<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03303443</url>
  </required_header>
  <id_info>
    <org_study_id>201508007RIN</org_study_id>
    <nct_id>NCT03303443</nct_id>
  </id_info>
  <brief_title>Empirical Mode Decomposition in the Electroencephalogram</brief_title>
  <official_title>Empirical Mode Decomposition in the Electroencephalogram During General Anesthesia Between Generations</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National Taiwan University Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>National Taiwan University Hospital</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      Bispectral index (BIS), a standard monitor for perioperative monitor of patient's conscious&#xD;
      level, is a noninvasive medical technique for monitoring and recording the electrical&#xD;
      activity of brain.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The electroencephalogram and BIS data have lots of information. Fourier transformation to&#xD;
      decompose EEG was first applied on the EEG signaling until now. The disadvantages of Fourier&#xD;
      transformation is hard to deal with physical signals, which was modulated by autonomic system&#xD;
      and factors. The Hilbert-Huang transform (HHT) was proposed to decompose EEG signal into&#xD;
      intrinsic mode functions (IMF) since 2004. HHT can obtain instantaneous frequency data and&#xD;
      work well for data that is nonstationary and nonlinear. HHT have been applied for wild&#xD;
      ranges, not only in the analysis of arrhythmia for medical and public health fields, but also&#xD;
      in the earthquake detection and earth physics detection…etc.&#xD;
&#xD;
      The relationship between frontal EEG patterns and general anesthesia remain poorly&#xD;
      understood. It can only say that the increase in frontal EEG power and shift power to lower&#xD;
      frequencies during general anesthesia from publications. The investigators are going to&#xD;
      compare the EEG signal between generations, try to find the difference in aging using&#xD;
      empirical mode decomposition method.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">May 15, 2016</start_date>
  <completion_date type="Actual">June 15, 2019</completion_date>
  <primary_completion_date type="Actual">June 15, 2019</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>EEG power calculation</measure>
    <time_frame>During the operation time.</time_frame>
    <description>The investigator use root mean square energy to calculate EEG power.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">60</enrollment>
  <condition>Depth of Anesthesia</condition>
  <arm_group>
    <arm_group_label>Younger</arm_group_label>
    <description>20-40 years old patients</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Elderly</arm_group_label>
    <description>over 60 years old patients</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        20-40y/o patients or over 60y/o patients without severe systemic disease who were scheduled&#xD;
        for general anesthesia.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. 20-40y/o or over 60y/o&#xD;
&#xD;
          2. Scheduled for low risk general anesthesia&#xD;
&#xD;
          3. Suitable for surgery after interviewed by anesthesiologist&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Not suitable for general anesthesia&#xD;
&#xD;
          2. High risk patient&#xD;
&#xD;
          3. Allergic to the EEG sensor&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>20 Years</minimum_age>
    <maximum_age>80 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Feng-Fang Tsai, MD</last_name>
    <role>Study Chair</role>
    <affiliation>National Taiwan University Hospital</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Feng-Fang Tsai</name>
      <address>
        <city>Taipei</city>
        <zip>100</zip>
        <country>Taiwan</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Taiwan</country>
  </location_countries>
  <verification_date>April 2019</verification_date>
  <study_first_submitted>December 1, 2015</study_first_submitted>
  <study_first_submitted_qc>October 4, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">October 6, 2017</study_first_posted>
  <last_update_submitted>June 4, 2020</last_update_submitted>
  <last_update_submitted_qc>June 4, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 5, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03303443
org study id: 201508007RIN
nct id: NCT03303443
lead sponsor: 
has dmc: No
Bispectral index (BIS), a standard monitor for perioperative monitor of patient's conscious
      level, is a noninvasive medical technique for monitoring and recording the electrical
      activity of brain.
The electroencephalogram and BIS data have lots of information. Fourier transformation to
      decompose EEG was first applied on the EEG signaling until now. The disadvantages of Fourier
      transformation is hard to deal with physical signals, which was modulated by autonomic system
      and factors. The Hilbert-Huang transform (HHT) was proposed to decompose EEG signal into
      intrinsic mode functions (IMF) since 2004. HHT can obtain instantaneous frequency data and
      work well for data that is nonstationary and nonlinear. HHT have been applied for wild
      ranges, not only in the analysis of arrhythmia for medical and public health fields, but also
      in the earthquake detection and earth physics detection…etc.

      The relationship between frontal EEG patterns and general anesthesia remain poorly
      understood. It can only say that the increase in frontal EEG power and shift power to lower
      frequencies during general anesthesia from publications. The investigators are going to
      compare the EEG signal between generations, try to find the difference in aging using
      empirical mode decomposition method.
observational model: Other
time perspective: Prospective
measure: EEG power calculation
time frame: During the operation time.
description: The investigator use root mean square energy to calculate EEG power.
arm group label: Younger
description: 20-40 years old patients
arm group label: Elderly
description: over 60 years old patients
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 20 Years
maximum age: 80 Years
healthy volunteers: No
last name: Feng-Fang Tsai, MD
role: Study Chair
affiliation: National Taiwan University Hospital
facility: 
country: Taiwan
responsible party type: Sponsor