Electroencephalography Might Improve Diagnosis of Acute Stroke and Large Vessel Occlusion

Abstract

Background and purpose: Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electroencephalography is rapidly sensitive to brain ischemia. This study examined the diagnostic utility of electroencephalography for acute stroke/transient ischemic attack (TIA) and for LVO.

Methods: Patients (n=100) with suspected acute stroke in an emergency department underwent clinical exam then electroencephalography using a dry-electrode system. Four models classified patients, first as acute stroke/TIA or not, then as acute stroke with LVO or not: (1) clinical data, (2) electroencephalography data, (3) clinical+electroencephalography data using logistic regression, and (4) clinical+electroencephalography data using a deep learning neural network. Each model used a training set of 60 randomly selected patients, then was validated in an independent cohort of 40 new patients.

Results: Of 100 patients, 63 had a stroke (43 ischemic/7 hemorrhagic) or TIA (13). For classifying patients as stroke/TIA or not, the clinical data model had area under the curve=62.3, whereas clinical+electroencephalography using deep learning neural network model had area under the curve=87.8. Results were comparable for classifying patients as stroke with LVO or not.

Conclusions: Adding electroencephalography data to clinical measures improves diagnosis of acute stroke/TIA and of acute stroke with LVO. Rapid acquisition of dry-lead electroencephalography is feasible in the emergency department and merits prehospital evaluation.

Keywords: brain; deep learning; early diagnosis; electroencephalography; transient ischemic attack.

Figure 1.

[A]. The Quick-20 dry-lead Cognionics headset and the EEG montage, having 17 leads and 27 bipolar lead-pairs (blue lines). [B]. EEG from a 69 year-old male 8.5 hours after stroke onset with right thalamocapsular infarct and NIHSS=9. [C] ROC curves for each model. The model combining clinical and EEG data using deep learning showed best diagnostic performance for both acute stroke/TIA (left; AUC=87.8) and for acute stroke with LVO (right; AUC=86.4).