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Empirical Mode Decomposition in the Electroencephalogram

Empirical Mode Decomposition in the Electroencephalogram During General Anesthesia Between Generations

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT03303443
Enrollment
60
Registered
2017-10-06
Start date
2016-05-15
Completion date
2019-06-15
Last updated
2020-06-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Depth of Anesthesia

Brief summary

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.

Detailed description

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.

Interventions

None listed

Sponsors

National Taiwan University Hospital
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
20 Years to 80 Years
Healthy volunteers
No

Inclusion criteria

1. 20-40y/o or over 60y/o 2. Scheduled for low risk general anesthesia 3. Suitable for surgery after interviewed by anesthesiologist

Exclusion criteria

1. Not suitable for general anesthesia 2. High risk patient 3. Allergic to the EEG sensor

Design outcomes

Primary

MeasureTime frameDescription
EEG power calculationDuring the operation time.The investigator use root mean square energy to calculate EEG power.

Countries

Taiwan

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026