Seizure Detection

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Seizure Detection

• Medical Instrumentation
• Professor Buechler
• Presenter Mohammad Habibi

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Outline

• Background
• EEG Signal
• Seizure detection Methods
• Computer simulation
• Conclusion and suggestions

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Facts About Epilepsy

• At least 2 million Americans and other 40-50
  million people worldwide (about 1 of population)
  suffer from Epilepsy.
• Epilepsy is the second most common brain disorder
  (after stroke)
• Epileptic seizures occur when a massive group of
  neurons in the cerebral cortex suddenly begin to
  discharge in a highly organized rhythmic pattern.

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Epileptic Seizures

• Seizures usually occur spontaneously, in the
  absence of external triggers.
• Seizures cause temporary disturbances of brain
  functions such as motor control, responsiveness
  and recall which typically last from seconds to a
  few minutes.
• Seizures may be followed by a post-ictal period
  of confusion or impaired sensorial that can
  persist for several hours.

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Types and Causes

• Partial seizures
• - Simple partial No change in consciousness,
  weakness, numbness, unusual smells or tastes.
  twisting muscles or limbs, turning the head to
  the side, paralysis, visual changes, vertigo
• -Complex partial seizures(temporal lobe)
  Consciousness is altered, walking in a circle,
  sitting and standing, smacking their lips
  together, deja vu , uncontrollable laughing,
  fear, visual
• Generalized seizures involve larger areas of the
  brain, often both hemispheres (sides)
• -Tonic-clonic (grand mal)
• -Absence (petit mal) Loss of consciousness
• -Myoclonic

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Causes

• Epilepsy is not associated with any particular
  disease
• Genetic factors
• Head injury
• Stroke/cerebrovascular disorders
• Metabolic disturbances
• Toxic causes
• Infections
• Tumors and space-occupying
• Degenerative disorders
• Brain damage in infancy
• Febrile seizures

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Electroencephalogram (EEG)

• is a tool for evaluating the physiological state
  of the brain.
• offers excellent spatial and temporal resolution
  to characterize rapidly changing electrical
  activity of brain activation
• captures voltage potentials produced by brain
  cells while communicating.
• In an EEG, electrodes are implanted in deep brain
  or placed on the scalp over multiple areas of the
  brain to detect and record patterns of electrical
  activity and check for abnormalities.

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EEG signal

• International Standard Electrode placement 10-20
  (book)
• 5 300 micro volts
• F dc 150 Hz

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(No Transcript)
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• T2,F8,T4,T6

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Seizure Detection Algorithms

• Time domain base
• Frequency domain base
• Time Frequency domain base

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Seizure Detection Algorithms

• Goytman 1997 - Frequency Spectrum
• Liu 1992 - Autocorrelation function
• Ivon 1998 Wavelet Transform

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Gotman

• based on the information in the frequency
  spectrum
• power spectrum differences
• compare with a group of thresholds
• Good result for periodic signals
• Accuracy in this method is based on the
  thresholds, nature of seizure, and the electrode
  connection

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Gotman
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Gotman method for a mix signal
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Liu (Time domain)

• seizure signal has a periodic property
• autocorrelation function between a seizure signal
  and a delayed version of itself,
• A non-seizure signal has some irregular peaks.
• A seizure signal contains regularly spaced peaks
  of the same frequency as the original signal.
• use these peaks to detect rhythmic seizure
  activity

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Liu
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Wavelet

• The FWT proved superior to the other methods for
  seizure detection
• Simultaneous time-frequency information with high
  resolution.
• Resolving high-frequency information in shorter
  time and low frequency information for longer
  time

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Example wavelet decomposition ability
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Digital implementation algorithm

• Step 1 FIR filter ( decomposing signal into
  epileptiform, and nonepileptiform)
• Step 2 distinguish seizure from single or short
  bursts
• Find foreground by passing through median filter
• find background by averaging on foreground signal
• Find RKFG-BG

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Wavelet filter used for simulation
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Wavelet base
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Conclusion

• Seizure has many types and shapes
• Detection method based on freq. or time wont
  have good specificity and sensitivity
• Need to analyze signal in time and freq. domains
  at the same time
• Neonatal seizure has periodic shape so Liu
  algorithm is well suited
• Wavelet based has excellent specificity and
  sensitivity
• What is very important in the seizure area?

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Appendix (Open Problems)

• Is the seizure occurrence random?
• If not, can seizures be predicted?
• If yes, are there seizure pre-cursors preceding
  seizures?
• If yes, what measurement can be used to indicate
  these pre-cursors?
• Does normal brain activity during differ from
  abnormal brain activity?