Track topics on Twitter Track topics that are important to you
Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (Pes) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative Pes. Approach: 1119 patients from the Stanford Sleep Clinic with PSGs containing Pes served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory (LSTM) neural network was implemented to achieve a context-based mapping between the non-invasive features and the Pes values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive Pes. Main results: The median difference between the measured and predicted Pes was 0.61 cmH2O with an interquartile range (IQR) of 2.99 cmH2O and 5th and 95th percentiles of -5.85 cmH2O and 5.47 cmH2O, respectively. The model performed well when compared to actual esophageal pressure signal (ρmedian=0.581, p=0.01; IQR = 0.298; ρ5% = 0.106; ρ95% = 0.843). Significance: A significant difference in predicted Pes was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for Pes, improving characterization of SDB events as obstructive or not. .
This article was published in the following journal.
Name: Physiological measurement
Estimation of activation energies within heterogeneous catalytic reactions is performed using machine learning and catalysts dataset. In particular, descriptors for determining activation energy are r...
We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is base...
Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient perfo...
Sleep-disordered breathing, including obstructive sleep apnoea and central sleep apnoea, is a common disorder among patients with chronic heart failure. Obstructive sleep apnoea is often treated with ...
Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary e...
In a prospective observational study, the investigators investigate the prevalence of sleep disordered breathing in patients with severe valvular regurgitation and the effect of using the ...
Obstructive sleep apnoea (OSA) is a condition that affects around one in 20 children. In children with OSA, repeated episodes of airway obstruction can severely disturb and fragment sleep,...
The diagnosis and treatment of sleep disordered breathing have come to the forefront of clinical medicine following recognition of the high prevalence and associated morbidity of sleep apn...
Only few prospective studies systematically investigated the prevalence of sleep disordered breathing in patients with stable chronic heart failure. Furthermore there is no report on the i...
Sleep disorder breathing (SDB) is a condition affecting 10% of children aged 2-6 years. It is a combination of snoring most nights during sleep, patchy sleep, short periods of stopping bre...
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
Sleep disorders disrupt sleep during the night, or cause sleepiness during the day, caused by physiological or psychological factors. The common ones include snoring and sleep apnea, insomnia, parasomnias, sleep paralysis, restless legs syndrome, circa...
Asthma COPD Cystic Fibrosis Pneumonia Pulmonary Medicine Respiratory Respiratory tract infections (RTIs) are any infection of the sinuses, throat, airways or lungs. They're usually caused by viruses, but they can also ...