JMIR Publications

ABSTRACT

Background:

Cerebral dysfunction is highly prevalent among patients with end-stage kidney disease (ESKD) receiving maintenance hemodialysis (HD), with reported cognitive impairment rates substantially exceeding those of age-matched populations. Longitudinal and cohort studies have demonstrated associations between HD exposure, reduced cerebral blood flow (CBF), white matter injury, and accelerated cognitive decline [1–5]. Intradialytic circulatory stress and dialysis disequilibrium further contribute to transient or cumulative cerebral injury [6–8]. Electroencephalography (EEG), particularly quantitative EEG (qEEG), provides high temporal resolution for detecting functional brain alterations related to metabolic and hemodynamic stress; however, its integration into routine dialysis monitoring remains limited.

Objective:

This review aims to synthesize current clinical and neurophysiological evidence regarding qEEG markers across the hemodialysis cycle and to outline key technical and translational considerations for implementing EEG-based cerebral monitoring in dialysis settings.

Methods:

A structured literature search was conducted in PubMed/MEDLINE, Embase, and IEEE Xplore (January 2005–February 2026). Studies involving adult HD patients assessed with EEG or qEEG were included. Evidence was organized into three temporal domains: pre-dialysis baseline burden, intradialytic physiological stress, and post-dialysis recovery. Foundational studies on dialysis encephalopathy were incorporated to contextualize modern quantitative analyses [9–11]. Engineering and digital biomarker frameworks relevant to real-world deployment were also reviewed [12–15].

Results:

Baseline EEG abnormalities in ESKD patients consistently demonstrate increased delta and theta power with reduced alpha activity, reflecting chronic uremic neurotoxicity and diminished functional reserve [16,17]. During HD, multimodal imaging studies show acute reductions in cerebral perfusion that correlate with transient cognitive decline and structural vulnerability [2,4,5]. Contemporary qEEG analyses indicate that spectral indices—particularly the alpha–delta ratio—and connectivity metrics are sensitive to intradialytic physiological fluctuations and may precede overt hypotension or neurological symptoms [18,19]. Implementation barriers include electrical interference, artifact contamination, asynchronous multimodal acquisition, and the requirement for individualized baselines. Emerging digital biomarker frameworks emphasize staged verification, analytical validation, and clinical validation prior to deployment in regulated care settings [12–14].

Conclusions:

Current evidence supports qEEG as a sensitive and non-invasive modality for detecting both chronic baseline cerebral dysfunction and acute intradialytic neurophysiological instability in HD patients. Transition from observational research toward clinical integration requires standardized analytic pipelines, artifact-resilient acquisition strategies, synchronized hemodynamic–neurophysiological data streams, and individualized baseline modeling. Framing the dialysis session as a structured physiological exposure provides a systems-oriented foundation for evaluating EEG-derived metrics within staged digital biomarker validation pathways. Clinical Trial: Not applicable.