EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks (2026)

Status: pending Scraped: 2026-08-12 Source: openalex Relevance: Direct

Authors: Hazal Su Bıçakcı Yeşilkaya, Mete Özbaltan, Nihan Özbaltan et al. DOI: 10.3390/bios16080400

Citation: Hazal Su Bıçakcı Yeşilkaya, Mete Özbaltan, Nihan Özbaltan et al. (2026), EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks, openalex. Type: Determine after review

Abstract

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Design

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Key Results

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Linked To

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Data Quality Assessment

Field	Assessment
Subject selection	How were subjects selected? Inclusion/exclusion criteria, sampling strategy, n=? Power analysis?
Data acquisition	How was data physically acquired? Device model (E4, Empatica, WHOOP, Polar, dedicated sensor), sampling rate, placement, firmware version.
Acquisition context	From whom, what were they doing during recording? Clinic, home, school, sleep-only, free-living, task-based? Naturalistic or controlled?
Gold standard labels	How were outcome events defined? Clinician-validated scale (ABCD, ABC, CAAS, VAS), event logs, direct observation, chart review? Inter-rater reliability reported?
Data validation	How were data fields validated? Artifact rejection, signal quality indices, cross-modal checks, manual inspection, automated QC pipeline?
Missingness	n=X subjects — do all have complete data? Missing at random, missing completely at random, or missing not at random? Is missingness correlated with outcome?