Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data
Recommended Citation
Saghafi S, Li Q, Neylan TC, Thomas TT, Stevens JS, Jovanovic T, Germine LT, Bucher MA, Huibregtse ME, Linnstaedt SD, An X, Harnett NG, Norrholm SD, Conti AC, Seligowski AV, Dillon DG, Vizer LM, McKibben LA, Albertorio-Saez LM, Beaudoin FL, Matson L, Calhoun VD, Harte SE, Bruce SE, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Swor RA, Pearson C, Peak DA, O'Neil BJ, Kessler RC, Koenen KC, McLean SA, Clifford GD, and Rad AB. Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data. IEEE Trans Biomed Eng 2025.
Document Type
Article
Publication Date
7-24-2025
Publication Title
IEEE transactions on bio-medical engineering
Abstract
BACKGROUND: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear.
METHODS: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP $ > 268 \frac{pg}{ml}$). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases.
RESULTS: Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively.
CONCLUSIONS: The results suggest that early sleep/wake data has promise for assisting with TBI identification.
SIGNIFICANCE: In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.
PubMed ID
40705576
ePublication
ePub ahead of print
