Quantitative Electroencephalography in Hemodialysis: Scoping Review and Translational Framework
Meng-Hsun Tsai1, Cheng-Hsiung Chan1
1Department of Mechanical Engineering, National Yunlin University of Science and Technology, No. 123, Section 3, University Road, Douliu, Yunlin, 64002, Taiwan, 886 5-534-2601 ext 4102, 886 5-532-1719.
Background:
Cognitive impairment and cerebral dysfunction are common among patients with end-stage kidney disease undergoing maintenance hemodialysis. Repeated intradialytic hemodynamic stress, including reductions in cerebral blood flow and episodes of intradialytic hypotension, has been associated with adverse neurological outcomes. Quantitative electroencephalography (qEEG) offers a noninvasive approach for continuous assessment of cerebral activity; however, its potential role in dialysis monitoring remains unclear because of methodological heterogeneity and implementation challenges.
Objective:
This scoping review aimed to synthesize current evidence regarding qEEG alterations across the hemodialysis cycle and to evaluate the technical requirements for translating qEEG-derived features into future dialysis-related cerebral monitoring systems.
Methods:
A structured literature search was conducted in PubMed/MEDLINE, Embase, and IEEE Xplore for studies published between January 2005 and February 2026. After removal of duplicates, 554 records were screened, and 70 full-text articles were assessed for eligibility. Sixty-two studies met the inclusion criteria and were included in the qualitative synthesis. Evidence was organized according to the 3 temporal domains of the hemodialysis cycle: predialysis baseline, intradialytic exposure, and postdialysis recovery. In addition to clinical findings, engineering-related evidence concerning electroencephalography hardware, electrode systems, signal processing pipelines, artifact mitigation, multimodal synchronization, edge computing, and digital biomarker validation was reviewed.
Results:
Patients receiving maintenance hemodialysis consistently demonstrated baseline spectral slowing characterized by increased delta and theta activity, and reduced alpha power. During dialysis, multimodal imaging studies reported cerebral blood flow reductions of approximately 10% to 15%, accompanied by dynamic qEEG changes, including alterations in the alpha-delta ratio and slow wave activity. Connectivity and complexity measures provided complementary information regarding network-level and recovery-related responses but showed substantial variability across acquisition conditions and analytical pipelines. Major implementation challenges included electrical interference, motion artifacts, electrode instability, asynchronous physiological data streams, and limited standardization of preprocessing methods. Comparative analysis indicated that acquisition quality, artifact rejection performance, synchronization accuracy, and individualized baseline modeling are critical determinants of translational feasibility.
Conclusions:
Current evidence indicates that qEEG-derived measures have been reported in association with both chronic and intradialytic neurophysiological changes in patients undergoing hemodialysis. However, available evidence remains heterogeneous and insufficient to support routine clinical deployment. Future implementation will require standardized acquisition protocols, robust artifact mitigation, synchronized multimodal monitoring, and prospective validation studies. At present, qEEG-derived features should be regarded as candidate digital biomarkers and components of a proposed translational monitoring framework rather than clinically validated monitoring tools.
