network (EANN) comprises multiple models and combines them to bear the desired output, as opposed to using a single model (Kourentzes, Barrow & Crone, 2014; Ripley & Ripley, 2001; Tay et al., 2013). Normally, an ensemble of models performs better than any individual model because mediocre effects are obtained in ensemble models (Baraldi et al., 2013; Zhou, Wu & Tang, 2002). In summary, the neural network is a powerful and effective mode for employ in data regression and model optimisation of nonstationary data. In biomedical fields, neural networks play a crucial role in the analysis of complicated physiological data (Amato et al., 2013).

    This study aimed to optimise an indicator index, known as the similarity and distribution index (SDI), that is derived from measurements of HRV (Huang et al., 2008). The SDI is proposed to evaluate the DoA from ECG signals occurring in the time domain during routine surgery, and thus differs from the methods previously described herein, which are based on extracting EEG spectrum features in the frequency domain. The time domain parameter is calculated by measuring the similarity between the statistical distributions of R–R interval measurements in consecutive data segments. In this study, results obtained using the proposed mode are compared with the expert assessment of consciousness flat (EACL), which is determined using the mediocre evaluation of five expert anaesthetists after data and patient observation. The model is then optimised by applying an EANN for estimating the DoA. Through SDI extraction in the time domain and EANN modelling targeting the EACL, results demonstrate that it is possible to foretell the DoA throughout an entire surgery.

    The remainder of this paper is divided into four sections. ‘Materials and Methods’ describes the generic anaesthesia used, patient participants and data analysis methods employed; ‘Results’ presents the results of processing and comparisons with the EACL; ‘Discussion’ presents the discussion and study limitations; and the conclusion is provided in ‘Conclusions’.

    Materials and Methods Ethics statement

    All studies were approved by the Research Ethics Committee, National Taiwan University Hospital (NTUH), Taiwan, and written informed consent was obtained from patients (No: 201302078RINC). During the experimental trial, the hospital endeavoured to ensure that whole scheduled surgery was performed very well on time.

    Standard anaesthetic procedure

    Anaesthesia is essential during surgery, and its associated procedures are outlined in Fig. 1 (Cornelissen et al., 2015). Anaesthesia generally involves end-tidal gas concentration over time, and routine anaesthetic drill consists of four stages: consciousness, induction, maintenance, and emergence (recovery) (Merry et al., 2010). Prior to surgery, patients were required to snitch nil by mouth for at least 8 h. After the electrodes were placed, each patient received the volume of anaesthetic agents preempt for the routine operation. Unconsciousness is usually induced by intravenous propofol, another analgesic drug (such as fentanyl), and a muscle relaxant medicine (such as nimbex). Gas anaesthetics (desflurane, sevoflurane) together with air and oxygen were used to maintain sedation for most patients after the mask had been placed, whereas propofol was employed in some cases. As the finish of surgery approached, additional drugs were administrated (such as morphine and atropine). Table 1 summarises particular information. generic anaesthesia was performed safely during whole stages by monitoring physiological signals, such as EEG, ECG, photoplethysmography (PPG), and intermittent vital signs of blood pressure (BP), heart rate (HR), pulse rate (PR), and pulse oximeter oxygen saturation (SpO2). If any of these observation signals underwent irregular changes, the anaesthetist adjusted the intraoperative yardstick anaesthesia machine correspondingly.

    Figure 1: Anesthetic procedure. Table 1:

    Patients clinical characteristics and demographics.

    Values are means (SD). Some eligible subjects are excluded by reasons described in Fig. 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 finish 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 Data recording

    ECG data acquired in this study were obtained from patients undergoing surgery at the NTUH using chest-mounted sensors and a MP60 anaesthetic monitor machine (Intellivue; Philips, Foster City, CA, USA). The machine was connected to a recording computer installed with real-time software developed by their research team using a Borland C+ + Builder 6 developing environment kit (Borland Company, C+ + version 6); this software collected data at a sampling rate of 500 Hz. The sampling rates of the EEG and PPG continuous waveforms were 128 Hz. Intermittent vital signs (such as BIS, HR, PR, BP and SPO2) were recorded every 5 s.

    Figure 2: Study protocol. In fact, patients before this collection era were consulted for their eligibility, dozens of cases were excluded for analysis such as technical and clinical reasons. The 110 remaining subjects are intact for four stages of analysis to evaluate depth of anaesthesia (DoA). Their demography information is shown in Table 1. Clinical data collection

    Prior to collecting data in this study, patients provided written consent for participation. Demographic and clinical data, including height, weight, age, gender, operation time, surgical procedure, and anaesthetic management, were acquired by hospital staff from anaesthesia recording sheets. Other data relating to the research procedure, such as cadaver movement and electrotome operation, were recorded by the research team. Regular hospital recordings and specific research notes were then integrated to serve as auxiliary clinical information.

    Patient participants

    Patients scheduled for elective surgical procedures were recruited from the preoperative clinic at NTUH in 2015. Eligibility criteria related to age, consent, and specific operation type. Those ineligible for inclusion were either (1) under 22 years old, (2) diagnosed with a neurological or cardiovascular disorder, or (3) undergoing surgery involving local anaesthetic rather than general. The selection procedure is illustrated in Fig. 2. According to these criteria, hundreds of patients were eligible for inclusion. However, it was unfortunately not possible to obtain data for whole eligible patients (technique failure, procedure interruption), and ultimately data for 110 patients were acquired. generic parameter information was obtained for whole 110 patients. However, anaesthetic drug management differed with respect to individuals, although propofol and fentanyl inductions were implemented for most patients (n = 100). The particular characteristics of the participants are provided in Table 1.

    ECG data preprocessing Data conditioning

    Data conditioning, or preprocessing, is critical for signal analysis for determining DoA and can overcome problems with compatibility and a want of analysis in advance. It generally consists of data format conversion, babel removal, and data rearrangement. Due to limitations with data collection storage, an ASCII file format was used in this study. Prior to implementing the algorithm, data were transferred into a MATLAB workspace and the notch filter was then used to remove 60 Hz power line noise. whole participant data sets were then manually inspected to determine specific segments of artefacts resulting in extremely abnormal QRS waveforms or ECG chain saturation (for example, electrical artefacts caused by medical tackle or cadaver movement), particularly for the R peak, which was previously impossible to recognise. Their algorithm was then applied to pre-processed data for further analysis.


    It is common information that no accurate yardstick index exists that is capable of symbolising a patient’s anaesthetic condition during clinical surgery. Therefore, five experienced anaesthesiologists were asked to plot subjective scores relating to ‘state of anaesthetic depth’ versus time, based on the data recordings referred to in the previous section and their own moneyed clinical experience. These scores thus represented an EACL. Criteria determined by the five anaesthesiologists with respect to their assessments of consciousness flat were based on both their clinical drill information and supporting information recorded by two research nurses. Any clinical events and signs potentially related to DoA were carefully recorded. Recorded information included (i) intermittent vital signs (such as HR, BP, SPO2); (ii) anaesthetic events, including induction of anaesthesia, tracheal intubation and extubation, the addition of muscle relaxant reversal drugs, and managing airway suction; (iii) surgical events, including the start and finish of surgical procedure and the happening of any specific noxious stimulus; (iv) clinical signs of the patient, including any types of movement and unusual responses and arousability during induction and emergence from anaesthesia; and (v) any other events that were considered relevant, such as patient demography.

    Figure 3: Flowchart design of expert assessment of consciousness flat (EACL). Recordings are clinically related BP, HR, SPO2 and drug administration records; assessments are done by five experienced experts by plotting the DoA curves with compass from 0 to 100. After using ANSYS to digitalize the curve value, they obtained the final gold yardstick by averaging the five doctors’ assessments. EACL: expert of assessment of consciousness level. Figure 4: One representative of EACL. From (A) to (E), it is the five doctors’ assessments, respectively; the final one (F) is the gold standard: EACL. The Red solid line is the imply value, the two green dashed line is imply ± std. Figure 5: Similarity and distribution index (SDI) definition protocol. ECG denotes step 1, R (n) means step 2. Step 3 includes D (n) and histogram. The histogram distribution is used for SDI computation.

    Based on these criteria (Liu et al., 2015), the assessment procedure used in this study to score DoA (Fig. 3) is described as follows. First, research nurses continually observed each patient’s condition to record the information described above. Each anaesthesiologist then made a continuous assessment and eminent changes in ‘the condition of anaesthetic depth’ of patients during the entire operation, based on hospital formal anaesthesia records. To maintain consistency with the BIS, scoring used the compass of 0–100, from brain extinct to fully awake (a score of 40–65 represents an preempt flat of anaesthesia during surgery). Finally, because original assessments were drawn by hand, the results were digitised using web-based software (webplotdigitizer; ANSYS, Canonsburg, PA, USA) (Dorogovtsev & Mendes, 2013) and resampled every 5 s using MATLAB interpolation to ensure concurrence with the BIS index. The result was then considered to exist an EACL. However, because the experience of each anaesthesiologist differed with respect to subjective EACL standards, and to minimise the consciousness flat oversight as much as possible, the data obtained from the five anaesthesiologists were averaged. design 4 shows one EACL case specimen from the five doctors and the imply value of the five scores, where it is evident that the imply value better represents absolute DoA.

    Data analysis SDI definition of HRV SDI protocol.

    The SDI is based on HRV recorded in ECG data. The SDI is a time domain parameter index representing the degree of similarity between consecutive data segments and is obtained by computing the statistical distribution of the R–R interval variability difference. design 5 shows details of the entire procedure used to compute the SDI from ECG recordings. The steps involved are as follows:

    Step 1. Extract the R peak of the ECG signal to obtain the instantaneous R–R interval, R n . Resample the data using the commonly used algorithm of Berger to 4 Hz (Berger et al., 1986).

    Step 2. calculate the dissimilarity between two consecutive heartbeat intervals: (1) D n = R n + 1 − R n n = 1 , 2 , 3 …

    Step 3. select any time point, t, and then select a data block, where the data obstruct contains M data points. Compare the statistical distribution of consecutive blocks, one from D(t − M + 1) to D(t), the other from D(t + 1) to D(t + M). Distribution histograms of both data blocks are generated using the identical cell size. The relative frequency of the D n value of the ith cell of the histogram is denoted P1(i) for the first data obstruct and P2(i) for the second. Determination of the cell number is described in ‘Data analysis’ fragment B below. For example, in the first data block, the data value compass is 0 to 0.5 s if 100 cells are chosen, and the cell width should exist set as 0.005 s. This means that P1(1) denotes the relative frequency between 0 and 0.005; that is, P 1 1 = relative frequency 0 < D n < 0 . 005 , P 1 2 = relative frequency 0 . 005 < D n < 0 . 010 and so on. This is the identical for the second data block.

    Step 4. After multiplying the relative frequency of corresponding cells in the histograms of both data blocks, the sum of the product value in whole cells is the SDI, as calculated using the following equation: (2) SDI = 1 − ∑ i = 1 n P 1 i ∗ P 2 i × 100 , where n is the number of cells and P1(i), P2(i) are the relative frequencies of each cell in the histograms of data blocks 1 and 2, respectively. Theoretically, lofty similarity between the distribution features of ECG data means that patients are in a stable physical condition during surgery and that they are under a condition of anaesthesia with lofty values of P1 × P2. When the sum is deducted by 1 and shows a lower SDI, the DoA is deeper. When the sum is multiplied by 100, the index value ranges from 0 to 100 and is consistent with clinically recognised consciousness levels, such as BIS values that compass from 0 (deep coma) to 100 (awake state), thus making it easier to determine the DoA.

    Implication of SDI value.

    Mathematically, the SDI is obtained from measuring features of the statistical distribution between two consecutive data segments. For a stable HR pattern, the consecutive data segments should hold lofty similarity and a histogram will demonstrate a consistent distribution when P 1 i and P 2 i fluctuate simultaneously. Under the condition of Eq. (2), the SDI is lower in this situation; therefore, a higher SDI symbolises a much more variable HR, which occurs frequently when a patient is awake or under minimal anaesthesia. In this instance, the SDI can exist expressed in accordance with the BIS index.

    Figure 6: The flood chart of ensemble artificial neural network (EANN) model construction. Figure 7: One case demo of SDI. (A) shows one SDI curve derived from a case ECG data, (B) one is the corresponding EACL, in which the blue thick line is the mediocre of other five doctors’ thin lines. Figure 8: Histogram distribution of correlation coefficient between SDI and EACL. Except one in negative correlation, others are positive values, of which most are located at lofty value section from 0.6 to 0.9. Table 2:

    The correlation coefficient comparison between EACL and both original SDI and ANN fitting SDI of 20 cases.

    The latter one has better performance except few cases. From p value (paired Student t test), the two groups are considered statistically significant. (P < 0.05 means statistically significant). Case Original SDI & EACL Fitting SDI & 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 ECG analysis

    Data from the 110 participants were analysed to obtain the SDI. For every case, the SDI was calculated using data from the entire operation procedure, including the awake, induction, maintenance, and recovery states. whole data were obtained under different types of anaesthetics to guarantee compatibility, and parameters were selected empirically. Because D n was in the compass of 0 ms to 0.5 ms, it was used as the length of the histogram. The number of cells used was 100–500, and the best performance was obtained for 250. Dividing the data compass into 250 cells required a cell width of 0.002, and the data block, M, was set as 128. Sample frequency D n of 4 Hz was used, and thus one data obstruct required 32 s. At any one time, 64 s of data (two 32 s data blocks) were required to calculate the SDI.

    ANN analysis

    The Pearson correlation coefficient was calculated for 110 intact cases. To measure the DoA accurately, regression analysis was conducted to compute the model. ANN analysis was utilised to determine the relationship between the SDI and EACL, thereby generating a more accurate output for evaluation. An ANN consists of three parts: an input layer, a hidden layer, and an output layer. In this study, a feedback propagation–type ANN was used, which is the most widely used character of ANN in machine learning. In previous studies (Huang et al., 2013; Liu et al., 2015; Sadrawi et al., 2015), nonlinear and nonstationary medical data were used with a back propagation network that had four layers: an input layer, two hidden layers with 17 and 10 neuron nodes, respectively, and an output layer. The number of nodes and layers used is widely known to strike the performance of an ANN, including the fitting result and time elapse. From an engineering perspective, three to four layers are mostly used (Kourentzes, Barrow & Crone, 2014; Ripley & Ripley, 2001). In this study, different ANN topologies were tested, where the performance of the network varied as a office of the data type. A final topology was selected that obtained the highest accuracy in the shortest time.

    Because the SDI data chain is being used as the input to obtain a result similar to the EACL, the SDI needed to exist consistent with the EACL for each case. As previously mentioned, there were variations in the subjective opinions of the five anaesthetists who completed the EACL, which thus resulted in a low correlation coefficient due to the different assessments. Therefore, 105 out of 110 data sets that had correlation coefficients higher than 0.3 (most of the value distribution was much higher than 0.3, as shown in the following ‘Statistical distribution of the correlation coefficient’ were used for ANN regression. In addition, 85 data sets were used separately in the model’s construction: 70% were used for training, 15% for validation, and 15% for testing. To enable selection of the best neural network, 1,000 epochs were set, and a big volume of data was employed to guarantee that the ANN model had a favourable fit. After the ANN model was generated, 20 sets of data were used for pure-testing of the ANN model to validate its performance.

    The modelling procedure was repeated 10 times to generate 10 ANN models for cross-validation, and the procedure involved was as follows. The initial weights were set randomly, and as mentioned previously, the training was set to 1,000 epochs. The data were finally used to create 20 models for testing of model accuracy. The data were acquired from regular surgical procedures conducted in the NTUH using valid and strict operating procedures and identical regimes. Each model was totally different, due to the randomness of the initial weights. The performance for the cross-validation of 10 models was then calculated to check the variability of the ANN models. The results showed that a different model was created each time ANN training was performed, despite using the identical data set for the training, validation, and testing. Cross-validation was conducted in a blind test to prove that there was no change in the regression result despite changes in the samples input.

    In addition, an EANN was employed to optimise the prediction results. Utilisation of an ensemble obtains higher accuracy than using other neural network approaches (Minku & Yao, 2012) and can address the trade-off between prediction diversity and accuracy within an evolutionary multiobjective framework (Chandra & Yao, 2004). As shown in Fig. 6, a single network model can exist established with the random creation of initial weights, scales, and parameters. In this study, 85 data cases were used to generate 10 ANN models with different initial weights, and the 10 ANN outputs were then averaged to validate the 20 cases for optimising the regression effect. Because each ANN generates a different result with a different error, the mediocre of the model outputs was calculated to overcome associated errors, thus creating an EANN to ameliorate results. whole data analyses were conducted with MATLAB (Mathworks, R2014b, US).

    Figure 9: dissimilarity between the original SDI and fitting SDI for correlation coefficient, imply absolute oversight (MAE) and district under curve (AUC). All of them (A) Correlation Coefficient; (B)Mean Absolute oversight and (C) AUC indicates that the fitting SDI has better performance. Table 3:

    The MAE between EACL and both original SDI and ANN fitting SDI of 20 cases.

    The latter one shows better performance except in a few cases. From p value (Paired Student t test), the two groups are considered statistically different indicating the fine ANN fitting results. (P < 0.05 means statistically significant). Case Original SDI & EACL Fitting SDI & 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 Table 4:

    The AUC between EACL and both original SDI and ANN fitting SDI of 20 cases.

    P value (Paired Student t test) demonstrate two groups are significantly different. The latter one has higher imply value and lower yardstick deviation. (p < 0.05 means statistically significant). Case Original SDI & EACL Fitting SDI & 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 Statistical analysis

    Statistical analysis was performed using SPSS (IBM v22, North Castle, NY, USA) and MATLAB. To evaluate the ANN effect, the performance of the original SDI was compared with the one random ANN regression–derived SDI. The Pearson correlation coefficient, imply absolute oversight (MAE), and district under the curve (AUC) for the EACL were computed and considered the gold standard. The receiver operating characteristic (ROC) curve was calculated to obtain the AUC, which is often used in medical fields during diagnosis of disease. The binary threshold used to distinguish between anaesthesia and consciousness was set to 65 (Johansen & Sebel, 2000). The parametric paired Student’s t-test was then used to assess the statistical significance. To prove the capability of the EANN-derived SDI to measure DoA, its relationship with EACL was analysed. Furthermore, the commonly used BIS was used as a reference. The identical significance test was furthermore undertaken between the two indices, thus demonstrating a solid and convincing result.

    Results Demonstration of typical SDI pattern

    Figure 7A shows a typical SDI trend for a representative patient, and Fig. 7B displays the corresponding EACL obtained from the scores of five experienced and professional anaesthesiologists. The DoA is shown to change throughout the operation, where a higher value denotes a lower flat of consciousness. After induction, the SDI falls sharply, although some variation exists in the maintenance period, and the SDI increases dramatically during emergence from anaesthesia. Generally, it corresponds with the fluctuations of EACL.

    Statistical distribution of the correlation coefficient

    To determine the coefficient distribution characteristics of whole 110 data sets, a histogram with a cell width of 0.1 was constructed (Fig. 8). Most of the data values are located in the compass from 0.6 to 0.8, with imply ± SD equal to 0.78 ± 0.16, which reflects a stalwart relationship with the EACL. Only five cases demonstrate extremely low correlation, these cases were just discarded.

    Comparison between performance of original SDI and SDI fitting using an ANN

    An ANN model can exist trained to model nonlinear behaviour and was used to accurately evaluate DoA in this study. Twenty data sets were used to quantify the optimisation effect, and a comparison was then made to validate the ANN effect. The correlation coefficients between the EACL and both the original SDI and ANN-derived SDI for cases 1 to 20 are presented in Table 2. It is evident that the ANN-derived SDI has significantly improved correlation with the EACL compared with the original SDI (p < 0.05). From the imply value of the statistics shown in Fig. 9A, it is limpid that the ANN-derived SDI has superior performance. Table 3 compares the MAE results in the shape of correlation coefficients. The MAE fitting results obtained for the ANN-derived values are much smaller than those obtained without the ANN, which demonstrates that the ANN performed favourably. It decreases the dissimilarity much from the EACL by showing the statistical results in Fig. 9B significantly (p < 0.05). In addition, the AUCs of both the original SDI and the ANN-derived SDI for the 20 cases were calculated, and the results are shown in Table 4. Furthermore, the ROC curve for one case is presented in Fig. 10 and proves that the optimised SDI evaluates the flat of consciousness more accurately. design 9C shows that the AUC of the ANN-derived SDI is 0.95 ± 0.05, much higher than that of the original SDI. The paired Student t-test was then used to determine the dissimilarity flat between the two groups. The comparison reveals a statistically significant dissimilarity (p < 0.05), indicating the favourable fitting result for the SDI using the ANN. From the relationship and the value difference, it is evident that the ANN-derived SDI measures the DoA more accurately than the original SDI.

    Figure 10: The receiver operating characteristic (ROC) curve of original SDI and artificial neural network (ANN) derived one. Both demonstrate the prediction of DoA features (AUC > 0.5). The ANN fitting SDI (blue curve) has larger AUC than the original SDI (red one), indicating better capacity to foretell DoA. Figure 11: One typical representative of the ANN regression result for SDI. The blue line represents the ANN derived output; it has more similar fluctuation cadence with EACL (black line). Relatively, the original SDI (red line) shows weaker relationship.

    A typical ANN-derived curve is displayed in Fig. 11; the results were derived from the case shown in Fig. 7. Clearly, the ANN-fitted SDI is superior to the original SDI, which varies sharply at the induction stage, whereas the ANN-derived SDI is basically consistent with the EACL. Furthermore, the original SDI reaches zero during the early maintenance period, which is definitely unreasonable.

    ANN blind cross-validation

    The results particular demonstrate that the ANN model improves the SDI performance. However, because only one ANN model test was conducted, a blind cross-validation test was conducted using the previously mentioned 20 cases to ensure that the ANN model was efficient. The results are presented in Table 5 and disclose that whole 10 ANN models used for the 20 cases provide similar results. The identical validation test was used for the MAE (Table 5). This demonstrated that the samples selected execute not strike the construction and effectiveness of the ANN.

    Table 5:

    The correlation coefficient and MAE (mean ± std) between 10 group ANNs fitting SDI and EACL of 20 cases.

    From the imply value comparison, it proves the ANN performance regardless of different input case data. Case Correlation coefficient MAE 1 0.8508 ± 0.0913 6.314 ± 3.1201 2 0.8346 ± 0.0952 4.8873 ± 1.9292 3 0.8417 ± 0.1025 5.8552 ± 2.6317 4 0.8378 ± 0.0972 5.1737 ± 2.2588 5 0.8398 ± 0.0945 4.9005 ± 2.1774 6 0.8459 ± 0.0933 4.9101 ± 2.1289 7 0.8448 ± 0.0921 4.8997 ± 2.2364 8 0.8158 ± 0.0976 6.0248 ± 2.5059 9 0.8340 ± 0.0959 5.4458 ± 2.4640 10 0.8507 ± 0.0899 5.5916 ± 2.5198 EANN-derived SDI compared with the BIS

    To further ameliorate the regression performance of the ANN, an EANN was utilised to foretell the DoA. design 12A shows that the ANNs had puny variance in terms of the correlation coefficient. The EANN has the highest correlation and the lowest yardstick deviation, thereby proving the superior performance of the EANN. In addition, the MAE distribution is shown in Fig. 12B. The individual ANNs had similar characteristics. In addition, the EANN has the lowest MAE, which is consistent with the correlation coefficient results.

    In comparison with the commonly used BIS, Fig. 13 shows that the EANN-derived SDI performs better than the BIS evaluation when referring to the EACL as the gold standard. Differences in terms of the correlation coefficient, MAE, and AUC are whole significant (p < 0.05 parametric paired Student’s t-test). They furthermore chose one representative case for which to plot the ROC curve for both the BIS and EANN-derived SDI (Fig. 14), where the AUC illustrates better discrimination between anaesthesia and consciousness. Tables 6 and 7 provide particular results for the EANN and BIS over 20 cases, respectively.

    Figure 12: The imply value and yardstick deviation statistics of ANNs and the EANN. (A) correlation coefficient; (B) imply absolute error. (A) shows that the ANN has puny fluctuation dissimilarity regardless of input training data in terms of correlation coefficient. The EANN has the highest correlation with lowest yardstick deviation to prove the better performance of EANN. MAE distribution is given in (B). As to individual ANN, they hold similar ability, but not significantly. Similar to the result of correlation coefficient, EANN has almost the lowest MAE. Figure 13: dissimilarity between the BIS and EANN derived SDI for correlation coefficient, MAE and AUC using EACL as gold standard. (A) means correlation coefficient, (B) denotes MAE and (C) shows AUC; whole of them attest the EANN derived SDI behaves better. Asterisk * represents the significant dissimilarity (p < 0.05, parametric paired student test). Figure 14: The ROC curve of BIS and EANN derived SDI from the representative case using EACL as gold standard. Both demonstrate fine capability of DoA prediction (AUC > 0.5). The EANN derived SDI (blue curve) has larger AUC than the BIS (red one), indicating better performance. Table 6:

    The correlation coefficient and MAE value between EACL and EANN fitting SDI of 20 cases.

    Compared with whole single ANN performance in Tables 4 and 5, the imply of correlation coefficient of 20 cases here is higher with lower yardstick deviation, while the MAE furthermore proves this with lower imply and yardstick deviation, acceptation 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 Table 7:

    The correlation coefficient, MAE value and AUC between EACL and BIS of 20 cases.

    These results are used to build comparison with EANN derived SDI. Significance test results are shown in Fig. 13. Generally, the BIS has weaker evaluation of DoA compared to EANN derived SDI in Table 6. Case Correlation coefficient Mean absolutely error AUC 1 0.7746 7.5005 0.9951 2 0.7798 4.9937 0.8878 3 0.621 17.7697 0.7919 4 0.3891 10.4033 0.9423 5 0.8188 6.4099 0.9995 6 0.555 20.6271 0.8773 7 0.7116 14.7956 0.9031 8 0.5617 6.1885 0.8036 9 0.574 9.7251 0.9884 10 0.7187 8.7184 0.9848 11 0.6139 8.8011 0.9703 12 0.694 9.9009 0.9302 13 0.6949 12.3573 0.976 14 0.6507 7.5062 0.996 15 0.5636 10.4242 0.861 16 0.663 8.0178 0.9758 17 0.8089 7.4653 0.9815 18 0.8937 8.8475 0.9942 19 0.7989 5.8428 0.9914 20 0.7553 9.0309 0.9782 Mean ± Std 0.6821 ± 0.1164 9.7663 ± 3.8673 0.9414 ± 0.0637 Discussion

    Doctors employ many observations and physiological vital signs to evaluate flat of consciousness during clinical operations. The medical parameters are usually HR, BP, and photoplethysmography (Merry et al., 2010). However, because these parameters cannot accurately delineate the actual DoA, researchers hold been developing recent methods for this purpose. For example, auditory evoked potential (AEP)- and EEG-related indices (which are mentioned in ‘Introduction’) such as BIS or entropy hold been employed to quantify DoA (Liu et al., 2015; Nishiyama, 2013; Rosow & Manberg, 2001), and such indices are powerful and effective to some extent. An SDI method, which is based on ECG signals, is proposed in this study to measure DoA. The SDI mode has a stalwart relationship with HRV, which is correlated with autonomic nervous system (ANS) function. Such office is seriously affected by anaesthesia (Hsu et al., 2012; Tarvainen et al., 2010), and because this fact is widely accepted in the territory of anaesthesia, the ECG has often been used in DoA research.

    Our point was to construct a practical ECG-derived index, and thus the SDI proposed in this study is constructed to correspond with the EACL, the gold yardstick that researchers adhere to when developing methods of measuring DoA. EACLs were thus obtained by their research team members, which involved a big amount of exertion and endeavour. Although DoA was clinically scored by experienced anaesthesiologists in this study, there were limitations associated with the subjective opinions of each anaesthesiologist, and it was thus necessary to collaboratively score inevitable cases. The point of this paper was to propose the employ of the SDI to measure DoA; thus, the SDI quiet requires inevitable future improvement with respect to the mathematical principles used. For example, the SDI is affected by ECG data fluctuations, which are related to the distribution and similarities between data obstruct points. Parameter selection details must furthermore exist further investigated. Moreover, it is necessary to obtain a clearer understanding of the comparisons made between the SDI and the BIS, AEP, or entropy. It is considered that both EEG-derived and ECG-derived indices provide specific and useful features, and therefore further research is required in this respect.

    The ANN regression model used herein was obtained from a predefined framework of an initial neural network based on their previous engineering research experience (Jiang et al., 2015; Liu et al., 2015; Sadrawi et al., 2015). However, it would exist profitable to investigate the ANN’s parameters, such as numbers of layers, number of nodes in each layers, and character of ANN (Hinton et al., 2012), and to contend the weights and expiration criteria for the maximum optimisation of the performance.

    Mathematically, the SDI does not delineate heart rate or HRV but quantifies the dissimilarity between two consecutive data blocks (as explained in detail in ‘Materials and Methods’). When the dissimilarity is higher, the SDI value is furthermore higher. The index is presumably affected by the shape of the distributions, as well as their similarity. If P1 and P2 are identical but both demonstrate either a uniform distribution (each value equally likely) or are deterministic (only a single value occurs in both), for example, different SDI will result. In the latter case, the SDI =1 − 12 = 0, and in the former case, SDI =1 − 100 × (1∕100)2 = 0.99, for n = 100. Therefore, the SDI not only measures similarity but is furthermore affected by D(n), which means it can delineate ECG data variability. Instead of simply using the correlation coefficient between the ECG and EACL as a definition of the SDI, which would exist less contingent on shape, they used the procedure outlined in ‘Data analysis’, fragment A, to define the final yardstick SDI. Although an ANN has a relatively complicated relationship with DoA, it is utilised for the regression and an output is obtained to quantify DoA, thus solving the nonlinearity between the SDI and DoA. In addition, when patients are conscious, the ANS has a regulation office that affects ECG signals. inevitable types of heart disease influence HRV (Mazzeo et al., 2011) and probably furthermore the SDI. It is thus imperative for us to validate and optimise this potential effect, even though the regression results emerge to exist favourable. They do, however, assume that the SDI is not currently suitable for employ in whole occasions, and research is thus required to explore and amend any problems with the algorithm.

    Although data from more than 100 cases were collected to build the SDI and the results demonstrate favourable performance, most of their cases were middle-aged patients. Therefore, it is necessary to obtain more data from youthful patients to verify their methodology (Cornelissen et al., 2015; Gemma et al., 2016), Surgery is conducted with respect to inevitable protocols and patient safety is always the priority; therefore, the anaesthetic drugs used for the patients in this study were whole chosen by experienced anaesthesiologists, who perhaps favoured the employ of particular drugs. Although other types of drugs could furthermore deliver successful outcomes (Mawhinney et al., 2012; Schwartz et al., 2010), the data obtained during the maintenance era were only related to the administration of propofol, sevoflurane, and desflurane (Table 1). It is thus necessary for us to obtain data based on the employ of other drugs such as medetomidine, isoflurane, and nitrous oxide (Kenny et al., 2015; Purdon et al., 2015), which may enhance index compatibility. In addition, mixed anaesthetic agents were given to the patients, which made it difficult to evaluate the capability of the SDI to reflect the employ of one specific drug regime. Furthermore, their data are obtained from routine surgery performed in a hospital and execute not involve any other clinically specific anaesthetic settings; thus, investigations of this aspect would furthermore exist useful. They will conduct future experiments using related data, and strict and rigorous comparisons will exist made between indices. Future efforts will exist made to investigate and update their algorithm and to determine the possibility of improving DoA evaluation accuracy through a combination with BIS or entropy, for example, or consideration of different surgical circumstances.

    Another issue to exist considered is the spectral analysis of the ANS. ANS office has been widely employed in the assessment of DoA using ECG frequency domain features (Guzzetti et al., 2015; Lin et al., 2014). Previous articles hold mainly focused on the ratio between lofty and low frequency powers. Galletly et al. (1994) described the spectral influence of several common anaesthetic agents on HRV, which provides directions for spectral fragment analysis. In addition, multitaper time frequency analysis was undertaken for autonomic activity dynamics evaluation in Lin et al. (2014). Nevertheless, future research on spectral analysis is required to pursue the promising and valuable integration with the present temporal analysis. Finally, although the results of this labor symbolise DoA from the perspective of the ANS, they furthermore aimed to provide an alternative to EEG-derived evaluation (Purdon et al., 2015; Samarkandi, 2006; Sleigh & Barnard, 2004). Based on the results of this research, it is considered that to overcome the disadvantages of EEG-based methods, studies should exist initiated using a combination of EEG- and ECG-based methods.


    In this study, physiological data from 100 participants were analysed to determine the capacity of their SDI algorithm to evaluate DoA. ECG data were used to derive the SDI, representing the differences in HRV to demonstrate the capacity of the SDI to measure DoA. To optimise prediction accuracy, ANN models were constructed and blind cross-validations were performed to conduct a regression test. In addition, an EANN was employed to overcome random errors and overfitting of the ANN models. This study indicated that HRV analysis has the potential to become another effective mode for the evaluation of DoA. However, because there is a current want of ideal measurement methods for the assessment of patient consciousness level, it is considered that incorporating the SDI into other methods would exist useful. Therefore, combining the employ of the SDI with other physiological medical signals relating to anaesthesia, such as EEG signal, would furthermore exist meaningful and helpful in improving the accuracy of DoA evaluation.

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