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

Test Code : P2020-012
Test appellation : IBM SPSS Data Collection Technical advocate Mastery v1
Vendor appellation : IBM
: 60 real Questions

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IBM IBM SPSS Data Collection

IBM Watson Studio: Product Overview and insight | killexams.com real Questions and Pass4sure dumps

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

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

final analysis

Watson is an umbrella for entire 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 outfit and technologies for data scientists, builders and subject remember specialists that requisite to explore data, construct models, and coach and set up machine discovering models at scale. The solution comprises outfit to participate 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’ choice 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.

https://o1.qnsr.com/log/p.gif?;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 scholarship of evolution and first-class tuning of models. statistics scientists and others can choose 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 subject rely consultants.

Interface

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

Integration

IBM Watson Studio connects a couple of IBM items, including SPSS Modeler and statistics Science event (DSX) together with open source tools, with the intention to bring a sturdy 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.

Pricing

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

IBM Watson Studio Overview and lines at a glance:

dealer and contours

IBM Watson Studio

ML heart 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 course 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 verisimilitude and the consumer event | killexams.com real 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 business smarter and blanketed keynotes and periods on such issues as synthetic intelligence, facts science, blockchain, quantum computing and cryptography. i used to be invited by course of IBM as a visitor to participate 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 fondness 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 be 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 be able to record about journey in a later redeem 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 augment using synthetic 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 entire customer interactions with a business will be managed devoid of human interplay via 2020.

customer taste 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 back groups mannequin the tactics that impress the consumer event. I attended a couple of sessions to learn about how IBM is leveraging the vim of IBM Watson to assist their clients with Watson Commerce and Watson customer event Analytics solutions. These options employ the power of synthetic intelligence (e.g., predictive analytics) to help how corporations can better manipulate customer relationships to augment client loyalty and circulation their company forward.

data Science Meets greater Analytics and Augmented fact

These information authorities from Aginity, IBM Analytics, H2O.ai and IBM Immersive Insights are improving how you rep 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 rep to these insights. Aginity is working with H2O.ai to exhibit the prerogative course to help your predictions via augmenting public statistics with enhanced records (with derived attributes) and stronger analytics to gain 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 business vertical, including finance, healthcare and media.

on the equal demonstration station, Alfredo Ruiz, lead of the Augmented verisimilitude software at IBM Analytics, showed me how his group (IBM Immersive Insights) is incorporating augmented verisimilitude into facts Science journey to back agencies superior retract 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 travail he is doing to enrich how Aginity and H2O.ai is improving the facts science process. retract a view at what he has to jabber beneath.

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

Posted by course 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 chance to talk with with many industry specialists who further to statistics science from a discrete standpoint than I do. while I heart of attention basically on the records and mathematics features of data science, many of my information friends course 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 aptitude set that you actually requisite to travail 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 plenary monitor in an pleasing session through which a couples therapist (Trisha Mahoney) helped resolve an controversy 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 business statistics science platform that allows them to comfortably collaborate, employ excellent open source outfit and rep their models into creation faster.

Analytics: Your aggressive competencies

For me, IBM feel 2018 was entire 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 bear on their business issues can be in a far better space to outperform their analytics-challenged competitors. This thought become illustrated via keynotes, sessions and conversations. by course of bringing distinctive statistics science authorities collectively to leverage the tools and methods of AI and computer/deep discovering will back you flux your business 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 | killexams.com real Questions and Pass4sure dumps

IBM's SPSS predictive analytics outfit include 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 slight 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 observe diverse strategies and algorithms to advocate the person ascertain assistance hidden in the statistics. The device can also aid in integrating and consolidating entire 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 basis desktop getting to know algorithms.
  • Extensions that supply persisted improvements to be 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 rush 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 convivial 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 aptitude to access greater than a hundred extensions, enabling clients to retract 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 grandiose help 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 detain 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 unvarying tools deliver superior statistical procedures that advocate linear and nonlinear statistical fashions, in addition to predictive simulation modeling, which bills for unclear inputs, geospatial analytics and customized tables.
  • SPSS records expert outfit pilot facts education, lacking values and statistics validity, decision 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 grandiose 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 | killexams.com real questions and Pass4sure dumps

    Introduction

    Anaesthesia is a significantly Important procedure used in almost entire surgery (Lan et al., 2012; Schwartz et al., 2010). general 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 general anaesthesia can taste 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 storm (Neumar et al., 2008). Accurate monitoring of the depth of anaesthesia (DoA) would thus contribute to improvements in the safety and trait of anaesthesia employ and would provide a superior taste for patients.

    A situation of general anaesthesia is produced by anaesthetics that act on the spinal cord and the stem 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 situation 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 silent not fragment of benchmark 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 apposite to infants or younger patients, thereby providing lower accuracy (Samarkandi, 2006), and second, the indices cannot generate precise DoA measurements for certain 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 involved algorithms and resources for pandemonium 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 advocate mainstream methods. In this respect, the electrocardiogram (ECG) provides Important 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 impress 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 general 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 pandemonium even when cheap electrode sensors are used. HRV analysis thus can be used to assess DoA. Moreover, interindividual variation is unvarying and is influenced by age, weight, and life habits, which means that the ECG-derived index more specifically reflects an individual’s anaesthetic situation than EEG-based indices that assume one index value indicates the same consciousness smooth for entire anaesthetics and patients (Purdon et al., 2015). Performing DoA research based on the HRV is thus valuable. However, it is Important 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 be obtained, resulting in an incorrect R–R interval distribution.

    An synthetic neural network (ANN) is an advanced modelling appliance used in statistics, machine learning, and cognitive science (Alpaydin, 2014; Kriegeskorte, 2015). This bio-inspired course supports self-learning from involved data by organizing training pattern set and resultant errors between the preferred output and the subsequent network output. It has the grandiose aptitude of non-linear, distributed, local, and parallel processing and adaptation and one of the most often used models in engineering applications. An ensemble synthetic 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 fair effects are obtained in ensemble models (Baraldi et al., 2013; Zhou, Wu & Tang, 2002). In summary, the neural network is a powerful and efficient course for employ in data regression and model optimisation of nonstationary data. In biomedical fields, neural networks play a crucial role in the analysis of involved 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 course are compared with the expert assessment of consciousness smooth (EACL), which is determined using the fair 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 show that it is possible to prognosticate the DoA throughout an entire surgery.

    The remnant of this paper is divided into four sections. ‘Materials and Methods’ describes the general 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 entire 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 retract nil by mouth for at least 8 h. After the electrodes were placed, each patient received the volume of anaesthetic agents appropriate 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 cease of surgery approached, additional drugs were administrated (such as morphine and atropine). Table 1 summarises minute information. general anaesthesia was performed safely during entire 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 benchmark 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 cease 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 term 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 carcass 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 entire eligible patients (technique failure, procedure interruption), and ultimately data for 110 patients were acquired. general parameter information was obtained for entire 110 patients. However, anaesthetic drug management differed with respect to individuals, although propofol and fentanyl inductions were implemented for most patients (n = 100). The minute characteristics of the participants are provided in Table 1.

    ECG data preprocessing Data conditioning

    Data conditioning, or preprocessing, is captious 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, pandemonium 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. entire 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 outfit or carcass movement), particularly for the R peak, which was previously impossible to recognise. Their algorithm was then applied to pre-processed data for further analysis.

    EACL

    It is common scholarship that no accurate benchmark index exists that is capable of symbolising a patient’s anaesthetic situation 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 wealthy clinical experience. These scores thus represented an EACL. Criteria determined by the five anaesthesiologists with respect to their assessments of consciousness smooth were based on both their clinical drill scholarship 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 cease of surgical procedure and the occurrence 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 smooth (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 sweep from 0 to 100. After using ANSYS to digitalize the curve value, they obtained the final gold benchmark 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 express value, the two green dashed line is express ± 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 situation to record the information described above. Each anaesthesiologist then made a continuous assessment and renowned changes in ‘the situation of anaesthetic depth’ of patients during the entire operation, based on hospital formal anaesthesia records. To maintain consistency with the BIS, scoring used the sweep of 0–100, from brain inanimate to fully awake (a score of 40–65 represents an appropriate smooth 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 be an EACL. However, because the taste of each anaesthesiologist differed with respect to subjective EACL standards, and to minimise the consciousness smooth oversight as much as possible, the data obtained from the five anaesthesiologists were averaged. pattern 4 shows one EACL case specimen from the five doctors and the express value of the five scores, where it is evident that the express 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. pattern 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. reckon the dissimilarity between two consecutive heartbeat intervals: (1) D n = R n + 1 − R n n = 1 , 2 , 3 …

    Step 3. choose any time point, t, and then select a data block, where the data obscure 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 same 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 obscure 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 sweep is 0 to 0.5 s if 100 cells are chosen, and the cell width should be 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 same 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 entire 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, towering 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 situation of anaesthesia with towering 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 sweep 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 towering similarity and a histogram will show 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 be expressed in accordance with the BIS index.

    Figure 6: The flux chart of ensemble synthetic 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 fair 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 towering 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. entire data were obtained under different types of anaesthetics to guarantee compatibility, and parameters were selected empirically. Because D n was in the sweep 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 sweep 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 obscure required 32 s. At any one time, 64 s of data (two 32 s data blocks) were required to reckon 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 nature 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 impress the performance of an ANN, including the fitting effect 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 function 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 be 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 large 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 sound 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 same 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 be 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 fair of the model outputs was calculated to overcome associated errors, thus creating an EANN to help results. entire data analyses were conducted with MATLAB (Mathworks, R2014b, US).

    Figure 9: dissimilarity between the original SDI and fitting SDI for correlation coefficient, express absolute oversight (MAE) and belt 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 estimable 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) show two groups are significantly different. The latter one has higher express value and lower benchmark 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, express absolute oversight (MAE), and belt 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 same significance test was also 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 smooth 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 entire 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 sweep from 0.6 to 0.8, with express ± SD equal to 0.78 ± 0.16, which reflects a sturdy relationship with the EACL. Only five cases show extremely low correlation, these cases were just discarded.

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

    An ANN model can be 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 express value of the statistics shown in Fig. 9A, it is transparent 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 smooth of consciousness more accurately. pattern 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 smooth between the two groups. The comparison reveals a statistically significant dissimilarity (p < 0.05), indicating the favourable fitting effect 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 synthetic neural network (ANN) derived one. Both show 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 aptitude to prognosticate DoA. Figure 11: One typical representative of the ANN regression effect for SDI. The blue line represents the ANN derived output; it has more similar fluctuation rhythm 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 minute 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 entire 10 ANN models used for the 20 cases provide similar results. The same validation test was used for the MAE (Table 5). This demonstrated that the samples selected Do not impress 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 express 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 help the regression performance of the ANN, an EANN was utilised to prognosticate the DoA. pattern 12A shows that the ANNs had slight variance in terms of the correlation coefficient. The EANN has the highest correlation and the lowest benchmark 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 entire significant (p < 0.05 parametric paired Student’s t-test). They also 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 minute results for the EANN and BIS over 20 cases, respectively.

    Figure 12: The express value and benchmark aberration statistics of ANNs and the EANN. (A) correlation coefficient; (B) express absolute error. (A) shows that the ANN has slight fluctuation dissimilarity regardless of input training data in terms of correlation coefficient. The EANN has the highest correlation with lowest benchmark aberration 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; entire 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 show estimable 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 entire single ANN performance in Tables 4 and 5, the express of correlation coefficient of 20 cases here is higher with lower benchmark deviation, while the MAE also proves this with lower express and benchmark deviation, sense that the EANN fulfill 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 gain 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 smooth of consciousness during clinical operations. The medical parameters are usually HR, BP, and photoplethysmography (Merry et al., 2010). However, because these parameters cannot accurately limn 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 efficient to some extent. An SDI method, which is based on ECG signals, is proposed in this study to measure DoA. The SDI course has a sturdy relationship with HRV, which is correlated with autonomic nervous system (ANS) function. Such function 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 benchmark that researchers adhere to when developing methods of measuring DoA. EACLs were thus obtained by their research team members, which involved a large amount of application 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 certain cases. The point of this paper was to propose the employ of the SDI to measure DoA; thus, the SDI silent requires certain 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 obscure points. Parameter selection details must also be 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 taste (Jiang et al., 2015; Liu et al., 2015; Sadrawi et al., 2015). However, it would be beneficial to investigate the ANN’s parameters, such as numbers of layers, number of nodes in each layers, and nature of ANN (Hinton et al., 2012), and to debate the weights and expiration criteria for the maximum optimisation of the performance.

    Mathematically, the SDI does not limn 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 also 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 show 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 also affected by D(n), which means it can limn ECG data variability. Instead of simply using the correlation coefficient between the ECG and EACL as a definition of the SDI, which would be less subject on shape, they used the procedure outlined in ‘Data analysis’, fragment A, to define the final benchmark 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 function that affects ECG signals. certain types of heart disease influence HRV (Mazzeo et al., 2011) and probably also the SDI. It is thus imperative for us to validate and optimise this potential effect, even though the regression results show to be favourable. They do, however, assume that the SDI is not currently suitable for employ in entire 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 green patients to verify their methodology (Cornelissen et al., 2015; Gemma et al., 2016), Surgery is conducted with respect to certain protocols and patient safety is always the priority; therefore, the anaesthetic drugs used for the patients in this study were entire chosen by experienced anaesthesiologists, who perhaps favoured the employ of particular drugs. Although other types of drugs could also deliver successful outcomes (Mawhinney et al., 2012; Schwartz et al., 2010), the data obtained during the maintenance term 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 Do not involve any other clinically specific anaesthetic settings; thus, investigations of this aspect would also be useful. They will conduct future experiments using related data, and strict and rigorous comparisons will be made between indices. Future efforts will be 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 be considered is the spectral analysis of the ANS. ANS function 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 towering 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 travail symbolise DoA from the perspective of the ANS, they also 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 be initiated using a combination of EEG- and ECG-based methods.

    Conclusions

    In this study, physiological data from 100 participants were analysed to determine the aptitude of their SDI algorithm to evaluate DoA. ECG data were used to derive the SDI, representing the differences in HRV to demonstrate the aptitude 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 efficient course for the evaluation of DoA. However, because there is a current want of pattern measurement methods for the assessment of patient consciousness level, it is considered that incorporating the SDI into other methods would be useful. Therefore, combining the employ of the SDI with other physiological medical signals relating to anaesthesia, such as EEG signal, would also be meaningful and helpful in improving the accuracy of DoA evaluation.


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