exploration of instantaneous amplitude and frequency features for epileptic seizure prediction ning...

Exploration of Instantaneous Amplitude and Frequency Features for Epileptic

Seizure Prediction

Ning Wang and Michael R. LyuDept. of Computer Science and Engineering

Chinese University of Hong Kong

IEEE 12th International Conference on BioInformatics and BioEngineering (BIBE 2012)

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Outline

IntroductionSeizure prediction featuresEvaluation methodologyPerformanceDiscussion & conclusion

Dept. of Computer Science & Engineering, Chinese University of Hong Kong

INTRODUCTION

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Epilepsy

Electroencephalogram (EEG)

Seizure prediction

Research focus


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EpilepsyNeurological disorder characterized by sudden recurring seizures.Affects 1% of world’s population

Second only to stroke.25% cases not well controlled by medication.

Aftermath of seizures causes most harm

Unpredictable time and place.Sudden lapse of attention or convulsion.Dyspnoea or physical injury.


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What happens today?Diagnosis using electroencephalogram (EEG)

Record electrical activity of brain using multiple electrodesMachine learning techniques applied to classify EEG data

Restricted to clinical environment


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EEG signal’s rhythmic pattern


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EEG with epileptic seizureMajor phases in a seizure cycle

Preictal – the period before seizure onset occurs.Ictal – the period during which seizure takes place.Postictal – the period after the seizure ends.Interictal – the time between seizures.


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Seizure diagnosis tasksTask Requirements Application scenarios

Seizure event detection

greatest possible accuracy, not necessarily shortest delay.

Apps. requiring an accurate account of seizure activity over a period of time.

Seizure onset detection

shortest possible delay, not necessarily highest accuracy.

Apps. requiring a rapid response to a seizure.e.g., initiating functional neuro-imaging studies to

localize cerebral origin of a seizure.

Seizure prediction

highest possible sensitivity, lowest possible false alarms, actionable warning time.

Apps. requiring quick reaction to a seizure by delivering therapy or notifying a caregiver,

before seizure onset.


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Current approaches

Two-step processing strategyComputationally expensive feature extraction

Involving high dimensional features;Lacking systematic analytical models.

e.g., bivariate pattern methods: 6300 parameters for 5 minute EEG signal (P. Mirowski et al., 2008, 2009); and spectral structure, short term temporal evolution: 432 parameters for 6 second EEG signal (A. Shoeb et al., 2010, 2011).

Standard machine learning techniques, e.g.,Support vector machine;Artificial neural networks;Mixture Gaussian models, etc.


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Research focusIdentify primary components existing in an EEG signal.

Derive compact yet comprehensive feature form to deliver distinctive EEG attributes.

Evaluate proposed feature extraction front-end under standard machine learning based disease prediction protocol.


SEIZURE PREDICTION FEATURES

Amplitude-frequency modulation EEG representation

Feature extraction overview

Dept. of Computer Science & Engineering, Chinese University of Hong Kong 11

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Primary components in EEG

A band-limited signal that describes the kth EEG rhythm

is characterized by two sequences: -- Amplitude of rhythm; -- Phase of rhythm.


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Feature extraction overview

Number of subbands Dimension of AIE, AIF feature vectors

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PERFORMANCE

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Database

Evaluation metrics

Experimental set-up

Performance


Freiburg EEG database

Epilepsy Center, the University Hospital of Freiburg, Germany.Intracranial EEG data:

recorded during invasive presurgical epilepsy monitoring.

21 patients: 8 males, 13 females.

For each patient: at least 100 min preictal data + approximately 24 hr

interictal data.


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Performance metricsSensitivity:– Number of seizures predicted correctly.Specificity:– Number of false alarms generated during interictal period per

hour.


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Parallel experimental sets evaluated byAccuracy F2 metric

with β = 2.

Stage Parameter Description

DataAt least 24 hr Duration of interictal record

At least 150 min Duration of preictal record

Feature extraction5 sec EEG epoch length

6 Number of EEG channels Training 5 fold Cross validation

SVM classificationlog2γ ~ [-10, 10] SVM radial basis function kernel parameter

log2C ~ [-10, 10] Cost parameter

Classification


PerformanceEpileptic seizure prediction results on a patient-by-patient basis.

18Dept. of Computer Science & Engineering, Chinese University of Hong Kong

PerformanceSensitivity: (1). 95.2%: 79 out of 83 seizures predicted successfully;(2). Perfect results for 16 out of 19 patients.

Specificity:(1). 0.130 FAs per hour;(2). Two-in-a-row post-processing: filtering out single positive detection.

19Dept. of Computer Science & Engineering, Chinese University of Hong Kong

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DiscussionPerformance comparison with works of Park (Park et al., 2010) and Williamson (Williamson et al., 2011) under same data set and evaluation standard, our method has been identified with

Higher sensitivity of predictionComparable specificity of prediction with less complex post-processing approachMore compact feature formPhysically meaningful feature parameters


Conclusion

Epileptic seizure prediction problem is handled from a feature-based perspective.

Efficacious signal representation is built to identify primary components of EEG signals.

Comprehensive and effective feature form is derived to improve state-of-the-art seizure prediction performance.


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Thank you very much!Q & A


exploration of instantaneous amplitude and frequency features for epileptic seizure prediction ning...

Documents

computer science engineering

seizure cyclepreictal

seizure ends

period of time

eeg datarestricted

minute eeg signal

harmunpredictable time

distinctive eeg attributes