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Related Concept Videos

Electroconvulsive Therapy01:30

Electroconvulsive Therapy

25
Electroconvulsive therapy (ECT), or shock therapy, remains a critical biomedical intervention for severe, treatment-resistant depression. While its origins can be traced back to Hippocrates' observations that malaria-induced convulsions alleviated mental illness, modern ECT has evolved significantly from its earlier, more primitive applications. First introduced in 1938 by Ugo Cerletti and his colleagues, ECT involves inducing controlled seizures using electrical currents. In its early...
25

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Related Experiment Video

Updated: Jun 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Ictal EEG Non-linear And High Order Spectral Analysis Methods In Electroconvulsive Therapy And Its Clinical Utility.

Hulegar A Abhishekh1, Jagadisha Thirthalli2, Vivek H Phutane3

  • 1Intern, Bangalore Medical College and Research Institute, Bangalore, India.

Indian Journal of Physiology and Pharmacology
|December 5, 2024
PubMed
Summary
This summary is machine-generated.

Higher bispectrum entropy in electroencephalogram (EEG) during electroconvulsive therapy (ECT) early on predicts better outcomes for schizophrenia patients. This EEG analysis offers a novel way to forecast treatment success.

Keywords:
EEGictalnon-linearoutcomeschizophrenia

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Area of Science:

  • Neuroscience
  • Computational Psychiatry

Background:

  • Electroencephalogram (EEG) analysis during electroconvulsive therapy (ECT) can predict clinical outcomes.
  • EEG signals are complex, non-linear, and non-stationary, requiring advanced analytical methods.
  • Non-linear and higher-order spectrum analyses offer better characterization of EEG signals compared to traditional methods.

Purpose of the Study:

  • To investigate the predictive value of non-linear and higher-order spectrum analyses of ictal EEG for clinical outcomes in schizophrenia patients undergoing ECT.
  • To determine if specific EEG measures recorded early in the ECT course can forecast treatment response.

Main Methods:

  • Schizophrenia patients undergoing ECT had their EEG recorded during seizures.
  • Non-linear measures including Approximate Entropy (ApEn), Sample Entropy (SamEn), Hurst exponent (H), correlation dimension (CD), and Largest Lyapunov exponent (LLE) were computed.
  • Higher-order spectrum analysis, specifically Bispectrum entropy (HOS. En), was also calculated from early ECT EEG sessions.

Main Results:

  • Bispectrum entropy (HOS. En) significantly predicted clinical outcome at two weeks (r = -0.434, p = 0.027 and r = -0.414, p = 0.036).
  • Other non-linear measures like ApEn, SamEn, H, CD, and LLE did not show a significant correlation with clinical outcome.

Conclusions:

  • Higher bispectrum entropy of ictal EEG during early ECT sessions is a significant predictor of better clinical outcomes in schizophrenia patients.
  • This finding highlights the potential of advanced EEG analysis techniques for personalized ECT treatment strategies.