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Automated spectrographic seizure detection using convolutional neural networks.

Peter Z Yan1, Fei Wang2, Nathaniel Kwok3

  • 1Department of Neurology, Weill Cornell Medicine, 525 E. 68(th) St F-610, New York, NY 10065, United States; Department of Health Policy & Research, Weill Cornell Medicine, 402 E. 67(th) St, New York, NY 10065, United States.

Seizure
|July 21, 2019
PubMed
Summary

Automated seizure detection using convolutional neural networks (CNNs) on EEG spectrograms shows promise for critically ill patients. This technology can improve the timely diagnosis of non-convulsive seizures, reducing associated morbidity and mortality.

Keywords:
Computer application in medicineCritical careElectroencephalogram quantitative analysisVideo-EEG monitoring

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Technology

Background:

  • Non-convulsive seizures are prevalent in critically ill patients.
  • Delayed diagnosis of seizures increases patient morbidity and mortality.
  • Continuous EEG (cEEG) monitoring is standard, but intermittent review delays treatment.

Purpose of the Study:

  • To develop and evaluate convolutional neural networks (CNNs) for automated seizure detection.
  • To improve the timeliness of seizure diagnosis and treatment in intensive care units.
  • To analyze the feasibility of using CNNs on EEG spectrograms for seizure detection.

Main Methods:

  • EEG data from adult (NYP) and pediatric (CHB) patients were converted into spectrograms.
  • Spectrograms were sampled as images, simulating a telemetry display for CNN training.
  • Four CNN models of varying complexity, based on VGG-net architecture, were trained and tested.

Main Results:

  • Two CNN models (4 and 7 convolution layers) achieved >90% sensitivity and specificity on the CHB dataset.
  • These models demonstrated >90% sensitivity and 75-80% specificity on the NYP dataset.
  • More complex (10 layers) and simpler (2 layers) CNN models showed suboptimal performance.

Conclusions:

  • Automated seizure detection on EEG spectrograms using CNNs is feasible.
  • The developed CNN models show potential for clinical application in seizure detection.
  • This approach may mitigate diagnostic delays, improving patient outcomes.