Telehealth and digital health innovations: A mixed landscape of access

J Phuong, P Ordóñez, J Cao, M Moukheiber… - PLOS Digital …, 2023 - journals.plos.org
In the wake of emergent natural and anthropogenic disasters, telehealth presents
opportunities to improve access to healthcare when physical access is not possible. Yet …

Computational sleep behavior analysis: A survey

S Fallmann, L Chen - IEEE Access, 2019 - ieeexplore.ieee.org
Sleep is a key marker of health, as it can either be a cause or a consequence. It is
traditionally studied in clinical environments using dedicated medical devices. Recent …

Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

A Carreño, I Inza, JA Lozano - Artificial Intelligence Review, 2020 - Springer
In recent years, a variety of research areas have contributed to a set of related problems with
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …

Investigation on factors related to poor CPAP adherence using machine learning: a pilot study

K Eguchi, T Yabuuchi, M Nambu, H Takeyama… - Scientific Reports, 2022 - nature.com
To improve patients' adherence to continuous positive airway pressure (CPAP) therapy, this
study aimed to clarify whether machine learning-based data analysis can identify the factors …

AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders

M Vaezi, M Nasri - Frontiers in Biomedical …, 2023 - publish.kne-publishing.com
Purpose: Sleep is a subconscious state, and the brain is active during it. Automatic
classification of sleep stages can help identify various diseases. In recent years, automatic …

SAS mobile application for diagnosis of obstructive sleep apnea utilizing machine learning models

C Haberfeld, A Sheta, MS Hossain… - 2020 11th IEEE …, 2020 - ieeexplore.ieee.org
In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface
(GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis …

A data-driven approach for continuous adherence predictions in sleep apnea therapy management

M Araujo, L Kazaglis, C Iber… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
The abandonment rate of patients who use CPAP devices for obstructive sleep apnea (OSA)
therapy is as high as 60%. However, there is growing evidence that timely and appropriate …

Homecare interventions as a Service model for Obstructive sleep Apnea: Delivering personalised phone call using patient profiling and adherence predictions

JS Joymangul, A Sekhari, O Grasset… - International Journal of …, 2023 - Elsevier
Abstract Background and Objective Obstructive Sleep Apnea (OSA) is a sleep disorder that
leads to different pathologies like depression and cardiovascular problems. The first-line …

Patient-centric approach to maximize CPAP therapy acceptance: AI-driven design, delivery, and monitoring of interventions

J Joymangul - 2022 - theses.hal.science
Obstructive Sleep Apnea (OSA) is a slee** disorder that manifests itself in various ways. In
the absence of treatment, it has a detrimental effect on the cardiovascular system, causes …

Defining and monitoring patient clusters based on therapy adherence in sleep apnea management

MKR Baddam, M Araujo… - 2021 IEEE 34th …, 2021 - ieeexplore.ieee.org
Obstructive Sleep Apnea (OSA) is a disorder in which breathing repeatedly stops and starts
due to recurrent episodes of partial and complete airway obstruction during sleep. One of …