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Editorial Board Members’ Collection Series: Wearable Computing Technologies for Healthcare...
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Editorial Board Members’ Collection Series: Wearable Computing Technologies for Healthcare Management

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 1817

Special Issue Editors

Dr. Michael O'Grady

E-Mail Website
Guest Editor
School of Computer Science, University College Dublin, Dublin, Ireland
Interests: ambient intelligence; pervasive computing; ubiquitous sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yogesan Kanagasingam

E-Mail Website
Guest Editor
1. School of Medicine, The University of Notre Dame, Fremantle, WA 6160, Australia
2. St John of God Midland Private and Public Hospitals, Midland, WA 6056, Australia
3. Department of Health Care Policy, Harvard Medical School, Cambridge, MA 02138, USA
Interests: telemedicine; digital health; ocular imaging; artificial intelligence; machine learning; wearables
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Collection entitled “Editorial Board Members’ Collection Series: Wearable Computing Technologies for Healthcare Management". This issue will be a collection of papers from researchers invited by the Editorial Board Members.

The aim is to provide a venue for networking and communication between Healthcare and scholars in the field of Wearable Computing Technologies for Healthcare Management. All papers will be fully open access upon publication after peer review. 

Dr. Michael O'Grady
Prof. Dr. Yogesan Kanagasingam
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

23 pages, 1129 KiB  
Article
Machine Learning and Wearable Technology: Monitoring Changes in Biomedical Signal Patterns during Pre-Migraine Nights
by Viroslava Kapustynska, Vytautas Abromavičius, Artūras Serackis, Šarūnas Paulikas, Kristina Ryliškienė and Saulius Andruškevičius
Healthcare 2024, 12(17), 1701; https://doi.org/10.3390/healthcare12171701 - 26 Aug 2024
Viewed by 1375
Abstract
Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology [...] Read more.
Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Wearable Computing Technologies for Healthcare Management)
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