Abstract Details

Title Wearable EEG and Machine-learning for Delirium Detection in Hospitalized Patients

Topic Epilepsy/Clinical Neurophysiology (EEG)

Presentation(s) P2 - Poster Session 2 (11:45 AM-12:45 PM)

Poster/Presentation
Number 11-006

Objective We aim to develop a new compact wearable EEG device and paired, automated analysis pipeline to monitor continuous EEG and predict delirium.

Background Delirium is a highly prevalent yet underdiagnosed neuropsychiatric condition which primarily affects older, hospitalized patients. Better objective diagnostic measures are needed to detect delirium reliably.

Design/Methods We applied a wireless, wearable single-channel EEG device to hospitalized patients admitted for at least one night and evaluated for delirium using the 3D-CAM. We evaluated device comfort and recording yield. Recordings were preprocessed, segmented into 4-second epochs, and used to extract 3,444 time-based, frequency-based, and non-linear EEG features. We trained machine-learning classifiers using XGBoost to predict delirium, using stratified cross-validation.

Results We enrolled 155 adult inpatients, 40 of whom met 3DCAM criteria for delirium (25.8%). Over 80% of participants found the device to be “very positive” or “positive” for comfort and willingness to wear again. 97% of patients found the device to have no impact on sleep. Our device reliably detected delirium with an AUROC of 0.80 using our comprehensive quantitative EEG feature set. Spectral features derived from the Catch22 and FOOOF packages as well as relative theta consistently ranked highly on Shapley analysis.

Conclusions Continuous wearable EEG is well-tolerated in hospitalized patients and yields high quality signals from which delirium can be reliably predicted with competitive accuracy. This wearable EEG system may serve as a crucial tool for neural monitoring, thereby accelerating the development of preventative strategies and targeted treatments for patients with delirium or other acute neurological illnesses.

Authors/Disclosures
Karen Mao PRESENTER	Ms. Mao has nothing to disclose.
Dillan Prasad, MD	Dr. Prasad has nothing to disclose.
Grace Steward, PhD	Dr. Steward has received personal compensation for serving as an employee of Northwestern University.
Haoqi Sun, PhD (Massachusetts General Hospital)	Dr. Sun has nothing to disclose.
Joseph J. Choi	Mr. Choi has nothing to disclose.
Alice D. Lam, MD, PhD	Dr. Lam has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Neurona Therapeutics. Dr. Lam has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Acadia Pharmaceuticals. The institution of Dr. Lam has received research support from Neurona Therapeutics. The institution of Dr. Lam has received research support from NIH. The institution of Dr. Lam has received research support from Alzheimer's Association.
Sydney Cash, MD (Massachusetts General Hospital)	Dr. Cash has received stock or an ownership interest from Beacon Biosignals.
M. B. Westover, MD, PhD (MGH)	Dr. Westover has received personal compensation in the range of $50,000-$99,999 for serving as a Consultant for Beacon Biosignals. Dr. Westover has stock in Beacon Biosignals. The institution of Dr. Westover has received research support from NIH. Dr. Westover has received publishing royalties from a publication relating to health care. Dr. Westover has a non-compensated relationship as a cofounder with Beacon Biosignals that is relevant to AAN interests or activities.
Eyal Y. Kimchi, MD, PhD (Northwestern University)	The institution of Dr. Kimchi has received research support from NIH. The institution of an immediate family member of Dr. Kimchi has received research support from NIH.

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