Instantaneous Heart Rate based sleep staging using deep learning models as a convenient alternative to Polysomnography Source: Sleep and Breathing Conference 2021 Year: 2021
Is pulse transit time (PTT) a good parameter to diagnose sleep apnea/hypopnea syndrome (SAHS)? Source: Eur Respir J 2005; 26: Suppl. 49, 117s Year: 2005
Workstation 3: Scoring sleep using AASM guidelines. A brief introduction to scoring respiratory events, arousals and limb movements. Source: International Congress 2015 – EW27 Hands-on polysomnography Year: 2015
Workstation 3: Scoring sleep using AASM guidelines. A brief introduction to scoring respiratory events, arousals and limb movements. Source: International Congress 2015 – EW25 Hands-on polysomnography Year: 2015
Do not go with just the flow: machine learning in oximetric versus flow-based sleep apnoea scoring Source: Virtual Congress 2020 – Screening methods and diagnostic tools for obstructive sleep apnoea Year: 2020
The use of pulse transit time (PTT) significantly improves detection of sleep respiratory events and recognition of micro-arousals in children Source: Eur Respir J 2002; 20: Suppl. 38, 574s Year: 2002
Automated sleep apnea detection from instantaneous heart rate using deep learning models Source: Sleep and Breathing Conference 2021 Year: 2021
Evaluation of automatic scoring algorithm for home sleep diagnosis Source: Annual Congress 2013 –Sleep disordered breathing in special situations III Year: 2013
Technical and digital polysomnography specifications for the new AASM sleep scoring rules Source: Annual Congress 2008 - Diagnostic aspects of sleep apnoea Year: 2008
Validation of automated versus manual scoring of cardio-respiratory sleep studies: A clinical audit Source: International Congress 2016 – Man versus machine: waves, frequency, and more in lung function Year: 2016
Evaluation of a multicomponent grading system for obstructive sleep apnoea: the Baveno classification Source: ERJ Open Res, 7 (1) 00928-2020; 10.1183/23120541.00928-2020 Year: 2021
ECG based detection of sleep apnea - a systematic comparison of 12 algorithms Source: Eur Respir J 2002; 20: Suppl. 38, 292s Year: 2002
A comparison of automated and manual sleep staging and respiratory event recognition in a portable sleep diagnostic device with in-lab sleep study Source: Virtual Congress 2020 – Screening methods and diagnostic tools for obstructive sleep apnoea Year: 2020
An audit comparing automated scoring to physiologist scoring of semi polysomnography sleep studies Source: International Congress 2016 – Man versus machine: waves, frequency, and more in lung function Year: 2016
Pharyngeal sensitivity assessment: a new tool for detecting sleep apnea? Source: Eur Respir J 2002; 20: Suppl. 38, 575s Year: 2002
Accuracy of residual events detection in a bench test and in clinical practice Source: Annual Congress 2013 –Twist in the tale: treating sleep disordered breathing - new technologies, new techniques Year: 2013
Deep learning for scoring sleep based on cardiorespiratory signals as compared to auto and multiple manual sleep scorings based on neurological signals Source: International Congress 2018 – New diagnostic tools for sleep and breathing and healthcare provision options Year: 2018
EEG scenarios and scoring Source: Sleep and Breathing Conference 2021 Year: 2021