This study aims to utilize HFO analysis to enhance existing SSEP modality and develop it as a bedside diagnostic tool for acute brain injury (ABI) detection in Extracorporeal Membrane Oxygenation (ECMO) patients.

Timely diagnosis of ABI in ECMO patients is challenging due to logistical complexities with computed tomography (CT) and magnetic resonance imaging (MRI). Integrating time–frequency analysis into routine SSEP monitoring for early ABI detection can facilitate timely medical decisions.

Consecutive SSEP data were collected from Johns Hopkins Intensive Care Units (ICUs), including 31 ECMO and 45 non-ECMO patients from 2016 to 2022. ABIs were determined using CT and MRI as clinically indicated. Using wavelet techniques, two SSEP-HFO components were quantified: HFOL (80–200 Hz) and HFOH (200–600 Hz), which were later fed to a Support Vector Machine (SVM) with a linear kernel.

ECMO patients with ABI (N = 22) exhibited suppressed HFOH (Median = −9.09, Interquartile Range (IQR) = [ −13.5; −4.73] dB) compared to patients without (N = 9, Median = −4.39, IQR = [−6.35; −3.28] dB, P = 0.035). The SVM classifier achieved an accuracy of 75 % and a sensitivity of 82 % for detecting ABI, outperforming SSEP-N20.

SSEP-HFO can potentially improve early detection of ABI in ECMO patients at the bedside.