Wong, Gardner, Krieger, Litt (2006) Zack Dvey-Aharon, March 2008

 What EEG is and how it may help to indicate seizures  Past work and the goal of study  HMM: A short Introduction  Model used in study and it’s restrictions  Methodology overview  Results and remarks  Criticism  Questions

 EEG = (or Electroencephalography) is the measurement of electrical activity produced by the brain as recorded from electrodes placed on the scalp.

 Seizure (epilepsy)

 In a case of a seizure, it is noticed in some of the areas\channel of the EEG signal.  In all of the cases there are pre-seizure spikes that appear very clearly.  Spikes and long disturbances can occur often even in a totally healthy patient.

 As we have seen, there are medical studies stating spikes appear differently before seizures, and that these can be therefore used for a predictive analysis.  No method convincingly demonstrated prospective seizure prediction sufficient.  The problematic tradeoff: Accuracy Vs. low FPR.  Writers claim that current top methods appear in research are (1) based on study design adding many assumptions and (2) Address only extreme cases with high rate of seizures, failing to handle FPR.

 HMM = Hidden Markov Models. These mathematical models are based on the Markovian Assumption:  (1) The observations are an outcome to a “hidden- state”, one of states in a chain that represent the state of the object.  (2) The probability to change from state to another depends only on the last (N) past transitions. (N- Markovian assumption)  (3) observations are stochastically distributed according to the current state.

 HMM parameters:  X – states of the model  Y – observations  A – a matrix that represents transition probabilities  B – a matrix that represent emission probabilities

 At first we train a three-state HMM, with states 1, 2, and 3 denoting the baseline, detected, and seizure states, respectively.  Model Restrictions: (1) aii = 1 - 1/Di * (2) a13 < a23 (3) b11 > b12, b21 < b22 (4) b33 = 1, b13 = b23 = b31 = b32 = 0 * where Di is the average duration of state i

 Training prediction algorithm using raw EEG signal, and labeled data of an expert states observation are added to train HMM network  Model is trained, and using the Viterbi algorithm, the most probable state sequence is found, clearing “transition” noise  Then the statistical association between seizure & detected states can be measured in order to validate the hypothesis.

 Algorithm shows two major achievements:  (1) Demonstrating on a specific prediction algorithm (of Gardner, 2006), HMM showed output can be smoother too lower FPR in more than 70%  (2) Using the algorithm as a post processing tool can increase detection ratio (demonstrated against that specific prediction algorithm, 17/29 against 5/29). Red arrows: False positives Black arrow: False negative

On Top: Global minima of the HMM training process On Bottom: How using the Model can help drop false-positives

 Enthusiasm caused writers to lose focus, from evaluating prediction algorithms to improving them.  All experiments were based on evaluating and improving one specific algorithm, which is not explained and in any aspect proved to be a very weak predictor.  Model definition can be improved very easily, adding more restrictions (as proved from B matrix results, for example).  The model also contains statistic estimations, against statement that methodology is free from study designs and data assumptions.

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