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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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IR Frequency Region: Alkyne and Nitrile Stretching01:22

IR Frequency Region: Alkyne and Nitrile Stretching

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Both alkyne (C≡C) and nitrile (C≡N) functional groups contain triple bonds and show stretching absorptions around the wavenumber range of 2100 to 2300 cm−1 in the diagnostic region of the IR spectra.
Comparing the stretching vibrational frequency of  C≡C triple bonds with that of double and single bonds, it is evident that C≡C triple bonds exhibit a higher stretching frequency than C=C double and C–C single bonds. Similarly, the C≡N triple bond...
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IR Frequency Region: Alkene and Carbonyl Stretching01:29

IR Frequency Region: Alkene and Carbonyl Stretching

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Double bonds in alkenes and carbonyl compounds exhibit stretching frequencies in the diagnostic region of the IR spectrum. In addition, alkenes exhibit vinylic C–H stretching and C–H out-of-plane bending absorptions that are useful for identifying substitution patterns.
Stretching frequencies are affected by several factors, such as resonance, inductive effects, ring strain, dipole moment, and hydrogen bonding. Consequently, the stretching frequency of the carbonyl double bond...
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Local Anesthetics: Clinical Application as Intravenous Regional Anesthesia01:16

Local Anesthetics: Clinical Application as Intravenous Regional Anesthesia

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Intravenous regional anesthesia or the Bier block technique is used to anesthetize a specific limb or extremity. It uses exsanguinated or blood-drained vessels to transport local anesthetics or LAs to the peripheral nerve trunks. Lidocaine without vasoconstrictors like epinephrine is most commonly used for this technique. Other drugs used are prilocaine, ropivacaine, and chloroprocaine. Bupivacaine is not recommended for this technique due to its high cardiac toxicity.
One of the advantages of...
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Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Related Experiment Video

Updated: Jan 25, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Localizing epileptogenic regions using high-frequency oscillations and machine learning.

Shennan A Weiss1, Zachary Waldman1, Federico Raimondo2,3

  • 1Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA.

Biomarkers in Medicine
|May 3, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning can analyze high-frequency oscillations (HFOs) to pinpoint seizure-generating brain regions, potentially improving epilepsy surgery outcomes. This approach uses HFO characteristics and location to distinguish abnormal from normal brain activity.

Keywords:
HFOartificial intelligenceepilepsyepilepsy surgeryepileptiform spikefast ripplehigh-frequency oscillationmachine learningphase–amplitude couplingrippleseizurewavelet

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Pathological high-frequency oscillations (HFOs) are key indicators of brain tissue causing seizures.
  • Identifying these HFOs is crucial for planning epilepsy surgery in patients with drug-resistant epilepsy.
  • Current methods for HFO detection and classification can be challenging.

Purpose of the Study:

  • To review machine learning strategies for identifying epileptogenic zones using HFO biomarkers.
  • To explore features of HFOs that can differentiate pathological from physiological events.
  • To emphasize the role of neuroanatomical localization in machine learning models for epilepsy.

Main Methods:

  • Review of machine learning techniques applied to HFO analysis.
  • Discussion of HFO features: rate, spectral content, duration, power, and phase-amplitude coupling.
  • Consideration of neuroanatomical localization within machine learning frameworks.

Main Results:

  • Machine learning offers promising strategies for analyzing HFOs to detect epileptogenic tissue.
  • Specific HFO features and their combination can improve the distinction between pathological and physiological HFOs.
  • Integrating neuroanatomical information enhances the accuracy of machine learning models.

Conclusions:

  • Machine learning can significantly advance the use of HFOs as biomarkers for epilepsy surgery planning.
  • Further development of machine learning algorithms incorporating detailed HFO features and localization is warranted.
  • Improved identification of epileptogenic tissue through these methods holds potential for better patient seizure control.