Evidence map›Paper›PMID 41142046›Full record

ReviewClinical neurophysiology practice2025

Home-Based sensing of the nervous system with clinical neurophysiology technologies: IFCN handbook chapter.

Christian Sandøe Musaeus, Pedro F Viana, Mark Cook, Jonas Duun-Henriksen, Sándor Beniczky, Preben Kidmose, Bart Vanrumste, Benjamin Filtjens, Troels Wesenberg Kjaer

Abstract readReview
In one paragraph

Review in Clinical neurophysiology practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Christian Sandøe MusaeusDanish Dementia Research Centre (DDRC), Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Inge Lehmanns vej 8, 2100 Copenhagen, Denmark.
Pedro F VianaInstitute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 9RT, United Kingdom.
Mark CookFaculty of Engineering and Information Technology, University of Melbourne, Australia.
Jonas Duun-HenriksenUNEEG Medical 3450 Allerød, Denmark.
Sándor BeniczkyDepartment of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.
Preben KidmoseDepartment of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, Aarhus N, Denmark.
Bart VanrumsteDepartment of Electrical Engineering (ESAT), KU Leuven, Belgium.
Benjamin FiltjensDepartment of Electrical Engineering (ESAT), KU Leuven, Belgium.
Troels Wesenberg KjaerAalborg University, Fredrik Bajers Vej 7, Aalborg East, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Home-based neurophysiological monitoring is improving the assessment and management of neurological conditions such as epilepsy. Technologies such as electroencephalography (EEG), electromyography (EMG), and accelerometry are increasingly integrated into wearable systems for at-home use. Due to an increasing amount of data from long-term monitoring, machine learning algorithms assist in automated data analysis. However, ensuring device accuracy, signal quality, and user compliance remains crucial for clinical useability. Objective: This chapter explores advances and challenges in at-home neurophysiological monitoring, with a primary focus on EEG systems and their applications.Content: The discussion highlights the technological advances and the challenges associated with at-home monitoring. The focus will be on EEG systems, as well as a discussion of EMG in epilepsy. Next, we will provide an overview of the clinical applications for home-based monitoring of epilepsy and sleep disorders. Lastly, we will briefly discuss emerging topics within home-based monitoring in movement disorders and neurodegenerative disorders. Conclusion: Future advancements are expected with new generations of wearable systems capable of providing long-term monitoring with minimal maintenance. Beyond epilepsy and sleep disorders, home-based technologies are also being investigated in other neurological diseases including movement disorders and neurodegenerative diseases showing the expanding scope of home-based technologies in neurology.

Indexed as

AccelerometryEEGEMGHome-based neurophysiologyMachine learningWearable neurological monitoring

Identifiers

PMID41142046
PMCPMC12547184

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.