A STOCHASTIC FRAMEWORK FOR EVALUATING SEIZURE PREDICTION

ALGORITHM USING HMMWong, Gardner, Krieger, Litt

(2006)

Zack Dvey-Aharon, March 2008

Agenda 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

What EEG is and how it may help to indicate seizures (1)

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

What EEG is and how it may help to indicate seizures (2)

Seizure(epilepsy)

What EEG is and how it may help to indicate seizures (3)

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.

Past work and the goal of study

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: A short Introduction

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: A short Introduction (2)

HMM parameters: X – states of the model Y – observations A – a matrix that represents transition

probabilities B – a matrix that represent emission

probabilities

Model used in study and it’s restrictions

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

Methodology overview

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.

Results and remarks (1) 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

Results and remarks (2)

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

Criticism 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.

Questions

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