Hidden Markov modelling of spike propagation from interictal MEG data.

Abstract:

:For patients with partial epilepsy, automatic spike detection techniques applied to interictal MEG data often discover several potentially epileptogenic brain regions. An important determination in treatment planning is which of these detected regions are most likely to be the primary sources of epileptogenic activity. Analysis of the patterns of propagation activity between the detected regions may allow for detection of these primary epileptic foci. We describe the use of hidden Markov models (HMM) for estimation of the propagation patterns between several spiking regions from interictal MEG data. Analysis of the estimated transition probability matrix allows us to make inferences regarding the propagation pattern of the abnormal activity and determine the most likely region of its origin. The proposed HMM paradigm allows for a simple incorporation of the spike detector specificity and sensitivity characteristics. We develop bounds on performance for the case of perfect detection. We also apply the technique to simulated data sets in order to study the robustness of the method to the non-ideal specificity-sensitivity characteristics of the event detectors and compare results with the lower bounds. Our study demonstrates robustness of the proposed technique to event detection errors. We conclude with an example of the application of this method to a single patient.

journal_name

Phys Med Biol

authors

Ossadtchi A,Mosher JC,Sutherling WW,Greenblatt RE,Leahy RM

doi

10.1088/0031-9155/50/14/017

subject

Has Abstract

pub_date

2005-07-21 00:00:00

pages

3447-69

issue

14

eissn

0031-9155

issn

1361-6560

pii

S0031-9155(05)94897-5

journal_volume

50

pub_type

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