Spike detection II: automatic, perception-based detection and clustering.
OBJECTIVE
We developed perception-based spike detection and clustering algorithms.


METHODS
The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record.


RESULTS
The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar.


CONCLUSIONS
An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.
Wilson
S B
SB
Persyst Development Corporation, Prescott, AZ 86303, USA. sbw@compuserve.com
Turner
C A
CA
Emerson
R G
RG
Scheuer
M L
ML
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D010465:Perception / Q000502:physiology*