A review of automated sleep stage scoring based on physiological signals for the new millennia

O Faust, H Razaghi, R Barika, EJ Ciaccio… - Computer methods and …, 2019 - Elsevier
Abstract Background and Objective Sleep is an important part of our life. That importance is
highlighted by the multitude of health problems which result from sleep disorders. Detecting …

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while sleeping. This reduction in …

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

M Radha, P Fonseca, A Moreau, M Ross, A Cerny… - NPJ digital …, 2021 - nature.com
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography
(PPG) could open the way for better sleep disorder screening and health monitoring …

Sleep stage classification with ECG and respiratory effort

P Fonseca, X Long, M Radha, R Haakma… - Physiological …, 2015 - iopscience.iop.org
Automatic sleep stage classification with cardiorespiratory signals has attracted increasing
attention. In contrast to the traditional manual scoring based on polysomnography, these …

[HTML][HTML] Studying the effect of long COVID-19 infection on sleep quality using wearable health devices: observational study

M Mekhael, CH Lim, AH El Hajjar, C Noujaim… - Journal of Medical …, 2022 - jmir.org
Background Patients with COVID-19 have increased sleep disturbances and decreased
sleep quality during and after the infection. The current published literature focuses mainly …

Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults

P Fonseca, T Weysen, MS Goelema, EIS Møst… - Sleep, 2017 - academic.oup.com
Abstract Study Objectives: To compare the accuracy of automatic sleep staging based on
heart rate variability measured from photoplethysmography (PPG) combined with body …

Sleep staging based on signals acquired through bed sensor

JM Kortelainen, MO Mendez… - IEEE Transactions …, 2010 - ieeexplore.ieee.org
We describe a system for the evaluation of the sleep macrostructure on the basis of Emfit
sensor foils placed into bed mattress and of advanced signal processing. The signals on …

Adaptive heartbeat modeling for beat-to-beat heart rate measurement in ballistocardiograms

J Paalasmaa, H Toivonen… - IEEE journal of biomedical …, 2014 - ieeexplore.ieee.org
We present a method for measuring beat-to-beat heart rate from ballistocardiograms
acquired with force sensors. First, a model for the heartbeat shape is adaptively inferred from …

An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification

T Willemen, D Van Deun, V Verhaert… - IEEE journal of …, 2013 - ieeexplore.ieee.org
Polysomnography (PSG) is considered the gold standard to assess sleep accurately, but it
can be expensive, time-consuming, and uncomfortable, specifically in long-term sleep …

Sleep/wake measurement using a non‐contact biomotion sensor

P De Chazal, N Fox, E O'HARE… - Journal of sleep …, 2011 - Wiley Online Library
We studied a novel non‐contact biomotion sensor, which has been developed for identifying
sleep/wake patterns in adult humans. The biomotion sensor uses ultra low‐power reflected …