The accuracy of wearable photoplethysmography sensors for telehealth monitoring: a scoping review
S Knight, J Lipoth, M Namvari, C Gu… - Telemedicine and e …, 2023 - liebertpub.com
Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly
used for remote patient monitoring, especially during the COVID-19 pandemic, for the …
used for remote patient monitoring, especially during the COVID-19 pandemic, for the …
The effect of training and testing process on machine learning in biomedical datasets
Training and testing process for the classification of biomedical datasets in machine learning
is very important. The researcher should choose carefully the methods that should be used …
is very important. The researcher should choose carefully the methods that should be used …
Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network
Q Yang, L Zou, K Wei, G Liu - Computers in biology and medicine, 2022 - Elsevier
Obstructive sleep apnea (OSA), which has high morbidity and complications, is diagnosed
via polysomnography (PSG). However, this method is expensive, time-consuming, and …
via polysomnography (PSG). However, this method is expensive, time-consuming, and …
Classification of sleep apnea based on EEG sub-band signal characteristics
Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops
during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological …
during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological …
Sparse spectrum based swarm decomposition for robust nonstationary signal analysis with application to sleep apnea detection from EEG
SV Bhalerao, RB Pachori - Biomedical Signal Processing and Control, 2022 - Elsevier
Background and motivation Time–frequency representation (TFR) of a signal finds its
application in numerous fields for non-stationary multicomponent signal analysis. Due to …
application in numerous fields for non-stationary multicomponent signal analysis. Due to …
Diagnostic Accuracy of Portable Sleep Monitors in Pediatric Sleep Apnea: A Systematic Review
V Landry, K Semsar-Kazerooni, T Chen… - Sleep Medicine …, 2024 - Elsevier
In recent years, a plethora of new type III and IV portable sleep monitors (PSM) have been
developed, although evidence regarding their diagnostic accuracy for use in children …
developed, although evidence regarding their diagnostic accuracy for use in children …
AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning
Abstract Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related
breathing disorder. It is caused by an increased upper airway resistance during sleep, which …
breathing disorder. It is caused by an increased upper airway resistance during sleep, which …
Estimation of body fat percentage using hybrid machine learning algorithms
Before obesity treatment, body fat percentage (BFP) should be determined. BFP cannot be
measured by weighing. The devices developed to produce solutions to this problem are …
measured by weighing. The devices developed to produce solutions to this problem are …
MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network
Sleep Apnea (SA) is a respiratory disorder that affects sleep. However, the SA detection
method based on polysomnography is complex and not suitable for home use. The …
method based on polysomnography is complex and not suitable for home use. The …
Feature selection techniques for a machine learning model to detect autonomic dysreflexia
Feature selection plays a crucial role in the development of machine learning algorithms.
Understanding the impact of the features on a model, and their physiological relevance can …
Understanding the impact of the features on a model, and their physiological relevance can …