{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T21:38:26Z","timestamp":1785533906932,"version":"3.56.0"},"reference-count":46,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Biomed Eng"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/94560","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T19:22:54Z","timestamp":1785525774000},"page":"e94560-e94560","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative Electroencephalography in Hemodialysis: Scoping Review and Translational Framework"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2529-3183","authenticated-orcid":false,"given":"Meng-Hsun","family":"Tsai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7463-892X","authenticated-orcid":false,"given":"Cheng-Hsiung","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1016\/j.kint.2016.11.015","article-title":"Loss of executive function after dialysis initiation in adults with chronic kidney disease","volume":"91","author":"Kurella Tamura","journal-title":"Kidney Int"},{"issue":"6","key":"R2","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1053\/j.ajkd.2015.12.028","article-title":"Cognition in people with end-stage kidney disease treated with hemodialysis: a systematic review and meta-analysis","volume":"67","author":"O\u2019Lone","journal-title":"Am J Kidney Dis"},{"issue":"2","key":"R3","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1212\/01.wnl.0000225182.15532.40","article-title":"Cognitive impairment in hemodialysis patients is common","volume":"67","author":"Murray","journal-title":"Neurology"},{"issue":"6","key":"R4","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1053\/j.ajkd.2016.11.015","article-title":"Cognitive decline and its risk factors in prevalent hemodialysis patients","volume":"69","author":"Drew","journal-title":"Am J Kidney Dis"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1177\/0271678X18818652","article-title":"Changes in cerebral oxygenation and cerebral blood flow during hemodialysis - A simultaneous near-infrared spectroscopy and positron emission tomography study","volume":"40","author":"Polinder-Bos","journal-title":"J Cereb Blood Flow Metab"},{"issue":"4","key":"R6","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1159\/000485961","article-title":"Cognitive function declines significantly during haemodialysis in a majority of patients: a call for further research","volume":"45","author":"Dasgupta","journal-title":"Blood Purif"},{"issue":"8","key":"R7","doi-asserted-by":"publisher","first-page":"2511","DOI":"10.1681\/ASN.2016060704","article-title":"Relationship between hypotension and cerebral ischemia during hemodialysis","volume":"28","author":"MacEwen","journal-title":"J Am Soc Nephrol"},{"issue":"5","key":"R8","doi-asserted-by":"publisher","first-page":"438","DOI":"10.3988\/jcn.2022.0357","article-title":"Changes in cerebral blood flow in patients who receive different durations of hemodialysis: an arterial spin labeling MRI study","volume":"19","author":"Xue","journal-title":"J Clin Neurol"},{"issue":"1-2","key":"R9","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1159\/000503776","article-title":"Intradialytic hypotension: mechanisms and outcome","volume":"49","author":"Sars","journal-title":"Blood Purif"},{"issue":"3","key":"R10","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1038\/ki.1994.84","article-title":"Dialysis disequilibrium syndrome: current concepts on pathogenesis and prevention","volume":"45","author":"Arieff","journal-title":"Kidney Int"},{"issue":"3","key":"R11","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/S0733-8619(18)31025-9","volume":"3","author":"Brenner","journal-title":"Neurol Clin"},{"key":"R12","unstructured":"Schomer DL Lopes da Silva FH , editors. Niedermeyer\u2019s Electroencephalography: Basic Principles, Clinical Applications and Related Fields. 6th ed. Lippincott Williams & Wilkins; 2011. ISBN: 978-0-7817-8942-4"},{"key":"R13","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.741446","article-title":"Background activity findings in end-stage renal disease with and without comorbid diabetes: an electroencephalogram study","volume":"15","author":"Jatupornpoonsub","journal-title":"Front Hum Neurosci"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.795237","article-title":"EEG delta\/theta ratio and microstate analysis originating novel biomarkers for malnutrition-inflammation complex syndrome in ESRD patients","volume":"15","author":"Jatupornpoonsub","journal-title":"Front Hum Neurosci"},{"issue":"1","key":"R15","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1111\/j.1525-139X.2007.00384.x","article-title":"Cognitive function in chronic kidney disease","volume":"21","author":"Madero","journal-title":"Semin Dial"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1681\/ASN.2018050462","article-title":"Investigating the relationship between cerebral blood flow and cognitive function in hemodialysis patients","volume":"30","author":"Findlay","journal-title":"J Am Soc Nephrol"},{"key":"R17","unstructured":"FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and Other Tools) Resource. Food and Drug Administration; National Institutes of Health; 2016. ISBN: 978-1-944883-10-4"},{"key":"R18","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0260-4","article-title":"Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)","volume":"3","author":"Goldsack","journal-title":"NPJ Digit Med"},{"issue":"2","key":"R19","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1159\/000500413","article-title":"Digital medicine: a primer on measurement","volume":"3","author":"Coravos","journal-title":"Digit Biomark"},{"issue":"7","key":"R20","doi-asserted-by":"publisher","first-page":"12847","DOI":"10.3390\/s140712847","article-title":"Dry EEG electrodes","volume":"14","author":"Lopez-Gordo","journal-title":"Sensors (Basel)"},{"issue":"3","key":"R21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/MEMB.2010.936545","article-title":"Wearable electroencephalography","volume":"29","author":"Casson","journal-title":"IEEE Eng Med Biol Mag"},{"issue":"1","key":"R22","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","journal-title":"J Neurosci Methods"},{"key":"R23","unstructured":"Makeig S Bell AJ Jung TP Sejnowski TJ . Independent component analysis of electroencephalographic data. In: Touretzky D Mozer MC Hasselmo ME , editors. Advances in Neural Information Processing Systems 8. MIT Press; 1996:145-151. ISBN: 978-0-262-10065-6"},{"issue":"3","key":"R24","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1109\/TITB.2012.2188536","article-title":"Artifact removal in physiological signals--practices and possibilities","volume":"16","author":"Sweeney","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"2","key":"R25","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jneumeth.2006.05.033","article-title":"Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis","volume":"158","author":"Castellanos","journal-title":"J Neurosci Methods"},{"issue":"11","key":"R26","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TBME.2015.2481482","article-title":"Real-time neuroimaging and cognitive monitoring using wearable dry EEG","volume":"62","author":"Mullen","journal-title":"IEEE Trans Biomed Eng"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2437951","article-title":"The internet of things for health care: a comprehensive survey","volume":"3","author":"Riazul Islam","journal-title":"IEEE Access"},{"issue":"1","key":"R28","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab581d","article-title":"EEG artifact correction strategies for online trial-by-trial analysis","volume":"17","author":"de Freitas","journal-title":"J Neural Eng"},{"issue":"7","key":"R29","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","journal-title":"Ann Intern Med"},{"issue":"10","key":"R30","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.11124\/JBIES-20-00167","article-title":"Updated methodological guidance for the conduct of scoping reviews","volume":"18","author":"Peters","journal-title":"JBI Evid Synth"},{"issue":"1","key":"R31","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/1364557032000119616","article-title":"Scoping studies: towards a methodological framework","volume":"8","author":"Arksey","journal-title":"Int J Soc Res Methodol"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0611-x","article-title":"Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach","volume":"18","author":"Munn","journal-title":"BMC Med Res Methodol"},{"key":"R33","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"issue":"5","key":"R34","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","journal-title":"Nat Med"},{"issue":"9","key":"R35","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"Liu","journal-title":"Nat Med"},{"issue":"5","key":"R36","doi-asserted-by":"publisher","first-page":"F782","DOI":"10.1152\/ajprenal.00438.2020","article-title":"Cerebral blood flow regulation in end-stage kidney disease","volume":"319","author":"Sprick","journal-title":"Am J Physiol Renal Physiol"},{"issue":"6","key":"R37","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1681\/ASN.0000000000000105","article-title":"Hemodialysis-related acute brain injury demonstrated by application of intradialytic magnetic resonance imaging and spectroscopy","volume":"34","author":"Anazodo","journal-title":"J Am Soc Nephrol"},{"issue":"5","key":"R38","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1681\/ASN.0000000000000299","article-title":"Update on hemodialysis-induced multiorgan ischemia: brains and beyond","volume":"35","author":"McIntyre","journal-title":"J Am Soc Nephrol"},{"issue":"11","key":"R39","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1681\/ASN.2019050461","article-title":"Intradialytic cerebral hypoperfusion as mechanism for cognitive impairment in patients on hemodialysis","volume":"30","author":"Wolfgram","journal-title":"J Am Soc Nephrol"},{"issue":"7","key":"R40","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1016\/j.ekir.2021.04.005","article-title":"Hemodialysis patients have impaired cerebrovascular reactivity to CO2 compared to chronic kidney disease patients and healthy controls: a pilot study","volume":"6","author":"Slessarev","journal-title":"Kidney Int Rep"},{"issue":"4","key":"R41","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1681\/ASN.2017101088","article-title":"Hemodialysis induces an acute decline in cerebral blood flow in elderly patients","volume":"29","author":"Polinder-Bos","journal-title":"J Am Soc Nephrol"},{"key":"R42","doi-asserted-by":"publisher","DOI":"10.1016\/j.smrv.2024.101951","article-title":"Sleep assessment using EEG-based wearables - A systematic review","volume":"76","author":"de Gans","journal-title":"Sleep Med Rev"},{"issue":"1","key":"R43","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-07613-x","article-title":"Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression","volume":"7","author":"Farzan","journal-title":"Sci Rep"},{"issue":"1","key":"R44","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s13534-018-00093-6","article-title":"Wearable EEG and beyond","volume":"9","author":"Casson","journal-title":"Biomed Eng Lett"},{"issue":"1","key":"R45","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01332-6","article-title":"Explainability for artificial intelligence in healthcare: a multidisciplinary perspective","volume":"20","author":"Amann","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"R46","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","journal-title":"Nat Med"}],"container-title":["JMIR Biomedical Engineering"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T19:22:56Z","timestamp":1785525776000},"score":1,"resource":{"primary":{"URL":"https:\/\/biomedeng.jmir.org\/2026\/1\/e94560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":46,"URL":"https:\/\/doi.org\/10.2196\/94560","relation":{},"ISSN":["2561-3278"],"issn-type":[{"value":"2561-3278","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]},"article-number":"v11i4e94560"}}