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Related Concept Videos

Epilepsy and Seizures: Overview01:24

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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
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Multi-day rhythms modulate seizure risk in epilepsy.

Maxime O Baud1,2,3,4, Jonathan K Kleen5, Emily A Mirro6

  • 1Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, 94143, USA. maxime.baud.neuro@gmail.com.

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|January 10, 2018
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Summary
This summary is machine-generated.

Researchers found that interictal epileptiform activity (IEA) in epilepsy patients follows predictable circadian and multi-day rhythms. Seizures tend to happen during the rising phase of these IEA patterns, suggesting a new way to predict seizure risk.

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Area of Science:

  • Neurology
  • Neuroscience
  • Biomedical Engineering

Background:

  • Epilepsy is characterized by unpredictable seizures, hindering preventative treatments.
  • Understanding seizure timing is crucial for developing proactive interventions.
  • Interictal epileptiform activity (IEA) is a marker of brain irritability between seizures, but its relationship to seizure timing is unclear.

Purpose of the Study:

  • To investigate the relationship between seizure timing and fluctuations in interictal epileptiform activity (IEA).
  • To determine if IEA exhibits predictable temporal patterns that correlate with seizure occurrence.
  • To explore the potential of IEA rhythms as a biomarker for predicting seizure risk.

Main Methods:

  • Utilized data from 37 subjects with implanted brain stimulation devices capable of detecting IEA and seizures over extended periods.
  • Analyzed electroencephalography (EEG) data to identify circadian and multidien (multi-day) periodicities in IEA.
  • Correlated the phase of IEA rhythms with the timing of seizure occurrence.

Main Results:

  • IEA was found to oscillate with both circadian and subject-specific multidien periods, commonly 20-30 days.
  • These multidien periodicities demonstrated robustness and stability over up to 10 years.
  • Seizures were observed to occur preferentially during the rising phase of the multidien IEA rhythms.

Conclusions:

  • IEA exhibits predictable circadian and multidien rhythms in epilepsy patients.
  • Seizure occurrence is linked to the phase of these IEA rhythms, particularly the rising phase of multidien cycles.
  • Combining phase information from circadian and multidien IEA rhythms offers a novel biomarker for assessing relative seizure risk.