Exploration of Instantaneous Amplitude and Frequency Features for Epileptic Seizure Prediction Ning Wang and Michael R. Lyu Dept. of Computer Science and Engineering Chinese University of Hong Kong IEEE 12 th International Conference on BioInformatics and BioEngineering (BIBE 2012)

Outline Introduction Seizure prediction features Evaluation methodology Performance Discussion & conclusion 2Dept. of Computer Science & Engineering, Chinese University of Hong Kong

INTRODUCTION 3 Epilepsy Electroencephalogram (EEG) Seizure prediction Research focus Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Epilepsy Neurological 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. 4Dept. of Computer Science & Engineering, Chinese University of Hong Kong

What happens today? Diagnosis using electroencephalogram (EEG) Record electrical activity of brain using multiple electrodes Machine learning techniques applied to classify EEG data Restricted to clinical environment 5Dept. of Computer Science & Engineering, Chinese University of Hong Kong

EEG signal’s rhythmic pattern 6Dept. of Computer Science & Engineering, Chinese University of Hong Kong

EEG with epileptic seizure Major 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. 7Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Seizure diagnosis tasks 8 TaskRequirementsApplication 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. Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Current approaches Two-step processing strategy Computationally 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. 9Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Research focus Identify 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. 10Dept. of Computer Science & Engineering, Chinese University of Hong Kong

SEIZURE PREDICTION FEATURES Amplitude-frequency modulation EEG representation Feature extraction overview Dept. of Computer Science & Engineering, Chinese University of Hong Kong 11

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. 12Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Feature extraction overview 13 Number of subbandsDimension of AIE, AIF feature vectors 55 Dept. of Computer Science & Engineering, Chinese University of Hong Kong

PERFORMANCE 14 Database Evaluation metrics Experimental set-up Performance Dept. of Computer Science & Engineering, Chinese University of Hong Kong

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. Dept. of Computer Science & Engineering, Chinese University of Hong Kong 15

Performance metrics Sensitivity: – Number of seizures predicted correctly. Specificity: – Number of false alarms generated during interictal period per hour. 16Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Parallel experimental sets evaluated by Accuracy F2 metric with β = 2. StageParameterDescription Data At least 24 hrDuration of interictal record At least 150 minDuration of preictal record Feature extraction 5 secEEG epoch length 6Number of EEG channels Training5 foldCross validation SVM classification log 2 γ ~ [-10, 10]SVM radial basis function kernel parameter log 2 C ~ [-10, 10]Cost parameter 17 Classification Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Performance Epileptic seizure prediction results on a patient-by-patient basis. 18 Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Performance Sensitivity: (1). 95.2%: 79 out of 83 seizures predicted successfully; (2). Perfect results for 16 out of 19 patients. Specificity: (1) FAs per hour; (2). Two-in-a-row post- processing: filtering out single positive detection. 19 Dept. of Computer Science & Engineering, Chinese University of Hong Kong

Discussion Performance 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 prediction Comparable specificity of prediction with less complex post- processing approach More compact feature form Physically meaningful feature parameters 20Dept. of Computer Science & Engineering, Chinese University of Hong Kong

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. Dept. of Computer Science & Engineering, Chinese University of Hong Kong 21

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