Inflammatory brain diseases have been associated to the presence of slow biphasic complexes (SBC)in the EEG. An automated method has been developed to identify them. A dataset of 128 EEGs was recorded from pediatric controls and patients showing encephalitis with different levels of severity. Experts assigned a severity score with 5 levels to each trace, considering both electrophysiological and clinical manifestations. The number, amplitude and location of SBCs were used to identify automatically the severity scores using a binary classification decision tree. True classifications have been obtained in the 64.1 % of cases (55.6% using a leave-one-out approach)and misclassifications were among close severity scores (about 80 % of accuracy was obtained considering 3 instead of 5 severity scores), indicating that SBCs may support the identification, assessment and follow-up of encephalitis.
[1]
A. Beaumanoir,et al.
Sporadic slow biphasic complex: description and clinical correlations
,
1985
.
[2]
A. Beaumanoir,et al.
[EEG in HIV infection].
,
1992,
Neurophysiologie clinique = Clinical neurophysiology.
[3]
A. Beaumanoir,et al.
L'EEG dans l'infection par le VIH
,
1992,
Neurophysiologie Clinique/Clinical Neurophysiology.
[4]
N. M. Vora,et al.
Burden of encephalitis-associated hospitalizations in the United States, 1998–2010
,
2013,
Neurology.
[5]
Amanda L. Piquet,et al.
The Clinical Approach to Encephalitis
,
2016,
Current Neurology and Neuroscience Reports.
[6]
L. Mesin,et al.
Automatic identification of slow biphasic complexes in EEG: an effective tool to detect encephalitis
,
2019,
Biomedical Physics & Engineering Express.