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 Bioljournal_title
Physics in medicine and biologyauthors
Ossadtchi A,Mosher JC,Sutherling WW,Greenblatt RE,Leahy RMdoi
10.1088/0031-9155/50/14/017subject
Has Abstractpub_date
2005-07-21 00:00:00pages
3447-69issue
14eissn
0031-9155issn
1361-6560pii
S0031-9155(05)94897-5journal_volume
50pub_type
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