Bridging Gaps in Neurocare: Evaluating the Feasibility of ECG Signal Extraction from EEG Data Through Fabric as a SensorTM - A Pilot Study
Presentation
Overview
Overview
Description
BACKGROUND: Traditional sleep monitoring relies on complex, multi-sensor systems that limit accessibility and long-term use outside clinical environments. This study evaluated the feasibility of extracting electrocardiographic (ECG) signals from electroencephalographic (EEG) data collected using a novel Fabric-as-a-Sensor (FaaS™) pillowcase with embroidered silver chloride (P-2A) conductive threads. METHODS: Adult participants (N=35) underwent EEG data collection during controlled supine and lateral positioning, followed by an extended rest period. Signals were preprocessed using channel quality assessment, artifact removal, and filtering (0.5–35 Hz). Cardiac-related activity was identified through visual inspection and quantified using the Pan-Tompkins algorithm for QRS complex detection. RESULTS: Results demonstrated that ECG signals could be intermittently detected within EEG recordings, particularly from auxiliary surface electrodes, with successful identification of R-peaks and calculation of physiologically plausible RR intervals. However, signal detectability was inconsistent and influenced by electrode placement, participant positioning, and signal-to-noise ratio. Embedded fabric as a sensor device demonstrates reduced sensitivity for capturing clear ECG morphology. DISCUSSION: These findings support the feasibility of extracting cardiac information from EEG data and highlight the potential for simultaneous brain–heart monitoring using a single, non-invasive system. While current limitations in signal consistency remain, this approach represents a promising step toward accessible, multimodal sleep monitoring technologies. Future improvements in sensor design and signal processing may enhance clinical applicability and support a more comprehensive assessment of sleep physiology and related disorders. Keywords - EEG; ECG; sleep monitoring; Fabric-as-a-Sensor (FaaS); brain–heart connection; signal processing; wearable technology; sleep physiology
