AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks.

Prajoona Valsalan, Mohammad Maroof Siddiqui

Abstract readReview

In one paragraph

Review in Clocks & sleep, 2026. 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

2 authors.

Prajoona ValsalanDepartment of Electrical and Computer Engineering, Dhofar University, Salalah 211, Oman.ORCID 0000-0002-0284-5600

Mohammad Maroof SiddiquiDepartment of Electrical and Computer Engineering, Dhofar University, Salalah 211, Oman.ORCID 0000-0003-1118-8443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO

Indexed as

CNN-BiLSTMedge computinghybrid deep learningInternet of Medical Thingsnetwork anomaly detectionSleep Health IoTsleep stage classificationwearable security

Identifiers

PMID42200970

DOI10.3390/clockssleep8020023

PMCPMC13214672

What Socratic holds

Textmetadata

LicenceCC BY

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 Socratic graph.